Intelligent Manufacturing and Energy Sustainability: Proceedings of ICIMES 2020 [213, 1 ed.] 9813344423, 9789813344426

Table of contents :
ICIMES 2020 Committees
Conference Committee
Editorial Board
International Advisory Committee
National Advisory Committee
Industry Advisory Committee
Organizing Committee
Preface
Contents
About the Editors
1 Metallographic Analysis of the Percentage of Carbon in the Test Tube Based on Artificial Vision
1.1 Introduction
1.2 Basis
1.3 Methodology
1.3.1 Selection and Cutting of the Material to Be Analyzed
1.3.2 Test Tube Mounting
1.3.3 Test Tube Roughing
1.3.4 Gross Roughing
1.3.5 Fine Roughing
1.3.6 Chemical Attack
1.3.7 Method of Interception (or Heyn)
1.3.8 Procedure Analysis with Privative Software
1.3.9 Grain Size Determination
1.4 Results Analysis
1.5 Conclusions
References
2 Machinability Study of “Nickel Material” in Deep Micro-holes Fabrication Through μECM
2.1 Introduction
2.2 Experimentation
2.2.1 Experimental Design
2.2.2 Measurement of Responses (“Dh,” “OC,” and “Dd”)
2.3 Results and Discussions
2.3.1 Analysis of Responses (“Dh,” “OC,” and “Dd”)
2.4 Development of Regression Models
2.5 Conclusions
References
3 Three-Dimensional FEM Analysis of Nanoparticle-Assisted Radiofrequency Ablation of Tissue-Mimicking Phantom
3.1 Introduction
3.2 Simulation Methodology
3.2.1 Governing Equations
3.2.2 Boundary Conditions
3.2.3 Models Used for Computing Effective Properties
3.3 Experimental Methodology
3.4 Results and Discussion
3.5 Conclusion
References
4 Investigations on Electrochemical Discharge Machining of Al2O3 Ceramics
4.1 Introduction
4.1.1 Basic Principle of ECDM Process
4.2 Design of Experiment
4.2.1 The Process Parameters
4.2.2 Selection of Machining Parameters
4.2.3 L9 Orthogonal Array
4.2.4 Parameter Selection
4.2.5 Experimental Runs
4.3 Experimental Work
4.3.1 Experimental Setup
4.3.2 Experimentation
4.4 Results and Discussion
4.4.1 Observations During Experimentation (MRR)
4.4.2 Observations During Experimentation. (Undercut/Overcut)
4.4.3 Taguchi Analysis
4.5 Conclusion
References
5 Design and Numerical Simulation of PCM-Based Energy Storage Device for Helmet Cooling
5.1 Introduction
5.2 Methodology
5.2.1 Design of PCM Packet and Its Mathematical Formulation
5.2.2 Boundary Conditions and Numerical Solution
5.3 Grid Independency Study and Validation
5.4 Results and Discussion
5.4.1 Effect of Orientation on Performance of PCM Packet
5.4.2 Variation of Melting Rate and Local Temperature Distribution
5.5 Conclusions
References
6 Numerical Simulation and Analysis of Tank Filling Time and Flow Sequence
6.1 Introduction
6.2 Methodology
6.3 Validation
6.3.1 Dam-Break Problem
6.3.2 Tank Filling Using Two Ingates
6.4 Results and Discussions
6.5 Conclusion
References
7 GA-Based Tuning of Integral Controller for Frequency Regulation of Hybrid Two-Area Power System with Nonlinearities and Electric Vehicles
7.1 Introduction
7.2 Modeling of Hybrid Two-Area Power System
7.3 Nonlinearities: Governor Dead Band and Generation Rate Constraint
7.4 Electric Vehicle
7.5 Genetic Algorithm
7.6 Results and Discussions
7.6.1 Hybrid Two-Area Power System Without Nonlinearities
7.6.2 Hybrid Two-Area Power System with Nonlinearities
7.6.3 Hybrid Two-Area System with Nonlinearities and Electric Vehicles
7.7 Conclusions
References
8 Design and Analysis of Vehicle Tyres with Phase Change Material for Anti-freezing
8.1 Introduction
8.2 Design of the PCM Embedded Anti-freezing Tyre
8.3 Theoretical Calculations for Temperature Measurement
8.4 Thermal Analysis
8.5 Results and Discussion
8.6 Conclusion
References
9 Experimentation and Mathematical Modelling: Indirect Forced Convection Solar Drying of Tomato with Novel Drying Chamber Arrangement Using Phase Change Material as Thermal Energy Storage
9.1 Introduction
9.2 Experimental Procedure
9.2.1 Experimental Set-Up
9.2.2 Procedure
9.2.3 Mathematical Modelling
9.3 Results and Discussions
9.3.1 Post-processing of Solar Drying Curves
9.3.2 Modelling of Solar Drying Curves
9.4 Conclusion
Reference
10 Effect of Indoor and Outdoor Conditions on the Performance of SHVCR System—An Experimental Study
10.1 Introduction
10.2 Description of Experimental Setup
10.3 Experimental Procedure
10.4 Results and Discussion
10.5 Conclusion
References
11 An Integrated Switching Pattern and Sensorless Speed Control for BLDC Motor Drive in Electric Vehicles
11.1 Introduction
11.2 Proposed BLDC Motor Drive for Electric Vehicle
11.3 Regenerative Braking Using Single Boost Converter
11.4 Battery Charging Using Buck Converter
11.5 Sensorless Control of BLDC Motor Drive
11.6 Results and Discussion
11.6.1 Simulation Results
11.6.2 Experimental Validation
11.7 Conclusions
References
12 An ANN Approach for Predicting the Wear Behavior of Nano SiC-Reinforced A356 MMNCs Synthesized by Ultrasonic-Assisted Cavitation
12.1 Introduction
12.2 Back Propagation Network
12.2.1 Implementation of Back Propagation Network
12.3 Weight Structure
12.3.1 Training
12.4 Network Architecture Optimization
12.5 Validation of the System
12.6 Conclusions
References
13 Multi-response Optimization of FSW Process Parameters of ZE42 Alloy Using RSM-Based Grey Relational Analysis
13.1 Introduction
13.2 Experimental Procedure
13.3 Effects of Experimental Design
13.3.1 Grey Relational Analysis (GRA)
13.4 Results and Discussion
13.4.1 Multiple Response Models Using GRA
13.4.2 Response Table for GRG Using S/N Ratio
13.4.3 Confirmation Check
13.5 Conclusion
References
14 Analysis and Modeling on Defects of Deep Micro-holes Fabrication in Stainless Steel Through μECM
14.1 Introduction
14.2 Experimentation
14.2.1 Experimental Design
14.2.2 Measurement of Responses ( “h”, “OC”, and “Dd”)
14.3 Results and Discussions
14.3.1 Analysis of Responses (“h”, “OC”, and “Dd”)
14.4 Development of Regression Models
14.5 Conclusions
References
15 An Iot-Based Smart Pet Food Dispenser
15.1 Introduction
15.1.1 Design and Fabrication
15.1.2 Calculations
15.1.3 Observations
15.1.4 Storage Volume
15.2 Dispenser Mechanism
15.2.1 Auger Feed Mechanism
15.2.2 3d Modelling
15.3 Prototyping
15.3.1 3d Printed Components
15.3.2 Final Prototype
15.4 Electronic Hardware and Software Components
15.4.1 Technical Specifications
15.4.2 STEPPER MOTOR
15.4.3 Blynk
15.4.4 Working
15.5 Result
15.6 Conclusion
References
16 Dynamic Performance Enhancement of Hybrid Tricycle by Design of Efficient Transmission System
16.1 Introduction
16.2 Literature Review
16.3 Transmission System
16.3.1 Resistance Force, Torque
16.3.2 Rear Wheel Selection
16.3.3 Resistance Force Calculation [4]
16.3.4 Manual Drive
16.3.5 Electric Drive
16.3.6 Maximum Acceleration Calculations
16.4 Validation [6]
16.4.1 Acceleration Test
16.4.2 Durability Test
16.4.3 Maneuverability Test
16.4.4 Utility Test
16.4.5 Gradient Test
16.5 Conclusion and Future Scope
References
17 Pyroelectric Energy Harvesting Potential in Lead-Free BZT-BST Ceramics
17.1 Introduction
17.2 Experimental Procedure
17.3 Results and Discussion
17.4 Conclusions
References
18 Implementation of Online Self-Tuning Fuzzy-PI (STFPI) Controller for Conical Tank System
18.1 Introduction
18.2 Conical Tank System
18.2.1 Process Modeling
18.2.2 Model Identification
18.3 PI-Controller Parameter Tuning by Ziegler–Nichols Tuning (ZNT) Method
18.4 Self-Tuning Fuzzy-PI (STFPI) Controller
18.4.1 Fuzzification of Input and Output Variables
18.5 Results and Discussions
18.6 Conclusion
References
19 Smart and Sustainable Shopping Cart for the Physically Challenged
19.1 Introduction
19.2 Methodology
19.3 Block Schematics of the Designed System
19.4 Results
19.5 Conclusion
References
20 Investigation of Surface Roughness in MQL Aided Turning of Al/Cu/Zr Alloy Using PCD Tool
20.1 Introduction
20.2 Materials and Methods
20.3 Results and Discussion
20.3.1 Analysis of Variance for the Response Variable
20.3.2 2FI Model for Surface Roughness
20.3.3 Optimization Using Desirability Function Analysis
20.4 Conclusions
References
21 Comparative Analysis on the Effect of Minimum Quantity Lubrication and Chilled Air Cooling During Turning Hardened Stainless Steel
21.1 Introduction
21.2 Experimental Analysis
21.2.1 MQL System
21.2.2 Chilled Air Coolant System
21.3 Optimization Model
21.4 Results and Discussion
21.4.1 Cutting Temperature
21.4.2 Surface Roughness
21.4.3 Results of Genetic Algorithm
21.5 Conclusions
References
22 Deposition of Single-Layer Oxide Films with Ion Beam Sputtering Technique on Super-Polished Ceramic Glass Substrates
22.1 Introduction
22.2 Experimental Work
22.3 Characterization of Thin Films
22.3.1 Optical Constants
22.3.2 Film Thickness and Surface Morphology
22.3.3 Stoichiometry of the Tantala Films
22.4 Results and Discussion
22.5 Conclusions
References
23 A Review on Latest Trends in Derived Technologies of Friction Stir Welding
23.1 Introduction
23.2 Variants of FSW
23.2.1 Friction Stir Extrusion (FSE)
23.2.2 Friction Stir Scribe Welding (FSS)
23.2.3 Friction Stir Dovetailing (FSD)
23.2.4 Friction Stir Interlocking (FSI)
23.3 Conclusions
References
24 Investigation on Hybrid Polyester Composite Comprising of Sisal and Coir as a Reinforcement and Fly Ash as Filler
24.1 Introduction
24.2 Materials, Methods and Testing
24.3 Results and Discussions
24.4 SEM Analysis
24.5 Conclusion
References
25 Thermal Performance Study of Double-Pass Solar Air Heater in Almora District Zone of Uttarakhand
25.1 Introduction
25.2 Mathematical Model
25.3 Computational Solutions
25.3.1 Geometrical Description
25.3.2 Meshing Details
25.4 Results and Discussion
25.5 Conclusion
References
26 Modeling and Optimal Control of Vehicle Air Conditioning System
26.1 Introduction
26.2 Plant Model of the Air Conditioning System
26.3 Nonlinearity of the Air Conditioning System
26.4 MPC Design for Cabinet Cooling
26.5 Simulation Results for the Controller
26.6 Summary and Future Work
Reference
27 Experimental and CFD Analysis of Artificial Dimples Surface Roughness by Using Application of Domestic Solar Water Heater
27.1 Introduction
27.2 Design Model and Experimental Model
27.3 Governing Equation
27.4 Punching Die Machine Tool
27.5 Experimental Results
27.5.1 Comparison of Plain Tube and Dimples Tube at 798, 830, 847, 840 W/M2
27.5.2 Velocity and Temperature of Different Cross Section Along with the Evacuated Dimples Tube
27.5.3 Velocity and Temperature of the Junction in Dimples Tube
27.6 Conclusion
References
28 Secure Privacy Analysis of HR Analytics—A Machine Learning Approach
28.1 Introduction
28.2 Related Study
28.3 Methodology
28.3.1 Logistic Regression
28.3.2 Homomorphic Encryption
28.4 Implementation Result
28.5 Conclusion
28.6 Future Enhancement
References
29 Identification of Parkinson's Disease Using Machine Learning and Neural Networks
29.1 Introduction
29.2 Related Work
29.3 Proposed System
29.3.1 Dataset—Description and Preprocessing
29.3.2 Machine Learning Algorithms Used
29.3.3 Classification Metrics
29.4 Results and Analysis
29.5 Conclusion
29.6 Declaration
References
30 Assessment of Forensics Investigation Methods
30.1 Introduction
30.2 Related Work
30.3 Logs of Operating System
30.4 Conclusion
References
31 Smart Tourism Development in a Smart City: Mangaluru
31.1 Introduction
31.2 Tourism Development in Mangaluru City
31.3 Smart Solutions to Tourism
31.3.1 Smart Traffic Management
31.3.2 Smart Water Management
31.3.3 Smart Waste Management
31.3.4 Smart Energy Management
31.4 Architecture for Smart Tourism
31.5 Conclusion
References
32 Big Data Analytics and Internet of Things in Health Informatics
32.1 Introduction
32.2 Biomedical Big Data
32.2.1 Big EHR Data
32.2.2 Medical Imaging Data
32.2.3 Clinical Text Mining Data
32.2.4 Big OMICs Data
32.3 Healthcare Internet of Things (IoT)
32.3.1 IoT Architecture
32.3.2 IoT Data Source
32.4 Studies Related to Big Data Analytics in Healthcare IoT
32.5 Challenges for Medical IoT and Big Data in Healthcare
32.6 Conclusion
References
33 Medicinal Leaves Recognition Using Contour-Based Segmentation
33.1 Introduction
33.2 Literature Review
33.3 Problem Definition
33.4 Methodology
33.4.1 Image Collection
33.4.2 Segmentation
33.4.3 Methodologies Used for Feature Extraction
33.4.4 Classification Using KNN Algorithm
33.5 Result and Observations
33.6 Conclusion
References
34 Deep Learning for Robot Vision
34.1 Introduction
34.2 Deep Learning
34.3 Convolutional Neural Networks
34.3.1 Fast RCNN
34.4 Generative Adversarial Networks
34.5 Restricted Boltzmann Machine
34.6 Recurrent Neural Networks
34.7 CNN Architectures
34.7.1 AlexNet
34.7.2 GoogLeNet
34.7.3 RGB-D
34.7.4 KITTI
34.7.5 ImageNet
34.7.6 CIFAR-100
34.8 Discussion
34.9 Conclusion
References
35 Deep Learning Approach for Prediction of Handwritten Telugu Vowels
35.1 Introduction
35.2 Methodology
35.2.1 ANNs, ML, and DL
35.2.2 Preprocessing Stage
35.2.3 CNN Architecture for Telugu Alphabet Recognition
35.3 Results and Discussions
35.4 Conclusions
References
36 Literature Review of Lean Methodology and Research Issues for Identifying and Eliminating Waste in Software Development
36.1 Introduction
36.2 Review of Literature
36.3 Research Gap
36.4 Conclusion
References
37 IQINN: Improve the Quality of Image by Neural Network
37.1 Introduction
37.2 Literature Survey
37.3 Methodology
37.3.1 Methodology Detail
37.4 Experiments
37.4.1 Result
37.4.2 Analysis
37.5 Conclusion
References
38 Traffic Monitoring System in Smart Cities Using Image Processing
38.1 Introduction
38.2 Image Processing
38.3 Proposed System
38.3.1 Acquisition of an Image
38.3.2 Image Scaling
38.3.3 RGB to Gray Conversion
38.3.4 Image Quality Improvisation
38.3.5 Detection of Edges
38.3.6 Comparison of Images
38.4 Results and Conclusion
References
39 Sensitivity Context-Aware PrivacyPreserving Sentiment Analysis
39.1 Introduction
39.2 Related Work
39.3 Proposed Methodology
39.3.1 SCA Anonymizationbased Sentiment Analysis
39.4 Results and Discussion
39.5 Conclusion and Future Work
References
40 Analysis of Heart Disease Data Using K-Means Clustering Algorithm in Orange Tool
40.1 Introduction
40.2 Heart Disease Dataset and Methodologies
40.3 K-means Clustering Algorithm Using in Orange Tool
40.4 Heart Disease k-means Clustering in Scatter Plot and Visualization of Data Implementation and Results
40.5 Conclusion
References
41 Development of Biomass Green Champo Leaf DRAM Memory Cell
41.1 Introduction
41.2 The Experiment
41.2.1 Realization of DRAM Cell on Green Biomass Circuit
41.3 Observations
41.4 Conclusion
References
42 An Unscented Kalman Filter Approach for High-Precision Indoor Localization
42.1 Introduction
42.2 System Model
42.3 Unscented Kalman Filter
42.3.1 Prediction Step
42.3.2 Update Step
42.3.3 Unscented Transformation
42.4 Results
42.5 Conclusion
References
43 Implementation of Energy Detection Technique for Spread Spectrum Systems
43.1 Introduction
43.2 Related Works
43.2.1 Sensing Time
43.2.2 Spread Spectrum Techniques in Wireless Communications
43.3 Theoretical Background
43.3.1 Direct Sequence Spread Spectrum (DSSS)
43.3.2 Frequency Hopping Spread Spectrum
43.4 Spectrum Sensing Technique
43.5 Methodology
43.6 Simulation Results
43.6.1 MATLAB Simulation of DSSS
43.6.2 MATLAB Simulation of FHSS
43.6.3 MATLAB Simulation of Energy Detection-Based Spectrum Sensing
43.7 Conclusion
References
44 Implementation of Low Area ALU Using Reversible Logic Formulations
44.1 Introduction
44.2 Literature Survey
44.3 Reversible Logic Gates
44.3.1 Feynman Gate
44.3.2 Fredkin Gate
44.4 Proposed Method
44.4.1 Proposed Reversible Full Adder
44.4.2 Proposed N-Bit Reversible Ripple Carry Adder
44.4.3 Proposed N-Bit Reversible Array Multiplier
44.4.4 Proposed Reversible Full Adder-Subtractor
44.4.5 Proposed N-Bit Reversible Adder-Subtractor
44.4.6 Proposed N-Bit Reversible ALU
44.5 Results and Discussions
44.6 Conclusion
References
45 Evaluation of Transfer Learning Model for Mango Recognition
45.1 Introduction
45.2 Material and Proposed Method
45.2.1 Collection of Sample Data
45.2.2 Transfer Learning Approach
45.3 Results and Discussion
45.4 Conclusion
References
46 An Inter-Comparative Survey on State-of-the-Art Detectors—R-CNN, YOLO, and SSD
46.1 Introduction
46.2 Literature Review
46.2.1 R-CNN
46.2.2 Fast R-CNN
46.2.3 Faster R-CNN
46.2.4 YOLOv1
46.2.5 YOLOv2
46.2.6 YOLOv3
46.2.7 SSD
46.3 Experimental Analysis
46.3.1 R-CNN, Fast R-CNN, and Faster R-CNN
46.3.2 Faster R-CNN, YOLO, and SSD
46.4 Conclusion
References
47 Diabetes Patients Hospital Re-admission Prediction Using Machine Learning Algorithms
47.1 Introduction
47.2 Literature Survey
47.3 Proposed Methodology
47.3.1 Data Exploration
47.3.2 Data Preprocessing and Feature Engineering
47.3.3 Decision Tree Classifier
47.3.4 Feature Importance
47.3.5 AdaBoost Classifier
47.3.6 Feature Importance
47.3.7 Hyperparameter Tuning of AdaBoost with GridSearchCV
47.4 Results and Discussions
47.4.1 Analysis of Classifiers
47.4.2 Identifying the Critical Factors
47.5 Conclusion and Future Work
References
48 Traffic Analysis Using IoT for Improving Secured Communication
48.1 Introduction
48.2 Related Work
48.3 Proposed Work
48.4 Experimental Results
48.5 Conclusion
References
49 Implementation of a Network of Wireless Weather Stations Using a Protocol Stack
49.1 Introduction
49.2 Data Logger System Architecture
49.3 Network Design
49.4 Results
49.5 Conclusion
References
50 Various Developments in the Design of Hovercrafts: A Review
50.1 Introduction
50.2 Saunders-Roe Nautical Models and Early Hovercrafts
50.2.1 Why Commercial Hovercrafts Failed?
50.2.2 Recent Designs of a Hovercraft
50.3 Summary and Discussions
50.4 Conclusions
References
51 Efficient Utilization of Home Energy During Pandemic—A Case Study
51.1 Introduction
51.2 Literature Review
51.3 Methodology
51.3.1 What is Hems?
51.3.2 Challenges
51.3.3 Implementation of Successful Home Energy Management Plan
51.3.4 Observations Made
51.4 Conclusion
References
52 Data Analytics Based Multimodal System for Fracture Identification and Verification in CBIR Domain
52.1 Introduction
52.2 Pre-processing
52.3 Segmentation and Feature Extractions
52.4 Data Analytics in Fracture Detection
52.5 Proposed Multimodal System: Frame and Data Analytics and Implementations
52.6 Verification and Validation
52.7 Conclusion
Reference
53 Solar PV-Driven Swaccha Jal
53.1 Introduction
53.2 Literature Survey
53.3 Procedures and Methodology
53.3.1 Details of the Electrical System
53.4 Results and Discussion
53.5 Conclusion and Future Scope
References
54 Field Performance Monitoring of Roof-Mounted SPV Systems: Application of Internet-Enabled Technologies
54.1 Introduction
54.2 Method of Study
54.2.1 Site Description
54.2.2 The Roof-Mounted SPV System at PCC4
54.3 Results and Discussion
54.4 Conclusions
References
55 Flow Modulation at Micro-combustor Inlet
55.1 Introduction
55.2 Physical and Numerical Descriptions
55.2.1 Geometrical Model
55.2.2 Numerical Methodology
55.3 Results and Discussion
55.4 Conclusions
References
56 Study on Performance of Phase Change Material Integrated Heat Pipe
56.1 Introduction
56.2 Methods and Materials
56.2.1 Heat Pipe Thermal Resistance and Thermal Resistance Network
56.3 Experimentation
56.4 Thermal Performance Analysis of PCM
56.5 Results and Discussion
56.5.1 Influence of Heating Power on Liquid Fraction
56.5.2 Variation in Wall Surface Temperature for Varied Power Input
56.6 Conclusion
References
57 Design and Implementation of Smart Charging for LMV
57.1 Introduction
57.1.1 EVSE Charging Levels
57.2 System Description
57.3 Block Diagram
57.4 CAN Data Extraction
57.5 Results
57.6 Conclusion
References
58 Experimental Transient Analysis of Radial Flow Clay Desiccant Packed Bed
58.1 Introduction
58.2 Preparation of Clay Desiccant
58.3 Experimental Setup
58.3.1 Desiccant Packed Bed
58.3.2 Instrumentation and Calibration
58.4 Experimental Procedure
58.5 Results and Discussion
58.6 Conclusions
References
59 Coral—A Smart Water Body Health Monitoring System
59.1 Introduction
59.2 Proposed Model
59.3 Implementation
59.4 Results and Discussion
59.5 Conclusion
References
60 Recent Investigation on Ultrasonic Machining of Aluminum Metal Matrix Composite
60.1 Introduction
60.2 Literature Review
60.3 Mechanical Aspects
60.4 Discussion and Conclusion
References
61 Military Reconnaissance and Rescue Robot with Real-Time Object Detection
61.1 Introduction
61.1.1 Surveillance and Reconnaissance
61.1.2 Search and Rescue
61.1.3 Robotic Arm
61.1.4 Artificial Intelligence
61.2 Methodology
61.2.1 Mechanical Body
61.2.2 Robotic Arm
61.2.3 Sensor Circuitry
61.2.4 Real-Time Object Detection
61.2.5 Graphical User Interphase (GUI)
61.3 Results and Discussion
61.3.1 Mechanical Body
61.3.2 Electrical Circuitry
61.3.3 Graphical User Interphase
61.3.4 Artificial Intelligence
61.3.5 Final Prototype
61.4 Conclusion
References
62 Finite Element Analysis and Design of a Four-Helical Coiled Single Lumen Microcatheter
62.1 Introduction
62.2 Modelling of Microcatheter
62.2.1 Designing of Microcatheter
62.3 Analysis of the Microcatheter
62.3.1 Boundary Conditions
62.4 Results
62.4.1 Stress Results
62.5 Conclusion
References
63 Wear Modeling Revisited Using Feedback Control Theory
63.1 Introduction
63.2 Modeling Using Control Theory
63.3 Conclusion
References
64 Performance Assessment of Improved Solar Still Design with Stepped-Corrugated Absorber Plate
64.1 Introduction
64.2 Methodology
64.3 Energy Efficiency
64.4 Stepped-Corrugated Solar Still
64.5 Experimental Results and Discussion
64.6 Conclusions
References
65 Parametric Analysis of Adhesively Bonded Single Lap Joint Using Finite Element Method
65.1 Introduction
65.2 Problem Definition
65.3 Finite Element Method
65.3.1 Mesh Sensitivity Analysis
65.3.2 Method Validation
65.4 Results and Discussion
65.4.1 Effect of Adhesive Size
65.4.2 Effect of Adhesive Thickness
65.4.3 Effect of Adhesive Width
65.4.4 Effect of Applied Load
65.5 Conclusion
References
66 Modelling and Analysis of Flat Disc Brake for Dynamic Vehicles
66.1 Introduction
66.2 Modelling of Disc Brake
66.3 Results and Discussion
66.4 Conclusion
References
67 Robust PV Fed Discrete Controller for Heating and Lighting Applications
67.1 Introduction
67.2 Methodology of Work
67.2.1 Mathematical Analysis of Zeta Converter
67.2.2 Modeling of Discrete Sliding Mode Control for Zeta Converter
67.3 Results and Discussion
67.4 Conclusion
References
68 Study of Effect of Variation of Parameters on the Performance of a Solar Still
68.1 Introduction
68.2 Single Basin Solar Still
68.2.1 Components for Construction of a Simple Passive Solar Still
68.2.2 Critical Parameters Affecting the Still Performance
68.3 Results and Discussion
68.3.1 Solar Radiation
68.3.2 Glass Cover Thickness
68.3.3 Depth of Feed Water
68.3.4 Basin Area
68.4 Conclusion
References
69 Friction and Wear Performance of Jatropha Oil Added with Molybdenum Disulphide Nanoparticles
69.1 Introduction
69.2 Materials and Experimental Procedure
69.2.1 Lubricant and Sample Preparation
69.2.2 Friction and Wear Tests
69.3 Results and Discussion
69.3.1 Analysis of Friction
69.3.2 Analysis of Wear
69.3.3 Wear Scar Analysis
69.4 Conclusion
References
70 Layer Based Fabrication of Human-Scaled Body Parts by Using Pneumatic Extrusion Method
70.1 Introduction
70.2 Material and Method
70.3 Results
70.4 Conclusion
References
71 Fuzzy-Based Power Management Strategy for Performance Improvement of Electric Vehicles
71.1 Introduction
71.2 Power Management Strategy
71.2.1 Frequency Sharing Based Strategy
71.3 Fuzzy Logic Controller for Power Management
71.4 Simulation Results
71.5 Conclusion
References
72 Design of Pitch Box-Mounting Tool
72.1 Introduction
72.2 Material Selection
72.3 Analysis of the Pitch Box-Mounting Tool
72.4 Results and Discussion
72.4.1 Pitch Box-Mounting Tool
72.5 Conclusion
72.6 Scope of Future Work
References
73 Heat Transfer Enhancement in Automobile Radiator Through the Application of CuO Nanofluids
73.1 Introduction
73.2 Preparation of CuO Nanofluid
73.3 Experimental Setup and Procedure
73.4 Experimental Data Reduction
73.4.1 Assessment of Heat Transfer Coefficient
73.4.2 Estimation of Nussle Number for Single-Phase Fluids
73.5 Experimental Results and Discussion
73.5.1 Base Fluid in Radiator
73.5.2 Nanofluid in Radiator
73.6 Conclusions
References
74 Positioning of Wind Turbine in a Wind Farm for Optimum Generation of Power Using Genetic Algorithm for Multiple Direction
74.1 Introduction
74.2 Turbine Modeling
74.3 Optimization Function
74.4 Genetic Algorithm
74.5 Results and Discussion
74.5.1 Selection of Wind Farm Location
74.5.2 Wind Farm Layout Optimization for Single Direction
74.5.3 Wind Farm Layout Optimization for Multiple Wind Directions
74.6 Conclusions
References
75 Eco-Efficiency and Business Performance Evaluation—Lean and Green Manufacturing Approach
75.1 Introduction
75.1.1 Lean and Green Manufacturing
75.1.2 Multi-criteria Decision Making (MCDM)
75.1.3 Business Performance Measures
75.2 Methodology
75.2.1 Flowchart
75.2.2 Identifying Enablers
75.2.3 Evaluation Using Analytic Hierarchy Process (AHP)
75.2.4 Identifying Ranks Using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
75.2.5 Calculating Driving and Dependency Using Interpretive Structural Modelling (ISM)
75.2.6 Business Performance Measure
75.3 Results and Discussion
75.4 Conclusion
References
Author Index

Citation preview

Smart Innovation, Systems and Technologies 213

A. N. R. Reddy Deepak Marla Margarita N. Favorskaya Suresh Chandra Satapathy   Editors

Intelligent Manufacturing and Energy Sustainability Proceedings of ICIMES 2020

123

Smart Innovation, Systems and Technologies Volume 213

Series Editors Robert J. Howlett, Bournemouth University and KES International, Shoreham-by-sea, UK Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form. Volumes on interdisciplinary research combining two or more of these areas is particularly sought. The series covers systems and paradigms that employ knowledge and intelligence in a broad sense. Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community. It also focusses on the knowledge-transfer methodologies and innovation strategies employed to make this happen effectively. The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities. The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions. High quality content is an essential feature for all book proposals accepted for the series. It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles. Indexed by SCOPUS, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago, DBLP. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/8767

A. N. R. Reddy Deepak Marla Margarita N. Favorskaya Suresh Chandra Satapathy •

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Editors

Intelligent Manufacturing and Energy Sustainability Proceedings of ICIMES 2020

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Editors A. N. R. Reddy Department of Mechanical Engineering Malla Reddy College Engineering and Technology Secunderabad, Telangana, India Margarita N. Favorskaya Department of Informatics and Computer Techniques Siberian State University of Science and Technology Krasnoyarsk, Russia

Deepak Marla Department of Mechanical Engineering Indian Institute of Technology Bombay Mumbai, India Suresh Chandra Satapathy School of Computer Engineering KIIT University Bhubaneswar, Odisha, India

ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-981-33-4442-6 ISBN 978-981-33-4443-3 (eBook) https://doi.org/10.1007/978-981-33-4443-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

ICIMES 2020 Committees

Conference Committee Chief Patron Sri. Ch. Malla Reddy, Founder Chairman, MRGI Patrons Sri. Ch. Mahendar Reddy, Secretary, MRGI Sri. Ch. Bhadra Reddy, President, MRGI Conference Chair Dr. V. S. K. Reddy, Principal Honorary Chairs Dr. Lakshmi C. Jain, University of Sydney, Sydney, Australia Dr. Margarita N. Favorskaya, Reshetnev Siberian State University of Science and Technology, Russia Publication Chair Dr. Suresh Chandra Satapathy, Professor, KIIT, Bhubaneswar, India Convener Dr. M. Murali Krishna, Dean—Academics Organizing Chair Dr. A. N. R. Reddy, HOD—Mechanical Engineering Organizing Secretary Dr. Srikar Potnuru, Associate Professor-Mechanical Engineering

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ICIMES 2020 Committees

Coordinator Prof. Harish Makena, Assistant Professor-Mechanical Engineering

Editorial Board Dr. A. N. R. Reddy, Malla Reddy College of Engineering and Technology, India Dr. Deepak Marla, Indian Institute of Technology Bombay, India Dr. Margarita N. Favorskaya, Siberian State University of Science and Technology, Russia Dr. Suresh Chandra Satapathy, KIIT, Bhubaneswar, India

International Advisory Committee Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr.

Narayanan Kulathu Ramaiyer, Universiti Malaysia Sarawak, Malaysia Abu Saleh Ahmed, Universiti Malaysia Sarawak, Malaysia Shahrol Mohamaddan, Universiti Malaysia Sarawak, Malaysia Jaesool Shim, Yeunagnam University, South Korea V. Vasudeva Rao, University of South Africa, South Africa Sinin Hamdan, Universiti Malaysia Sarawak, Malaysia Amiya Bhaumik, Lincoln University College, Malaysia Bhaskar Kura, University of New Orleans, LA, USA Devarayapalli K. C., Yeunagnam University, South Korea Raja V. Pulikollu, Electric Power Research Institute, North Carolina, USA Nguyen Dang Nam, Duy Tan University, Vietnam Angel Sanz Anderes, UPM, Madrid, Spain S. V. Prabhakar, Yeunagnam University, South Korea Yequing Bao, University of Alabama, USA Sabastian Franchini, UPM, Madrid, Spain

National Advisory Committee Dr. G. Balu, DOAD, DRDL, Telangana, India Dr. K. Vijay Kumar Reddy, Jawaharlal Nehru Technological University Hyderabad, Telangana, India Dr. P. K. Jain, Centre for carbon Materials, ARCI, Hyderabad, Telangana, India Dr. Susanta Kumar Sahoo, NIT Rourkela, Odisha, India Dr. K. S. Reddy, Indian Institute of Technology Madras, Tamil Nadu, India Dr. G. Raghavendra, National Institute of Technology Warangal, Telangana, India

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Dr. T. Gangadhar, National Institute of Technology Tadepalligudem, Andhra Pradesh, India Dr. U. S. Paul Russel, Air India, India Dr. V. P. Chandra Mohan, National Institute of Technology Warangal, Telangana, India Dr. P. Narsimha Reddy, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India Dr. Swami Naidu, National Institute of Technology Raipur, Chhattisgarh, India Dr. Vemuri Laxmi Narayana, Rajamahendri Institute of Engineering and Technology, Andhra Pradesh, India Dr. P. H. V. Sesha Talpa Sai, Malla Reddy College of Engineering and Technology, Hyderabad, India

Industry Advisory Committee Mr. Mr. Mr. Mr.

Uma Shankar, Farm Division, Mahindra and Mahindra, Zaheerabad, India Sunil Maheshwari, Adroitec Engineering Solutions Pvt. Ltd. India Uddagiri Vidyasagar, TCS, Hyderabad, India Narva Pavan Kumar, Verizon, India

Organizing Committee Dr. V. Madhusudhana Reddy, Professor Dr. T. Siva Kumar, Professor Dr. T. Lokeswara Rao, Professor Dr. Trisekhar Reddy, Professor Dr. B. Jain A. R. Tony, Professor Prof. Sridhar Akarapu, Assistant Professor Prof. Akhila Kakera, Assistant Professor

Preface

The International Conference on Intelligent Manufacturing and Energy Sustainability (ICIMES 2020) was successfully organized by Malla Reddy College of Engineering and Technology, an UGC Autonomous Institution, during August 21–22, 2020, at Hyderabad. The objective of this conference was to provide opportunities for the researchers, academicians and industry persons to interact and exchange the ideas, experience and gain expertise in the cutting-edge technologies pertaining to Industry 4.0. Research papers were received and subjected to a rigorous peer review process with the help of editorial board, program committee and external reviewers. The editorial committee has finally accepted 19.5% manuscripts for publication in a single volume with Springer SIST series. Our sincere thanks to the Chief Guest and Keynote speaker Ir. Dr. Lim Soh Fong Faculty of Engineering, University Malaysia Sarawak (UNIMAS) Sarawak, Malaysia. Our special thanks to all the session chairs for their immense support. The Session Chairs are: 1. Ir. Dr. David Chua Sing Ngie, Faculty of Engineering, University Malaysia Sarawak, (UNIMAS), Sarawak, Malaysia 2. Dr. Shahrol bin Mohamaddan, Faculty of Engineering, Shibaura Institute of Technology (SIT), Tokyo, Japan 3. Dr. Elammaran Jayamani, Faculty of Engineering, Swinburne University of Technology, Sarawak Campus, Malaysia 4. Dr. Deepak Marla, Professor, Department of Mechanical Engineering, IIT Bombay, India. 5. Dr. D. K. Charyulu, Research Professor, Yeungnam University, South Korea. 6. Dr. S. V. Prabhakar Vattikuti, Research Professor, Yeungnam University, South Korea. We are indebted to the editorial board, program committee and external reviewers who have produced critical reviews in a short time. We express our heartfelt thanks to our Chief Patron Sri. Ch. Malla Reddy, Founder Chairman, MRGI; Patrons Sri. Ch. Mahendar Reddy, Secretary, MRGI; Sri. Ch. Bhadra Reddy, President, MRGI; Conference Chair Dr. V. S. K. Reddy; ix

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Convener Dr. M. Murali Krishna; Organizing Chair Professor Dr. A. N. R. Reddy; Organizing Secretary Dr. Srikar Potnuru; and Coordinator Mr. M. Harish for their valuable contribution to successfully conduct the conference. Last but certainly not least, our special thanks to all the authors without whom the conference would not have taken place. Their technical contributions have made our proceedings rich and praiseworthy. Hyderabad, India Mumbai, India Siberian, Russia Bhubaneswar, India

Dr. A. N. R. Reddy Dr. Deepak Marla Dr. Margarita N. Favorskaya Dr. Suresh Chandra Satapathy

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Metallographic Analysis of the Percentage of Carbon in the Test Tube Based on Artificial Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . Luigi O. Freire, Luis M. Navarrete, Byron P. Corrales, and Jefferson A. Porras Machinability Study of “Nickel Material” in Deep Micro-holes Fabrication Through lECM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Md. Zishanur Rahman, Alok Kumar Das, and Somnath Chattopadhyaya Three-Dimensional FEM Analysis of Nanoparticle-Assisted Radiofrequency Ablation of Tissue-Mimicking Phantom . . . . . . . . Santosh Shiddaling Naik, Bhanu Prakash Bonthala, and Ajay Kumar Yadav

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Investigations on Electrochemical Discharge Machining of Al2O3 Ceramics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Vijay, T. Sekar, N. Muthukumaran, and K. Vijayakumar

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Design and Numerical Simulation of PCM-Based Energy Storage Device for Helmet Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nagaraju Dora, Ch Ramsai, and Ch Srinivasa Rahul

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Numerical Simulation and Analysis of Tank Filling Time and Flow Sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akshay Saxena, Mayank Parasher, Mukul Anand, Nikhil Garg, Supradeepan Katiresan, and P. S. Gurugubelli GA-Based Tuning of Integral Controller for Frequency Regulation of Hybrid Two-Area Power System with Nonlinearities and Electric Vehicles . . . . . . . . . . . . . . . . . . . . K. R. Roshin and E. K. Bindumol

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Design and Analysis of Vehicle Tyres with Phase Change Material for Anti-freezing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Venkata Ramana, Sai Rohan Gogulapati, K. Adithya Sharma, K. Sanjay, and N. Varun Raj Experimentation and Mathematical Modelling: Indirect Forced Convection Solar Drying of Tomato with Novel Drying Chamber Arrangement Using Phase Change Material as Thermal Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Sabareesh, K. John Milan, C. Muraleedharan, and B. Rohinikumar

10 Effect of Indoor and Outdoor Conditions on the Performance of SHVCR System—An Experimental Study . . . . . . . . . . . . . . . . . Surender Kumar and Rabinder Singh Bharj

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11 An Integrated Switching Pattern and Sensorless Speed Control for BLDC Motor Drive in Electric Vehicles . . . . . . . . . . . . . . . . . . 101 M. U. Deepa and G. R. Bindu 12 An ANN Approach for Predicting the Wear Behavior of Nano SiC-Reinforced A356 MMNCs Synthesized by Ultrasonic-Assisted Cavitation . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Suneel Donthamsetty and Penugonda Suresh Babu 13 Multi-response Optimization of FSW Process Parameters of ZE42 Alloy Using RSM-Based Grey Relational Analysis . . . . . . 125 Ramanan Gopalakrishnan, Darwins Anantha Kanakaraj, Bino Prince Raja Dennis, and Ajith Raj Rajendran 14 Analysis and Modeling on Defects of Deep Micro-holes Fabrication in Stainless Steel Through lECM . . . . . . . . . . . . . . . . 135 Md. Zishanur Rahman, Alok Kumar Das, and Somnath Chattopadhyaya 15 An Iot-Based Smart Pet Food Dispenser . . . . . . . . . . . . . . . . . . . . . 147 M. V. R. Durga Prasad, M. Anita, and T. Malyadri 16 Dynamic Performance Enhancement of Hybrid Tricycle by Design of Efficient Transmission System . . . . . . . . . . . . . . . . . . 165 Amol Waddamwar, Suyog Kulkarni, and P. R. Dhamangaonkar 17 Pyroelectric Energy Harvesting Potential in Lead-Free BZT-BST Ceramics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Satyanarayan Patel 18 Implementation of Online Self-Tuning Fuzzy-PI (STFPI) Controller for Conical Tank System . . . . . . . . . . . . . . . . . . . . . . . . 185 M. Lakshmanan, V. Kamatchi Kannan, K. Chitra, and S. Srinivasan

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19 Smart and Sustainable Shopping Cart for the Physically Challenged . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Prashant Kumar Soori, Kiran Mathews Abraham, and Mohamed Al-Mujtaba Ali Idris Osman 20 Investigation of Surface Roughness in MQL Aided Turning of Al/Cu/Zr Alloy Using PCD Tool . . . . . . . . . . . . . . . . . . . . . . . . . 207 Md. Rezaul Karim, Sabbir Hossain Shawon, Shah Murtoza Morshed, Abir Hasan, and Juairiya Binte Tariq 21 Comparative Analysis on the Effect of Minimum Quantity Lubrication and Chilled Air Cooling During Turning Hardened Stainless Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Israt Sharmin, Mahjabin Moon, and Faysal Hasan Asik 22 Deposition of Single-Layer Oxide Films with Ion Beam Sputtering Technique on Super-Polished Ceramic Glass Substrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Laxminarayana Gangalakurti, K. Venugopal Reddy, Chhabra Inder Mohan, Atchaih Naidu Varadharajula, and Radhika Kanakam 23 A Review on Latest Trends in Derived Technologies of Friction Stir Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Maddela Narender, V. Ajay Kumar, and Aluri Manoj 24 Investigation on Hybrid Polyester Composite Comprising of Sisal and Coir as a Reinforcement and Fly Ash as Filler . . . . . . . . . . . . 251 M. L. Darshan, Srikumar Biradar, and K. S. Ravishankar 25 Thermal Performance Study of Double-Pass Solar Air Heater in Almora District Zone of Uttarakhand . . . . . . . . . . . . . . . . . . . . . 261 Divya Joshi, Satyendra Singh, and Sandeep Kandwal 26 Modeling and Optimal Control of Vehicle Air Conditioning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Nassim Khaled and Harsha Mathur 27 Experimental and CFD Analysis of Artificial Dimples Surface Roughness by Using Application of Domestic Solar Water Heater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 M. Arun, Debabrata Barik, K. P. Sridhar, and G. Vignesh 28 Secure Privacy Analysis of HR Analytics—A Machine Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 V. Kakulapati 29 Identification of Parkinson’s Disease Using Machine Learning and Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Ved Abhyankar and Rushikesh Tapdiya

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30 Assessment of Forensics Investigation Methods . . . . . . . . . . . . . . . . 317 Pranay Chauhan and Pratosh Bansal 31 Smart Tourism Development in a Smart City: Mangaluru . . . . . . . 325 A. N. Parameswaran, K. S. Shivaprakasha, and Rekha Bhandarkar 32 Big Data Analytics and Internet of Things in Health Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Pawan Singh Gangwar and Yasha Hasija 33 Medicinal Leaves Recognition Using Contour-Based Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 B. R. Pushpa, K. B. Amaljith, and N. Megha 34 Deep Learning for Robot Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Mamilla Keerthikeshwar and S. Anto 35 Deep Learning Approach for Prediction of Handwritten Telugu Vowels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Ch. Prathima and Naresh Babu Muppalaneni 36 Literature Review of Lean Methodology and Research Issues for Identifying and Eliminating Waste in Software Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Mona Deshmukh and Prateek Srivastava 37 IQINN: Improve the Quality of Image by Neural Network . . . . . . 389 Priyanka Birajdar and Bashirahamad Momin 38 Traffic Monitoring System in Smart Cities Using Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Syed Qamrul Kazmi, Munindra Kumar Singh, and Saurabh Pal 39 Sensitivity Context-Aware PrivacyPreserving Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 A. N. Ramya Shree, P. Kiran, and Sharan Chhibber 40 Analysis of Heart Disease Data Using K-Means Clustering Algorithm in Orange Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Sarangam Kodati, Kumbala Pradeep Reddy, G. Ravi, and Nara Sreekanth 41 Development of Biomass Green Champo Leaf DRAM Memory Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Gaurang K. Patel, Jitendra P. Chaudhari, and S. P. Kosta 42 An Unscented Kalman Filter Approach for High-Precision Indoor Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 Yashwant Yerra, D. Ram Kumar Reddy, and P. Sudheesh

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43 Implementation of Energy Detection Technique for Spread Spectrum Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 T. Anjali, T. S. Aparna, M. Meera, A. Parvathy, and Gayathri Narayanan 44 Implementation of Low Area ALU Using Reversible Logic Formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455 Niveditha Duggi and Swaminadhan Rajula 45 Evaluation of Transfer Learning Model for Mango Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 Chanki Pandey, Prabira Kumar Sethy, Santi Kumari Behera, Sharad Chandra Rajpoot, Bitti Pandey, Preesat Biswas, and Millee Panigrahi 46 An Inter-Comparative Survey on State-of-the-Art Detectors—R-CNN, YOLO, and SSD . . . . . . . . . . . . . . . . . . . . . . . 475 B. Bhavya Sree, V. Yashwanth Bharadwaj, and N. Neelima 47 Diabetes Patients Hospital Re-admission Prediction Using Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Sneha Grampurohit 48 Traffic Analysis Using IoT for Improving Secured Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 K. Santhi Sri, P. Sandhya Krishna, V. Lakshman Narayana, and Reshmi Khadherbhi 49 Implementation of a Network of Wireless Weather Stations Using a Protocol Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 Segundo G. Vacacela and Luigi O. Freire 50 Various Developments in the Design of Hovercrafts: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519 Jhansi Reddy Dodda, N. V. Srinivasulu, and Balem Rahul Reddy 51 Efficient Utilization of Home Energy During Pandemic—A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529 A. P. Nikitha, Mir Mohammed Junaid Basha, M. N. Vijayakumar, and M. S. Archana 52 Data Analytics Based Multimodal System for Fracture Identification and Verification in CBIR Domain . . . . . . . . . . . . . . . 539 H. Manjula Gururaj Rao and G. S. Nagaraja 53 Solar PV-Driven Swaccha Jal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 Rahul Virmani, Isha Rajput, Satish Kumar Gupta, Sarthak Singhal, Rupali Gupta, and Harsh Kapil

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54 Field Performance Monitoring of Roof-Mounted SPV Systems: Application of Internet-Enabled Technologies . . . . . . . . . . . . . . . . . 559 Navneet Raghunath, M. K. Deshmukh, and Sandip S. Deshmukh 55 Flow Modulation at Micro-combustor Inlet . . . . . . . . . . . . . . . . . . 571 Arees Qamareen, Shahood S. Alam, and Mubashshir A. Ansari 56 Study on Performance of Phase Change Material Integrated Heat Pipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 G. Gnaneshwar, G. Sundara Subramanian, N. S. Hari Thiagarajan, Lakshmi Narayanan, and D. Senthil Kumar 57 Design and Implementation of Smart Charging for LMV . . . . . . . . 591 A. Jeevitha, K. Vasudeva Banninthaya, and G. S. Srikanth 58 Experimental Transient Analysis of Radial Flow Clay Desiccant Packed Bed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601 Abhijeet Boche and Ravikiran Kadoli 59 Coral—A Smart Water Body Health Monitoring System . . . . . . . . 609 Saket Vaibhav, R. Shakthivel, Nikhil Suresh, S. Jyothsna, Arijit Datta, and K. Chitra 60 Recent Investigation on Ultrasonic Machining of Aluminum Metal Matrix Composite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619 Rajkumar Ashok Patil-Tekale, Aditya Gadekar, Yash Gadhade, Laukik Parakh, R. Balaji, and Ashish Selokar 61 Military Reconnaissance and Rescue Robot with Real-Time Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 Rakshana Ismail and Senthil Muthukumaraswamy 62 Finite Element Analysis and Design of a Four-Helical Coiled Single Lumen Microcatheter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649 Mallapi Debashree Gayatri Reddy, Ruby Mishra, and Manoranjan Mohapatra 63 Wear Modeling Revisited Using Feedback Control Theory . . . . . . . 659 M. Hanief and M. S. Charoo 64 Performance Assessment of Improved Solar Still Design with Stepped-Corrugated Absorber Plate . . . . . . . . . . . . . . . . . . . . 667 Aasawari Bhaisare, Unmesh Wasnik, Aniket Sakhare, Pawan Thakur, Akash Nimje, Abhishek Hiwarkar, Vikrant Katekar, and Sandip Deshmukh 65 Parametric Analysis of Adhesively Bonded Single Lap Joint Using Finite Element Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675 Abdul Aabid, Sher Afghan Khan, Turki Al-Khalifah, Bisma Parveez, and Asraar Anjum

Contents

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66 Modelling and Analysis of Flat Disc Brake for Dynamic Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687 K. Viswanath Allamraju 67 Robust PV Fed Discrete Controller for Heating and Lighting Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697 K. Viji, K. Chitra, and K. Uma Maheswari 68 Study of Effect of Variation of Parameters on the Performance of a Solar Still . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 Twinkle Rane, Parthsarathi Mulay, Namrata Kala, and Archana Thosar 69 Friction and Wear Performance of Jatropha Oil Added with Molybdenum Disulphide Nanoparticles . . . . . . . . . . . . . . . . . . 715 Zahid Mushtaq and M. Hanief 70 Layer Based Fabrication of Human-Scaled Body Parts by Using Pneumatic Extrusion Method . . . . . . . . . . . . . . . . . . . . . . 723 O. Y. Venkata Subba Reddy, V. Venkatesh, A. N. R. Reddy, and A. L. S. Brahma Reddy 71 Fuzzy-Based Power Management Strategy for Performance Improvement of Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . 733 J. S. Rakhi and T. Rajeev 72 Design of Pitch Box-Mounting Tool . . . . . . . . . . . . . . . . . . . . . . . . 745 K. S. Prakasha, Shrishail Kakkeri, and D. Amaresh Kumar 73 Heat Transfer Enhancement in Automobile Radiator Through the Application of CuO Nanofluids . . . . . . . . . . . . . . . . . . . . . . . . . 757 M. Chandra Sekhara Reddy and Veeredhi Vasudeva Rao 74 Positioning of Wind Turbine in a Wind Farm for Optimum Generation of Power Using Genetic Algorithm for Multiple Direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769 Khalid Anwar and Sandip Deshmukh 75 Eco-Efficiency and Business Performance Evaluation—Lean and Green Manufacturing Approach . . . . . . . . . . . . . . . . . . . . . . . 779 R. Kishore, R. Pradeep, Suyash Roy, K. Ravi Teja, M. S. Narassima, K. Ganesh, and S. P. Anbuudayasankar Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791

About the Editors

Dr. A. N. R. Reddy Professor of Mechanical Engineering and Head of the Department, obtained his B.Tech. and M.Tech. from JNT University, Hyderabad, India, and Ph.D. from Universiti Malaysia Sarawak, Malaysia. Dr. A. N. R. has received many prestigious research innovation and academic excellence awards that include Malaysian Government International Student Award ‘Malaysian International Scholarship’; Zamalah Graduate Scholarship; Two Silver medals and best Paper Presentation Awards for his state-of-the-art for research innovations in biofuels and nano catalysts. His main research domains are bioenergy, pyrolysis of biomass, synthesis of nano materials, spectrophotometry, applied & fluid mechanics, modelling, optimization, DOE and TRIZ. Dr. A. N. R., as a Principle Investigator, has successfully completed AICTE, Government of India sponsored research project entitled ‘Multi Objective Optimization of Production Process Parameters using Evolutionary Algorithms’, and guided many PG and UG projects. He is a life member of several professional associations such as ORSI, ISTAM, IndACM, ISTE, EWB, SAE India and ISSMO. Having over 20 years of service in both academics and research, he has more than 35 publications to his credit in various ISI/ Scopus indexed journals and Conference proceedings. Dr. A. N. R. was an Organizing Chair and Editor for SIST Series ‘Proceedings of Intelligent Manufacturing and Energy Sustainability 2019’, also he is actively involved in organizing trainings, seminars, conferences, FDPs and workshops for the benefit of academia. Dr. Deepak Marla is currently working as Assistant Professor in the Department of Mechanical Engineering at the Indian Institute of Technology Bombay (IIT Bombay). He has obtained Ph.D. from IIT Bombay and had done his postdoctoral work from the Technical University of Denmark and University of Illinois at Urbana–Champaign. His work is in the domain of micro-/nano-manufacturing using advanced techniques that involve lasers, electric discharges, electrochemical reactions, plasmas and micro-tools. His research focuses on gaining a fundamental

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About the Editors

insight into these processes through a synergetic use of multi-physics modelling and simulation, and experiments with an eye on addressing critical challenges at the process level. Dr. Margarita N. Favorskaya is Professor and Head of Department of Informatics and Computer Techniques at Reshetnev Siberian State University of Science and Technology, Russian Federation. Professor Favorskaya is a member of KES organization since 2010, the IPC member and Chair of invited sessions of over 30 international conferences. She serves as Reviewer in international journals (Neurocomputing, Knowledge Engineering and Soft Data Paradigms, Pattern Recognition Letters, Engineering Applications of Artificial Intelligence), Associate Editor of Intelligent Decision Technologies Journal, International Journal of Knowledge-Based and Intelligent Engineering Systems and International Journal of Reasoning-based Intelligent Systems, Honorary Editor of the International Journal of Knowledge Engineering and Soft Data Paradigms, Reviewer, Guest Editor and Book Editor (Springer). She is the author or the co-author of 200 publications and 20 educational manuals in computer science. She co-authored/co-edited seven books for Springer recently. She supervised nine Ph.D. candidates and is presently supervising four Ph.D. students. Her main research interests are digital image and video processing, remote sensing, pattern recognition, fractal image processing, artificial intelligence and information technologies. Suresh Chandra Satapathy is currently working as Professor, KIIT Deemed to be University, Odisha, India. He obtained his Ph.D. in Computer Science Engineering from JNTUH, Hyderabad, and Master’s degree in Computer Science and Engineering from National Institute of Technology (NIT), Rourkela, Odisha. He has more than 27 years of teaching and research experience. His research interest includes machine learning, data mining, swarm intelligence studies and their applications to engineering. He has more than 98 publications to his credit in various reputed international journals and conference proceedings. He has edited many volumes from Springer AISC, LNEE, SIST and LNCS in the past, and he is also the editorial board member in few international journals. He is a senior member of IEEE and a life member of Computer Society of India. Currently, he is National Chairman of Division-V (Education and Research) of Computer Society of India.

Chapter 1

Metallographic Analysis of the Percentage of Carbon in the Test Tube Based on Artificial Vision Luigi O. Freire, Luis M. Navarrete, Byron P. Corrales, and Jefferson A. Porras Abstract This research was born with the purpose of accrediting metallographic analysis tests aimed at microstructure composition techniques and morphological analysis of metallic materials, which is mainly used in the national vehicle bodywork industry. It is based on the use of artificial vision tools. It is necessary to have the requirements of the NTE INEN ISO / IEC 17025: Standard 2006 that stipulates the General Requirements for the competence of testing and calibration laboratories. An intercomparison test of results is implemented to validate the traceability of measurements and to allow technical analysts to demonstrate knowledge of how results are obtained through commercial metallographic analysis software. The comparison of an acquisition, post-processing and results generation protocol of a commercial image processing tool, with another one developed in-house using free software is known as OpenCV.

1.1 Introduction Metallographic testing laboratories need to prove competent to guarantee a level of confidence in their results reports [1]. Center for the Promotion of Metal-Mechanical Production CFPMC is in the implementation of a Metallographic Analysis Laboratory, in which the need to have validation techniques developed by the laboratory is present to guarantee whether the methods used are valid according to the requirements L. O. Freire (B) · L. M. Navarrete · B. P. Corrales · J. A. Porras Universidad Técnica Cotopaxi, Latacunga, Ecuador e-mail: [email protected] L. M. Navarrete e-mail: [email protected] B. P. Corrales e-mail: [email protected] J. A. Porras e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_1

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of normative requirements of the INEN ISO/IEC 17025 standard, which provides compliance to both national and international traceability [2]. The software acquired for the first implementation is the Stream Basic version 1.9 of the Olympus brand and has tools for acquisition of metallographic images in red green and blue “RGB (red, green, blue)” [3] and grayscale. In addition, it allows capturing photos for further analysis using tools to support the metallographic characterization of steels. However, according to what the quality standard for laboratories imposes, a greater knowledge is required of how these results are obtained by technological means and how these can be compared to demonstrate their validity. The most common forms for this purpose are the use of standard test tubes, inter-laboratory tests or proprietary methods developed by the laboratory. Ordinary carbon steels are essentially iron and carbon alloys with a content of up to 1.2% carbon and from 0.25 to 1% manganese, as well as smaller amounts of other elements; but in general, most steels contain less than 0.5% carbon [4]. Historically worldwide, a corresponding level of production of 90% has been registered for carbon steels, and the remaining 10% would be alloyed steels. In the compact vehicle industry, more and more alloy steels are used due to the need to lighten weights in the self-supporting structures. According to the national technological level, the car body manufacturers use square profiles in their carbon steel structures, obtained under the reference designation ASTM A36. The task of evaluating the technical competence of a metallography laboratory is based on the verification of compliance with the specific requirements of this reference material, in order to be subsequently evaluated so that a laboratory is competent, this being the Ecuadorian Accreditation Service (Servicio de Acreditación Ecuatoriano SAE) [5]. For these reasons, the CFPMC considers it of great value to start with an intercomparison study of analyzed results of the commercial software image processing with an alternative, and it becomes one of the first steps reached within the evaluation of the technical competence and its formal recognition [6].

1.2 Basis The research is carried out in the CFPMC of the Honorable Gobierno Provincial de Tungurahua (HGPT), exclusively in the Metallographic Analysis Laboratory, Climatic Tests and Thermal Treatments, being this a space to be demanded by the Metalworking, Construction and Energy Industry. The UNE-EN ISO/IEC 17025 standard was designed to be used by testing and calibration laboratories when developing management systems for their quality, administrative and technical activities. When working under the regulations of this standard, their technical competence and the validity of their results are recognized, responding to the demands of the organizations or entities and giving themselves credibility to their clients [7, 8].

1 Metallographic Analysis of the Percentage of Carbon …

3

1.3 Methodology The procedure and the preparation of the material were done according to ASTM E-3 [9], which specifies the different procedures for the correct preparation of metallographic test tube.

1.3.1 Selection and Cutting of the Material to Be Analyzed The area selections to analyze the square profile were: in the cross section to analyze the microstructure and in the flat section to observe the inclusions. The sections in the metallographic test tube were cut to avoid affecting the thermal structure of the material. The sample must be manipulatable [10], but due to the small size, these samples must be encapsulated to proceed to the next process.

1.3.2 Test Tube Mounting The test tube was mounted on phenolic resin with the help of the metallographic press according to standardized procedure [10]. The amount of resin that was used was 15 gr. at a pressure of 4 bar with a heating temperature of 170 °C and a cooling temperature of 60 °C, with an encapsulation time of 8 min.

1.3.3 Test Tube Roughing The surface of the test tube passes through the roughing metallographic, in order to flatten the surface and leave the same list for the polishing machine.

1.3.4 Gross Roughing Once the test tube was encapsulated, the section to be analyzed was polished, the abrasive process starts by using the disk number 120 during 5 min, the disk number 220 was used for a period of 15 min and finally placed disk number 600 for 20 min [10]. In case the disk 600 is not efficient, it has a number 1200 abrasive disk. This disk is recommended to be used to obtain a specular surface (it is the ideal surface in which the law of reflection is perfectly fulfilled (incident angle = reflected angle) [11].

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a.100X

b.200X

c.500X

Fig. 1.1 Magnification

The initial speed of each polishing was 4 level (50 rpm), and as soon as the polishing process advances, the speed increases from level 8–10 (200–300 rpm) [12].

1.3.5 Fine Roughing The fine polishing is finally performed, and the DiaMax Poly 3 um diamond paste is used, the knob is changed to oil, a constant drip flow is regulated. The polishing time must be that necessary for the surface to be speculated [10].

1.3.6 Chemical Attack Counting with a washed and dried test tube, it was chemically attacked with Nital (alcohol and nitric acid), from 2 to 5% of Nital to oxidize the section to be analyzed, in this procedure, the pearlite darkens and differs from the ferrite. After 35 s, the acid is removed with abundant water, and again, it is dried with a current of air by means of the compressor to later observe the test tube in the microscope, proceed to the microscope and observe the microstructure at 100×, 200× and 500× (Fig. 1.1).

1.3.7 Method of Interception (or Heyn) The grain size is estimated by counting by means of a divided glass screen, the number of grains intersected by one or more straight lines. The length of the line in millimeters, divided by the average number of grains intersected by it, gives the average grain intercept length. An intercept is a segment of the test line that passes over a grain. An intersection is a point where the test line is cut by a grain edge. Either of the two can be counted

1 Metallographic Analysis of the Percentage of Carbon …

5

with identical results in a single-phase material. When intercepts are counted, the segments at the end of the test line that penetrate into a grain are scored as a half intercept. When intersections are counted, the endpoints of the test line are not intersections and are not counted except when they touch exactly one edge of grain, then 1/2 intersection must be noted. An intersection coinciding with the union of three grains should be noted as 11/2 as marked by ASTM E 112 [13]. G = (6.643856 log 10 × NL) − 30288

(1.1)

NL = N i/(L/M)

(1.2)

Being: G NL Ni L M

Grain size Number of grains per mm Intercepted grains Length of the online test Magnification.

1.3.8 Procedure Analysis with Privative Software The microstructure of the metallographic test tube, where it is observed that it corresponds to a ferritic–pearlite steel, that is to say, a carbon steel with a low percentage of carbon. For this, the software marks the carbon grains with red (Fig. 1.2). Next, the calculation of the percentages relative to the red color is shown. 255 100% 90 x x = 35.294%

Fig. 1.2 Magnification 200× and image segmentation

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The percentage of pearlite is 32.654%  %C − 0.008 ∗ 100 %P = 0.8 − 0.008 

(1.3)

%C = 0.2875%

1.3.9 Grain Size Determination In the determination of the grain size, the interception method has been used, with which the grain size was determined (Fig. 1.3): The following tables show the quantification by the divisions made in the horizontal as well as the vertical direction (Table 1.1). Vertical Direction In the vertical direction, the grain number was determined by computer-aided drawing tools. In addition, its size was calculated by the recommended formulation as shown below: Fig. 1.3 Measuring grain size measured with CAD tools

Table 1.1 No. of grains

No. of lines

No. of grains vertical lines

No. of grains horizontal lines

1

51

71

2

55

70

3

53

72

4

56

70

5

54

71

1 Metallographic Analysis of the Percentage of Carbon …

No. of Grainsaverage =

51 + 55 + 53 + 56 + 54 = 53.8 grains 5

500 µm = 9.29368 µm 53.8 grains   E G = 10 − 6.64391 log 10

E=

7

(1.4) (1.5) (1.6)

G vertical = 10.211357 ∼ = 10 The grain size obtained is 10.211357, the average diameter is 9.29368 µm, and therefore, the grain size in the vertical direction is 10. Horizontal Direction This is obtained in the same way as in the vertical direction as it is presented in the following development: Grains numbers(average) = 71 + 70 + 72 + 70 + 71 = 70.8 grains E = 9.322 µm G Horizontal = 10.2025 ≈ 10 The grain size obtained is 10.2025, the average diameter is 9.322 µm, and therefore, the grain size in the horizontal direction is 10. When making the comparison of microstructures, it can be clearly seen that the grain size is number 10, when compared with the photograph of the Metals Handbook (Fig. 1.4). The process is summarized in the conversion of RGB color spaces to grayscale and the conditioning through different filtering algorithms in order to have an image banalization that contains information close to reality. The post-processing is done with pixel count programming to discriminate and differentiate the characteristics Fig. 1.4 Comparison with pattern images of the metal handbook [14]

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of metallographic structures. This process generates as a result of the percentage corresponding to the clearest images represented by the ferrite and the darker ones that represent the pearlite, according to the material analyzed. As a synthesis of the implemented algorithm, it can be summarized that the image of the microscope is taken, and as a first step, a grayscale image transformation is carried out, as every real image has a certain noise inherent to the sensor, it is necessary to implement a noise reduction stage, in this case as a first step, a Gaussian smoothing with a minimum kernel is used to then perform a Laplacian convolution. Next, it is necessary to make an “opening” to delimit and improve the contours of each analyzed grain, thus leaving the image ready to perform a threshold search that minimizes the intraclass disagreement, defined as a weighted sum of disagreement of two classes, being the weights, the probability of separating the two classes by a threshold based on the maximum disagreement among the elements of the present classes. Once the dynamic binarization has been carried out, as a next step, it is necessary to delimit the contours of the structures found in the image to calculate the percentage represented by the black regions in relation to the blank regions present in the image analyzer, which is 572 px per side. The filtering stage has been determined for convolution and morphological processing in their respective stages of erosion and expansion according to what is described in the main program. The result of what is described can be seen in the following images. The binarization shown in Fig. 5d is the penultimate process of post-treatment step to obtain the percentages of ferrite and pearlite in the case of carbon steels obtained by image capture using the microscope, and this is compared to the total pixels of the image and is shown as a percentage (Fig. 1.6).

1.4 Results Analysis The implemented algorithm, it can be summarized that the image of the microscope is taken and as a first step a grayscale image transformation is carried out, as every real image has a certain noise inherent to the sensor, it is necessary to implement a noise reduction stage, in this case as a first step a Gaussian smoothing with a minimum kernel is used to then perform a Laplacian convolution. Next, it is necessary to make an “opening” to delimit and improve the contours of each analyzed grain, thus leaving the image ready to perform a threshold search that minimizes the intraclass disagreement, defined as a weighted sum of disagreement of two classes, being the weights, the probability of separating the two classes by a threshold based on the maximum disagreement among the elements of the present classes. Once the dynamic binarization has been carried out, as a next step, it is necessary to delimit the contours of the structures found in the image to calculate the percentage

1 Metallographic Analysis of the Percentage of Carbon …

9

Fig. 1.5 Filtering of images

Fig. 1.6 Pixel count

represented by the black regions in relation to the blank regions present in the image analyzer, which is 572 px per side. The metallographic sample obtained through a standard preparation process guaranteed the quality and reliability of the observations of the material and its digital acquisition through the payment program Stream Basic. The software Scope Photo, used in the conventional process for the determination of the percentage of pearlite, depends on the experience and the visual acuity of the technician to aim at the correct segmentation of reds within the image. This practice is common in the laboratory because the commercial software does not have solvers for direct post-treatment in this aspect.

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The developed process allowed to determine percentages close to those of an experienced staff and its direct calculation, demonstrating that the technical competence is valid according to this essay. The result obtained from the percentage of pearlite maintains a difference somewhat greater than 10% because it no longer depends on the experience of the technician and the method used, and it should be noted that the system corrects the possible human failure that may exist in the evaluation of each trial. Another important fact is the recalculation of equivalent carbon C = 0.2499% with a variation of 3%.

1.5 Conclusions The type test presented as a reference by the Ecuadorian Accreditation Service allowed evaluating and knowing all the gaps in the data acquisition system that the laboratory has, being a clear starting source to comply with the technical part of the developed test. Based on the classical method, it can be determined that the analyzed specimen of a square profile of [25 × 3 mm] is probably an ASTM A36 steel because it is a carbon steel with a content of 0.26% C, the size grain obtained by the intersection method was grain size number 10. Metallographic analysis methods require special attention to image processing to generate reliable results. The system presented allows the addition of new characteristics for material analysis according to technical requirements, this being the starting point for new jobs such as the determination of grain size.

References 1. Standars Council of Canada, https://www.scc.ca/ 2. Servicio de Acreditación Ecuatoriano, https://www.acreditacion.gob.ec/ 3. R.A.B. Fernandes, B. Diniz, R. Ribeiro, M. Humayun, Artificial vision through neuronal stimulation. Neurosci. Lett. 519, 122–128 (2012) 4. W.F. Smith, Fundamentos de La Ciencia e Ingeniería de Materiales (Xoncepción Fernandez Madrid, España, 2018). 5. R. Mendoza, Elaboración de la documentación y el manual de calidad bajo la norma ISO/IEC 17025 para el laboratorio de Física en la facultad de Mecánica con fines de acreditación ante el Organismo de Acreditación Ecuatoriano (OAE), Riobamba (2014) 6. L&S Consultores C.A., www.lysconsultores.com 7. International Organization for Standardization, https://www.iso.org/ 8. International Electrotechnical Commission, https://www.iec.ch/ 9. T. Kevin O’brien, Composite materials: fatigue and fracture, vol. 4, ed. by W.W. Stinchcomb , N.E. Ashbaugh (1993) pp 507–537 10. ASTM: Standard practice for Preparation of Metallographic Test tube, Annual Book of ASTM standards (2009), pp. 1–8

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11. E. Rivas, Comportamiento Espectral de Superficies (Technical report, Optativa Sensores Remotos LTA, 2019). 12. TECMICRO S.A., https://tecmicro.es/ 13. ASTM: Standard Test Methods for Determining Average Grain Size, Annual Book of ASTM standards (2013), pp. 1–27 14. ASTM, Metals Handbook Desk, ASM International, Annual Book of ASTM standards (1998)

Chapter 2

Machinability Study of “Nickel Material” in Deep Micro-holes Fabrication Through µECM Md. Zishanur Rahman, Alok Kumar Das, and Somnath Chattopadhyaya

Abstract In current years, demands of parts and products with deep micro-holes have fast raised. Micro-electrochemical machining (μECM) is one of the costeffective techniques and a better alternative for the fabrication of deep micro-holes in hard-to-machine materials with precise dimensions and good surface finish. Selection of suitable electrolyte material plays most important role in μECM of a particular material. Quality of deep micro-holes can be highly controlled by selecting suitable electrolyte material as well as its concentration. In this research, aqueous solution of H2 SO4 electrolyte (acidic) is used for the study of machinability characteristics of “nickel material” in deep micro-holes fabrication through the process of μECM. To the best of author’s knowledge a very few studies has been attempted in deep microholes fabrication in “nickel material” through μECM. In this study, all experiments are conducted using Taguchi L9 (33 ) OA design with fabricated cylindrical tungsten micro-tool electrode of diameter 108 μm. Machining parameters are optimized using Taguchi technique, and ANOVA is employed to investigate the influence of these parameters on the response outputs such as average diameter (Dh), overcut (OC), and diameter difference (Dd). In last, the dominant machining parameters for the responses have been found out, and the regression models have been developed.

Md. Z. Rahman (B) · A. K. Das · S. Chattopadhyaya Department of Mechanical Engineering, Indian Institute of Technology (ISM), Dhanbad, India e-mail: [email protected] A. K. Das e-mail: [email protected] S. Chattopadhyaya e-mail: [email protected] Md. Z. Rahman Department of Mechanical Engineering, Nalanda College of Engineering, Chandi (Nalanda), DST, Govt. of Bihar, Nalanda, Bihar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_2

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2.1 Introduction Tool-wear rate, MRR, surface roughness, over cut, aspect ratio, diameter error, etc., are some of the inherent characteristics of machinability which are commonly used for measuring the machining performance. A low cutting force diminishes the tool-wear and enhances machining performance, and a low surface roughness on machined surface indicates the favorable cutting performance with acceptable surface quality. The conventional methods for micromachining encounter various problems such as residual stresses, unintended heat generation near cutting zone, poor surface quality, high cutting force, rapid wear of tool, and extensile burr formation. μECM is one of the cost-effective techniques to machine micro-components with reasonably precise dimensions and good quality of surface finish for hard-tomachine and exotic materials needed for various industrial applications, especially in aerospace industries, electronic, and computer [1–5]. These days, demands of products with micro-holes have increased. Wire drawing dies, miniature oil sprayers, turbine blades, miniature mixers, cooling channels, spinner holes, miniature oil atomizers, inkjet printer nozzle, diesel fuel injection nozzles, drug delivery orifices, etc., are some of the widely used products which contain the micro-holes [6–9]. Economy of production cost for deep micro-hole fabrication is necessary along with precise dimension and good quality of surface finish. Considering these requirements, μECM have turned out to be a useful and effective alternative for producing deep microholes in exotic and hard-to-machine materials [10, 11]. Acidic electrolytes have more advantages for μECM process as the reaction products dissolved in the electrolytic solution during the electrolysis. This permit the inter electrode gap (i.e., gap between the micro-tool electrode and the micro-hole) to be produced as small as possible [12]. In this research, an acidic electrolyte, i.e., aqueous solution of H2 SO4 electrolyte, is used for the study of machinability characteristics of “nickel material” in deep micro-holes fabrication through μECM process. Pulsed DC voltage (V), electrolyte concentration (Mol/L), and pulse frequency (KHz) are chosen as machining parameters in order to investigate their effects on response outputs, i.e., average diameter of micro-hole “Dh” [μm], radial overcut “OC” [μm], and diameter difference of microhole “Dd” [μm]. All experiments are conducted using Taguchi L9 (33 ) orthogonal array (OA) design with fabricated cylindrical tungsten micro-tool electrode of diameter 108 μm. Machining parameters has been optimized using Taguchi technique. In last, the dominant machining parameters for the responses have been found out by employing ANOVA, and the regression models have been developed.

2.2 Experimentation An in-house sinking type μECM setup (as shown in Fig. 2.1a) is used to fabricate deep through micro-holes in nickel plate. Anodic workpiece of nickel material having thickness 1050 μm is clamped on a fabricated fixture inside the machining chamber,

2 Machinability Study of “Nickel Material” in Deep …

15

Fig. 2.1 a μECM setup, b enlarge view of machining chamber, c setup configuration

and the machining chamber is filled with the H2 SO4 electrolyte as shown in Fig. 2.1b, c. During the fabrication of micro-hole, the pulsed DC power supply is connected across the micro-tool electrode (cathode) and the workpiece (anode). For holding this micro-tool electrode, ultra-precision spindle-collet is used. This cathodic micro-tool electrode is emerged just 2 mm deep inside electrolyte during all the experiments. For each of the experiments, fresh electrolyte is used for maintaining the uniform pH of the electrolyte which is also important to get accurate experimental results. After many trial runs, the feasible working range (low level and upper level) of each machining parameters is decided. All the experiments are conducted with a maximum constant tool feed of 50 μm/minute and a constant duty cycle of 49%.

2.2.1 Experimental Design Orthogonal array, control factors, and response factors are selected according to Taguchi design technique. The selected control factors for this study are three machining parameters: pulsed DC voltage (V), electrolyte concentration (Mol/L), and pulse frequency (KHz). For three control factors, three level tests for each factor are taken as given in Table 2.1. Average diameter of micro-hole “Dh” [μm], radial overcut “OC” [μm], and diameter difference of micro-hole “Dd” [μm] are chosen as response factors for the experimentation. To accommodate three control factors (machining parameters) and their three levels, standard Taguchi’s L9 (33 ) OA design is selected for achieving the objectives of how the controlled parameters influence the response factors (output), and what are the optimum machining parameters to

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Table 2.1 Control factors and its values for the experiments Control factors (machining parameters)

Code

Levels 1

2

3

Pulse DC voltage (V)

X

9

11

13

Electrolyte concentration (Mol/L)

Y

0.4

0.6

0.8

Pulse frequency (KHz)

Z

160

180

200

Table 2.2 Design of experiments (L9 OA-design) and corresponding results Exp. run

Mach. Param. and levels

Designation

di (μm)

do (μm)

Dh (μm)

OC (μm)

Dd (μm)

X

Y

Z

1

1

1

1

X1Y 1Z1

703

541

622

257.0

162

2

1

3

1

2

2

X1Y 2Z2

731

561

646

269.0

170

3

3

X1Y 3Z3

788

594

691

291.5

4

194

2

1

2

X2Y 1Z2

797

601

699

295.5

196

5

2

2

3

X2Y 2Z3

849

641

745

318.5

208

6

2

3

1

X2Y 3Z1

944

698

821

356.5

246

7

3

1

3

X3Y 1Z3

932

680

806

349.0

252

8

3

2

1

X3Y 2Z1

1006

734

870

381.0

272

9

3

3

2

X3Y 3Z2

1115

807

961

426.5

308

obtain minimum average diameter, minimum overcut, and minimum diameter difference of micro-hole. According to Taguchi’s L9 orthogonal array design (Table 2.2), nine experimental runs are conducted based on deep through micro-holes fabrication in nickel plate through the process of μECM using H2 SO4 electrolyte (aqueous). Microscopic views of fabricated micro-hole at experimental run-2 are explained in Fig. 2.2.

2.2.2 Measurement of Responses (“Dh,” “OC,” and “Dd”) In this study, response output such as “Dh,” “OC,” and “Dd” are calculated for each machined micro-hole by using equation number 2.1, 2.2, and 2.3, respectively, which are formulated according to the geometry of fabricated micro-hole through μECM. A metallurgical microscope (Model: BX51M of OLYMPUS) was used to measure the diameters di and do. (i)

Average diameter; Dh =

di + do 2

(2.1)

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Fig. 2.2 Microscopic views of fabricated micro-hole at experimental run-2

(ii) Radial overcut; OC =

Dh − Dt 2

(2.2)

(iii) Diameter difference; Dd = di − do

(2.3)

where di do Dt

Diameter of micro-hole at entrance (μm) Diameter of micro-hole at exit (μm) Diameter of micro-tool electrode (μm).

2.3 Results and Discussions Under this section, all experimental results are analyzed through S/N ratio and ANOVA. The optimum machining parameters required for the minimum “Dh,” minimum “OC” and minimum “Dd” are obtained by using Eq. 2.4 in which “y” is the observed data. For all “Dh,”, “OC,” and “Dd,” S/N ratios and level values are calculated using MINITAB-17 software. Table 2.2 depicts the design of experiments and their corresponding results. The level of a response output with the greatest S/N ratio gives an optimal level, regardless of the type of response characteristics (such as “Dh,” “OC,” “Dd”). For analyzing the effects of machining parameters on “Dh,” “OC,” and “Dd,” main effects plot of S/N ratios and interaction plot are generated as shown in Figs. 2.3, 2.4, 2.5 and 2.6. S/N ratio equation:

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Fig. 2.3 a Main effects plot of SN ratio and b interaction plot for “Dh”

Fig. 2.4 a Main effects plot of SN ratio and b interaction plot for “OC”

Fig. 2.5 a Main effects plot of SN ratio and b interaction plot for “Dd”

For smaller is the better characteristic, (minimize): S 1  2  = 10 log y N n

(2.4)

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Fig. 2.6 Comparison between experimental and predicted values of a “Dh” b “OC” c “Dd”

2.3.1 Analysis of Responses (“Dh,” “OC,” and “Dd”) Figures 2.3a, 2.4a, and 2.5a reveal that the “Dh,” “OC,” and “Dd” obtained are the minimum (optimal) at the first level of pulsed DC voltage (X1), the first level of electrolyte concentration (Y 1), and the third level of pulse frequency (Z3). As a result, optimal parameter for all “Dh,”, “OC,” and “Dd” is X1Y 1Z3, i.e., puled DC voltage 9 V, electrolyte concentration 0.4 Mol/L, and pulse frequency 200 KHz. According to the S/N ratio table, it is observed that pulsed DC voltage (V) has more influence, electrolyte concentration (Mol/L) has moderate influence, and pulse frequency (KHz) has less influence on “Dh,”, “OC,” and “Dd” all in the fabrication of deep micro-holes in nickel plate through μECM process under H2 SO4 electrolyte. Main effect plot of mean for “Dh,”, “OC,” and “Dd” as shown in Figs. 2.3b, 2.4b, and 2.5b indicate that “Dh,”, “OC,” and “Dd” all increase with increases of electrolyte concentration as well as pulsed DC voltage and decreases with increase of pulse frequency [13–15]. ANOVA has been applied for significance level α = 0.05 (or confidence level = 95%). Control factors (machining parameters) with P-value obtained < 0.05 are acknowledged as statistically significant contribution. Following are the ANOVA results for: (i)

Average diameter (Dh) of fabricated micro-holes; illustrate that the pulsed DC voltage has more influence (77.60%) on the “Dh” which are statistically significant, while electrolyte concentration (20.48%) on the “Dh” has moderate influence, which are also statistically significant in the fabrication of deep microhole in nickel plate through μECM process under H2 SO4 electrolyte. Pulse frequency has least influence on “Dh,” which is statistically not significant. The error contribution is 0.54.89% for “Dh.” (ii) Overcut (OC) in fabricated micro-holes; illustrate that the pulsed DC voltage has more influence (77.60%) on the “OC” which are statistically significant, while electrolyte concentration (20.48%) on the “OC” has moderate influence, which are also statistically significant in the fabrication of deep micro-hole in nickel plate through μECM process under H2 SO4 electrolyte. Pulse frequency has least influence on “OC,” which is statistically not significant. The error contribution is 0.89% for “OC.”

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(iii) Diameter difference (Dd) in fabricated micro-hole; illustrate that pulsed DC voltage (81.73%) on the “Dd” has more influence, which are statistically significant, while the electrolyte concentration (17.39%) on the “Dd” has moderate influence, which are also statistically significant in the fabrication of deep micro-hole in nickel plate through μECM process under H2 SO4 electrolyte. Pulse frequency has least influence on “Dd,” which is statistically not significant. The error contribution is 0.25% for “Dd.”

2.4 Development of Regression Models Regression modeling has been done for obtaining the relationship between cutting parameters [“X,” “Y,” and “Z”] and response outputs [“Dh”, “OC” and “Dd”] using statistical software “MINITAB-17.” After neglecting insignificant coefficient, the developed regression models are: (a) Regression model of average diameter; Dh = 891.3 − 51X − 530Y − 1.025Z + 2.75X 2 + 270.8Y 2 − 0.000833Z 2 + 45.83X Y + 0.1083X Z

(2.5)

For which R2 = 99.94% (b) Regression model of radial overcut; OC = 391.6 − 25.5X − 265Y − 0.5125Z + 1.375X 2 + 135.4Y 2 − 0.000417Z 2 + 22.92X Y + 0.05417X Z

(2.6)

For which R2 = 99.96% (c) Regression model of diameter difference Dd = 353.3 − 41.67X − 231.7Y − 0.08333Z + 2.417X 2 + 216.7Y 2 − 0.001667Z 2 + 8.333X Y + 0.05X Z

(2.7)

For which R2 = 99.91% In regression model analysis, usually R2 value is used for validating the developed regression models, and R2 value should lie between 0.8 and 1.0 [16]. In current study, the developed regression models [Eqs. (2.5), (2.6) and (2.7)] are consistent because of R2 is greater than 90%. The predicted values obtained from developed regression models are compared with the experimental values of “Dh,”, “OC,” and “Dd” as shown in Fig. 2.6a–c, respectively. These figures predict that the variations between predicted and experimental values are very minimal. Therefore, the developed regression models of second-order are statistically significant for “Dh,”, “OC,”

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and “Dd.” Hence, these models [Eqs. (2.5), (2.6), and (2.7)] can be used for further analysis.

2.5 Conclusions This article has been focused on the machinability study of nickel material in deep micro-hole fabrication through the process of μECM using fabricated tungsten micro-tool electrode (cylindrical) under H2 SO4 electrolyte. Following conclusions are summarized on the ground of experimental results and their analysis: • Pulsed DC voltage 9 V, electrolyte concentration 0.4 Mol/L, pulse frequency 200KHz, duty cycle 49%, and feed rate 50 μm/min are the optimum parameters for minimum average diameter “Dh,” minimum over cut “OC” and minimum diameter difference “Dd” in the fabrication of deep micro-hole in nickel plate through μECM process under H2 SO4 electrolyte. • Interaction plot for “Dh,”, “OC,” and “Dd,” indicate that “Dh,” “OC,” and “Dd” all increase with increase of electrolyte concentration as well as pulsed DC voltage. • ANOVA results for “Dh” and “OC” indicate that the pulsed DC voltage has more influence (77.60%) on the “Dh” and “OC,” both which are statistically significant, while electrolyte concentration (20.48%) on the “Dh” and “OC” both has moderate influence, which are also statistically significant in the fabrication of deep micro-hole in nickel plate through μECM process under H2 SO4 electrolyte. Pulse frequency has least influence on “Dh” and “OC,” which is statistically not significant. The error contribution is 0.54.89% for “Dh” and “OC” both. • ANOVA results for “Dd” indicate that the pulsed DC voltage (81.73%) on the “Dd” has more influence, which are statistically significant, while the electrolyte concentration (17.39%) on the “Dd” has moderate influence, which are also statistically significant in the fabrication of deep micro-hole in nickel plate through μECM process under H2 SO4 electrolyte. Pulse frequency has least influence on “Dd” which are statistically not significant. • Since the developed regression models are statistically significant, these models can be used for further analysis. • Experimental results indicate that still there is need of development in electrolyte or development in electrolyte control system or development in electrodes to improve the machinability characteristics (such as overcut, aspect ratio, and diameter difference of micro-hole) of “nickel material” in deep micro-hole fabrication through μECM.

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References 1. T. Geethapriyan, V. Thulasikanth, V. Singh, A.C.A. Raj, T. Lakshmanan, A. Chaudhury, Performance characteristics of electrochemical micro ma-chining of tungsten carbide. Mater. Today Proc. 27(3), 2381–2384 (2020) 2. X.L. Chen, G.C. Fan, C.H. Lin, B.Y. Dong, Z.N. Guo, X.L. Fang, N.S. Qu, Investigation on the electrochemical machining of micro groove using masked porous cathode. J. Mater. Process. Technol. 276, 116406 (2020) 3. A. Kadirvel, P. Hariharan, S. Gowri, Experimental investigation on the electrode specific performance in micro-EDM of die-steel. Mater. Manuf. Process 28(4), 390–396 (2013) 4. S.S. Park, M. Malekian, Mechanistic modeling and accurate measurement of micro end milling forces. CIRP Ann. Manuf. Technol. 58, 49–52 (2008) 5. M.Z. Rahman, A.K. Das, S. Chattopadhyaya, Microhole drilling through electrochemical processes: a review. Mat. And Man. Proc. 33(13), 1379–1405 (2017) 6. B. Muralikrishnan, J. Stone, Fiber deflection probe uncertainty analysis for micro holes. NCSLI Meas. 3(1), 38–44 (2016) 7. M.L. Wang, J.Z. Li, Z.Y. Yu, X. Li, Micro-drilling of pre-sintered alumina ceramic. Adv. Mater. Res. 1120–1121, 27–31 (2015) 8. X.L. He et al., Micro-hole drilled by EDM-ECM combined processing. Key Eng. Mater. 562– 565, 52–56 (2013) 9. M. Rusli, K. Furutani, Performance of micro-hole drilling by ultrasonic-assisted electrochemical discharge machining. Adv. Mater. Res. 445, 865–870 (2012) 10. Y.J. Chang, Y.C. Hung, C.L. Kuo, J.C. Hsu, C.C. Ho, Hybrid stamping and laser micromachining process for micro-scale hole drilling. Mater. Manuf. Process. 32(15), 1685–1691 (2017) 11. D. Zhu, W. Wang, X.L. Fang, N.S. Qu, Z.Y. Xu, Electrochemical drilling of multiple holes with electrolyte-extraction. CIRP Ann. Manuf. Technol. 59, 239–240 (2010) 12. R.J. Leese, A. Ivanov, Electrochemical micromachining: an introduction. Adv. Mech. Eng. 8(1), 1–13 (2016) 13. B. Ghoshal, B. Bhattacharyya, Shape control in micro borehole generation by EMM with the assistance of vibration of tool. Prec. Eng. 38, 127–137 (2014) 14. M.A.H. Mithu, G. Fantoni, J. Ciampi, M. Santochi, On how tool geometry, applied frequency and machining parameters influence electrochemical micro-drilling. CIRP J. Manuf. Sci. Technol. 5, 202–213 (2012) 15. P. Agrawa, A. Manoria, H. Jain, R. Thakur, Value engineering of micro manufacturing using ecm and its applications. IJMERR 1, 2 (2012) 16. E. Kuram, B. Ozcelik, Multi-objective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill. Measurement 46(6), 1849–1864 (2013)

Chapter 3

Three-Dimensional FEM Analysis of Nanoparticle-Assisted Radiofrequency Ablation of Tissue-Mimicking Phantom Santosh Shiddaling Naik, Bhanu Prakash Bonthala, and Ajay Kumar Yadav

Abstract Radiofrequency ablation (RFA) is a minimally invasive procedure to damage the cancer cells. In RFA, heat is generated only at the center zone of the tumor, and this heat has to propagate up to the periphery of the tumor. Since the thermal conductivity of phantom is low, it reduces heat transfer rate, and time required for complete ablation of tumor will be more. Since the ablation time is one of the main concerns, it is required to reduce it below the standard time (≈7.3 min). The ablation time can be reduced by injecting the nanoparticles into the tumor. In this paper, numerical studies are conducted on PAG phantom to analyze the effect of nanoparticle assisted RFA on the ablation time. Results indicate that in case of nanoparticles assisted RFA, heat conduction rate increases and takes lesser time (17.56% less) to ablate the tumor completely than that in conventional RFA.

3.1 Introduction In radiofrequency ablation, cancer tissue is ablated by passing electric current in radiofrequency range (350–500 kHz). This causes resistive heating due to which temperature rises, and when the temperature reaches 50 °C, tissue undergoes coagulative necrosis [1]. In conventional RFA, heat conduction rate in tumor is very low due to which ablation time is more. To decrease the ablation time, different treatment methods have been emerged, including microwave ablation [2]. Different electrode designs have also been done viz multiple electrode, bipolar electrode, internally cooled electrode, and perfusion electrode [3]. From the literature studies, it is found S. S. Naik (B) · B. P. Bonthala · A. K. Yadav Department of Mechanical Engineering, NIT Karnataka, Surathkal, Mangalore 575025, India e-mail: [email protected] B. P. Bonthala e-mail: [email protected] A. K. Yadav e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_3

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that a lot of research has been done on the development of alternative RFA electrode design, and little attention has been focused to improving the thermal properties of the tumor in order to improve the destruction of the tumor within less time during the radiofrequency ablation treatment. In this study, iron oxide nanoparticles (thermal conductivity = 40 W/mK [4]) are used to enhance the thermal properties of the tumor (phantom).

3.2 Simulation Methodology We modeled a monopolar (14 gauge, multitine (9 tines)) RF ablation electrode, deployed up to 2 cm. This 9-tine electrode is inserted in the center of the PAG phantom (Fig. 3.1). We performed FEM analysis under the temperature-controlled mode (target set temperature 95 °C) and checked the ablation time for the same tumor size (3 cm diameter) for different cases viz., without nanoparticles and with nanoparticles. We selected the temperature-controlled mode to avoid excessive temperature at the center of tumor so as to avoid the charring of tumor. To validate the developed model, we also conducted an experimental in vitro study using multitine electrode on polyacrylamide gel (PAG) phantom. The material properties used in the numerical study for different domains are presented in Table 3.1 [5, 6] and Table 3.2 [5]. The study is carried out numerically in COMSOL Multiphysics software. Fig. 3.1 3D model representing a tumor (phantom) and multitine monopolar electrode

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Table 3.1 Electrical and thermophysical properties of the phantom, human liver, and electrode FEM region

Electrical conductivity σ (S/m)

Thermal conductivity k(W/m K)

Specific heat capacity c(J/kg-K)

Density ρ(kg/m3 )

Phantom

0.117

0.59

3676

1069

Human liver

0.148

0.52

3800

1060

Electrode

108

18

840

6450

Table 3.2 Kinetic parameters of tissue and tumor

Kinetic properties

Liver

Tumor

Frequency Factor f (1/s)

7.39 ×

1039

Same as liver

Activation energy A (J/mol)

2.577 × 105

Same as liver

3.2.1 Governing Equations The electric voltage distribution within the tumor due to applied voltage on RF electrode can be calculated by using the generalized Laplace equation [7], ∇ · (σ ∇V ) = 0

(3.1)

where σ (S/m) is the electrical conductivity and V (volt) is the applied voltage. The heat energy per unit volume deposited in the tissue is expressed as is given by, Qs = σ · E 2

(3.2)

where E (volt/m) is electric field distribution within the tumor. Temperature distribution within tumor (phantom) is given by Pennes bioheat equation [8], ρc

∂T = ∇(k · ∇T ) + Q s + Q m − ρb cb wb (T − Tb ) ∂t

(3.3)

where ρ, c, k, and T are the density (kg/m3 ), specific heat (J/kg K), thermal conductivity (W/m K), and temperature (K) of tumor (phantom), respectively. ωb , ρ b , cb, and T b are the blood perfusion rate (1/s), density of blood (kg/m3 ), specific heat (J/kg K), and temperature of blood, respectively. Qm (W/m3 ) is the heat generated by metabolic activity (negligible), and Qs (W/m3 ) is the resistive heat generated at the center of the tumor. The induced thermal damage is computed by using the first-order Arrhenius rate equation, t (t) =

A

f e RT dt 0

(3.4)

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Here, T is the tissue temperature at time t, A is the activation energy, f is the frequency factor, and R is the universal gas constant (8314 J/kmol K) [5]. A damage integral Ω = 1.0 (corresponds to 63% probability of cell death), and isothermal temperature 50 °C has been assumed to be the point at which cancerous cells undergoes irreversible damage [1].

3.2.2 Boundary Conditions Initial temperature and voltage of an entire tissue domain have been set to 37 ˚C (core body temperature) and zero volts, respectively. The multitine electrode boundaries have been set to variable voltage source computed by the PID controller. The electric potential on one side of the tumor was set to zero (grounded). Electrical insulation condition has been set for the insulated trocar part. For the other inner boundaries of the FEM model, electrical and thermal continuity boundary conditions have been applied.

3.2.3 Models Used for Computing Effective Properties The effective thermal conductivity of nanoparticle enriched tumor is calculated by using Maxwell’s model [9]. Maxwell is the first person to theoretically investigate conduction of suspended particles. The equation used to calculate effective thermal conductivity is, keff = kt +

3(kn − kt )v (kn + 2kt ) − (kn − kt )v

(3.5)

where k eff , k n , k t are thermal conductivities of nanoparticle enriched tissue, nanoparticle, and liver tissue, respectively.v is the volume fraction of nanoparticles. The effective density and effective specific heat can be approximated as [4], ρeff = ρt (1 − v) + ρn v

(3.6)

ceff = ct (1 − v) + cn v

(3.7)

where ρ eff , ρ n , ρ t are densities of nanoparticle enriched tissue, nanoparticle, and liver tissue, respectively, and ceff , cn, ct are specific heats of nanoparticle enriched tissue, nanoparticle, and liver tissue, respectively.

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Fig. 3.2 Experimental setup of RF ablation

3.3 Experimental Methodology In order to validate the results obtained from numerical study, an experimental study is done on the PAG tissue-mimicking phantom gel [6] using multitine electrode. Here, the electrode is deployed to 2 cm according to manufacturer’s standard. RITA model 1500× RF generator is used for generating high-frequency (460 kHz ± 5%) current. The maximum power output of the RF generator is 250 ± 2 W. Experimental setup is shown in Fig. 3.2. Two K type thermocouples are inserted at 15 and 20 mm from the electrode centerline. Temperature readings at these two locations are acquired by the data acquisition module.

3.4 Results and Discussion Comparison is made between simulation and experimental data at two different locations, one at 15 mm from the electrode center line and another at 20 mm from the electrode center line as shown in Fig. 3.3. It is apparent that the simulated results are in good agreement with the experimental results for the case without nanoparticles. Hence, the developed numerical model can be used for further study with different cases. Numerical simulation is carried out at a target set temperature of 95 °C for 3 different cases, without nanoparticles, with 0.02 volume fraction of nanoparticles (v), and 0.06 volume fraction of nanoparticles (v). The temperature at three different points (P1, P2, and P3) is plotted with respect to time to find the treatment time for ablation of 15 mm radius tumor (3 cm diameter tumor). The points P1 and P2 are at 15 mm from electrode centerline, and point P3 is at 20 mm from the electrode

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Fig. 3.3 Comparison of numerical and experimental temperature value at a 15 mm from the centerline of electrode and b 20 mm from the centerline of the electrode

centerline as shown in Fig. 3.4. It is important to note that even after achieving temperature criteria (i.e., 50 °C) up to 15 mm from center, still there is a chance of recurrence of tumor cells because the region within 5 mm from the tumor boundary contains some viable tumor cells. Hence, to avoid the local tumor recurrence, a margin of at least 5 mm (P3) around the tumor boundary should be completely ablated along with the tumor. Case 1: Without Nanoparticles Numerical simulation is carried out without nanoparticle assistance to find the ablation time. Temperature distribution obtained during the ablation process is shown in Fig. 3.5. From Fig. 3.5, it can be seen that temperature raise at point P2 is more since it is nearer to the tip of the electrode. And at point, P3 temperature raise is less since Fig. 3.4 Points P1, P2, and P3 at which temperature is monitored

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Fig. 3.5 Temperature variation at points P1, P2, and P3 with time (without nanoparticles)

it is far from the electrode tip (i.e., at 20 mm away from electrode tip). So, treatment time is decided based on the temperature at point P3. It is found that at point P3, time required for reaching 50 °C without nanoparticle assistance is 438 s (7.3 min). Hence, treatment time for complete ablation of 15 mm radius (3 cm diameter) tumor is 7.3 min. Case 2: With Nanoparticles (v = 0.02) Figure 3.6 shows the temperature distribution at three points P1, P2, and P3. Since the nanoparticles increases heat transfer rates and the heat get transferred to points P1, P2, and P3 faster when compared to that without nanoparticles. It is seen that temperature raise at point P3 is fast and reaches 50 °C in 402 s (6.7 min). Hence, the ablation time decreases from 438 to 402 s, and the percentage decrease is 8.21%. Case 3: With Nanoparticles (v = 0.06) The temperature distribution at point P1, P2, and P3 for v = 0.06 is shown in Fig. 3.7. Temperature raise at point P3 is faster and reaches 50 °C in 361 s (6.01 min). The percentage decrease in ablation time compared to case 1 is 17.56%.

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Fig. 3.6 Temperature variation at points P1, P2, and P3 with time (v = 0.02)

Fig. 3.7 Temperature variation at points P1, P2, and P3 with time (v = 0.06)

3.5 Conclusion A three-dimensional FEM model has been developed to study the influence of iron oxide nanoparticles on treatment time. In order to validate the results obtained from the developed FEM model, experimental in vitro study is carried out on PAG based phantom. Good agreement is found for temperature distribution obtained from numerical and experimental in vitro study. It is found that RFA with nanoparticle

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assistance has decreased the ablation time and percentage decrease in ablation time is 8.3% and 17.56% for the second and third case, respectively. Hence, we can conclude that percentage decrease in ablation time depends on nanoparticle concentration. Acknowledgements Authors would like to acknowledge Ministry of Human Resource Development, Government of India, for providing the Grant (Grant number IMP/2018/002096) to pursue the present research work. The authors would also like to acknowledge National Institute of Technology Karnataka for providing essential infrastructure to carry out the present research.

References 1. S. Singh, R. Repaka, Temperature-controlled radiofrequency ablation of different tissues using two-compartment models. Int. J. Hyperth. 33(2), 122–134 (2017) 2. A. Facciorusso, M. Di Maso, N. Muscatiello, Microwave ablation versus radiofrequency ablation for the treatment of hepatocellular carcinoma: a systematic review and meta-analysis. Int. J. Hyperth. 32(3), 339–344 (2016) 3. B. Zhang, M.A.J. Moser, E.M. Zhang, Y. Luo, C. Liu, W. Zhang, A review of radiofrequency ablation: large target tissue necrosis and mathematical modelling. Phys. Medica 32(8), 961–971 (2016) 4. Q. Li, Y. Yang, H. Liu, L. Zhang, Numerical analysis of electromagnetically induced heating and bioheat transfer for self-formed magnetic fluids hyperthermia. Gongneng Cailiao/J. Funct. Mater. 50(1), 1210–1214 (2019) 5. G. Zorbas, T. Samaras, Simulation of radiofrequency ablation in real human anatomy. Int. J. Hyperth. 30(8), 570–578 (2014) 6. Z. Bu-Lin, H. Bing, K. Sheng-Li, Y. Huang, W. Rong, L. Jia, A polyacrylamide gel phantom for radiofrequency ablation. Int. J. Hyperth. 24(7), 568–576 (2008) 7. S. Singh, R. Repaka, Numerical study to establish relationship between coagulation volume and target tip temperature during temperature-controlled radiofrequency ablation. Electromagn. Biol. Med. 37(1), 13–22 (2018) 8. S. Singh, A. Bhowmik, R. Repaka, Thermal analysis of induced damage to the healthy cell during RFA of breast tumor. J. Therm. Biol. 58, 80–90 (2016) 9. X.Q. Wang, A.S. Mujumdar, A review on nanofluids—part I: theoretical and numerical investigations. Brazil. J. Chem. Eng. 25(4), 613–630 (2008)

Chapter 4

Investigations on Electrochemical Discharge Machining of Al2 O3 Ceramics M. Vijay, T. Sekar, N. Muthukumaran, and K. Vijayakumar

Abstract The machining of ceramics materials in the conventional machining process is a tedious one. This research work attempts the investigations on electrochemical discharge machining (ECDM) used to material removal of ceramics such as aluminum oxide (Al2 O3 ) with the working medium of the NaOH electrolyte. This machining method will be a better alternative method for industrial applications. The key effort of this work is to attain better material removal rate (MRR) and to minimize the overcut problem. The process parameters are selected for machining such as applied voltage, electrolyte concentration with three different levels. The results reveal that machining of ceramics can be done by electrochemical discharge machining, and the maximum material removal rate is obtained by using the higher concentration of electrolyte.

4.1 Introduction The unconventional machining process is used to combine with two processes to better machining performance. The electrochemical discharge machining (ECDM) M. Vijay (B) · T. Sekar Department of Mechanical Engineering, Government College of Technology, Coimbatore, Tamilnadu, India e-mail: [email protected] T. Sekar e-mail: [email protected] N. Muthukumaran Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India e-mail: [email protected] K. Vijayakumar Department of Mechanical Engineering, TPEVR Government Polytechnic College, Vellore, Tamilnadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_4

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is one of the hybrid un-conventional machining process involving the techniques of electrochemical machining (ECM) and electro-discharge machining (EDM). Singh et al. [1] describe a hybrid process in their review of the hybrid process also known by different terms based on using a different combination of energy sources, process methods, machining platform. Electro-discharge machining (EDM) is the process to machine the complex shape of the component. Equbal et al. [2] discussed the EDM process in their paper that the electric energy is converted into thermal energy to remove material by the electro-thermal process. The required heat energy is produced by a spark between tool and workpiece. An inner electrode gap (IEG) is kept in the middle of the tool and workpiece, to preventing mechanical contact of the two electrodes; this is the important parameter in the EDM process. Industries are adapting this process for machining of hard and brittle ceramic materials, such as aluminum oxides, zirconium oxides, silicon nitrides, etc. In the ECM process, the electrical source is used with chemical energy to remove the material. Selvan et al. [3] in their review they observed the ECM is combined with other machining process produce a very good result such non-conducting ceramic materials have wide industrial applications in bearings, computer parts, artificial joints, cutting tools, electrical and thermal insulators, electronic devices, aerospace components, etc. The hybrid machining process like ECDM is used to machining hard material like Al2 O3. Ramkumar et al. [4] revealed in their research the ECDM is one of the efficient processes to remove material such as electrically non-conducted hard material like Al2 O3 . In the ECDM process, MRR is depended upon various process parameters like inter electrode gap (IEG), applied voltage, electrolyte concentration, pulse frequency, and duty ratio. Mediliyegedara et al.[5] in their research proposed a software control architecture to provide a control algorithm to control the process parameter IEG to obtain the great surface finish and maximum MRR. Wüthrich et al. [6] in their review they observed the MRR of ECDM depends on the material to be machined, electrolyte, applied voltage, and temperature. Sharma et al. [7] in their review paper they studied an electrolyte concentration is the most influential parameter affecting material removal rate. Mallick et al. [8] in their research revealed the MRR is influenced by electrolyte concentration and apply voltage and also revealed an OC (overcut) is directly proportional to applied voltage and electrolyte concentration. Mallick et al. [9] in their experimental studies found the 25% concentration of electrolyte influence of an overcut. Sarkar et al. [10] in their research they successfully machined silicon nitride ceramics and also find optimum parameters like applied voltage 50 V, electrolyte concentration 22% wt, and IEG 39 mm. Gupta et al. [11] in their experimental research work they used Al2 O3 workpiece material to make a hole by varying pulse frequency and revealed an increase of duty ratio by increasing pulse frequency. Singh et al. [12] in their review they studied various non conducting material MRR with varying process parameters with KOH and observed MRR, also they suggest being use abrasives like SiC, alumina in ECDM. This research focused on the ECDM process to machining the Al2 O3 with NaOH and also finds the optimum process parameter which is used to real-life industrial applications.

4 Investigations on Electrochemical Discharge Machining …

35

Fig. 4.1 Working principle of ECDM

4.1.1 Basic Principle of ECDM Process The working principle ECDM is as shown in Fig. 4.1. It is the combination of a thermal and chemical mechanism. [13] When a potential difference is applied between the two electrodes [14] (tool and work piece) which are kept a few microns’ apart gas bubbles start forming at both electrodes [15]. In this experimental work, the material going to be machining is aluminium oxide (Al2 O3) as working material. The Al2 O3 is difficult to machine in traditional machining without thermal damage and fracture due to its nature of brittleness. The NaOH is used as an electrolyte solution to machining the workpiece and also flush away the debris.

4.2 Design of Experiment 4.2.1 The Process Parameters The process parameters’ ranges for the experiments are given below: Electrode—Tungsten carbide. Workpiece—Aluminium oxide. Workpiece thickness—3 mm. Electrolyte—NaOH (with various concentrations).

4.2.2 Selection of Machining Parameters The feasible range of machining parameters for the material magnesium alloy is recommended as follows. • Voltage in the range of 80–100 V • Electrolytic concentration in the range of 25–35 g/l.

36 Table 4.1 Machining parameters and their levels

M. Vijay et al. Parameters

Levels Low

Medium

High

Voltage (V)

80

90

100

Electrolyte concentration (g/l)

25

30

35

4.2.3 L9 Orthogonal Array To conduct the experiments Taguchi’s L9 orthogonal array is selected. The design of experiments based L9 orthogonal array of Taguchi method having, Factors = 2; Levels = 3; and Runs = 9.

4.2.4 Parameter Selection Table 4.1 depicts the process parameters and the level of their value on experiments.

4.2.5 Experimental Runs Table 4.2 shows the number of experimental runs to be conducted while machining the material obtaining by the L9 orthogonal array design of the experiment. Table 4.2 L9 orthogonal array

S. No.

Voltage (V)

Electrolyte concentration (g/l)

1

80

25

2

80

30

3

80

35

4

90

25

5

90

30

6

90

35

7

100

25

8

100

30

9

100

35

4 Investigations on Electrochemical Discharge Machining …

37

4.3 Experimental Work 4.3.1 Experimental Setup Figure 4.2 shows the overall experimentation setup of ECDM. Figure 4.3 shows the machining chamber of the ECDM. In hybrid ECDM system, the DC power supply with pulsed voltage is applied in the middle of the cathode tungsten tool, and the copper electrode acts as anode engaged in a strong electrolytic solution. The copper plate with a greater surface area is used as a secondary electrode and positioned at a distance of 20–40 mm present from the tool tip. The tungsten tool continuously very nearly contacts the ceramic workpiece while machining the workpiece. The machining table is fully covered by electrolyte solution to the desired level to the workpiece must be immersed in the electrolyte solution and the top surface of the electrolyte solution should keep 2 or 3 mm above the workpiece surface. The top surface of the workpiece, and secondary electrode, is

Fig. 4.2 Electrochemical discharge machining setup

Fig. 4.3 Machining chamber

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retained parallel to the XY plane. To machining the complex shape on the workpiece, the work holding device is fixed in the X- and Y-direction moving a table or machine vice versa. Make sure the workpiece can be moved along any preferred profile to machining in the x–y plane. The ECDM setup must attach the tool feed mechanism throughout machining by way of this tool feed mechanism unit.

4.3.2 Experimentation The workpiece of aluminium oxide is machined in the ECDM machine. The values of the material removal rate are measured in each trial and each parametric condition. The chamber is filled with electrolytes, as per the criteria. The level of electrolyte in the electrolyte chamber is maintained in such a way that the machining zone (work piece and tool electrode) is immersed (2–3 mm above the work piece) during the machining process.

4.4 Results and Discussion Table 4.3 shows the observed data and the calculated material removal rate of the machining of Al2 O3 . The workpiece has measured before machining and after machining to calculate the material removal rate. Table 4.3 Material removal rate (MRR) Initial weight (g)

Final weight (g)

MRR (g/min) × 10−3

S. No.

Electrolyte concentration (g/l)

Voltage (v)

1

25

80

5.643

5.601

1.400

2

25

90

5.601

5.512

2.966

3

25

100

5.512

5.417

3.166

4

30

80

12.559

12.480

2.633

5

30

90

12.480

12.395

2.833

6

30

100

12.395

12.297

3.266

7

35

80

12.252

12.101

5.033

8

35

90

12.101

11.943

5.266

9

35

100

11.943

11.750

6.433

4 Investigations on Electrochemical Discharge Machining …

39

4.4.1 Observations During Experimentation (MRR) Time duration = 30 min, (Each Cycle). Tool = Tungsten Carbide. Tool Diameter = 2.4 mm. Table 4.3 depicts the result of the material removal rate of Al2 O3 in g/min. This table values are clearly indicating that Al2 O3 is machined using ECDM with NaOH. From the above table values, we clearly infer that experimental run no: 9 achieved more MRR than other, in the 9th run we used the parameters are electrolyte concentration is 35 g/l and the applied voltage is 100 V.

4.4.2 Observations During Experimentation. (Undercut/Overcut) Time duration = 30 min. Tool = Tungsten Carbide. Tool Diameter = 2.4 mm. Table 4.4 infers the result of the undercut or overcut defects that happen in the workpiece after machining of Al2 O3 . These table values are clearly indicating that the ECDM process is easy to machining the hard and brittle material also have some quality issues such as under cut or overcut. This problem should be studied and eliminated in future work to effective machining and achieve better MRR. From the table values indicating that the experimental run 2nd is achieved very less undercut deviation. The run parameters’ electrolyte concentration is 25 g/l, the applied voltage is Table 4.4 Under/overcut S. No.

Electrolyte concentration (g/l)

Voltage (V)

Hole diameter(mm)

Undercut/overcut

1

25

80

2.1

0.3(undercut)

2

25

90

2.2

0.2(undercut)

3

25

100

2.0

0.4(undercut)

4

30

80

1.5

0.9(undercut)

5

30

90

2.9

0.5(overcut)

6

30

100

3.0

0.6(overcut)

7

35

80

2.8

0.4(overcut)

8

35

90

2.9

0.5(overcut)

9

35

100

3.5

1.1(overcut)

40 Table 4.5 Rank of the parameters

M. Vijay et al. Level

Electrolyte concentration (g/l)

Voltage (V)

1

−55.92

−60.65

2

−71.97

−52.62

3

−45.49

−60.12

Delta

26.48

8.03

Rank

1

2

90 V, and 7th run is very less over cut problem and parameters are 35 g/l concentration and 80 V applied voltage. Table 4.4 clearly shows higher concentration, and higher voltage leads to higher overcut and the moderate value of both electrolyte concentrations which lead to the optimal machined hole.

4.4.3 Taguchi Analysis Table 4.5 shows that electrolyte concentration is the most significant process parameter for both material removal rate and optimal machining of the produced hole. Figure 4.4 shows the effect of concentration and voltage in signal-to-noise ratio. Figure 4.5 shows the effect of concentration in signal-to-noise ratio for three different voltages.

4.5 Conclusion The ECDM process with NaOH electrolyte and tungsten carbide tool using various process parameters to conducting experiments concludes the following, 1. From this experimental work, the hard and brittle material such as Al2 O3 is effectively machined without any fracture or thermal defects. 2. In this work, the electrolyte concentration plays a major role in the material removal rate and overcut of the alumina. Run No: 9 with the parameters’ electrolyte concentration is 35 g/l, and the applied voltage is 100 V 3. From the Run No: 7, the nominal MRR and minimum overcut defects allowed with the parameters’ electrolyte concentration is 35 g/l, and the applied voltage is 80 V 4. This is basic experimentation to finalize whether the machining can be done in ceramics are not in the ECDM process. This work shows the machining of high-density ceramic like alumina can be done in electrochemical discharge machining.

4 Investigations on Electrochemical Discharge Machining …

41

Main Effects Plot for SN ratios Data Means

concentration

voltage

-45

M ean of S N ratios

-50 -55 -60 -65 -70 -75 25

30

Signal-to-noise: Larger is better

Fig. 4.4 Main effect plot for SN ratios

35

80

90

100

42

M. Vijay et al.

Interaction Plot for SN ratios Data Means

-40

voltage 80 90 100

S N ratios

-50

-60

-70

-80 25

30

35

concentration Signal-to-noise: Larger is better

Fig. 4.5 Interaction plot for SN ratios

Acknowledgements The authors acknowledge the financial support provided by Government College of Technology, Coimbatore, under TEQIP—III (Resolution 33/BoG/13).

References 1. K. Singh, S.K. Maurya, S. Kumar, A Review on Introduction to the Hybrid Machining Process 2. A. Equbal, A.K. Sood, Electrical discharge machining: an overview of various areas of research. Manuf. Ind. Eng. 13(1–2) (2014) 3. C.P. Selvan, S.R. Madara, S.S. Sampath, Review of the Current State of Research and Development in Electro-Chemical Machining 4. J. Ramkumar, M. Singh, Overview of Hybrid Machining Processes (HMPs) 5. T.K.K.R. Mediliyegedara, A.K.M. De Silva, D.K. Harrison, J.A. McGeough, New developments in the process control of the hybrid electrochemical discharge machining (ECDM) process. J. Mater. Process. Technol. 167(2–3), 338–343 (2005) 6. R. Wüthrich, V. Fascio, Machining of non-conducting materials using the electrochemical discharge phenomenon-an overview. Int. J. Mach. Tools Manuf. 45(9), 1095–1108 (2005) 7. S. Sharma, R.R. Mishra, V. Kumar, S. Rajesha, Effective parameters of electrochemical discharge machining–a review. Int. J. Mech. Prod. Eng. 2(5), 37–41 (2014) 8. B. Mallick, M.N. Ali, B.R. Sarkar, B. Doloi, B. Bhattacharyya, Parametric analysis of electrochemical discharge micro-machining process during profile generation on the glass, in 5th

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9.

10. 11.

12. 13.

14.

15.

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International & 26th All India Manufacturing Technology, Design, and Research Conference (AIMTDR 2014) (pp. 12–14) (2014) B. Mallick, B.R. Sarkar, B. Doloi, B. Bhattacharyya, Analysis of the effect of ECDM process parameters during micro-machining of glass using a genetic algorithm. J. Mech. Eng. Sci. 12(3), 3942–3960 (2018) B.R. Sarkar, B. Doloi, B. Bhattacharyya, Parametric analysis on electrochemical discharge machining of silicon nitride ceramics. Int. J. Adv. Manuf. Technol. 28(9–10), 873–881 (2006) P.K. Gupta, J.P. Bhamu, C.S. Rajoria, N.K. Lautre, V. Agarwal, Effect of duty ratio at different pulse frequency during hole drilling in ceramics using electrochemical discharge machining, in MATEC Web of ConferencesEDP Sciences, vol. 77 (pp. 10004) (2016) B. Singh, R.O. Vaishya, Analyses of output parameters of ECDM using different abrasives–a review. Int. J. Mater. Sci. 12(2), 307–314 (2017) B. Jiang, S. Lan, K. Wilt, J. Ni, Modeling and experimental investigation of the gas film in the micro-electrochemical discharge machining process. Int. J. Mach. Tools Manuf. 90, 8–15 (2015) S.P. Rajagopal, V. Ganesh, A.V. Lanjewar, M.R. Sankar, Past and current status of hybrid electric discharge machining (H-EDM) processes. Adv. Mater. Manuf. Charact. 3(1), 111–118 (2013) F. Klocke, T. Herrig, M. Zeis, A. Klink, Experimental investigations of cutting rates and surface integrity in wire electrochemical machining with rotating electrode. Procedia CIRP 68, 725–730 (2018)

Chapter 5

Design and Numerical Simulation of PCM-Based Energy Storage Device for Helmet Cooling Nagaraju Dora, Ch Ramsai, and Ch Srinivasa Rahul

Abstract Thermal energy storage systems have gained importance in the designing of cooling system for micro-electronic and energy-efficient devices. An attempt has been made for designing cooling technique in the helmet namely PCM packet and its performance analysis was carried out numerically. The PCM packet consists of PCM filled pipes and passage for air. The device is intended to provide thermal comfort by reducing the outside air temperature to comfort temperature with the help of latent heat storage PCM material. Two PCM packet positions vertical, horizontal and PCM pipes are filled with paraffin (RT50) PCM. The results of both the systems are presented in terms of liquid fraction, local temperature distribution of PCM, and average air outlet temperature. It is predicted from the results that outside air temperature decreased with the PCM packet held in horizontal position.

5.1 Introduction The rapid innovation in the development of electronic components and paramount challenging parameters are size and power consumption without compromising in performance and efficiency. Electronic component works on electric energy where certain amount of energy is lost in the form of excess heat proceeds to build up temperature. Increase in temperature decreases reliability and leads to component failure. In order to overcome the impediment, the equipment should be designed with energyefficient cooling systems such as conventional sinks with capable fluids, extended surfaces, and heat pipes. An innovative energy storage device incorporated with PCM N. Dora (B) · C. Srinivasa Rahul Mechanical Engineering Department, GITAM Deemed to be University, Visakhapatnam, India e-mail: [email protected] C. Srinivasa Rahul e-mail: [email protected] C. Ramsai Graduate Engineer Trainee (GET), TVS Motors, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_5

45

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material is one of the remarkable solutions which has played vital role toward the energy-efficient system. In recent years, PCM has a wide range of application such as building cooling, automotive, textile, and solar. Several researchers have carried out numerous works on latent heat storage systems and the following will be explained. Francis et al. investigated the influence of heat transfer fluid inlet boundary conditions such as mass flow rate and temperature on thermal energy storage system performance [1]. For the domestic application point of view, temperature ranges between 0 and 60 °C suitable for heating applications. Vertical cylindrical tube incorporated with PCM is investigated numerically and validated with experimentation results to explore the understanding of heat transfer mechanism for various latent heat conditions [2]. Their results pointed that heat transfer by means of conduction during initial phase through the tube walls and later convection has played key role. Energy storage system for building cooling was proposed to reduce the dependence on mechanical ventilation atleast in peak demand [3] period. It is observed for the pertinent parameters considered in the analysis that the solidification rate of PCM is good in rectangular type storage than cylindrical one. They have drawn the conclusions for various inlet HTF temperatures. When implementing the PCM in the particle thermal energy storage (TES) system, the important parameters are shape of the container and stability of solid–liquid interface during melting. The shape optimization of TES storage system was proposed for transient operating conditions to fill the gap between energy demands and supply while using PCM materials [4]. Similarly, several researchers had carried numerical/experimental studies. They verified the influence of pertinent parameters volume of the fin, the number of fins, and fin base temperature on the PCM melting process subjected to different heat fluxes [5–7]. They have proposed various fin configurations and predicted system efficiency can be enhanced with 24% with optimized unit. An experimental investigation has been carried out on PCM storage systems, and phase transition interactions were analyzed on purview of conduction/convection mechanisms [8]. It is perceived that effective heat transfer coefficient can be obtained only with natural convection in case of analyzing the performance of PCM storage system. Researchers have anticipated the performance of PCM in different container shapes such as hemi-cylindrical, vertical rectangular, and effect of rectangular channel inclination through experimentally [9–12]. The novelty of the present work is to develop a numerical model to design an energy-efficient device while incorporating PCM materials prior to experimentation. Authors have proposed a design namely PCM packet for helmet cooling certainly helpful for site engineers, traffic police officers, etc. In the initial stage, 2D simulations were conducted to see the influence of PCM encapsulation shape cooling the outside atmospheric air.

5 Design and Numerical Simulation of PCM-Based Energy Storage Device …

47

5.2 Methodology The performance of the PCM packet for helmet cooling has been studied numerically using finite volume method-based ANSYS FLUENT 15.0 software. Pertained to design of energy storage devices for engineering application, it is essential to predict its behavior using any commercially approved software as mentioned by several researchers. The actual physical idea of the packet design is formulated and corresponding boundary conditions explained below.

5.2.1 Design of PCM Packet and Its Mathematical Formulation The actual design of proposed idea for helmet cooling is represented as graphically in Fig. 5.1. The packet consists of four pipes and these surround the air passage in which diameter is equal to one pipe. The shape of the packet is similar to helmet shape, and dimensions of PCM packet periphery were chosen from realistic helmet. A small fan is mounted near the air inlet to suck hot air from ambient. It allows the passage of air throughout the pipe length and simultaneously PCM absorbs heat from air, as result of conversion of PCM phase from solid to liquid. It is assumed that the initial PCM temperature is lower than air subsequently air temperature decreases. The cool air will be directed to throughout head as well as face of the person, and then he/she feels comfortable. The performance of PCM packet and its influence on air temperature has been studied by considering the 2D computational domain. The computational calculation of PCM-based thermal energy storage device is time-consuming and hence 2D projection of prototype is chosen, which consists of two PCM pipes that surround the air pipe. The geometry and structured mesh of computational domain are generated using ICEM CFD 15.0 software as shown in Fig. 5.2. The generated computational mesh quality is measured in terms of orthogonal quality and it is observed as 1. RT50

Fig. 5.1 Graphical representation of PCM packet for helmet cooling

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Fig. 5.2 a Geometry modeling and b structured meshing of computational domain

paraffin has chosen as PCM and its thermo-physical properties were considered with temperature variation. These properties are inserted in the FLUENT solver using user-defined functions (UDFs). The governing equations continuity, momentum, and energy equations are given below from Eqs. (5.1) to (5.6).  − ∂ρ → +∇ · ρV =0 ∂t − → − ∂V → − → − → − → → ρ + ρ V · ∇ V = −∇ p + μ∇ 2 V + ρ − g (T − T0 ) + S ∂t

(5.1)

(5.2)

where momentum source term − → (1 − δ)2 − → S = 3 Amushy V δ +ε

(5.3)

Amushy is the mushy zone constant, which generally varies from 104 to 108 (kg/m3 s), and in the present work, it was taken as 106 kg/m3 s. Energy equation  − ∂ →  (ρ H ) + ∇ · ρ V H = ∇ · (k∇T ) ∂t

(5.4)

where the velocity of liquid PCM is represented with V and T 0 is reference temperature. Since enthalpy-porosity approach was used, no explicit tracking of solid–liquid interface and liquid fraction presence in the domain represents the melting process. Accordingly, equation for enthalpy as follows

5 Design and Numerical Simulation of PCM-Based Energy Storage Device …

49

T H = Href +

C p dT + δL

(5.5)

T ref

Here, H ref is enthalpy at the reference temperature T ref , δ is PCM liquid fraction, and L is the latent heat. Liquid fraction during melting of PCM is defined as follows

δ=

⎧ ⎪ ⎨ 0,

if T < Ts H s , if Ts < T < Tl = TTl −T −Ts ⎪ L ⎩ 1, if T > T l

(5.6)

5.2.2 Boundary Conditions and Numerical Solution The 2D computational domain is presented in Fig. 5.2, and outer walls are adiabatic since the material used for casing is engineering plastic (ABS). Air inlet is specified with time-dependent temperature which follows sinusoidal boundary condition and the temperature range is 20–40 °C. Also, air velocity is specified by assuming fully developed laminar flow and pressure outlet specified at air pipe outlet. The initial temperature of PCM is assumed as 15 °C. Finite volume method is chosen to discretize the governing equations, and enthalpy-porosity technique was used to solve PCM phase change phenomenon. Staged grid approach is used while discretizing the equations and SIMPLER algorithm is employed to solve pressure–velocity coupling terms. The residuals for governing equations such as continuity, momentum, and energy are specified as 1E06, 1E−06, and 1E−08, respectively. Reports of local temperature, liquid fraction, and average temperature of PCM were defined and also monitored while conducting simulations. After successful trials of three different time steps (1, 0.5, and 0.1 s), it is found that time step 0.1 s has given converged solutions. Further, simulations were performed using 0.1 s time step with 30 iterations. The computational calculations are performed in 32 GB RAM, 8 core in house work station.

5.3 Grid Independency Study and Validation Four different meshes are generated using ICEM CFD and conducted the simulations using aforementioned boundary conditions to predict the optimum grid. The PCM liquid fraction is chosen as output for all three meshes for 3000 s simulation time. The variation of RT50 PCM’s liquid fraction for different mesh is presented in Fig. 3a.

50

N. Dora et al.

Fig. 5.3 a Mesh independency study of PCM packet and b comparison of present model results with a numerically obtained liquid fraction of PCM in enclosure heating from the bottom wall (constant wall temperature boundary condition)

Further the present computational procedure is validated with numerical results of Arasu and Mujumdar [13]. They considered a simple benchmark domain of square cavity subjected to horizontal and vertical heating while the cavity filled with PCM. In this connection, the same cavity size, boundary conditions, and simulation setting were implemented. The validation of both the results is presented in Fig. 3b and then it is observed that the results are agreed with an error of 0.05 liquid fraction value.

5.4 Results and Discussion The selection of PCM material, boundary conditions, and location of heaters inside an enclosure is paramount for the application of optimum heat transfer. In this regard, melting behavior, time required for PCM melting, and temperature variation of PCM at various locations inside the device play an important role. Numerical simulation using ANSYS FLUENT 15.0 software was used to analyze the effect of pertinent parameters on PCM performance.

5.4.1 Effect of Orientation on Performance of PCM Packet The normal displacement of the solid–liquid interface with time for variable inlet air temperature is presented in Fig. 5.4. Initially, the PCM shape is same as container, as hot air flows through the pipe and then heat transferred to PCM through convection. Further natural convection in the PCM propagates the displacement of interface within the pipe it leads to higher melting rates. It can be seen from Fig. 5.4 that both

5 Design and Numerical Simulation of PCM-Based Energy Storage Device …

51

Fig. 5.4 a Local temperature distribution of left side PCM and right side PCM and b uniquely colored temperature contours

horizontal and vertical positions ensure complete phase change of PCM from solid to liquid approximately 185 min. Moreover, the solid–liquid interface of horizontal position at time t = 90 min clearly exhibits the buoyancy force effect and it is perceived that the melting rate is not symmetry as like in vertical position.

5.4.2 Variation of Melting Rate and Local Temperature Distribution The time required to melt the phase change material is paramount for latent heat storage systems in the engineering applications. The melting rate of RT50 paraffin for various positions is plotted in Fig. 5.5. Also, local temperature was obtained to make sure rise in PCM average temperature with time. The results obtained from the present study reveals horizontal position is more favorable to TES application. It is evident from Fig. 5.5a that the time required for PCM melting in both top and bottom PCM pipes is almost same, whereas for vertical position PCM-left melted earlier than PCM-right which leads to non-equilibrium cool air temperature. Moreover, outlet air temperature variation for horizontal PCM packet position is shown in Fig. 5.6. It is observed that air temperature range is 29.5–25 °C by the end of complete liquefaction of PCM for an average inlet air temperature 35 °C.

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Fig. 5.5 Liquid fraction of PCM packet held in a horizontal position, b vertical position and temperature variation, c local PCM temperature variation, and d average PCM temperature Fig. 5.6 Variation of air outlet temperature for PCM packet horizontal position

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5.5 Conclusions The performance of RT50 PCM-filled packet for helmet cooling subjected to variable inlet air temperature has been investigated numerically. The comparison of validation studies results has achieved good agreement. Further, mesh independency study was conducted to ensure optimum time step for transient numerical simulation. The PCM packet position is optimized for helmet cooling. It is found PCM packet placed in horizontal direction ensured phase change transition and its melting rate. Local temperature distribution of PCM with time has been plotted which ensures the PCM melting rate as well as stability of solid–liquid interface. The results of PCM melting and isotherms presented and compared for both horizontal and vertical direction. It is observed that air outlet temperature decreased with time as PCM changes from solid to liquid.

References 1. F. Agyenim, N. Hewitt, P. Eames, M. Smyth, A review of materials, heat transfer and phase change problem formulation for latent heat thermal energy storage systems (LHTESS). Renew. Sustain. Energy Rev. 14, 615–628 (2010) 2. H. Shmueli, G. Ziskind, R. Letan, Melting in a vertical cylindrical tube: numerical investigation and comparison with experiments. Int. J. Heat Mass Transf. 53, 4082–4091 (2010) 3. A.H. Mosaffa, F. Talati, H. Basirat Tabrizi, M.A. Rosenc, Analytical modeling of PCM solidification in a shell and tube finned thermal storage for air conditioning systems. Energy Build. 49, 356–361 (2012) 4. A. Al-abidi, B.M. Sohif, K. Sopian, M.Y. Sulaiman, T.M. Abdulrahman, CFD applications for latent heat thermal energy storage. Renew. Sustain. Energy Rev. 20,353–363 (2013) 5. H.M. Ali, A. Arshad, Experimental investigation of n-eicosane based circular pin-fin heat sinks for passive cooling of electronic devices. Int. J. Heat Mass Transf. 112, 649–661 (2017) 6. R. Baby, C. Balaji, Thermal management of electronics using phase change material based pin fin heat sinks. J. Phys.: Conf. Ser. 395, 012134 (2012) 7. T. Rozenfeld, Y. Kozak, R. Hayat, G. Ziskind, Close-contact melting in a horizontal cylindrical enclosure with longitudinal plate fins: demonstration, modeling and application to thermal storage. Int. J. Heat Mass Transf. 86, 465–477 (2015) 8. A. Sciacovelli, F. Gagliardi, V. Verda, Maximization of performance of a PCM latent heat storage system with innovative fins. Appl. Energy 137, 705–715 (2015) 9. N.S. Bondareva, M.A. Sheremet, Conjugate heat transfer in the PCM-based heat storage system with finned copper profile: application in electronics cooling. Int. J. Heat Mass Transf. 124, 1275–1284 (2018) 10. M.J. Huang, P.C. Eames, B. Norton, N.J. Hewitt, Natural convection in an internally finned phase change material heat sink for the thermal management of photovoltaics. Solar Energy Mater. Solar Cells. 95, 1598–1603 (2011) 11. N.S. Dhaidan, A.F. Khalaf, Experimental evaluation of the melting behaviours of paraffin within a hemicylindrical storage cell. Int. Commun. Heat Mass Transf. 111, 104476 (2020) 12. Z. Li, L. Lv, J. Li, Combination of heat storage and thermal spreading for high power portable electronics cooling. Int. J. Heat Mass Transf. 98, 550–557 (2016) 13. S. Wang, A. Faghri, T.L. Bergman, A comprehensive numerical model for melting with natural convection. Int. J. Heat Mass Transfer. 53,1986–2000 (2010)

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14. H. Zennouhi, W. Benomar, T. Kousksou, A. AitMsaad, A. Allouhi, M. Mahdaoui, T. El Rhafikia, Effect of inclination angle on the melting process of phase change material. Case Stud. Therm. Eng. 9, 47–54 (2017) 15. X. Sun, Q. Zhang, M.A. Medina, K. OkLee, Experimental observations on the heat transfer enhancement caused by natural convection during melting of solid–liquid phase change materials (PCMs). Appl. Energy 162, 1453–1461 (2016)

Chapter 6

Numerical Simulation and Analysis of Tank Filling Time and Flow Sequence Akshay Saxena, Mayank Parasher, Mukul Anand, Nikhil Garg, Supradeepan Katiresan, and P. S. Gurugubelli

Abstract The tank filling problem can be put under a broad umbrella of free surface flows. Free surface flows are characterized by phases separated by a distinct interface. In this paper, we present a discussion on tank filling problems, wherein a tank is filled by the liquid entering from two in gates. Parametric studies are performed by varying the inlet velocity and orientation of the ingate to predict the fill time, flow sequence, and track the velocities at different positions in the tank, using ANSYS Fluent. An optimal velocity was chosen to ensure there is no rapid flow movement and overflow. Providing the orientation to the ingates helped to improve the volume fill rate, though some cases showcase rapid bubble formation, and receded flow movements.

6.1 Introduction Free surface flow phenomena can be observed in the wave breaking, dam break, rise of the liquid bubble in a viscous medium, microchannel flow and the jet breaking, in the design of hydrodynamic structures, predicting the flow reversal patterns and fuel shut off behavior in automotive tanks, studying the filling sequences and flow stability, bubble entrapment involved in a casting process, and other applications which include packing bottles with liquid, diesel carriers like earth-moving trucks and diesel ships. Simple marker and cell (SMAC) is one of the earliest methods developed to study free surface flows. To model free surface flows, the Volume of Fluid method (VOF) is used. Hirt and Nicholas [1] discussed the Lagrangian and Eulerian methods for treating complex-free boundary flows with their drawbacks. The VOF method was used which helps in tracking the interface by solving for the function F, whose values correspond to whether a particular cell is filled, empty, half-filled representing an interface. Shin and Lee [2] used A modified volume of fluid (VOF) method based on four node elements in 2D geometry was proposed and A. Saxena · M. Parasher · M. Anand · N. Garg · S. Katiresan (B) · P. S. Gurugubelli Mechanical Engineering Department, BITS Pilani Hyderabad Campus, Hyderabad, Telangana 500078, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_6

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found that VOF technique is useful for solving moving interface system to analyze dam-break problems as well as mold filling problems for different configurations using a finite element method for incompressible flow with a moving free surface or interface. Zendejoa and Flores [3] discussed the meshfree methods, which were found to be far more superior to traditional mesh-based methods for treating fluid dynamics problems with rapidly changing domains like dam-break problems. A comprehensive yet simplified lumped parameter model as discussed by Fackrell et al. [4] to determine the pressure and flow rate associated with the automotive fuel tank to predict premature shutoff. Sinha [5] provided an insight into the complex unsteady wave processes that occur during pumping of fuel into the fuel tank and the pressurization of trapped air within the tank dome, both of which can produce shutoff. Whalley [6] explored the possible links between flooding and slugging in two-phase gas–liquid flow by consideration of several simple experiments which consisted of timing the emptying and filling of bottles of liquid. Martin et al. [7] studied the static and dynamic response of a hydraulic tank partially filled with hydraulic oil. In other research by Fernando [8] and Heitsch [9] hydrogen tank filling problem was considered where along with mass balance, energy balance equations are also used to model the problem. Kang et al. [10] performed a similar study and analyzed the flow rate, fluid velocity, total filling time associated with a gas well, by developing a mathematical model and using thermodynamic and hydrodynamic equation sets to represent process elements like gas wells, tankers and pipes. Kim and Kim [11] have discussed the flow sequence, filling time of the tank with two ingates, along with the effect of ingate spacing. However, to the best knowledge of the authors, there are very limited studies that have been undertaken to introduce the influence of ingate orientation and velocity magnitude variations at the inlet. These contribute to the filling time of the tank, also inducing rapid flow movements and thus have been studied in the present work.

6.2 Methodology The simulations are performed using ANSYS Fluent [12]. The multiphase option is chosen to perform the simulations. Eulerian–Multi VOF model with phases as water and air are taken with phase interactions specified. The VOF model in ANSYS Fluent utilizes Eulerian framework for both the phases with special treatment of interface. All phases share a single set of governing equations, with mixture properties volume fraction weighted. This mode is utilized for solving stratified or free surface flows and flows having a distinct interface. The VOF model does not solve for the interface directly, it solves for the motion of the phases and indirectly tracks the interface.  ∂(ρ) + ∇ · (ρU ) = Sk k ∂t

(6.1)

6 Numerical Simulation and Analysis of Tank Filling Time …

57

∂(ρU ) + ∇ · (ρUU ) = −∇.π + ρg + F ∂t

(6.2)

∂αk + (Uk · ∇)αk = Sαk ∂t

(6.3)

  n      ∂ ρq αq + ∇ · ρq αq v = Sαq + m pq − m q p ∂t p=1

(6.4)

 n+1  n  ρq αq − ρq αq ∂t

+



⎤n ⎡ n     n+1 m pq − m q p ⎦ ∇ · ρq αq v = ⎣ Sαq +

nb

p=1

(6.5) where ρ the density of the given phase, U the phase field velocity, π the pressure force, α represents a specified phase. Phase continuity equations are continuity equations solved for a particular phase, represented by k. The discretization scheme is a coupled momentum pressure equation, where the velocity in the continuity equations is of the latest time step, and the coupled equations are solved at n + 1 time step too. A mesh that satisfies the courant number criteria is chosen. The mathematical form of boundary conditions used are as follows: At the inlet, the uniform velocity profile is assumed u = U (specified). No slip boundary conditions at the wall u = 0; v = 0. At the outlet vent atmospheric pressure is specified, gauge pressure is taken to be 0.

6.3 Validation To gain confidence into the work, validation studies are performed, covering (i) the dam-break problem and (ii) two ingates water tank problem.

6.3.1 Dam-Break Problem In the dam-break problem, a fluid column is collapsed under the action of gravitation, as the containing dam is removed suddenly, the water which is stored with the help of the dam starts flowing. The dimensionless position of the leading edge with respect to dimensionless time plotted in Fig. 6.1 is in good agreement with Kim and Kim [11].

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Fig. 6.1 Dimensionless position of the leading edge with respect to dimensionless time

Dam-Break validation

4

Simulation Results

3.5

Z/L

3

Kim-Kim[11]

2.5 2 1.5 1 0

1

2

3

t√(2 / )

6.3.2 Tank Filling Using Two Ingates Validation is performed on the 2D domain shown in Fig. 6.2, as given by Kim and Kim [11]. The volume filling with respect to time is monitored by VOF method and finally validated with marker-and-cell (MAC) method. Different cases are shown in Table 6.1 Marker-and-cell method (MAC) is adopted for simulating unsteady incompressible viscous flows with a free surface, on a uniform Cartesian staggered grid to predict the velocity field. The boundary ingate height sizes are specified, with the fluid inflow velocity of 4 m/s and free slip conditions at the wall. The orthogonal mesh of 5 cm is used. The results are shown in Fig. 6.3 are in good agreement with Kim and Kim [11]. 0.2

0.2 0.05

Inlet Pressure Outlet L 0.1 Tank

0.1

0.05

1.5

Upper Ingate 0.6 0.25 Lower Ingate D 1.8 Fig. 6.2 The geometry of the water tank with two ingates

6 Numerical Simulation and Analysis of Tank Filling Time … Table 6.1 Different cases considered for validation

59

Case number Position of upper gate Diameter of lower gate (L) (D) Case 1

0.75

0.05

Case 2

0.75

0.1

Case 3

0.95

0.05

Case 4

0.95

0.1

Fig. 6.3 Comparison and validation of volume as a function of filling time for case 1 to case 4

6.4 Results and Discussions The present work focuses the effect of ingate orientation and the inlet velocity during the filling of a tank. The domain for this study is shown in Fig. 6.4. Velocity should be sufficient to avoid any overflow and rapid movements. Two parametric studies are performed which include changing the inlet velocity and changing the orientation of the upper ingate. In the first study, the inlet velocity is varied from 1 to 5 m/s in the steps of one. The optimum velocity is chosen for the second study, wherein the orientations are changed from 10° (anticlockwise) to 15° (clockwise) in the steps of 5°. Velocity variations and the volume fill time results and plots are monitored. The flow sequence at various time steps for different orientations is shown in Fig. 6.5. The time-dependent flow variation is greatly affected in the manner the flow enters, the velocity of flow and configuration of the domain. The lower ingate has more contribution toward filling the tank, hence the velocity tracking is done at a point just above the ingate. From Fig. 6.6a it is clear that with increase in the inlet velocity, the lower ingate velocity variations show a gradual increase. Also Fig. 6.6b the upper velocity shows a similar trend for all the five inlet velocity values. After providing inclination to the upper ingate, there was a reduction in the velocity variations compared to the ideal case. In the case of upper velocity variations, it is observed that when the inclination is of 10°, max velocity variation is obtained. While the lowest variations are when the inclination is of −5° downwards. It is concluded

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0.2

0.2

0.05

Inlet Pressure Outlet 0.75 Upper Ingate 0.1

0.1

Tank

0.05

1.5

0.6 0.25 Lower Ingate 0.1 1.8 Fig. 6.4 Geometry of the water tank with two ingates

Fig. 6.5 Flow sequence at different time steps for inclination of 5°

4 3 2 1

0

2 3 4 5 Time (seconds)

7

4 3 2 1 0

0

1

10 deg(anticlock) 5 deg(anticlock) 5 deg 10 deg 15 deg 0 deg 2 3 4 5 6 7 Time (seconds)

1 m/s 2 m/s 3 m/s 4 m/s 5 m/s

Volume Filled -Study I

Volume Filled (m3)

6

Lower Ingate variation -Study II

5 Velocity(m/s)

1

2.5 2 1.5 1

6 5 4 3 2 1 0

4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

61

Upper Ingate variation - Study I 2 m/s 1 m/s 3 m/s 4 m/s 5 m/s

0

1

2

3 4 5 Time (seconds)

0

5 Time (sec)

Fig. 6.6 Results for different parametric studies

6

0

10 deg 5 deg -5 deg -10 deg 2 4 6 Time (seconds)

8

Volume Filled - Study II 2.5 2 1.5

10 deg 5 deg -5 deg -10 deg -15 deg 0 deg

1

0

7

Upper ingate variation - Study II

0.5

0.5 0

Velocity(m/s)

0

Velocity (m/s)

Lower Ingate Variation - Study I 1m/s 2 m/s 7 3 m/s 4 m/s 6 5 m/s 5

Volume (m3)

Velocity (m/s)

6 Numerical Simulation and Analysis of Tank Filling Time …

0

1

2 3 Time (sec)

4

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that no matter what the orientation angle is, the velocity at the upper ingate point is lower than the ideal. From the Volume vs Time, i.e., Fig. 6.6e graph it can be inferred from the slope of the linear region that with the inlet velocities 1, 2, 3 m/s, show a slow filling rate. So finally 4 m/s is taken as the optimum inlet velocity considering overflow, bubble formation and fill time. As the flow progresses, there is an increase in the level of water, thereby increasing the pressure at the bottom of the tank, which resists the inlet flow through the lower ingate and the water tending to move toward the outlet vent causes bubble formation and restricted flow, and it is seen as a deviation from the linear region in the graph. In Fig. 6.6f, the ideal case is found to be at the lowermost end of the graph. 10° inclination gives the best volume fill time, whereas the clockwise inclinations require more time for the tank to fill. And also, they showcase more deviations from the linearity of the curve and are thus associated with the more rapid and turbulent movement of water. So it is concluded that to optimize the fill time of a tank, providing counter-clock inclinations to the upper ingate would be more effective in reducing the time taken to fill the tank.

6.5 Conclusion ANSYS Fluent was used to simulate the problem involving two ingates. The problem was validated with the standard dam-break and the two ingates problem. The results were satisfactory and after gaining confidence we moved on to our main problem where we performed parametric studies to find the influence of upper ingate orientation and the inlet velocity on the filling time of a water tank. It was observed that • The velocity of water in the lower ingate is greater than the upper ingate so from the flow sequence it is clear that the lower ingate has more contribution to the filling of the tank, even when the gates are oriented differently. • In the parametric study of velocity from the volume v/s time graph, it is observed that increasing the velocity does not necessarily indicate faster filling time, as the influence of bubble entrapments and rapid flow movements come in consideration, and led to receding in filling rate of water. • From the orientation studies, it is observed that there is an increment in velocity magnitude with an inclination in the anticlockwise direction, compared to the case where no orientation was given. • Volume filling rate was improved after including orientation angles, and there are rapid movements of water which are represented by the deviations from the linearity of the curve.

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References 1. C.W. Hirt, B.D. Nichols, Volume of fluid (VOF) method for the dynamics of free boundaries. J. Comput. Phys. 39(1), 201–225 (1981) 2. S. Shin, W.I. Lee, Finite element analysis of incompressible viscous flow with moving free surface by selective volume of fluid method. Int. J. Heat Fluid Flow 21(2), 197–206 (2000) 3. F.R. Saucedo-Zendejo, E.O. Resendiz-Flores, A new approach for the numerical simulation of free surface incompressible flows using a meshfree method. Comput. Methods Appl. Mech. Eng. 324, 619–639 (2017) 4. S. Fackrell, M. Mastroianni, G.W. Rankin, Model of the filling of an automotive fuel tank. Math. Comput. Model. 38(5–6), 519–532 (2003) 5. N. Sinha, R. Thompson, M. Harrigan, Computational simulation of fuel shut-off during refueling (No. 981377). SAE Technical Paper (1998) 6. P.B. Whalley, Two-phase flow during filling and emptying of bottles. Int. J. Multiph. Flow 17(1), 145–152 (1991) 7. M.Ž. Martin Moˇcilana, CFD simulation of hydraulic tank. Procedia Eng. 609–614 (2017) 8. F. Olmos, V.I. Manousiouthakis, Gas tank fill-up in globally minimum time: theory and application to hydrogen. Int. J. Hydrogen Energy 39(23), 12138–12157 (2014) 9. M. Heitsch, D. Baraldi, P. Moretto, Numerical investigations on the fast filling of hydrogen tanks. Int. J. Hydrogen Energy 36(3), 2606–2612 (2011) 10. B.S. Kang Cen, Numerical modeling of the dynamic filling process of high-pressure tankers for marginal gas wells. J. Nat. Gas Sci. Eng. (2019) 11. N.H. Kim, G.B. Kim, The numerical simulation of the water-filling in the water tank with two ingates. KSCE J. Civil Eng. 11(4), 215–226 (2007) 12. ANSYS, ANSYS Fluent User Guide. Retrieved from ANSYS Fluent User Guide (n.d.). https:// www.afs.enea.it/project/neptunius/docs/fluent/html/ug/main_pre.htm

Chapter 7

GA-Based Tuning of Integral Controller for Frequency Regulation of Hybrid Two-Area Power System with Nonlinearities and Electric Vehicles K. R. Roshin and E. K. Bindumol Abstract Electric vehicles (EVs) provide a new step toward the planning and efficient control of power grids with its vehicle-to-grid capability. It serves as a reliable backup for frequency control (FC). This paper aims to analyze the FC using automatic generation control (AGC) in a hybrid two-area system incorporated with EVs, which act as controllable loads along with conventional generating units under abnormal conditions. Integral controller of AGC is the key factor of the system, in which the controller parameters are to be optimally tuned with the help of suitable tuning method to arrive the frequency deviation as zero. Physical limitations or nonlinearities are added in the analysis of this system, to obtain a realistic and accurate result. Potential of the control technique is analyzed in MATLAB/Simulink environment in view of transient response parameters and deviations in tie-line power under perturbations.

7.1 Introduction Growth rate in electricity supply industry (ESI) is not commensurate with growth in demand, leading to energy deficit and can even jeopardize the stable system operation. A planned and effective power network can meet the requirements of fast-growing demand [1]. Electrical power system consists of multiple generating units associated with necessary control units. The major challenges faced by the power system operators are the interchanging of power between different control areas and adjustment in frequency within the rated values [2]. The power supplied by the supplier should be of good quality, economical, reliable and satisfy necessary safety requirements. Moreover, the system frequency and K. R. Roshin (B) · E. K. Bindumol Electrical Engineering Department, Government Engineering College, Thrissur, Kerala, India e-mail: [email protected] E. K. Bindumol e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_7

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bus voltage should not be varied from ±5% of rated frequency and rated voltage, respectively. In this situation, an automatic control mechanism named automatic generation control (AGC) is adopted to cancel the effect of load variation, thereby keeping a constant frequency and bus voltage level. To keep constant frequency and continuous interchange of power with other areas, AGC provides necessary control signals and regulates the active power output of multiple generators according to variations in tie-line loading and system frequency. Load frequency control (LFC) maintains the constant frequency at any load perturbations or external disturbances [3]. The main task of LFC is to reduce the error in the tie-line power exchange at the time of power exchanges between generator units [4]. Under any circumstances with abrupt change in real power load of an area, the system itself should be balanced by transferring sufficient power between areas with no external support. The economic conflict between the areas can be avoided with its own load frequency controller. Several research works are carried out in this area to develop a suitable method to overcome the unstable frequency issues in hybrid power systems interconnected through a tie line. The effectiveness of AGC in power systems with multiple power sources is discussed in [5–8]. The prominence of AGC in three-area system with generation rate constraint (GRC) is explained in ref [9]. Deregulated power system incorporating with various control methods for AGC is discussed in Ref. [10]. Several researchers have proposed both conventional and heuristic optimization tools like gradient-type Newton–Raphson algorithm, linear matrix inequalities, fuzzy and model predictive control [11, 12], etc., to optimize the control parameters for AGC in deregulated power system. In this paper, an attempt is made to analyze frequency response of a hybrid two-area system which consists of thermal, gas and hydro in both areas in MATLAB/Simulink environment. Genetic algorithm (GA) tool is used to optimize the controller gains of each area by adding some nonlinearities in the hybrid system. Moreover, an aggregated electric vehicle model is added in the system to analyze its effect on the performance of the whole system.

7.2 Modeling of Hybrid Two-Area Power System With large growing demand and to increase the reliability, the best way is to interconnect the nearby power plants. Tie line is the transmission line through which power is shared from one area to its neighboring area. In this work, two control areas are selected in such a way that combination of hydro, thermal and gas units is put in both areas along with load. The simplified diagram of the power system is shown in Fig. 7.1. The two areas are connected with a tie line. Nonlinearities are added in both areas and controllable load, and EV is connected in area 2. Simulink platform is used to model hybrid two-area system with nonlinearities and EVs, which is shown in Fig. 7.2. The system parameters are shown in Table7.1.

7 GA-Based Tuning of Integral Controller for Frequency Regulation …

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Fig. 7.1 Simplified diagram of hybrid two-area system

Fig. 7.2 Simulink modeling of hybrid two-area system with nonlinearities

7.3 Nonlinearities: Governor Dead Band and Generation Rate Constraint Multi-area system is susceptible to inter-area oscillations due to nonlinearities like governor dead band (GDB) and generation rate constraint (GRC) in the system [13,

68 Table 7.1 System parameters

K. R. Roshin and E. K. Bindumol D, f , B1 , B2 , T 12

0.0145p.uMW/Hz, 60 Hz, 0.4311p.u.MW/Hz, 0.0433

Inertia Load: K ps1 = K ps2 , T ps1 = T ps2

1/D Hz/p.u.MW, 2H/(Df) s

Steam: N 1 , N 2 , T sg , K r , T r , T t , Rth

0.8, −0.2/π, 0.06 s, 0.3, 10.2 s, 0.3 s, 2.4 Hz/p.u.MW

Hydro: T gh , T rs , T rh , T w , Rhyd 0.2 s, 4.9 s, 28.749 s, 1.1 s, 2.4 Hz/p.u.MW Gas: Bg , C g , X g , Y g , T cr , T f , T cd

0.049 s, 1, 0.6 s, 1.1 s, 0.01 s, 0.239 s, 0.2 s

EV: K av , Rav , T d , C h

0.5, 2.4 Hz/p.u.MW, 0.05 s, 1000

14]. GDB is the total magnitude of the sustained change in speed within which there is no change in valve position. Physical constraint of GRC set a limit on the rate of change of generating power due to physical limitations of turbine. Physical constraints of GRC and GDB are included for more accuracy, which are highlighted in Fig. 7.2. The Fourier coefficients in the GDB model are chosen to be 0.8 −0.2/π for N 1 ,N 2 , respectively. Since there is an upper threshold on the rate of possible change in active power for hydro and thermal units, the GRC limits are defined. For the thermal unit, a 10% per minute is used for generation rate constraint for rising and falling rates. As for the hydro unit, 270% and 360% per minute are used as rising and falling rate of generation.

7.4 Electric Vehicle High penetration of renewable energy sources is becoming a challenging task for the suppliers, which may lead to the expansion of the existing AGC capacity requirements. A new technology named vehicle-to-grid (V2G) is one of the best options for frequency regulation of power system connected with electric vehicles [15]. An aggregate model of EV fleet can be seen in Fig. 7.3 [16]. Battery charger and primary frequency control (PFC) are the main components of individual PEV model. The stored energy in battery is used to propel the EV,

Fig. 7.3 Aggregate model of the EV fleet

7 GA-Based Tuning of Integral Controller for Frequency Regulation …

69

and power exchange between supply system and battery is controlled by battery charger [17]. In the model of battery charger, the dynamic response of the closedloop power control system is considered as a first-order transfer function with a small time constant T d (= 50 ms). ΔPav(max) and ΔPav(min) are the upward and downward primary reserves of an EV, which are obtained after taking the average battery charger power of an EV as 5.41 kW. At over- and under-frequency problems, the maximum amount of primary reserve that can be consumed and injected back to grid is taken as ΔPav(max) and ΔPav(min) . C h represents the number of controllable EVs in the entire fleet that can contribute to frequency control. Upper and lower limits, Δf u and Δf l , of dead-band function and an average droop coefficient Rav are the compositions of primary frequency control of PEV. It depends on primary reserve of PEV and charging power. Participation factor K av depends on EVs’ state of charge. Furthermore, a converter charging/discharging capacity of 3 kW is assumed for this study.

7.5 Genetic Algorithm Genetic algorithm tool can be used for optimizing the controller parameters of AGC to damp out the oscillations; thereby, the system can be brought back into normal condition. Flowchart of GE is shown in Fig. 7.4. In designing the controller, with step response of load deviation, minimization of integral of square error (ISE) is taken as Fig. 7.4 Flowchart for genetic algorithm to find integral controller parameter

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K. R. Roshin and E. K. Bindumol

the objective function. Practical studies inferred that ISE functions will have more minimization in overshoot and give fast response with shorter settling time. So, the cost function Fc as the objective function is given in Eq. (7.1) where  f 1 and f 2 are the frequency changes in areas 1 and 2, respectively, and PT ie is the tie-line power transfer between the areas.   2 dt Fc = ∫  f 12 +  f 22 + Ptie

(7.1)

GA uses a roulette wheel selection, dependent crossover and adaptive feasible mutation with initial population of 50. Equation (7.2) represents the fitness function to compute the integral parameters in area 1 and area 2. FitnessFunction, FF =

1 1 + Fc

(7.2)

7.6 Results and Discussions Hybrid two-area system is controlled with the use of integral controller which is tuned by genetic algorithm. Simulink platform is used to model the hybrid twoarea system incorporated with various power sources in two areas with and without nonlinearities.

7.6.1 Hybrid Two-Area Power System Without Nonlinearities Hybrid two-area system includes three generating units, a, hydro, reheat thermal and gas. Each unit has its own control loop. Nonlinearities are not yet introduced in the system. First and foremost, a classical method of trial and error is used to evaluate the integral constants, and the values of K i1 and K i2 are taken as −0.1 and −0.1, respectively. On simulation, for a step disturbance of 0.01pu, the peak overshoot for area 1 is 0.02496 Hz, while the settling time is 14.6895 s for area 1 and that of area 2 is 0.01366 Hz and 20.8578 s respectively. Figure 7.5a, b shows the deviation in frequency and tie-line power subjected to a sudden disturbance in area 1. Observation shows that peak overshoot along with settling time remains within limits. It is seen that at the starting, tie-line deviation and frequency deviation are stable and then oscillate in between and finally get settled again. This is because of the cumulative effect of disturbances in two areas. A properly designed and tuned controller will push the system to stable condition faster. In this scenario, genetic algorithm tool is used to tune the integral controller parameters of AGC [11]. Convergence process of GA is shown in Fig. 7.6 that shows the progression of the two variables to their final optimized values.After 55

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Fig. 7.5 Simulation results on trial and error a Frequency deviation, b tie-line power transfer

Fig. 7.6 Convergence process of GA

iterations, the obtained values for K i1 and K i2 are −0.3755 and −0.0304, respectively. On simulation, the peak overshoot for area 1 is 0.02409 Hz, while the settling time is 11.29 s for area 1 and that of area 2 is 0.01255 Hz and 12.89 s, respectively. Figure 7.7a, b shows the frequency deviation and tie-line power deviation when a sudden disturbance occurs in area 1. It shows the variations in frequency change of two areas and tie-line power change between area 1 and area 2. It is inferred that oscillations are damped out in a faster rate with the use of tuned controller parameters.

7.6.2 Hybrid Two-Area Power System with Nonlinearities The nonlinearities GRC and GDB are applied in the same hybrid system to observe the reflection of nonlinearities in dynamic performance. From GA, after 50 iterations, the obtained values for K i1 and K i2 are −0.134 and 0.008, respectively. On simulation with these values, it is found that the peak overshoot for area 1 is 0.0314 Hz, while the settling time is 31.63 s for area 1 and that of area 2 is 0.0203 Hz and 37.37 s, respectively. Figure 7.7c, d shows the system response in two areas subjected to a sudden disturbance. From the figures, it is observed that the oscillations are persisting

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Fig. 7.7 Simulation results on GA a, c, e frequency deviation, b, d, e tie-line power transfer

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for a longer time, and it is very difficult to settle down the frequency deviation to zero with the effect of high nonlinearity. Even though the controller parameters are optimally tuned, due to the severity of nonlinearities, more time is taken to settle down.

7.6.3 Hybrid Two-Area System with Nonlinearities and Electric Vehicles Electric vehicles are incorporated in the hybrid two-area power system in addition with nonlinearities. The fleet has 1000 EVs. GA tool gives the values of K i1 and K i2 are −0.1853 and −0.0907, respectively. With the use of revaluated controller parameters, settling time in two areas is found to be 19.47 and 27.07 s, and their peak overshoots are 0.0313 and 0.0123 Hz. With the presence of nonlinearities, both settling time and peak overshoot are more than those without nonlinearities. But EV plays a role of controllable load so as to damp out the oscillations in a faster rate. Hence, both dynamic oscillations are reduced than with the case with nonlinearities so that both deviations are successfully brought back to zero as shown in Fig. 7.7e, f. The transient response parameters of hybrid two-area system incorporated with nonlinearities and EV are given in Table 7.2. Though the introduction of maximum amount of nonlinearities made the system to react in a realistic manner with higher settling time, the incorporation of EVs into the system makes a reduction in settling time by 38.44 and 27.56% in area 1 and 2, respectively. So, as the load increases, the system frequency decreases. The constant frequency, i.e., Δf = 0, is maintained through these tie lines with the sharing of power between areas. Thus, LFC maintains the power flow between different areas with a constant frequency. Table 7.2 Transient response parameters of hybrid two-area power system with and without nonlinearities and EV Parameters Hybrid two-area power system

Settling time (s)

Trial and error, without GDB and GRC

GA, without GDB and GRC

GA, with GDB and GRC

GA, with GDB, GRC and EVs

Area 1

Area 2

Area 1

Area 2

Area 1

Area 2

Area 1

Area 2

14.689

20.857

11.29

12.89

31.63

37.37

19.47

27.07

Peak overshoot (Hz)

0.0250

0.0137

0.02409

0.01255

0.0314

0.0203

0.0313

0.0123

Peak time (s)

0.7488

1.5846

0.7517

1.4142

0.8475

1.6399

0.8471

3.6327

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7.7 Conclusions In response to disturbance in system loads, LFC is used to regulate and control the frequency of the generated power within its area. To see the effectiveness of EVs in transient response of hybrid two-area system, the controller gains are properly tuned with a heuristic method. Genetic algorithm is proposed to tune the parameters of integral controller for frequency regulation problem. An integral square error of deviation in frequency and tie-line power of both areas is taken as the objective function to enhance the system transient response. Obtained results emphasize that the settling time was reduced with the tuned controller parameters in case of nonlinearities introduced in the two-area system. Incorporation of EV acts as a controllable load which reduces the frequency deviation to zero if there is a mismatch. The potentiality of GA is proved in damping the deviations in frequency and tie-line power in each area using the Simulink platform.

References 1. E.K. Bindumol, C.A. Babu, J. Meenu, K.P. Drisya, IBFSA for computation of power loss and voltage profile in RDS, in International Conference on Electrical, Electronics, and Optimization Techniques (2016) 2. H. Bevrani, Q. Shafiee, Intelligent DR contribution in FC of MAPS. IEEE Trans. Smart Grid. 9(2), 1282–1291 (2016) 3. C.L. Wadhwa, Electrical Power Systems. New Age Int. Publisher, New Delhi (2010) 4. M. Rajasi, C. Kalyan, K.P. Bhavesh, LFC of a single area HPS by using integral and LQR based I controllers, in Proceedings of the National Power Systems Conference (NPSC) (2018) 5. A. Rajeshkumar, M. Manmohan Singh, S. Kumar, To analyse the perf. of 2 area PS in AGC based on MATLAB. Int. Res. J. Eng. Tech. 02 (2015) 6. I. Seyedmahdi, G.P. Garcia, An aggregate model of PEVs for primary frequency control. IEEE Trans. Power Syst. 30(3), 1475–1482 (2015) 7. A. Oshnoei, S.M. Rahmat Khezri, Automatic generation control incorporating electric vehicles, in Electric Power Components and Systems (2019) 8. P. Sanki, M. Basu, P. Pal, Study of AGC in Two Area Thermal Interconnected PS Consisting WPG and SPG (EDCT, Kolkata, 2018), pp. 1–6 9. E. Reihani, M. Motalleb, Thornton, Providing frequency regulation reserve services using DRS. Energy Convers. Mgt. 124, 439–452 (2016) 10. S. Afsharnia, K. Dehghanpour, Electrical DS contribution to frequency control in PS: a review on technical aspects. Renew. Sust. Energy Rev. 41, 1267–1276 (2015) 11. P. Xie, J. Zhu, P. Xuan, Optimal controller design for AGC with BES using BFA, IEEE Power ES General Meeting, Chicago, IL, pp. 1–1 (2017) 12. S. Shokoohi, R. Khezri, S. Golshannavaz, FL based fine-tuning approach for robust LFC in a MAPS. Electr. Power Compon. Syst. 44(18), 2073–2083 (2016) 13. J. Morsali, M.T. Hagh, K. Zare, AGC of interconnenction MSPS with GDB and GRC nonlinearity effects, in Conference on Thermal Power Plants, Tehran (2016), pp. 12–17 14. J. Morsali, K. Zare, MGSO optimised TID-based GCSC damping contribution in coordination with AGC for diverse-GENCOs multi-DISCOs PS with considering GDB and GRC nonlinearity effects. IET Gxn, Trans. Distrub. 11(1), 193–208 (2017) 15. S. Jaiswal, M. Ballal, Optimal load management of PEVs with DSM in V2G application (ITEC, Pune, 2017), pp. 1–5

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16. M.I. Mohamed, F.B. Ahmed,LFC for multi area smartgrid based on advanced control techniques. Alexandria Eng. J. 57, 4021–4032 (2018) 17. Z. Hu, H. Zhang, Z. Xu, Evaluation of achievable V2G capacity using aggregate PEV model. IEEE Trans. Power Syst. 32(1), 784–794 (2016)

Chapter 8

Design and Analysis of Vehicle Tyres with Phase Change Material for Anti-freezing P. Venkata Ramana, Sai Rohan Gogulapati, K. Adithya Sharma, K. Sanjay, and N. Varun Raj Abstract In transportation, automobiles run on tyres for their movement on the road ways. There are various problems faced by the tyres, one of them is cold weather conditions especially in countries and places where temperatures drop below 0 °C and where the roads are covered with layers of ice or snow which clogs in the treadings of the tyre decreasing their grip on the road and also reduces tyre pressure due to change in density of air. These factors not only affect the tyres performance, but are also responsible for the fatal accidents occurring on the roads due to skidding and slipping of tyres. This work deals with the problems faced on account of tyres in cold weather conditions and tries to eliminate them with a different approach by employing phase change materials (PCM) which release high amounts of heat while freezing and absorb heat when they are melting. Embedding these PCMs in the vehicle tyres generate sufficient heat that can reduce the clogging of ice on the tyres. The present work analyses the effect of the position of the PCMs on the performance of the tyres. The PCMs are embedded around the circumference of the tyre such that angle between PCMs is 90°,120°,180°and 360º, respectively, on four different tyres. Theoretical and simulation analyses are carried out to study the effect of PCMs on the temperature difference generated. From the analysis, it is observed that PCM is quite effective in transfer of heat to tyres in cold conditions, and it is effective and efficient when positioned at 360°, i.e. when attached on complete inner wall of a tyre. P. Venkata Ramana · S. R. Gogulapati (B) · K. Adithya Sharma · K. Sanjay · N. Varun Raj Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, Telangana 500075, India e-mail: [email protected] P. Venkata Ramana e-mail: [email protected] K. Adithya Sharma e-mail: [email protected] K. Sanjay e-mail: [email protected] N. Varun Raj e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_8

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8.1 Introduction Automobiles have been playing a major part in the field of transportation such as transporting of humans and goods. However, these automobiles face many problems while being used in extreme cold weather conditions such as cooling down of engines, ignition lags, problems in drive train causing decrease in stability, thickening of engine oil, etc. Also, there is one more problem which is of importance, i.e. tyres losing their grip due to accumulation of snow between tyre treads and on roads. This problem may lead to fatalities as vehicle tyres are the most important component in ensuring adequate contact/grip during acceleration, braking or cornering. Earlier, these issues of tyres were dealt by usage of winter tyres, which did not provide promising results for daily use and practicality [1–3]. So, this present work deals with problems faced by the tyres in cold conditions which include, initially, clogging of snow or ice in tyre treadings which can result in sliding/skidding of vehicle tyres (Fig. 8.1) on roads accumulated with snow [4]. Besides, there is also the problem of flat tyres and losing of air pressure in tyres. These problems cause major road accidents and harm people’s lives [5, 6]. These are the major issues not only faced by cold weather countries (i.e Canada, USA & Russia) but are also a great obstacle for the Indian Army in the northern states. One of the solutions to the problem of clogging of snow to the tyres is that material used for the construction of tyres should be such that it will maintain the temperature of the tyres to avoid clogging. It would be very difficult to find a single material to meet the properties of strength of the tyre and also maintain temperatures. This problem leads to idea of thinking of a material which may be embedded in the regular tyre material just to maintain the right temperature to avoid clogging of snow to the tyre. One such material option is use of phase change materials (PCMs) which are those materials which release high amounts of heat while freezing and similarly absorb heat when they are melting, storing heat in form of latent heat (Fig. 8.2). They are basically available in solid or viscous liquid state, and they change their phase (solid to liquid or vice versa) on heating and cooling, respectively. The PCMs have limited Fig. 8.1 Vehicle skidding and sliding on snowy road

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Fig. 8.2 Phase transformation of PCM

usage in various fields like textiles, room ceilings, refrigerators, air conditioners and solar panels [7, 8].

8.2 Design of the PCM Embedded Anti-freezing Tyre A modern tyre consists of tread and body; its constituents encompass synthetic rubber, natural rubber, fabric and wire, along with carbon black and other chemical compounds. The purpose of the tread is to provide traction, while the body contains compressed air and provides comfort. In this present work, it is planned to embed the phase change material in the body portion of the tyre such that the influence of the amount and position of phase change material on avoiding the clogging of snow on the tyres is studied. The phase change material employed in the present study is OM-11 which is an organic material type with working temperature range 5–16 °C. The PCM OM-11 is selected on a basis that it is suitable for the embedding on the circumference of the tyre and temperatures created for the experimentation and analysis which provide better and accurate results. The PCMs are embedded around the circumference of the tyre such that angle between PCMs is 90°, 120°, 180° and 360º, respectively, on four different tyres as shown in Fig. 8.3. For the construction of this anti-freezing tyre, the PCM is contained into small tough envelopes so that the material does not leak, while it is liquefied, and these material contained envelopes are placed on the inner walls of tyres, i.e. outer diameter for the metal rims of tyres. Due to PCM’s unique nature of releasing heat while liquefying, the envelopes will help in containing the PCM and the heat released by the material is conducted firmly throughout the tyre surface by the rims. It helps the tyre to reduce clogging of ice in the rim and the treads and helps in melting snow around the tyre by convection heat transfer which ultimately reduces accidents due to slipping of tyres.

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Fig. 8.3 3D solid models showing PCM at various positions for different case: 90°, 120°, 180° and 360º

8.3 Theoretical Calculations for Temperature Measurement The process of transfer of heat from phase change material to tyre and vice versa happens through the process of conduction since both the materials are in complete contact. So, the rate of heat transfer and change in temperature of the tyre surface is mathematically calculated by using formula of conduction between two surfaces in contact (Eq. 8.1) and specific heat equation (Eq. 8.2) for all the cases. In these mathematical calculations, few constant values are considered, and few assumptions are made. The constant values that are considered for calculations are thermal conductivity of PCM (K), dimensional parameters of PCM capsule tube, radius and height (R and H), specific heat of tyre (C), thickness of tyre wall (D) and mass of tyre (m). The assumptions include time taken for transfer of heat (t) which is assumed to be 5 h based on average time taken by PCM to undergo complete phase change, initial temperature of tyre, (T i ) = −2 °C, based on creating cold weather condition and working temperature of PCM which is taken as 12 °C. Below is the theoretical procedure to find out the final temperature of tyre surface which is exposed to ice and cold weather. K A(T ) Q = . t d

(8.1)

where Q/t is heat transfer rate (t, time taken = 5 h = 18,000 s); K is thermal conductivity of PCM, 0.118 W/mk; A is area of cross section of PCM tube, 2π RH; D is wall thickness of tyre, 0.12 m; T is change in temperature. T = (temperature of PCM) − (temperature of Tyre).

(8.2)

From (8.2), T = ((273 + 12) − (273 − 2)) = 14 °C Area of Cross Section = 2π R H.

(8.3)

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Table 8.1 Summary of theoretical calculations Position of PCM

90°

120°

180°

360°

Time taken for heat transfer, i (hrs)

5

5

5

5

Heat input, Q (kJ)

8.4

11.5

16.8

33.9

Initial temperature, T i (°C)

−2

−2

−2

−2

Final temperature, T f (°C)

3.94

1.99

0.97

9.97

From (8.3) for Case 1: 90° = (2π * (0.152) * (0.115))/2 = 0.068 m; Case 3: 120° = (2π * (0.152) * (0.115))/3 = 0.0457 m; Case 2: 180° = (2π * (0.152) * (0.115))/4 = 0.034 m; Case 1: 360° = 2π * (0.152) * (0.115) = 0.1376 m; Heat transfer rate (Q/t) from (8.1): Case 1: (360°) Q = (0.118 × 0.137 × 14)/0.12) * 18,000 = 33.94 kJ. Similarly for Case 2: (180°), Q = 8.42 kJ; Case 3: (120°), Q = 11.32 kJ; Case 4: (90°), Q = 16.85 kJ. From the equation, specific heat of tyre and the final temperature can be calculated. Q = mCT.

(8.4)

where m is mass of tyre, 2 kg; C is specific heat of tyre, 1417 K; t is time taken for heat transfer, 5 h; T is change in temperature = (T f − T i ); T i is initial temperature, −2 °C; T f , final temperature. So, from (4), the final temperature of tyre surface (Tf ) for all cases, Case 1: 90°, T f = (16,850.4/(2 * 1417)) − 2 = 3.94580 °C; Case 2: 120°, T f = (11,324.46/(2 * 1417)) − 2 = 1.995928 °C; Case 3: 180°, T f = (8425.2/(2 * 1417)) − 2 = 0.972900 °C; Case 4: 360°, T f = (33,948.6/(2 * 1417)) − 2 = 9.9790 °C. The summary of all calculations is presented in Table 8.1.

8.4 Thermal Analysis A thermal analysis has been carried out using parameters gained from the above theoretical calculations so as to find out the final surface temperature of the tyre in all the cases that are considered. The thermal analysis is carried out by applying load cases through applied temperature and internal heat. This analysis is performed in Autodesk Fusion 360 Software which can be seen in Fig. 8.4. The thermal analysis of the tyre embedded with PCM at 90° shows that final temperature of the surface is 3.895 °C for given thermal loads obtained in mathematical calculations. The loads applied for the CASE-1 are applied temperature of −2 °C on the tyre surface, applied temperature of 12 °C on the phase change material and thus an internal heat load of 0.93 W/s to the phase change material. The final result diagram shows the peak temperature in red and lowest temperature point in dark blue. The resulting temperature of the simulation is very close to the value obtained through theoretical calculations. This indicates that the analysis is carried out properly with

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Fig. 8.4 Thermal analysis simulation of tyres with PCMs at various positions

least deviation. For the same thermals loads, i.e. applied temperature of −2 and 12 °C and due to change in the amount of phase change material, the internal heat loads for 120°, 180°and 360º positioned PCMs are 0.62 W/s, 0.466 W/s and 1.88 W/s, respectively. The final temperature of the surface is 1.957 °C, 0.968 °C and 9.94 °C, respectively, for given thermal loads obtained in mathematical calculations.

8.5 Results and Discussion After detailed mathematical calculations and thermal analysis study on the tyre with phase change material (PCM) placed on inner wall of tyres in various positions with respect to the centre (i.e. at 90°, 120°, 180° and 360°), the final outer surface temperatures of the tyres are obtained and are presented in Table 8.2 and Fig. 8.5. From Table 8.2 and Fig. 8.5, it is observed that the temperature of tyre surface increased from sub-zero temperature (−2 °C) to above zero temperatures in various cases, implying that heat transfer is taking place between the PCM and tyre in the cold weather condition and also suggesting that ice that is clogged in the tyre treads can melt. Through the results obtained, it can be deduced that maximum heat transfer and maximum temperature change of −2 °C to approximately 10 °C are occurring

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Table 8.2 Comparison between final temperatures of theoretical and simulation studies Position of PCMs

Final temperature (T f °C) (from theoretical calculations)

Final temperature (T f °C) (from simulation analysis)

90°

3.94

3.859

120°

1.99

1.957

180°

0.97

0.968

360°

9.97

9.94

Fig. 8.5 Final temperatures of the tyre

Theoritical Simulation

Final Temperature (Tf)oC

10 8 6 4 2 0

90°

120°

180°

360°

Position of PCMs

in the tyre where the PCM is placed completely on inner walls of tyre (360°). This is evident in both theoretical and simulation studies. Similarly, minimum heat transfer and minor temperature change are occurring in the tyre where the PCM is placed as two patches of angle 180° to each other inside the tyre. Out of the four cases studied, the PCM material used is least in this case, and the temperature change is also less. This clearly indicates that more the amount of PCM, more the temperature difference which would thus help in melting the snow/ice clogged in the tyres. It means for phase change material to be effective and efficient in its working, it has to be placed completely around the inner walls of tyre.

8.6 Conclusion In present work, an attempt is made to address the problems associated with the vehicle tyres that are used in freezing temperatures. The problems are slipping and damage of tyres due to the ice or snow getting accumulated in tyre treads and causing accidents in cold weather conditions. To resolve this issue, in the present work, phase change material (PCM), a material that releases heat while freezing and absorbs heat

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while melting in cold conditions, is embedded along the circumference of the tyre. This material helps any type of tyre to withstand under cold conditions without manual work of removing snow. The PCMs are embedded around the circumference of the tyre such that angle between PCMs is 90°, 120°, 180° and 360º, respectively, on four different tyres. Heat absorption and dissipation, pressure difference, cost of installation, efficiency and tyre life are analysed. The mathematical and simulation studies are performed with few assumptions to show their effectiveness in usage. After performing mathematical calculations, the results were correlated with thermal analysis done in Fusion 360. From the analysis it is observed that: a. Phase change material (PCM) is quite effective in transfer of heat to tyres in cold conditions. b. PCM is effective and efficient when positioned at 360°, i.e. when attached on complete inner wall of a tyre. c. The melting of ice or snow takes place at an average time period of 5 h. Even though this might seem a little longer period, it helps the tyre in not accumulating snow further and causes less damage and avoids accidents. d. This method is economical, ecological, non-toxic and also does not need external energy as it works by the temperature changes around it. Acknowledgements The authors would like to thank the Management, Principal, Head of the Department of Mechanical Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, for their support in carrying out this work.

References 1. S.W. Brown, W.G. Vanlaar, R.D. Robertson, Winter tires: a review of research on effectiveness and use. Traffic Injury Research Foundation (2013) 2. V. Žuraulis, G. Garbinˇcius, P. Skaˇckauskas, O. Prentkovskis, Experimental study of winter tyre usage according to tread depth and temperature in vehicle braking performance. Iranian J. Sci. Technol. Trans. Mech. Eng. 44(1), 83–91 (2020) 3. E. Anne, E. Russ, The Composition of a Tyre: Typical Components (Oxen. The Waste & Resources Action Programme, Oxen, 2006). 4. K.J. Walu´s, Z. Olszewski, Analysis of tire-road contact under winter conditions. Proc. World Cong. Eng. 3, 6–8 (2011) 5. H. Takagi, Winter skidding accidents on roads surfaces covered with snow and ice under studdedtyre regulation, in Conference—Xth PIARC International Winter Road Congress (1998) 6. H. Takagi, Winter skidding accidents on road surfaces covered with snow and ice under studdedtire regulation, in Xth PIARC International Winter Road Congress Permanent International Association of Road Congresses, 3(Technical Report) (1998) 7. H. Mehling, L.F. Cabeza, Phase change materials and their basic properties, in Thermal Energy Storage for Sustainable Energy Consumption (Springer, Dordrecht, 2007), pp. 257–277 8. M. Rahman, A. Hamja, H.N. Chowdhury, Phase Change Materials: Characteristics and Encapsulation

Chapter 9

Experimentation and Mathematical Modelling: Indirect Forced Convection Solar Drying of Tomato with Novel Drying Chamber Arrangement Using Phase Change Material as Thermal Energy Storage V. Sabareesh, K. John Milan, C. Muraleedharan, and B. Rohinikumar Abstract The scope of indirect forced convection solar drying is better than other methods due to reduction in drying time and better quality of dried products. The current work focuses on experimental and mathematical modelling of tomato drying using the method at two different flow rates of 0.153 and 0.077 kg/s. Experimentation on drying of tomato is performed using a novel drying chamber arrangement with phase change material used as thermal energy storage in solar collector. With the experimental data, mathematical modelling is considered in the present study. There is a reduction in drying time by 6 h at a flow rate of 0.153 kg/s compared to open sun drying, and at a flow rate of 0.077 kg/s, the drying time gets reduced by 5 h. The best model is found to be the parabolic model for drying tomato with R 2 = 0.9806 and RMSE = 0.03175.

9.1 Introduction The drying and storage of crops having high initial moisture content (more than 80%) is a challenge. One of the crops having high water content and that is widely V. Sabareesh (B) · K. J. Milan · C. Muraleedharan · B. Rohinikumar Department of Mechanical Engineering, National Institute of Technology Calicut, Kozhikode, Kerala 673601, India e-mail: [email protected] K. J. Milan e-mail: [email protected] C. Muraleedharan e-mail: [email protected] B. Rohinikumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_9

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cultivated by the farmers is tomato. Most of the farmers, especially in India, resort to open sun drying (OSD), and due to high fungal attack, there is huge post-harvest loss in this method adopted. Compared to this method, indirect forced convection solar drying can be preferred [1, 2]. In indirect forced convection solar drying, the products to be dried are safely arranged in the trays of drying chamber, and air is supplied by an external blower which reaches the drying chamber after getting heated in the solar air heater. In the present study, the V-corrugated sheets of the absorber plate of the solar air collector are filled with paraffin wax as phase change material (PCM) to ensure heating of air even during low sunshine hours. The experiment is performed at two different flow rates of 0.153 and 0.077 kg/s using a novel chamber drying arrangement. In conventional arrangement of solar dryers, the hot air entry from solar collector is from the bottom or side of the chamber. This results in considerable increase in moisture content of the drying air available at each tray which adversely affects the process of drying and results in increased drying time. In the current experiment, the hot air enters from the solar collector horizontally through a duct that splits into three leading into each of the three drying trays. So, the problem mentioned is possibly eliminated. The solar drying chamber is designed using Ansys Fluent-19.0 [3, 4] with an objective to maximize the distribution of air inside the drying chamber. The exit of the drying air is also horizontal along the length of the chamber through vents. The drying chamber is also made from unconventional material known as fibre-reinforced plastic (FRP) which has desirable properties of low thermal conductivity (0.35 W/m K) and less coefficient of thermal expansion (20 × 10−6 m/m K) and moderate tensile strength (270 MPa). After obtaining the experimental results, mathematical modelling is performed to study the best model for the drying in drying chamber.

9.2 Experimental Procedure 9.2.1 Experimental Set-Up The experimental set-up consists of a solar collector, solar drying chamber centrifugal air blower and orifice meter. The blower is of standard rating 0.5 HP at 2800 rpm. This blower supplies fresh surrounding air into the solar collector (solar air heater) having dimensions 1.2 m length, 0.75 m width and 0.25 m height, which is packed with PCM modules (Fig. 9.1). The PCM module is filled with PCM, and ends are sealed. The PCM modules are arranged in two out of every three corrugations in spaces of corrugated sheet so as to study collector temperature with and without using PCM. It can be observed from Fig. 9.1 that there is provision for both horizontal and vertical passage of hot air from the solar collector. When the air enters horizontally in the manner shown, the vertical entry is made restricted and vice versa. In the present

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Fig. 9.1 Photograph of the experimental set-up

study, experimentation is performed with horizontal entry and horizontal exit of air only.

9.2.2 Procedure The fresh sample of tomato is divided into three portions each of approximately 1.2 kg mass. One portion is kept under direct sunlight in open air for drying. The second portion is kept in three drying trays of the designed drying chamber for solar drying, while the third portion is kept for electric hot air oven drying at 60 °C until a constant dry weight is obtained. An electronic weighing balance (accuracy: ±0.001 kg) is used to measure the hourly mass of the sample in drying chamber as well as open sun drying. J-type thermocouples are connected to various points inside the solar collector to measure the temperature. The solar radiation data used is from Davis Vantage Pro weather station. The experiment is performed at N.I.T Calicut campus, Kozhikode (11.2588 °N, 75.7804 °E), Kerala. Moisture content (Mt ) at any time t [5] can be defined in terms of dry basis or wet basis. On wet basis, it can be defined as, Mt =

W (t) − Wd . W (t)

(9.1)

Initial moisture content percentage (Mi ) can be defined as the percentage of moisture content initially present in the sample before drying starts. Mathematically, it is expressed as, Mi =

W (0) − Wd × 100 W (0)

W (0): initial mass of the sample to be dried. Wd : final dry weight of sample (final dry weight of sample in hot air oven).

(9.2)

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W (t): weight of the sample at any time ‘t’. On dry basis, it can be defined as: Mt =

W (t) − Wd × 100 W (d)

(9.3)

In general, wet basis is preferred for indicating moisture percentages.

9.2.3 Mathematical Modelling Since moisture removal rate is higher at flow rate of 0.153 kg/s, drying data at this flow rate is selected in modelling of tomato using the three models—Henderson and Pabis model [6–8], page model [6–8] and parabolic model [6, 7]. The moisture ratio (M.R) of the sample at any time [7] can be obtained using Eq. (9.4). M.R =

Mt Mi

(9.4)

where Mt and Mi are the moisture content at any time t, the initial moisture content (kg water/kg on wet basis) respectively, and t is the drying time. All models are fitted to the experimental data of M.R versus time, and the best model is selected based on the statistical parameters mentioned below [9]. Coefficient of determination 2  2 N  − MRpred,i − MRexp,i i=1 MRexp,i − MRexp mean,i R = 2 N  i=1 MRexp,i − MRexp mean,i    N   MRpred,i − MRexp,i 2  Root mean square error (RMSE) = N i=1 2

(9.5)

(9.6)

From the drying models considered, the fitted curve with the highest value of   coefficient of determination R 2 and lowest root mean square error (RMSE) is selected as the best model. The curve fitting is done with the help of regression analysis tool in MS-Excel Worksheet 2013.

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Temperature with PCM

Temperature without PCM 80

800

60

600

40

400

20

200 0

Time

0

Temperature (°C)

Solar Radiation(W/

)

Average solar radiation and Average collector temperature Radiation (W/m2) 1000

Fig. 9.2 Average solar radiation and average collector temperature

9.3 Results and Discussions 9.3.1 Post-processing of Solar Drying Curves The use of paraffin wax as PCM keeps an average collector temperature of 4 °C above the nominal collector temperature for heating the air even during times of low solar radiation. The maximum average solar radiation received is 798 W/m2 (Fig. 9.2). The final mass of sample in hot air oven after placing it for 24 h is 0.107 kg. At an air flow rate of 0.153 kg/s, the initial sample of 1.24 kg (initial moisture content on wet basis is 91.3%) is dried to 0.144 kg (25.69%) in 12 h. Meanwhile, the sample of 1.2 kg (91.08%) is dried to 0.153 kg (30%) in open sun in 18 h. Hence, there is a saving in drying time of 6 h using drying chamber compared to open sun drying (OSD) (Fig. 9.2). In the case of air flow rate of 0.077 kg/s, the sample of 1.192 kg (91%) is dried to 0.115 kg (6.95%) in 22 h, while in open sun drying, 1.198 kg (91.1%) of sample is reduced to 0.115 kg (6.95%) in 27 h. Therefore, there is a saving of 5 h in chamber drying. It can be observed from Fig. 9.2 that drying curve at flow rate of 0.153 kg/s is steeper than that of drying curve at 0.077 kg/s. Therefore, it can be inferred that the moisture removal rate is higher in the former.

9.3.2 Modelling of Solar Drying Curves With the help of experimental data of drying tomato in the dryer at 0.153 kg/s, three models are applied to find the best fitting model among them. The constants of each model are obtained (refer Table 9.1), and the model with high R 2 and low RMSE value is selected as the best fitting model. The predicted moisture curve is obtained as exponential when Henderson and Pabis model is applied to drying tomato as shown in Fig. 9.3. R 2 value is obtained as 0.913 and RMSE to be 0.06425.

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Table 9.1 Modelling results of drying tomato S. No.

Model

Model equation e−kt

1

Henderson and Pabis

MR = A

2

Page model

MR = A e−kt

3

Parabolic

MR = a + bt 2 + ct

n

Constants

R2

RMSE

A = 1.1437 k = 0.087

0.913

0.06425

k = 0.0694 n = 0.8972

0.942

0.0884

a = 1.0092 b = −0.0421 c = −0.0007

0.9806

0.03175

Mass of the sample (kg)

Drying data of the tomato sample 1.5

Dryer (0.077 kg/s)

OSD ( 0.077 kg/s)

Dryer (0.153kg/s)

OSD (0.153 kg/s)

1

0.5

0

0

5

10

15

20

25

30

35

Drying time( hour) Fig. 9.3 Drying data of the tomato sample at two different flow rates (OSD: open sun drying)

After the Henderson and Pabis model, page model having MR = e−kt is applied to the same experimental data. In the case of page model, determination of constants is predicted by converting this into a linear equation. This can be done by taking natural logarithm on both sides [6, 10]. n

ln(MR) = −kt n

(9.7)

ln(−ln(MR)) = ln(k) + n ln(t)

(9.8)

From a plot of ln (−ln (MR)) versus ln (t) using experimental data, a line is fitted to determine the constants k and n (Table 9.1). Then using the values of n and k, moisture curve is obtained. R 2 value is obtained as 0.942 and RMSE as 0.0884 for the predicted moisture curve of page model. After considering the Page model and Henderson and Pabis models, the parabolic model is applied to the experimental data. The predicted moisture curve is having R 2 0.9806 and RMSE 0.03175. From the models considered for drying tomato (Fig. 9.4), it can be observed that parabolic model is found to be the best fitting model with highest R 2 and lowest RMSE value among the three models considered.

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Drying Models

Fig. 9.4 Comparison of different drying models with experimental moisture ratio (MR)

Moisture ratio (kg/kg)

1.2

MR experimental

Page Model

Parabolic Model

Hendersonand Pabis

1 0.8 0.6 0.4 0.2

Drying time (hours) 0

2

4

6

8

10

12

14

16

9.4 Conclusion The conventional drying method of open sun drying faces several drawbacks like fungal attack, contamination, etc., especially in the case of drying high moisture content raw crops like tomato. Indirect forced convection solar drying is the best possible method of drying and storage in such cases. By using the novel configuration of solar drying chamber and integration of paraffin wax as phase change material in the solar collector, there is a reduction in drying time by 6 h at a flow rate of 0.153 kg/s compared to open sun drying, and at a flow rate of 0.077 kg/s, the drying time gets reduced by 5 h. Among the three models considered for drying tomato sample at a flow rate of 0.153 kg/s in the drier, the best model obtained is parabolic model with R 2 0.9806 and RMSE 0.03175. Acknowledgements The authors would like to thank MHRD for funding the research work through TEQIP-III.

Reference 1. M. Augustus Leon, S. Kumar, S.C. Bhattacharya, A comprehensive procedure for performance evaluation of solar food dryers. Renew. Sustain. Energy Rev. 6, 367–393 (2002) 2. A.B. Lingayat, V.P. Chandramohan, V.R.K. Raju, V. Meda, A review on indirect type solar dryers for agricultural crops—dryer setup, its performance, energy storage and important highlights. Appl. Energy 258 (2020) 3. P. Demissie, M. Hayelom, A. Kassaye, A. Hailesilassie, M. Gebrehiwot, M. Vaneirschot, Design, development and CFD modeling of indirect solar food dryer. Energy Procedia 158, 1128–1134 (2019) 4. T. Norton, B. Tiwari, D.-W. Sun, Computational fluid dynamics in the design and analysis of thermal processes: a review of recent advances. Crit. Rev. Food Sci. Nutr. 53, 251–75 (2013)

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5. M.N. Musembi, K.S. Kiptoo, N. Yuichi, Design and analysis of solar dryer for mid-latitude region. Energy Procedia 100, 98–110 (2016) 6. S.B. Mariem, S.B. Mabrouk, Drying characteristics of tomato slices and mathematical modeling. Int. J. Energy Eng. 4(2A), 17–24 (2014) 7. O. Badaoui, S. Hanini, A. Djebli, H. Brahim, A. Benhamou, Experimental and modeling study of tomato pomace waste drying in a new solar greenhouse: evaluation of new drying models. Renew. Energy (2018) 8. K. Sacilik, R. Keskin, A.K. Elicin, Mathematical modelling of solar tunnel drying of thin layer organic tomato. J. Food Eng. 73, 231–238 (2006) 9. Y. Bahammou, Z. Tagnamas, A. Lamharrar, A. Idlimam, Thin-layer solar drying characteristics of Moroccan horehound leaves (Marrubium vulgare L.) under natural and forced convection solar drying. Solar Energy 188, 958–969 (2019) 10. I. Doymaz, Air-drying characteristics of tomatoes. J. Food Eng. 78, 1291–1297 (2007)

Chapter 10

Effect of Indoor and Outdoor Conditions on the Performance of SHVCR System—An Experimental Study Surender Kumar and Rabinder Singh Bharj

Abstract This paper presents an experimental study on the solar hybrid vapor compression refrigeration (SHVCR) system in indoor and outdoor conditions. This system was tested in indoor condition and compared with the outdoor condition. The energy consumption and temperature variation inside the refrigerator were recorded in both conditions. The effect of meteorological parameters on the performance of SHVCR system was analyzed. The results showed that the performance of this system was depended on indoor and outdoor conditions. The refrigerator cooling rate in indoor condition was faster as compared to outdoor condition. The per day energy consumption of this system in the outdoor condition was 112 Wh to maintain average lower temperature −12 °C inside the refrigerator chamber. The per day energy consumption of this system in the outdoor condition was recorded 78 Wh. The energy consumption of this system was 30% lower in indoor condition as compared to outdoor condition. The proposed SHVCR system was able to store lifesaving vaccines and perishable foodstuffs.

10.1 Introduction Nowadays, the global demand of refrigeration and air conditioning is continuously increasing for the duration of the summer season. This rising demand for refrigeration and air conditioning has led to a significant increase in the sector of energy consumption and production [1]. The refrigeration system is consumed about 17% of total global energy and 40% consumed in the building sector. The energy production and consumption process are responsible for leading greenhouse gas emissions (GHGE). The GHGE produces the negative effects in the urban areas environment [2]. This GHGEs have been marked with a frightening rise in the last century. Due to ruin environment by the GHGEs, the death of 940,000 children recorded in 2016. After that, the United Nations initiative agreement with global countries to reduce gas emissions by S. Kumar (B) · R. S. Bharj Mechanical Engineering Department, National Institute of Technology, Jalandhar, Punjab, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_10

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2020. The global countries are encouraged to invest in green technologies that thrive solar hybrid refrigeration technologies [3]. The VCR technology-based systems are used almost 80% in global refrigeration industries due to higher COP (0.7–3.5) [4]. The global population raising continuously which demanding more perishable foodstuffs, medicines, refrigeration, and air conditioning because the living standard of peoples rising continuously. About 40% of foodstuffs required refrigeration in the global cold chain supply. However, only 15% of perishable foodstuffs such as fruits, vegetables, milk products, meat, medicines are using this facility at the current time [5]. The developing countries energy production sector highly depends on fossil fuels. The cost and scarcity of fossil fuels are another problem in front of the global energy sector. Most industrial, institutional, residential, and commercial buildings are required refrigeration and air conditioning (RAC) in the current time era. These RAC systems are less energy efficient; therefore, they consume higher grid energy. About 85% of these RAC systems are operated with the grid electricity and diesel engine generator-set which again causes the GHGE [6]. The solar hybrid RAC systems are operated on grid electricity (major energy sources). The solar energy is the second energy sources which assist the battery charging system in the daytime. Therefore, the solar-assisted RAC system less depends on the grid electricity. The global renewables share capacity by different region in 2019 as shown in Fig. 10.1 [7, 8]. India plays an important role in the share of global renewable by using solar PV (46%), wind (16%), hydro (5%), and bioenergy (1%). Energy consumed in different sector worldwide is shown in Fig. 10.2.

Fig. 10.1 Global renewable share in capacity by different region

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Fig. 10.2 Energy consumed in different sectors worldwide [8]

The refrigerator of SHVCR system requires less energy as compared to conventional AC refrigerator. This paper is focused on the performance study of SHVCR system. The proposed SHVCR system is capable to solve the storage problem of perishable foodstuffs, medicines, and vaccines [9–20].

10.2 Description of Experimental Setup The SHVCR system is experimentally analyzed in this study. This system is used in hybrid mode of energy. The series of experiments are conducted in indoor and outdoor conditions.The experimental setup of the SHVCR system is shown in Fig. 10.3. The experimental setup consists of PV module (12 V 150 W), solar charge controller (10 A), refrigerator (12 V 240 L), battery charger (15 A), and lead-acid battery (12 V 105 Ah). The energy produced by the solar panel is supplied to the battery bank through the MPPT charge controller in the day time. The SMPS charger is used to charge battery bank of the SHVCR system with the grid electricity. The experimental setup operates on solar energy in day time. But it operates on grid electricity when unavailability of solar energy in night time or cloudy day.The battery energy is used in experimental setup when unavailability of both type energy (solar and grid electricity). The R134a is the working refrigerant in the refrigerator of the SHVCR system.

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Fig. 10.3 Experimental setup of SHVCR system

10.3 Experimental Procedure The performance of SHVCR system is analyzed in indoor and outdoor conditions. The six experiments are conducted at two different locations. The series of experiments are conducted in the ME Department of the NIT Jalandhar. The three outdoor condition experiments were carried out in between 21 and 25 July 2019, and the other three indoor condition experiments carried out in between 26 and 30 July 2019. The experimental and meteorological data are recorded from 8:00 am to 5:00 pm in the experimental day. The average temperature inside the refrigerator is recorded using six thermocouples data. Two digital energy meters are used to measure voltage, current, power, and energy of the SHVCR system. One energy meter is used to measure solar panel energy production and the other is used to measure the DC refrigerator energy consumption. The following experimental conditions were maintained: • The inner side temperature of refrigerator is recorded in 10 min time interval. • The door of DC refrigerator is kept open for 5 h to maintain thermal equilibrium with ambient air before starting new experiment. • The door of the DC refrigerator was kept closed during the experimental time. • The grid electricity is automatically cut when solar energy is available.

10.4 Results and Discussion The performance of the SHVCR system is analyzed at thermostat position 7 with the no-load condition. This system is tested under indoor and outdoor experimental

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conditions. The indoor experiments are performed at room temperature and the outdoor condition experiments are performed in an open environment condition. The environmental parameters were recorded according to NIT Jalandhar weather condition which is shown in Fig. 10.4. The performance of solar panel is tested at standard condition (at 25 °C cell temperature and 1000 W/m2 solar intensity) which is shown in Fig. 10.5. Figure 10.6 represents the current and power consumption of the DC refrigerator in indoor and outdoor conditions. Temperature decreasing rate inside the DC refrigerator directly effects on power consumption. In the indoor condition, the maximum current and power consumption were recorded 1.16 A and 14.7 W, respectively. In the outdoor condition, the maximum current and power consumption were recorded 1.54 A and 16.8 W, respectively. Figure 10.7 shows the temperature variation and energy consumption of the DC refrigerator in indoor and outdoor conditions. At thermostat position 7, all experiments of the DC refrigerator are performed at no-load condition. The chamber of DC refrigerator maintains the lower average temperature −12 °C in the outdoor experimental condition. The DC refrigerator consumed per

Fig. 10.4 Environmental parameters in the experimental days in indoor and outdoor conditions

Fig. 10.5 Solar panel performance in standard condition

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Fig. 10.6 Current and power consumed by the DC refrigerator in indoor and outdoor conditions

Fig. 10.7 Temperature variation and energy consumption of the DC refrigerator in indoor and outdoor conditions

day energy 112 Wh at the outdoor condition. The initial (pull-down) cycle time for the indoor condition is lower as compared to the outdoor condition. In the indoor condition experiments, the DC refrigerator maintains the lower average temperature (−14 °C) inside the chamber. The ambient air temperature is highly effect on the energy consumption of the DC refrigerator.

10.5 Conclusion The present study was focused on the SHVCR system tested in different operating conditions. This system was able to store perishable foodstuffs, medicines, and vaccines. The effect of indoor and outdoor conditions on the performance of this system was analyzed in Jalandhar city (Punjab state) of India. This system was

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analyzed under real weather conditions for six days in the summer season (21–30 June 2019). The experiments were performed on the DC refrigerator of the SHVCR system in no-load condition (at 7th thermostat position). The following main conclusions were drawn from this study: • The intensity of solar radiation falls on the PV panel was recorded in between 310 and 1150 W/m2 , at that time the DC compressor was running with rotational speed range 1900–3600 rpm. • The results showed that the cooling rate in indoor condition was faster as compared to outdoor condition. • The DC compressor of this system was consuming an average of 78 Wh energy per day to maintain the minimum lower temperature (−14 °C) in indoor condition. • The DC compressor of this system was consuming an average of 112 Wh energy per day in outdoor condition. • The energy consumption of this system was recorded 30% lower in indoor condition as compared to outdoor condition. • The COP of this system was higher 1.56 at lower speed (1900 rpm), and its lower value (1.46) at the highest speed (3600 rpm). • The battery backup of the SHVCR system was working for 28 h in indoor condition and 25 h in outdoor condition.

References 1. O. Marc, F. Lucas, F. Sinama, E. Monceyron, Experimental investigation of a solar cooling absorption system operating without any backup system under tropical climate. Energy Build. 42(6), 774–782 (2010) 2. C. Piselli, V.L. Castaldo, A.L. Pisello, How to enhance thermal energy storage effect of PCM in roofs with varying solar reflectance: experimental and numerical assessment of a new roof system for passive cooling in different climate conditions. Sol. Energy 192, 106–119 (2019) 3. M. Souissi, Z. Guidara, A. Maalej, Numerical simulation and experimental investigation on a solar refrigerator with intermittent adsorption cycle. Sol. Energy 180, 277–292 (2019) 4. V.W. Bhatkar, V.M. Kriplani, G.K. Awari, Alternative refrigerants in vapor compression refrigeration cycle for a sustainable environment: a review of recent research. Int. J. Environ. Sci. Technol. 10(4), 871–880 (2013) 5. R. Gugulothu, N.S. Somanchi, H.B. Banoth, K. Banothu, A review on solar-powered air conditioning system. Procedia Earth Planet. Sci. 11, 361–367 (2015) 6. H. Ling, C. Chen, S. Wei, Y. Guan, C. Ma, G. Xie, Z. Chen, Effect of phase change materials on indoor thermal environment under different weather conditions and over a long time. Appl. Energy 140, 329–337 (2015) 7. IIR, The Role of Refrigeration in the Global Economy. https://sainttrofee.nl/wp-content/upl oads/2019/01/NoteTech_29-World-Statistics.pdf 8. L. Ming, W. Liuling, Z. Xizheng, L. Qing, Study of a solar trough concentrating system for application of solar energy refrigeration, in Proceedings of ISES World Congress 2007 (Springer, Berlin, Heidelberg, 2008), pp 556–560 9. S.K Gundu, A. Joshi, G. Raj, S. Vahora, M. Dubey, M. Shyam, Performance evaluation of solar– biogas hybrid cold storage system for transient storage of horticultural produce, in Concentrated Solar Thermal Energy Technologies (Springer, Singapore, 2018), pp. 211–216

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10. G. Li, S. Shittu, X. Zhao, X. Ma, Preliminary experiment on a novel photovoltaic-thermoelectric system in summer. Energy. 188, 116041 (2019) 11. R. Opoku, S. Anane, I.A. Edwin, M.S. Adaramola, R. Seidu, Comparative techno-economic assessment of a converted DC refrigerator and a conventional AC refrigerator both powered by solar PV. Int. J. Refrig. 72, 1–11 (2016) 12. J.M. Lukuyu, R.E. Blanchard, P.N. Rowley, A risk-adjusted techno-economic analysis for renewable-based milk cooling in remote dairy farming communities in East Africa. Renew. Energy 130, 700–713 (2019) 13. S.M.A. Rahman, A.A. Hachicha, C. Ghenai, R. Saidur, Z. Said, Performance and life cycle analysis of a novel portable solar thermoelectric refrigerator. Case Stud. Thermal Eng. 19, 100599 (2020) 14. M.P. Islam, T. Morimoto, Thermodynamic performances of a solar-driven adsorption system. Sol. Energy 139, 266–277 (2016) 15. A. Allouhi, T. Kousksou, A. Jamil, Y. Agrouaz, T. Bouhal, R. Saidur, A. Benbassou, Performance evaluation of solar adsorption cooling systems for vaccine preservation in Sub-Saharan Africa. Appl. Energy 170, 232–241 (2016) 16. S. Kumar, R.S. Bharj, Comparative analysis on battery used in solar refrigerated e-rickshaw in India, in Intelligent Manufacturing and Energy Sustainability (Springer, Singapore, 2020), pp. 239–248 17. H. Wu, Q. Liu, G. Xie, S. Guo, J. Zheng, B. Su, Performance investigation of a novel hybrid combined cooling, heating and power system with solar thermochemistry in different climate zones. Energy. 190, 116281 (2020) 18. E.M. Salilih, Y.T. Birhane, Modelling and performance analysis of directly coupled vapor compression solar refrigeration system. Sol. Energy 190, 228–238 (2019) 19. K.V. Kumar, L. Paradeshi, M. Srinivas, S. Jayaraj, Optimum composition of alternative refrigerant mixture for direct expansion solar-assisted heat pump using ANN and GA, in Concentrated Solar Thermal Energy Technologies (Springer, Singapore, 2018), pp. 199–209 20. P.S. Pandey, M.K. Aghwariya, P. Ranjan, G. Rani, The real-time hardware design and simulation of a thermoelectric refrigerator system based on Peltier effect, in Proceeding of International Conference on Intelligent Communication, Control, and Devices (Springer, Singapore, 2017), pp. 581–589

Chapter 11

An Integrated Switching Pattern and Sensorless Speed Control for BLDC Motor Drive in Electric Vehicles M. U. Deepa and G. R. Bindu

Abstract The vital area in brushless direct current (BLDC) motor drive used in electric vehicle drive is motoring, regenerative braking, battery charging, and speed control. To attain these, separate circuits should be used. Switching pattern for the first three modes integrated with sensorless speed control for which finds application in electric vehicle is highly warranted in the present scenario. This paper addresses in detail these four areas. The proposed switching pattern enables the same converter for motoring operation of BLDC motor to be used during regenerative braking as well as rectifier in charging mode of battery from the AC grid. Line voltage detection-based sensorless speed control method is also discussed in the paper so that reliability of the system can be improved. The effectiveness of the proposed method is verified using simulation and also virtual hall sensor signals are compared with actual hall sensor signals experimentally under variable speeds.

11.1 Introduction Nowadays, electric vehicles (EVs) are widely used due to their environmental friendly and economic considerations. EVs driven by high-efficiency permanent magnet motors and controllers, which are powered by alternative energy sources, give a clean, efficient, and environment friendly system [1, 2]. EVs use the energy stored in batteries which limit its driving range accompanied by high cost [3]. Regeneration, which refers to transfer of energy back to the supply/battery, is a solution to this problem since it can increase the range as well as the battery life due to reduced depth of discharge [4, 5]. M. U. Deepa (B) · G. R. Bindu Department of Electrical Engineering, College of Engineeritng Trivandrum, Thiruvananthapuram 695025, India e-mail: [email protected] G. R. Bindu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_11

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At present, the electric vehicles driven by BLDC motor use separate circuits for driving, regenerative braking, and battery charging modes. Plug in hybrid EV using AC/DC PWM buck converter for battery charging [6] and BLDC motor driven hybrid EV with hybrid storage, incorporating battery and super capacitor [7], are developed to improve efficiency. A four-quadrant brushless electronically commutated sensorless motor drive is done in [8] to get better control of torque. For controlling the BLDC motor in all four quadrants, a bidirectional DC-DC converter is presented, with recovery of energy at all stopping operations [9]. A bidirectional DC-DC converter with reduced circuitry is investigated for increasing the effectiveness of power transfer in BLDC motor fed EV [10]. A simple method without any extra circuit or complex algorithm is suggested, to convert kinetic energy into electrical energy for feeding it back to the battery [11]. A novel regenerative technique, for effective regeneration in EV is also proposed, in which the motor inverter is used as a two-stage boost converter [12]. Conventionally, hall sensor signals are used for position detection of the rotor which causes addition of the cost and reliability issues. Sensorless operation of BLDC motor can be divided into five categories [13]. The back emf detection method is the most popular one and can be implemented effectively. The commutation signals are extracted directly from the line-to-line voltage of a BLDC motor, which is in phase with actual hall sensor signal [14]. Hence, conventional control algorithm cannot be modified. A simple low-pass filter circuit and low-cost operational amplifier-based comparators are required here [15]. An integrated switching scheme which combines motoring, regenerative braking, battery charging, along with a sensorless speed control is proposed in the paper. Section 11.2 deals with the proposed drive system for BLDC motor used in electric vehicles. In Sect. 11.3, regenerating braking aspects and its switching pattern of BLDC motor drive are presented. Section 11.4 discusses the charging operation of battery. The sensorless approach of BLDC-based on line voltage detection is explained in the Sect. 11.5. In Sect. 11.6, the simulation results of BLDC motor drive incorporating regenerative braking and charging of battery from the AC grid are analysed. An experimental verification is also presented in this section. Conclusion of the paper based on the results obtained is provided in the last section.

11.2 Proposed BLDC Motor Drive for Electric Vehicle Figure 11.1 shows BLDC motor drive used in EVs which consists of a three-phase voltage source inverter with insulated gate bipolar transistor (IGBT) as switching devices, battery, and line voltage detection circuit for sensorless operation. The proposed system is a BLDC motor drive with adjustable switching scheme as detailed in Sects. 11.3 and 11.4 for regenerative braking and charging the battery from grid, respectively, without using additional circuit. Pulse width modulation (PWM) is used for controlling the inverter switches which in turn controls the speed of BLDC motor. The operation modes can be changed by changing the position of switch between

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Fig. 11.1 Proposed scheme for BLDC motor drive used in electric vehicle

S1 and S2. Virtual hall sensor signals are generated from line-to-line voltage of the converter to develop commutation signals.

11.3 Regenerative Braking Using Single Boost Converter The schematic diagram for single stage boost converter for regenerative braking of BLDC motor of electric vehicle is shown in Fig. 11.2. Here, the boost converter helps to improve or step up the voltage as per the requirement of the battery and BLDC motor winding inductance acts as the boost inductor. In the regenerative braking mode, all the upper switches of VSI are in off position, and the lower switches of VSI are switched ON and OFF with a predefined duty ratio. For every 60° of a full cycle switching pattern will change. At the time of regenerative Fig. 11.2 Schematic diagram of three-phase voltage source inverter under regenerative braking mode of operation

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braking, the input voltage of boost converter V i = E b , where E b is the back emf of the motor. The output voltage from the boost converter is V o = V b , where V b is the battery voltage. R and L are the resistance and inductance of one phase of the motor, respectively. Assuming that the boost converter operates in CCM, the following relation can be derived, if the resistance is neglected: 1 Vb = Eb (1 − D)

(11.1)

Here, the duty cycle (D) depends on the E b and V b .

11.4 Battery Charging Using Buck Converter For charging mode, operation switch is kept at position S 2 as shown in Fig. 11.3. In this method, the switching pattern of the power switches in the three-phase voltage source inverter in a BLDC motor driver is changed to achieve charging operation of the battery. Hence, the same motor driver is converted into rectifier and buck converter at the time of charging mode. The single-phase AC grid voltage is rectified to DC with the full bridge diode rectifier using the diodes in first two limbs of three-phase voltage source inverter. This DC voltage level is reduced to the battery voltage level by setting a suitable value of duty cycle for the buck converter to charge the battery. Let V o be the output voltage and V i be the input voltage of buck converter. Then, Vo Vi

(11.2)

(Vi − Vo )D fs L 1

(11.3)

Duty Cycle, D = The inductor ripple current I L is given as, I L =

The values of filter inductance can be calculated using Eq. (11.4) Fig. 11.3 Charging mode of operation of the converter from AC supply

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Parameter

Design values

Output voltage (V o )

48 V

Input voltage (V i )

340 V

Input AC voltage (V ac )

240 V

Inductance (L 1 )

27 mH

Capacitance (C 1 )

1000 µF

Switching frequency (fs)

10 kHz

L1 =

Vo (Vi − Vo ) I L f s Vi

(11.4)

where f s is the switching frequency and I L is the inductor ripple current which is usually taken as 20% of the output current. The values of output capacitance can be calculated from Eq. (11.5) C1 =

I L 8 f s V o

(11.5)

Buck converter filter parameters L 1 and C 1 for charging the battery from the grid is calculated using Eqs. (11.4) and (11.5). Here, the buck converter is designed to convert the fully rectified DC output of the bridge rectifier V i to the maximum value of battery voltage V o . The values obtained from the calculations are given in Table 11.1.

11.5 Sensorless Control of BLDC Motor Drive Among the different sensorless control techniques, back emf detection method is the commonly used one. In conventional terminal voltage sensing method shown in Fig. 11.4a, the zero crossing points are leading 30 electric degrees of the ideal commutation points, and a phase-shifting circuit is needed to get the exact commutation points. This phase-shifting circuit provides variable time delays when operating under different speeds, and hence, control algorithm becomes complex to achieve good performance. In this paper, the commutation signals are extracted directly from the line-to-line voltage which is in phase with actual hall sensor signal as shown in Fig. 11.4b. It uses a simple RC low-pass filters and low-cost comparators. Terminal voltages V a , V b , and V c are passed through a low-pass filter to avoid high frequency components. V ac , V ba , and V cb are used to generate hall sensor signals H1, H2, and H3, respectively. This line-line voltage is passed through a zero crossing detector to get the exact commutation points. These virtual hall sensor signals are almost same as actual hall sensor signals. An optocoupler is used at the output of comparator to isolate power and control circuit and to reshape the voltage.

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Fig. 11.4 a Conventional back emf detection circuit for sensorless control b line voltage detection method for sensorless control

11.6 Results and Discussion 11.6.1 Simulation Results The simulations are performed on a BLDC motor with variable speeds of operation as shown in Fig. 11.5. BLDC motor specifications are provided in Table 11.2. From Fig. 11.5, it is clear that BLDC motor drive accelerates for duration of 0.4 s

Fig. 11.5 Speed versus time curve

Table 11.2 BLDC motor specification

Parameter

Values

Power output

750 W

Voltage

48 V

Input AC voltage (V ac )

240 V

Stator inductance

0.835 mH

Stator resistance

0.18 

Motor torque

5 Nm

11 An Integrated Switching Pattern and Sensorless Speed …

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Fig. 11.6 Motor torque versus time curve

and remains at constant speed from 0.4 to 1.2 s. After 1.2 s, the system is under regenerative braking mode. The variations of motor torque with respect to time are shown in Fig. 11.6. The load torque of 5 Nm is applied up to 0.8 s and from 0.8 s the load torque decreases and becomes zero at 1.2 s and the motor torque also follows the same pattern. For regenerative braking mode of operation, both load torque and motor torque act in reverse direction with respect to the normal condition and it is visualised by applying negative load torque as the reference. Negative value of motor torque after 1.2 s indicates that power is negative and feeding back to the supply side. The variation of battery current is shown in Fig. 11.7. The motor is accelerated to a maximum speed of 1500 rpm, during which battery shows a positive current, indicating it is discharging. After 1.2 s, the battery current becomes negative indicating that it is charging mode. AC supply voltage 240 V is applied as the input to the rectifier and it is rectified as 340 V as shown in Fig. 11.8. This rectified voltage is applied to the buck converter for charging the battery from AC supply. Output of the buck converter shown in Fig. 11.9 is the battery charging voltage. From the simulation results, it is clear that the proposed switching scheme achieves the same objective of motoring, regenerative braking, and charging of battery as

Fig. 11.7 Battery current versus time curve

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Fig. 11.8 Input voltage of the buck converter

Fig. 11.9 Battery voltage when charging from AC supply

discussed in [6, 9], but with less circuit complexity. This simplicity in circuitry is obtained by utilising less number of circuit components.

11.6.2 Experimental Validation The experimental validation of sensorless control scheme of three-phase brushless DC motor based on line voltage detection is done in a prototype of 12 V BLDC motor with rated speed 3000 rpm, rated current 4.4 A, and rated torque of 0.12 N m for hardware implementation as shown in Fig. 11.10. Fig. 11.10 Experimental setup for sensorless control of BLDC motor

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Comparison between the actual hall sensor signal from BLDC motor and virtual hall sensor signals obtained from conventional back emf detection circuit is shown in Fig. 11.11. Hall sensor signal obtained from BLDC motor and virtual hall sensor signals obtained from line voltage detection scheme is shown in Fig. 11.12. From Fig. 11.12, it is clear that the virtual hall sensor signals (VH) agree in phase with the actual hall signals (H).

Fig. 11.11 Actual hall sensor signals and virtual hall sensor signals from conventional back emf detection method

Fig. 11.12 Actual hall sensor signals and virtual hall sensor signals from line voltage detection method

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11.7 Conclusions Technological advancements in power electronic devices have made a huge impact in the area of electric vehicle. An integrated approach in circuitry with such devices can hence improve the performance of electric vehicles. This paper proposes such a scheme where motoring, regenerative braking, and battery charging are carried out by a single converter with the help of a switching pattern. Further improvement in the performance is achieved by back emf based on a sensorless speed control. The analysis of the proposed system is done using MATLAB–SIMULINK. The results obtained from simulation and experiment also validate the same. As a future scope, artificial intelligence-based automatic controllers may be introduced for the smooth switching between different modes of operation of converter.

References 1. X. Nian, F. Peng, H. Zhang, Regenerative braking system of electric vehicle driven by brushless DC motor. IEEE Trans. Industr. Electron. 61(10), 5798–5808 (2014) 2. A. Kumar, P.R. Thakura, Close loop speed controller for brushless DC motor for hybrid electric vehicles, in Nanoelectronics, Circuits and Communication Systems (Springer, Singapore, 2019), pp. 255–268 3. J.K. Ahn, K.H. Jung, D.H. Kim, H.B. Jin, H.S. Kim, S.H. Hwang, Analysis of a regenerative braking system for hybrid electric vehicles using an electromechanical brake. Int. J. Autom. Technol. 10(2), 229–234 (2009) 4. Y.F. Lian, Y.T. Tian, L.L. Hu, C. Yin, A new braking force distribution strategy for electric vehicle based on regenerative braking strength continuity. J. Central South Univ. 20(12), 3481– 3489 (2013) 5. C.S. Joice, S.R. Paranjothi, V.J.S. Kumar, Digital control strategy for four quadrant operation of three phase BLDC motor with load variations. IEEE Trans. Industr. Inf. 9(2), 974–982 (2012) 6. K.Y. Kim, S.H. Park, S.K. Lee, T.K. Lee, C.Y. Won, Battery charging system for PHEV and EV using single phase AC/DC PWM buck converter, in 2010 IEEE Vehicle Power and Propulsion Conference (2010), pp. 1–6 7. F. Naseri, E. Farjah, T. Ghanbari, An e_cient regenerative braking system based on battery/supercapacitor for electric, hybrid, and plug-in hybrid electric vehicles with BLDC motor. IEEE Trans. Veh. Technol. 66(5), 3724–3738 (2016) 8. C. Gnanavel, T.B. Immanuel, P. Muthukumar, P.S.L. Kanthan, Investigation on four quadrant operation of BLDC MOTOR using spartan-6 FPGA, in International Conference on Soft Computing Systems (Springer, Singapore, 2018), pp. 752–763 9. S. Tiwari, S. Rajendran, Four quadrant operation and control of three phase BLDC motor for electric vehicles, in 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia) (2019), pp. 577–582 10. M.A. Khan, A. Ahmed, I. Husain, Y. Sozer, M. Badawy, Performance analysis of bidirectional DCDC converters for electric vehicles. IEEE Trans. Ind. Appl. 51(4), 3442–3452 (2015) 11. P.B. Bobba, K.R. Rajagopal, Compact regenerative braking scheme for a PMBLDC motor driven electric two-wheeler, in 2010 Joint International Conference on Power Electronics, Drives and Energy Systems & 2010 Power, India (2010), pp. 1–5 12. M. Bahrami, H. Mokhtari, A. Dindar, Energy regeneration technique for electric vehicles driven by a brushless DC motor. IET Power Electron. 12(13), 3397–3402 (2019)

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13. J.P. Johnson, M. Ehsani, Y. Guzelgunler, Review of sensorless methods for brushless DC, in Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No. 99CH36370) vol. 1 (1999), pp. 143–150 14. D. Chowdhury, M. Chattopadhyay, P. Roy, Modelling and simulation of cost effective sensorless drive for brushless DC motor. Procedia Technol. 10, 279–286 (2013) 15. D. Ari_yan, S. Riyadi, Hardware implementation of sensorless BLDC motor control to expand speed range, in 2019 International Seminar on Application for Technology of Information and Communication (iSemantic) (2019), pp. 476–481

Chapter 12

An ANN Approach for Predicting the Wear Behavior of Nano SiC-Reinforced A356 MMNCs Synthesized by Ultrasonic-Assisted Cavitation Suneel Donthamsetty and Penugonda Suresh Babu Abstract Artificial neural networks (ANN) are a science that attempts to mimic the system of human mind in tackling issues. Many researchers have been conveyed for modeling and forecast of wear properties of metal matrix composites (MMCs) by ANN method. But this technique is not yet used for metal matrix nanocomposites (MMNCs) so far. ANN is an incredible asset to foresee properties of MMNCs, if it is properly trained. In the current work, a back propagation neural network model for assessing wear characteristics of MMNCs is proposed, in which aluminum (A356) reinforced with different weight percentages (wt.% of 0.1, 0.2, 0.3, 0.4 and 0.5) of nano-silicon carbide (SiC) MMNCs is fabricated with ultrasonic-assisted cavitation. Taken the tested results of wear characteristics using pin on disk apparatus at different loads of 30 and 40 N, which are utilized to develop and test the model. Compared to pure aluminum alloy, the wear resistance of MMNCs is increased (Donthamsetty S, Babu PS, in Int. J. Autom. Mech. Eng. 14(4):4589–4602, [1]) and able to predicting the wear within minimal error by using ANN.

12.1 Introduction The MMNCs are capable materials to be utilized in numerous areas like car, aviation, and so on. Because of the little (nano) measured fortifications, mixing with the phase interface gets improved due to the increased surface region which prompts to boost properties of materials, at a little volume part of the fortification too.

S. Donthamsetty (B) · P. S. Babu Department of Mechanical Engineering, Narasaraopeta Engineering College (Autonomous), Andhra Pradesh, Narasaraopet, Guntur 522601, India e-mail: [email protected] P. S. Babu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_12

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(a)

(b)

Fig. 12.1 a Ultrasonic transducer, b ultrasonic generator

The nanoparticles are created by high energy ball milling for fortification. Because of more exterior to volume ratio of nano elements, causes in accumulation and grouping in base matrix material, causes bad impact on the properties of MMNCs. This problem can be solved by ultrasonic-assisted cavitation, where a ultrasonic probe, transducer and generator, power source (Fig. 12.1a, b) supported stir-casting method is using for uniform scattering of nanoparticles to increase the stuffs of the composite materials [2, 3]. Majority of casting processes are suitable to cast aluminum and its alloys to have good solidification properties, quality, and quantity. These castings melt at low temperatures and show good fluidity and have low melting temperatures. In this work, A356 was chosen as the matrix material due to its high castability, and SiC as the support material in view of its extraordinary wettability [4]. The data processing paradigm of an ANN is influenced how biological nervous structures such as the brain data process. Neurons are comprised of several layers of ANN. An ANN implements in cycles, and entire system of neurons is implemented in chain reaction to some input.

12.2 Back Propagation Network Many kinds of ANNs are proposed, based on specific computing capabilities of the brain of human. The selection of a specific neural system relies on the implementation. Back propagation network has become increasingly important for the artificial neural networks available owing to the weaknesses in other networks available. The network is a multi-layer network, which includes input and output layers with at least one hidden layer (Fig. 12.2).

12 An ANN Approach for Predicting the Wear Behavior of Nano …

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Fig. 12.2 Structure of back propagation neural network

Based on the use and severity of the difficulty and the quantity of inputs and outputs, the number of hidden layers and their neurons is to be fixed. To simulate through network, we use nonlinear and log-sigmoid transfer functions. Based on its uses, back propagation network is preferred for current work, and neural network tool box of Matlab® version7 software has been used for predicting the wear behavior of A356 matrix nanocomposites. Figure 12.2 shows the structure of ANN for the current study.

12.2.1 Implementation of Back Propagation Network Usage of back propagation model comprises of two stages. Every neuron gets info and gives some output. Neurons are associated within a layer and among various layers. Link strength within the system, else identified as weight structure, is at first thought to be arbitrary esteemed and is fixed by preparing. When system constraints are fixed, output is gotten by giving the input designs to the system, called as testing of the system. Training is based on gradient decent rule that inclines to regulate weights and decrease system error in the system.

12.3 Weight Structure Each info layer neuron is associated with every one of concealed layer neurons, by between layer associations. Each association conveys a weight factor, which is at first assigned by a random generator calculation.

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12.3.1 Training Wear tests are conducted and specimen wear progress is measured with progress in sliding distance. Sliding distance, normal load, and reinforcement percentages are fed as inputs to the network to obtain wear predictions. The activation, a1 = W i j1 ∗ I T + φ

(12.1)

where Ii Output of the input layer, Wij1 Weight structure between input and hidden layers φ The bias. Then O1 =

1 (1 + exp(−a1 ))

(12.2)

12.4 Network Architecture Optimization Accomplishment of the execution of neural systems is affected by the selection of parameters of the system, which impacts the exactness of execution of neural systems. Heuristic strategy for streamlining is utilized to conquer the danger of nearby minima by using momentum rate, which is fixed as 0.1 in the current system. As a trade-off, the value is fixed as 0.45 for the current system. The inputs are sliding distance, percentage of fortification, and normal load for the network to foresee wear volume. Input patterns are given to the network for training and by trial and error, the size of the network is optimized. Figure 12.3 shows the difference of total error by changing the number of hidden layer neurons for a single and two Fig. 12.3 Optimization of number of hidden layers in neural networks

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Fig. 12.4 Optimization of number of iterations in neural networks

hidden layers. To evaluate the quantity of neurons in the hidden layer, test information is introduced to the system to look difference in the error with change in number of hidden layer neurons. The outcomes introduced in Fig. 12.3 demonstrate that the error is least with one hidden layer having four neurons. Henceforth, the quantity of hidden layer neurons is kept as four and size of the system is fixed as 3–4–1. Figure 12.4 displays the difference of error with the number of iterations demonstrating that error firstly declines speedily with intensification in number of iterations and slowly steadies. Mazahery and Shabani [5] noted that there is increase in porosity with incremental nanoparticles volume fraction in aluminum alloy reinforced with nano-silicon carbide. Also found that there is increase in wear resistance by sliding test comparatively. Through observation found that there is consistent with practical measurements for Al matrix composites by using ANN and FEM. In the investigations of Tofigh and Shabani [6], with additions of 3.5 vol% of SiC nanoparticles found enhancement in yield strength, hardness, and ultimate tensile strength and also proved that this reinforced composite has more resistant to wear compared to un-reinforced composite. To forecast the optimum properties of nanocomposites obtain by using ANN with 6 neurons in 1 hidden layers with Bayesian regularization strategy. In the studies of Canakci1 et al. [7], by using ANN, agreeable outcomes came compared to measurement in AA2024/B4C composites, which saves lot of experimental time and money. And there is an increase in AA2024 alloy reinforced with B4 C particles when compared to un-reinforced 2024 Al alloy. Based on the performed tribological tests by Miladinovi´c et al. [8], it can be reasoned that A356/10SiC/1Gr gives good tribological characteristics, or the minimum wear rate. And for the reduction of experiments and the predictions of the tribological behavior of hybrid composites, ANN was used. In the opinion of Ceschini et al. [9], among various production routes fluid state MMNCs treating directions are good as they are comparatively simple, reduction in cost, and possibly scalable to industrial level for the manufacture of near-net shape products. It was observed by Donthamsetty and Babu [1], that wear resistance of A356 reinforced with SiC nanoparticles is increased by 53.735% at 30 N and 47.04% at 40 N when matched to pure Al alloy along with increase in hardness. In the studies of Donthamsetty [2], it was proved that with the escalation in fortification

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ratio, hardness and tensile strength of A356-nano SiC-reinforced composites are improved by reducing the ductility. And also with help of SEM images, it was found that the fortifications are uniformly spreaded in Al matrix. Shabani and Mazahery [10] understand that ANN models are most useful to avoid the experimentation cost and studied the various ANN training algorithms to investigate the mechanical and wear properties of Al-Al2 O3 MMCs. Ekka et al. [11] matched the two different MMNCs formed with SiC and Al2 O3 as reinforcements and found the SiC linked MMNCs revealed good wear resistance compared to other. Also techniques like Taguchi, regression, and ANN are used to develop a model to forecast the prepared MMNC’s wear rates. Devadiga et al. [12] confirmed that the ANN is good forecasting method through which they found that the parameters of quantity of reinforcement, time of ball milling and sintering mostly affect the properties of multi-walled carbon nanotubes (MWCNT) and fly ashes (FA)/Al composites formed by powder metallurgy. Megahed et al. [13] established the models of ANN, ANOVA, and multiple regression to forecast the wear rate of stir-casted Al-Si–Al2 O3 MMC. Out of selected parameters to do this process, load and sliding distance improved the wear rate. Bhattacharjee and Chanda [14] forecasted the wear rate of Al2219-TiC MMC by using ANN technique which reduces the cost, human efforts, time, wrong calculations, and accidents. Veeresh Kumar et al. [15] used back propagation method of ANN to forecast the wear rate of Al6061-TiO2 MMC and Taguchi concept is applied for experimental design. The Experimental and ANN results are in good agreement.

12.5 Validation of the System By fixing the system considerations, the system is trained using 90% of practical data obtained while testing pure A356, 0.1 to 0.5 wt% SiC-reinforced composites at 30 N, 40 N to get steady weight structures which causes the system is verified for balance 10% data as input patterns. Every information set limited inputs such as regular load, sliding distance, weight percentage of reinforcement and the output is wear volume predicted by the neural network. The suggested models are proved after matching the forecasted results with the achieved results. Figures 12.5 and 12.11 show the matching of expected and achieved values for pure alloy at 30 N and 40 N, respectively. Figures 12.6, 12.7, 12.8, 12.9 and 12.10 show the matching of expected and achieved values at 30 N for 0.1 to 0.5 wt% nanocomposites, respectively. Similarly, Figs. 12.12, 12.13, 12.14, 12.15 and 12.16 show the matching of expected and achieved values at 40 N for 0.1 to 0.5 wt% nanocomposites, respectively. It can be observed from the above figures that the forecasted values are matching with the achieved values with maximum absolute error of 10%.

12 An ANN Approach for Predicting the Wear Behavior of Nano … Fig. 12.5 Comparision of predicted and experimental values for pure alloy at 30 N

Fig. 12.6 Comparision of predicted and experimental values for 0.1 wt% nanocomposite at 30 N

Fig. 12.7 Comparision of predicted and experimental values for 0.2 wt% nanocomposite at 30 N

119

120 Fig. 12.8 Comparision of predicted and experimental values for 0.3 wt% nanocomposite at 30 N

Fig. 12.9 Comparision of predicted and experimental values for 0.4 wt% nanocomposite at 30 N

Fig. 12.10 Comparision of predicted and experimental values for 0.5 wt% nanocomposite at 30 N

S. Donthamsetty and P. S. Babu

12 An ANN Approach for Predicting the Wear Behavior of Nano … Fig. 12.11 Comparision of predicted and experimental values for pure alloy at 40 N

Fig. 12.12 Comparision of predicted and experimental values for 0.1 wt% nanocomposite at 40 N

Fig. 12.13 Comparision of predicted and experimental values for 0.2 wt% nanocomposite at 40 N

121

122 Fig. 12.14 Comparision of predicted and experimental values for 0.3 wt% nanocomposite at 40 N

Fig. 12.15 Comparision of predicted and experimental values for 0.4 wt% nanocomposite at 40 N

Fig. 12.16 Comparision of predicted and experimental values for 0.5 wt% nanocomposite at 40 N

S. Donthamsetty and P. S. Babu

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12.6 Conclusions In the current work, a neural system is designed to foresee the wear volume of the silicon carbide nanoparticle-reinforced A356 metal matrix nanocomposites of five different MMNCs with SiC weight percentages 0.1, 0.2, 0.3, 0.4, and 0.5. These were formed by stir-casting method assisted with high energy ball milling for fortification, ultrasonic-assisted cavitation for even distribution of nanoparticles in the A356 matrix to avoid grouping. Taken values of wear with the help of pin on disk apparatus at different loads of 30 N and 40 N, this work indicates the ability of ANN to foresee the wear properties of nanocomposites, for which a back propagation neural network model ANN is proposed for assessing wear characteristics of MMNCs. From the above Figs. 12.5, 12.6, 12.7, 12.8, 12.9, 12.10, 12.11, 12.12, 12.13, 12.14, 12.15 and 12.16, the predicted wear values are matched with the achieved results to validate the developed model. The model is found to be capable of predicting wear within 10% error. Acknowledgements The authors express thanks to UGC-New Delhi for okaying grant of Rs. 10.36 Lakhs under Major Research Project, with Ref. No. 33-401/2007 (SR) dated 28-2-2008.

References 1. S. Donthamsetty, P.S. Babu, Experiments on the wear characteristics of A356 MMNCs fabricated using ultrasonic cavitation. Int. J. Autom. Mech. Eng. 14(4), 4589–4602 (2017) 2. S. Donthamsetty, Investigations on mechanical properties of A356 nano composites reinforced with high energy ball milled SiC nanoparticles with ultrasonic assisted cavitation (with a comparison of micro composite). Int. J. Nanoparticles (IJNP) 6(1), 38–49 (2013) 3. A.V. Muley, Ultrasonic probe assisted stir casting method for metal matrix nano-composite manufacturing: an innovative method. Int. J. Mech. Prod. Eng. 3, 111–113 (2015) 4. J.G. Kaufman, E.L. Rooy, Aluminum Alloy Castings Properties, Processes, and Applications (ASM International 2004) 5. G.B. Veeresh Kumar, R. Pramod, P.S. Shivakumar Gouda, C.S.P. Rao, Artificial neural networks for the prediction of wear properties of Al6061-TiO2 composites, in IOP Conference Series: Materials Science and Engineering, vol. 225, pp. 012046 (2017) 6. A.A. Tofigh, M.O. Shabani, Applying various training algorithms in data analysis of nano composites. Acta Metallurgica Slovaca 19(2), 94–104 (2013) 7. A. Canakci1, T. Varol, S. Ozsahin, S. Ozkaya, Artificial neural network approach to predict the abrasive wear of AA2024-B4C composites. Univ. J. Mater. Sci. 2(6), 111–118 (2014) 8. S. Miladinovi´c, V. Rankovi´c, M. Babi´c, B. Stojanovi´c. Prediction of tribological behavior of aluminium matrix hybrid composites using artificial neural networks, in 15th International Conference on Tribology, Kragujevac, Serbia, pp. 142–149 (2017) 9. L. Ceschini, et al., Aluminum and Magnesium Metal Matrix Nanocomposites, Engineering Materials (Springer Nature Singapore Pte Ltd., 2017). https://doi.org/10.1007/978-981-102681-2_2 10. M.O. Shabani, A. Mazahery, Artificial Intelligence in numerical modeling of nano sized ceramic particulates reinforced metal matrix composites. Appl. Math. Model. 36, 5455–5465 (2012) 11. K.K. Ekka, S.R. Chauhan, Varun, Dry sliding wear characteristics of SiC and Al2 O3 nanoparticulate aluminium matrix composite using taguchi technique. Arab J. Sci. Eng.

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12. U. Devadiga, R.R.P. Kumar, P. Fernandes, Artificial neural network technique to predict the properties of multiwall carbon nanotube-fly ash reinforced aluminium composite. J. Mater. Res. Technol. 8(5), 3970–3977 (2019) 13. M. Megahed, D. Saber, M.A. Agwa, Modeling of wear behavior of Al–Si/Al2O3 metal matrix composites. Phys. Met. Metall. 120(10), 981–988 (2019) 14. A.D. Bhattacharjee, D. Chanda, A machine learning advent in the prediction analysis of wear behavior of TiC reinforced Al2219 metal matrix composite. Int. J. Comput. Sci. Netw. 7(2) (2018) 15. A. Mazahery, M.S. Shabani, Nano-sized silicon carbide reinforced commercial casting aluminum alloy matrix: experimental and novel modeling evaluation. Powder Technol 217, 558–565 (2012)

Chapter 13

Multi-response Optimization of FSW Process Parameters of ZE42 Alloy Using RSM-Based Grey Relational Analysis Ramanan Gopalakrishnan, Darwins Anantha Kanakaraj, Bino Prince Raja Dennis, and Ajith Raj Rajendran Abstract This study presents the multi-objective optimization using grey relational analysis (GRA) of friction stir welding (FSW) parameters of ZE42 alloy utilizing of 1.2 mm diameter H13 wire. Input parameters for welding process perform a predominant part in calculating expected quality in weld. The research has been carried out in accordance with the response surface methodology (RSM). The input parameters preferred were the welding speed, axial force, tool pin profile, and tool speed. The responses for quality targets preferred are the ultimate tensile strength (UTS) and hardness strength. Grey relational analysis has been preferred in optimizing the input parameters instantaneously allowing for output variables in much variable. Determination of optimal parameters combination is stated as A3 B3 C 3 D3 when welding speed at 1150 rpm, tool speed at 60 m/min, cylindrical tool pin profile at zero, and axial force at 5 N. ANOVA method finds its total weldment quality over different level of input parameters.

R. Gopalakrishnan (B) Aeronautical Engineering, ACS College of Engineering, Bangalore, Karnataka 560074, India e-mail: [email protected] D. A. Kanakaraj Automobile Engineering, Noorul Islam Centre for Higher Education, Nagercoil, Tamilnadu 629180, India e-mail: [email protected] B. P. R. Dennis Aeronautical Engineering, SJC Institute of Technology, Bangalore, Karnataka 562101, India e-mail: [email protected] A. R. Rajendran Aeronautical Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana 500100, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_13

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13.1 Introduction ZE42 alloy known as identic nickel–iron-based super alloy possesses outstanding hardness with substantial conflict oxidation including carburization at elevated temperatures [1]. It has been chosen as the most unavoidable material in power plants of next generation. Exclusive mechanical property pools with high temperature resistance and also corrosion resistance with varieties the preferred alloy beneficial for numerous applications comprising continuous exposure to higher range of temperatures in atmospheres which supports corrosion [2]. FSW welding practices an electrode and is safeguarded using argon gases safeguards from atmospheric contaminants, the group of molten weld wire [3]. Weld quality is preferred from directly or indirectly over process parameters on the weldments mechanical and weld bead geometry. Weld joint quality has a crucial requirement on the process parameter of weld [4]. Author expressed those manufacturers controlling the input parameters control in obtaining a better welded joint with the desired weld quality [5]. Normally, skilled operators select inputs on trial and error method that consumes time corresponding to new welded metal in the idea of finding a welded joint along the essential specifications. Many authors have published that computational analysis and evolutionary algorithms have been extensively involved in developing numerical expressions for the input parameters in the idea of determining the quality welding input parameters which may provide desired weld quality [6]. Kaushik et al. [7] used Taguchi method in optimizing input parameter along with its wear behavior in achieving the parameter that is efficient at most with aluminum composite. Ghangas et al. [8] examined the effect of process parameter for AA7036 T6 alloy on hardness of armor alloy for FSW by central composite design approach. Ramanan and Edwin et al. [9] acquired mathematical model equations for wire cut EDM parameters of aluminum composites and determined optimum responses using grey relational parameters for automated manufacturing. Prabhu et al. [10] found the FSW optimum responses for dissimilar pipe joints containing Taguchi method with the determination that greater range of heat input ended up with tensile strength of lower values. Padmanabhan et al. [11] improved the tensile strength on AZ31B alloy using GRA approach. Hakan et al. [12] detailed the application of friction stir welding process in optimizing parametric combination in the idea of yielding promising tensile strength and weld beam. Neela Rajan et al. used the transesterified jatropha oil input parameters of getting optimum using GRA and concluded with inclusion of heating time in achieving optimum parameters [13]. Above-mentioned literatures conclude that, it is strong that only a few works were performed in optimization of ZE42 alloy using GRA. Another strong inference would be that GRA is involved in optimizing the friction stir welding input for achieving the anticipated quality response. This work presents optimization using GRA on the FSW weld parameters. Two responses are tensile strength and hardness. In the responses, the grey relational grade (GRG), the greatest significant factor has been found. ANOVA method has been applied in finding the influence of individual factors.

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Table 13.1 Input process factors and their levels Parameters

Symbol

Level 1

Level 2

Level 3

Level 4

Level 5

Units

Tool speed

N

950

1050

1150

1250

1350

RPM m/min

Welding speed

S

20

40

60

80

100

Tool pin profile

P

−2

−1

0

+1

+2

–

Axial force

F

3

4

5

6

7

N

13.2 Experimental Procedure ZE42 alloy plates of aspect 100 × 50 × 6 mm were welded by FSW process weld mechanism. From literatures and trail run experiments the operating input parameters increase the quality of FSW are recognized as axial load, tool rotational speed (TRS), welding speed and tool pin profile used [14]. Between the four influence parameters identified, two parameters were ought to be altered in machine. Numerous trials are completed in selecting the process parameters with upper and lower values [6]. Response surface method has been chosen, and the experiments have been performed consequently [9]. The input welding parameter and levels are detailed in Table 13.1. TRS was offered in welding machine and performed variation as per the estimated axial load. To estimate axial load generated in FSW process, a dynamometer is connected on the surface of specimen where the specimen and spindle get connection. Accordingly fixture is designed to place above a probe and is connected with dynamometer to calculate axial load produced through FSW process. The altered vertical milling machine, parameters achieved [6] for FSW process are axial load, tool pin profile, tool rotational speed, and welding speed used. The hardness and tensile test were carried out according to ASME standard. The experimental results are discussed in the response and contour plots.

13.3 Effects of Experimental Design Design of experiments (DOE) is applied to identify the process parameters that contribute the best response. This design will be useful to relate the responses with input parameters [4]. To analyze the effect of FSW welding process parameters N, S, P, and F the response plot and contour plots are presented in Figs. 13.1 and 13.2. Figure 13.1 represents surface plot of hardness that tends to increase with axial load of 5 kN with increase in TRS between 950 and 1350 rpm. Likewise thread cylindrical shape tool pin profile produces high UTS with TRS of above 1150 rpm and other tool pins fail at various TRS. Welding speed also produces maximum strength with high TRS. From this, it is evident that plates with similar joints produce high strength. Figure 13.2 shows contour plot of hardness and UTS which tends to increase as axial load increases from 950 to 1350 rpm. Likewise tool pin profile produces high UTS with TRS of above 1150 rpm. The quality of the welding is done using the tensile

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Surface Plot of H v s F, S Hold Values N 1150 S 60

Hold Values N 1150 P 0

80

H

80

H

70

70 7.5

7.5 60

6.0

P

0

2

6.0

60

F

4.5

-2

30

3.0

S

Surface Plot of UTS vs P, S

F

4.5 60

3.0

90

Surface Plot of UTS vs F, N Hold Values N 1150 F 5

Hold Values S 60 P 0

180

17 5

UTS 160

UTS 2 0

140 30

S

60

90

150 7 .5

125 6. 0

1 00

P

1000

-2

4.5 1100

N

1 2 00

F

3.0

1 30 0

Fig. 13.1 Surface response plots for effect of process parameters with the responses Contour Plot of UTS vs F, P

Contour Plot of UTS vs S, N

7

6

80

Hold Values N 1150 S 60

5

UTS < 100 100 – 120 120 – 140 140 – 160 > 160

90

Hold Values P 0 F 5

70

S

F

100

UTS < 160 160 – 165 165 – 170 170 – 175 > 175

60 50

4

40 30

3 -2

-1

0

1

20 950

2

1000 1050 1100 1150 1200 1250 1300 1350

P

N

Contour Plot of H vs P, N

P

1

2 50 60 70 80 80

Hold Values S 60 F 5

0

H < 65 65 – 70 70 – 75 75 – 80 > 80

1

Hold Values N 1150 F 5

0

-1

-1

-2 950

Contour Plot of H vs P, S H < 50 – 60 – 70 – >

P

2

1000 1050

1100 1150 1200 1250 1300 1350

N

-2 20

30

40

50

60

70

S

Fig. 13.2 Contour plots for effect of process parameters with the responses

80

90

100

13 Multi-response Optimization of FSW Process Parameters …

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testing, which is a simple method to find the reaction of material when applied to tension. The ultimate tensile strength of base material ZE42 alloy was 150 MPa. The entire welded region showed increased strength as compared to that of the other materials combination.

13.3.1 Grey Relational Analysis (GRA) Initial step begins with the transformation of response data from the weld response values. Equation (13.1) has been used to find larger, better and subsequent analysis of S/N ratio values [6]. Through GRA, originally the input data are normalized by incorporating this normalized data, grey relational coefficient is assessed, and the GRC has been achieved by mean values related to nominated experiment data [9].

13.3.1.1

Grey Relational Generation

In GRA criterion, a linear data preprocessing method for the MRR is higher the better [5] and is conveyed as: X i∗ (k) =

yi (k) − minyi (k) maxyi (k) − minyi (k)

(13.1)

Correspondingly the normalized data processing for SR is lower the better and can be articulated [5] as: Yi (k) =

13.3.1.2

maxyi (k) − yi (k) maxyi (k) − minyi (k)

(13.2)

Grey Relational Coefficient

Determination of grey relation coefficient has been achieved by Eq. (13.3) as illustrated below [5] ∈i (k) =

min + ωmax oi (k) + ωmax

(13.3)

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Grey Relation Grade

In the grey relation grades square measure estimated by considering mean of the GRC related with observation as presented in Eq. (13.4). Q 1  Γi = i(k) M 1

(13.4)

13.4 Results and Discussion FSW on ZE42 alloy is carried out according to Box–Behnken design to investigate the effect of the FSW parameters, specifically, welding speed, tool speed, pin profile and axial force on the output responses, ultimate tensile strength, and hardness [14]. Box–Behnken design is to be widely used for optimization techniques because of the advantage of optimizing multifactor problems with optimum number of experimental runs. It is a statistical tool that allows the independent evaluation of the responses with maximum number of experiments. In determining the preferred set of FSW parameters for welding the ZE42 alloy better and effort has been given consistently.

13.4.1 Multiple Response Models Using GRA By using GRA complicated optimization problem can be solved effectively. To have better contributing response parameters such as ultimate tensile strength, hardness of ZE42 alloy has been studied [14] using ANOVA and presented in Table 13.2. Table 13.2 ANOVA table for FSW of ZE42 alloy Source

SeqSS

AdjSS

Adj MS

N

DF 2

0.014672

0.007724

0.003862

F 0.67

S

2

0.054051

0.00507

0.002535

S

2

0.041542

0.057846

0.028923

F

2

0.284986

0.172981

0.08649

Error

18

0.021201

0.010332

0.00574

Total

26

0.416308

P

% contribution

0.523

3.5243

0.44

0.006

13.0936

5.04

0.018

9.9788

15.07

0.001

68.4558

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Fig. 13.3 Main effect plot of grey relation grade

13.4.2 Response Table for GRG Using S/N Ratio The average GRC values for each set of the FSW parameters are prepared and were determined by average in all the levels of process parameters [10]. It illustrates optimal value of input parameters. Figure 13.3 details the foremost effect plot of grey relation grade. It reveals that A3 B3 C 3 D3 is optimal response which represents the welding speed of 1150 rpm, tool speed 60 m/min, pin profile of zero, and axial force of 5 N. Welding speed was the foremost prompting factor, shadowed by the tool speed and axial force [13]. In Fig. 13.3, the graph shows the impact of rotational speed on responses which improves that with rise in speed from 950 to 1350 rpm grade decreases. In Fig. 13.3 second graph presents that the influence of welding speed increases over the strength of FSW process [11]. From third and fourth graph, it is states that with rise in welding speed and pin profile from gains after that reduced. On rise of axial force from 2 to 7 N, the strength level increased with it and then moderated at higher level [12].

13.4.3 Confirmation Check While analyzing the FSW parameters, the important thing is to confirm the hardness and also the tensile strength by accompanying the validation experimentations is

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Table 13.3 Validation of results against developed GRA model for FSW of ZE42 alloy Response

Process parameters A rotational speed

B welding speed

Output parameters C pin profile

D axial force

Hardness (BHN)

UTS (MPa)

Initial

1250

40

Square

6

83.6

180.1

Optimal

1150

60

Cylinder

5

86.14

183.45

Experiment

1150

60

Cylinder

5

89.31

187.26

presented in Table 13.3. Here A4 B2 C 4 D4 is a quality parameter has been found during FSW process via the GRA. Consequently, the state A3 B3 C 3 D3 of the quality response level has been considered as consent check. As perceived in Table 13.3, hardness goes up from 86.14 to 89.31 BHN and UTS value occurs from 183.45 to 187.26 MPa. As per the discussions of results which increased the strength of FSW process and confirming that the quality distinctiveness is highly considerable by enhancing via this confirmation test [15]. Significant effect in parameters and its percentage contribution gets changed compared to the parameter behavior with balanced response.

13.5 Conclusion From results, response surface method with grey relational analysis plays a vital role in optimizing the FSW multiple responses such as hardness and UTS. An optimum set of test parameters of GRC for quality weld surfaces has been identified as the tool speed of 60 m/min, welding speed of 1050 rpm, cylindrical pin profile at zero, and axial force of 5 N. ANOVA results predicted that the axial force (68%) exerted a significant impact on FSW responses followed by welding speed (13%) and pin profile (9%). This study has shown that the measured responses of welded ZE42 alloy are greatly increased by grey-based response surface method for improvement in weld quality.

References 1. B. Gleeson, B.T. Li, Cyclic oxidation of chromia-scale forming alloys: lifetime prediction and accounting for the effects of major and minor alloying additions. Mater. Sci. Forum 461, 427–438 (2004) 2. K. Deepandurai, R. Parameshwaran, Multiresponse optimization of FSW parameters for cast AA7075/SiCp composite. Mater. Manuf. Proc. 31(10), 1333–1341 (2016) 3. P. Kumar, K.P. Kolhe, S.J. Morey, C.K. Datta, Process parameters optimization of an aluminium alloy with pulsed gas tungsten arc welding (GTAW) using gas mixtures. Mat. Sci. Appl. 2(04), 251 (2011)

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4. U.G.A.G.I. Taguchijeve, V.T.P. FSW, Application of grey relation analysis (GRA) and Taguchi method for the parametric optimization of friction stir welding (FSW) process. Mater. Tehnol. 44, 205 (2010) 5. P.K. Sahu, S. Pal, Multi-response optimization of process parameters in friction stir welded AM20 magnesium alloy by Taguchi grey relational analysis. J. Mag. Alloys 3(1), 36–46 (2015) 6. A.K. Darwins, M. Satheesh, G. Ramanan, Experimental analysis of friction stirs welding of dissimilar alloys AA7075 and mg ZE42 using butt joint geometry. Int. J. Mech. Prod. Eng. Res. Dev. 8(1), 505–512 (2017) 7. N. Kaushik, S. Singhal, Hybrid combination of Taguchi-GRA-PCA for optimization of wear behavior in AA6063/SiCp matrix composite. Prod. Manuf. Res. 6(1), 171–189 (2018) 8. G. Ghangas, S. Singhal, Modelling and optimization of process parameters for friction stir welding of armor alloy using RSM and GRA-PCA approach. Mater. Res. Exp. 6(2), 026553 (2018) 9. G. Ramanan, J.E.R. Dhas, M. Ramachandran, Optimization of material removal rate and surface roughness for wire electric discharge machining of AA7075 composites using grey relational analysis. Int. J. Veh. Str. Syst. 9(5), 309–312 (2017) 10. S.R. Prabhu, A.K. Shettigar, M.A. Herbert, S.S. Rao, Multi-objective optimization of FSW process variables of aluminium matrix composites using taguchi-based grey relational analysis. Adv Comp Methods Manuf. 133–144 (2019) 11. G. Padmanaban, V. Balasubramanian, Optimization of pulsed current gas tungsten arc welding process parameters to attain maximum tensile strength in AZ31B magnesium alloy. Trans. Nonferrous Metals Soc. China 21(3), 467–476 (2011) 12. G. Diju Samuel, G. Ramanan, D. Bino Prince Raja, Prediction of responses in FSW processed hybrid composites using soft computing technique, J. Comp. Theor. Nanosci. 16(2), 463–466 13. A. Hakan, A. Bayram, E. Ugur, Y. Kazancoglu, G. Onur, Application of Grey relational analysis and Taguchi method for the parametric optimization of friction stir welding process. Mater. Technol. 44(4), 205–211 (2010) 14. A.K. Darwins, M. Satheesh, G. Ramanan, Influence of cylindrical threaded tool pin profile on the mechanical and metallurgical properties of FSW of ZE42 magnesium alloy, in Proceedings of ICDMC 2019 (pp. 253–262) (2020) 15. R.R. Neela Rajan, G. Ramanan, R. Rajesh, Multi-objective optimization of transesterified Jatropha curcas oil using response surface methodology and grey relational analysis. Int. J. Ambient Energy 1–12 (2019)

Chapter 14

Analysis and Modeling on Defects of Deep Micro-holes Fabrication in Stainless Steel Through µECM Md. Zishanur Rahman, Alok Kumar Das, and Somnath Chattopadhyaya

Abstract Fabrication of micro-holes in difficult-to-machine and exotic materials is a routine requirement for various products such as wire drawing dies, miniature oil sprayers, turbine blades, miniature mixers, cooling channels, spinner holes, miniature oil atomizers, inkjet printer nozzle, diesel fuel injection nozzles, and drug delivery orifices. Electrochemical micro-machining (μECM) is one of the cost-effective techniques and a better alternative for the micro-holes fabrication in such difficult-to-cut materials with good surface finish. In view of minimizing the defects of deep microholes fabrication, an analysis and regression modeling on defects of deep micro-holes fabrication in stainless steel plate through μECM process has been done. In this study, three defects such as “depth of curve formed at entrance of micro-hole”, “overcut”, and “diameter difference in micro-hole” have been analyzed in deep micro-holes fabrication through μECM process. All experiments are conducted using fabricated cylindrical tungsten micro-tool electrode of diameter 108 μm. Machining parameters are optimized using Taguchi technique and ANOVA is employed to investigate the influence of these parameters on the response outputs, i.e., “depth of curve formed at entrance of micro-hole”, “overcut”, and “diameter difference in micro-hole”.

Md. Z. Rahman (B) · A. K. Das · S. Chattopadhyaya Department of Mechanical Engineering, Indian Institute of Technology (ISM), Dhanbad, India e-mail: [email protected] A. K. Das e-mail: [email protected] S. Chattopadhyaya e-mail: [email protected] Md. Z. Rahman Department of Mechanical Engineering, Nalanda College of Engineering, Chandi (Nalanda), DST, Government of Bihar, Bihar Sharif, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_14

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14.1 Introduction Micromachining of difficult-to-machine and exotic materials is a very challenging field. In spite of the cost-effectiveness of the micromachining process, precise dimensions with the good surface finish are essential for micromachining. μECM is one of the cost-effective techniques to machine micro-components with reasonably good surface finish and precise dimensions in hard-to-machine and exotic materials needed for various industrial applications especially in aerospace industries, electronic, and computer [1–6]. Economy of production cost for deep micro-hole fabrication is necessary along with precise dimension and good surface quality. Considering these requirements, μECM has turned out to be a useful and effective alternative for fabricating deep micro-holes in exotic and hard-to-machine materials [7,8]. Taper generation and curve formation at entrance of deep micro-hole is one of the serious problems in μECM process. Reasons behind these taper generation and curve formation at entrance of deep micro-hole are following: (i) Due to difficulty of the electrolyte flow inside the deep part of micro-hole, material is not dissolved easily. (ii) Electrolyte resistance inside the deep part of micro-hole increases because of the gas bubbles formation due to electrolyte boiling. Then the current mainly flows to the side wall and outside of the tool whose electric-resistance is comparatively small, hence the entrance of the micro-hole becomes large. (iii) During the electrolytic dissolution of stainless steel material, there may be the formation of chromium oxide layer at a specific range of the voltage. This formation of passive layer may cause the taper generation of micro-hole. (iv) Differences in machining time between entrance and end/exit of hole may cause taper generation in micro-holes [9]. Figure 14.1 shows different defects of deep micro-hole fabricated through μECM process.

Fig. 14.1 Different defects of deep micro-hole fabricated through μECM process

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In this study, an aqueous solution of H2 SO4 electrolyte is used for the analysis and regression modeling on defects of deep micro-holes fabrication in stainless steel plate through μECM process. Pulsed DC voltage (V), electrolyte concentration (Mol/L), and pulse frequency (KHz) are chosen as machining parameters in order to investigate their effects on response outputs, i.e., depth of curve formed at entrance of micro-hole “h” [μm], radial overcut “OC” [μm], and diameter difference in micro-hole “Dd” [μm]. All experiments are conducted using Taguchi L9 (33 ) orthogonal array (OA) design with fabricated cylindrical tungsten micro-tool electrode of diameter 108 μm. Machining parameters are optimized using Taguchi technique. In last, the dominant machining parameters for the responses are found out by employing ANOVA and the regression models are developed to correlate the relation between machining parameters and responses.

14.2 Experimentation An in-house sinking-type μECM setup (as shown in Fig. 14.2a) is used to fabricate deep through micro-holes in stainless steel plate. Anodic workpiece of stainless steel material having thickness 770 μm is clamped on a fabricated fixture set inside the machining chamber and the machining chamber is filled with the H2 SO4 electrolyte as shown in Fig. 14.2b, c. During the fabrication of micro-hole, the pulsed DC power supply is connected across the micro-tool electrode (cathode) and the workpiece

Fig. 14.2 a μECM setup, b enlarge view of machining chamber, and c setup configuration

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(anode). For holding this micro-tool electrode, ultra-precision spindle collet is used. This cathodic micro-tool electrode is emerged just 2 mm deep inside the liquid electrolyte during all the experiments. For each of the experiments, fresh electrolyte is used for maintaining the uniform pH of the electrolyte which is also important to get accurate experimental results. After many trial runs, the feasible working range (low level and upper level) of each machining parameters is decided. All the experiments are conducted with a maximum constant tool feed of 100 μm/minute and a constant duty cycle of 49%.

14.2.1 Experimental Design Orthogonal array, control factors, and response factors are selected according to Taguchi design technique. The selected control factors for this study are three machining parameters: pulsed DC voltage (V), electrolyte concentration (Mol/L), and pulse frequency (KHz). For three control factors, three level tests for each factor are taken as shown in Table 14.1. Depth of curve formed at entrance of micro-hole “h” [μm], radial overcut “OC” [μm], and diameter difference in micro-hole “Dd” [μm] are chosen as response factors for the experimentation. To accommodate three control factors (machining parameters) and their three levels, standard Taguchi’s L9 (33 ) OAdesign is selected for achieving the objectives of how the controlled parameters influence the response factors (output), and what are the optimum machining parameters to obtain minimum “depth of curve formed at entrance of micro-hole”, minimum “overcut”, and minimum “diameter difference in micro-hole”. According to Taguchi’s L9 orthogonal array design (Table 14.2), nine experiments are conducted based on deep through micro-holes fabrication in stainless steel plate through μECM process using H2 SO4 electrolyte (aqueous). Microscopic views of fabricated micro-hole at experimental run-1 are depicted in Fig. 14.3.

14.2.2 Measurement of Responses ( “h”, “OC”, and “Dd”) In this study, response outputs such as “OC” and “Dd” and “Dh” are calculated for each machined micro-hole by using equation number (1) and (2) and (3), Table 14.1 Control factors and its values for the experiments Control factors (machining parameters)

Code

levels 1

2

3

Pulse DC voltage (V )

X

8

10

12

Electrolyte concentration (Mol/L)

Y

0.2

0.3

0.4

Pulse frequency (KHz)

Z

150

175

200

1

1

1

2

2

2

3

3

3

2

3

4

5

6

7

8

9

X

Machining Param. and levels

1

Exp. run

3

2

1

3

2

1

3

2

1

Y

2

1

3

1

3

2

3

2

1

Z

X3Y3Z2

X3Y2Z1

X3Y1Z3

X2Y3Z1

X2Y2Z3

X2Y1Z2

X1Y3Z3

X1Y2Z2

X1Y1Z1

Designation

697

606

526

595

510

453

477

416

375

di (μm)

Table 14.2 Design of experiments (L9 OA-design) and corresponding results

581

510

446

505

440

391

401

354

319

do (μm)

639

558

486

550

475

422

439

385

347

Dh (μm)

214

160

116

192

139

104

170

124

94

“h” (μm)

265.5

225.0

189.0

221.0

183.5

157.0

165.5

138.5

119.5

“OC” (μm)

116

96

80

90

70

62

76

62

56

“Dd” (μm)

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Fig. 14.3 Microscopic views of fabricated micro-hole at experimental run-1

respectively, which are derived according to the geometry of fabricated micro-hole (Fig. 14.1) through μECM process. A metallurgical microscope (model: BX51M of OLYMPUS) was used to measure the diameters “di”, “do” and “h”. Radial overcut; OC = [Dh−Dt]/2

(14.1)

Diameter difference; Dd = di−do

(14.2)

Average diameter; Dh = [di+do]/2

(14.3)

where di = Diameter of micro-hole at entrance (μm). do = Diameter of micro-hole at exit (μm). Dt = Diameter of micro-tool electrode (μm).

14.3 Results and Discussions Under this section, all experimental results are analyzed through S/N ratio and ANOVA. The optimum machining parameters required for the minimum “h”, minimum “OC”, and minimum “Dd” are obtained by using Eq. 4 in which “y” is the observed data. For all “h”, “OC”, and “Dd”, S/N ratios and level values are calculated using MINITAB-17 software. Table 14.2 depicts the design of experiments

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and their corresponding results. The level of a response output with the greatest S/N ratio gives an optimal level, regardless of the type of response characteristics (such as “h”, “OC”, and “Dd”). For analyzing the effects of machining parameters on “h”, “OC”, and “Dd”, main effects plot of S/N ratios and interaction plot are generated as shown in Figs. 14.4, 14.5, 14.6 and 14.7. S/N ratio equation: For smaller is the better characteristic, (minimize): 1  2 S = 10log ( y ) N n

(14.4)

14.3.1 Analysis of Responses (“h”, “OC”, and “Dd”) Figures 14.4a, 14.5a and 14.6a reveal that the “h”, “OC”, and “Dd” obtained are the minimum (optimal) at the first level of pulsed DC voltage (X1), the first level of

Fig. 14.4 a Main effects plot of SN ratio and b interaction plot for “h”

Fig. 14.5 a Main effects plot of SN ratio and b interaction plot for “OC”

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Fig. 14.6 a Main effects plot of SN ratio and b interaction plot for “Dd”

electrolyte concentration (Y1), and the third level of pulse frequency (Z3). As a result, optimal parameter for all “h”, “OC”, and “Dd” is X1Y1Z3, i.e., pulsed DC voltage 8 V, electrolyte concentration 0.2Mol/L, and pulse frequency 200 KHz. According to the S/N ratio tables of “h”, it is observed that electrolyte concentration (Mol/L) has more influence, pulsed DC voltage (V) has moderate influence, and pulse frequency (KHz) has less influence on “h”. According to the S/N ratio tables of “OC” and “Dd”, it is observed that pulsed DC voltage (V) has more influence, electrolyte concentration (Mol/L) has moderate influence, and pulse frequency (KHz) has less influence on “OC” and “Dd” in the fabrication of deep micro-holes in stainless steel plate through μECM process under H2 SO4 electrolyte. Interaction plot for “h”, “OC”, and “Dd” as shown in Figs. 14.4b, 14.5b and 14.6b indicate that “h”, “OC”, and “Dd” increase with an increase of electrolyte concentration as well as pulsed DC voltage. ANOVA has been applied for significance level α = 0.05 (or confidence level = 95%). Control factors (machining parameters) with P-value obtained < 0.05 are acknowledged as statistically significant contribution. Following are the ANOVA results for: (i)

Depth of curve formed at entrance of micro-hole “h”: ANOVA results for “h” illustrate that the electrolyte concentration has more influence (86.09%) on the “h” which is statistically significant, while pulsed voltage (13.00%) has moderate influence on the “h” which is also statistically significant in the fabrication of deep micro-hole in stainless steel through μECM process under H2 SO4 electrolyte. Pulse frequency has least influence on “h” which is statistically not significant. The error contribution is 0.3% for “h”. (ii) Overcut “OC” in fabricated micro-holes: ANOVA results for “OC” illustrate that the pulsed DC voltage has more influence (64.34%) on the “OC” which is statistically significant, while electrolyte concentration (34.26%) has moderate influence on the “OC” which is also statistically significant in the fabrication of deep micro-hole in stainless steel through μECM process under H2 SO4 electrolyte. Pulse frequency has least influence on “OC” which is statistically not significant. The error contribution is 0.54% for “OC”.

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(iii) Diameter difference “Dd” in fabricated micro-hole: ANOVA results for “Dd” illustrate that pulsed DC voltage (57.08%) on the “Dd” has more influence, which are statistically significant, while the electrolyte concentration (40.59%) on the “Dd” has moderate influence, which are also statistically significant in the fabrication of deep micro-hole in stainless steel plate through μECM process under H2 SO4 electrolyte. Pulse frequency has least influence on “Dd”, which is statistically not significant. The error contribution is 0.63% for “Dd”.

14.4 Development of Regression Models Regression modeling has been done for obtaining the relationship between cutting parameters [“X”, “Y”, and “Z”] and response outputs [“h”, “OC”, and “Dd”] using statistical software “MINITAB-17”. After neglecting insignificant coefficient, the developed regression models are: (a) Regression model of depth of curve formed at entrance of micro-hole;

h = 102.0 − 3.333X − 276.7 Y − 0.09333 Z + 0.3333 X ∗ X + 766.7 Y ∗ Y + 0.000533Z ∗ Z + 25.00 X ∗ Y − 0.01333 X ∗ Z

(14.5)

For which R2 = 99.90% (b) Regression model of radial overcut;

OC = −39.50 + 26.83 X − 245.8 Y − 0.2567 Z − 0.8333 X ∗ X + 375.0 Y ∗ Y + 0.000400Z ∗ Z + 33.33 X ∗ Y + 0.006667 X ∗ Z

(14.6)

For which R2 = 99.92% (c) Regression model of diameter difference; For which R2 = 99.82% In regression model analysis, usually R2 value is used for validating the developed regression models and R2 value should be lie between 0.8 and 1.0 [10]. In current study, the developed regression models (Eqs. (14.5), (14.6), and (14.7)) are consistent because of R2 is greater than 90%. The predicted values obtained from developed regression models are compared with the experimental values of “h”, “OC”, and “Dd” as illustrated in Fig. 14.7a–c, respectively. These figures predict that the variations between predicted and experimental values are very minimal. Therefore, the

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Fig. 14.7 Comparison between experimental and predicted values of a “h”, b “OC”, and c “Dd”

developed regression models of second order are statistically significant for “h”, “OC”, and “Dd”. Hence, these models (Eqs. (14.5), (14.6), and (14.7)) can be used for further analysis. Dd = 180.0 − 29.50 X − 290.0 Y + 0.2800 Z +1.750 X ∗ X + 433.3 Y ∗ Y − 0.000533 Z ∗ Z +16.67 X ∗ Y − 0.01333 X ∗ Z

(14.7)

14.5 Conclusions This article focuses on analysis and regression modeling on defects of deep microholes fabrication in stainless steel through μECM process using fabricated tungsten micro-tool electrode (cylindrical) under H2 SO4 electrolyte. Following conclusions are summarized on the ground of experimental results and their analysis: • Pulsed DC voltage 8 V, electrolyte concentration 0.2 Mol/L, and pulse frequency 200 kHz, duty cycle 49%, and feed rate 100 μm are the optimum parameters for minimum “depth of curve formed at entrance of micro-hole (h), minimum “overcut (OC)”, and minimum “diameter difference in micro-hole (Dd)”. It indicates that low voltage, low concentration of H2 SO4 electrolyte, and high pulse frequency play more effective role in minimizing the defects of deep micro-hole fabrication in stainless steel through μECM process. • Interaction plot for “h”, “OC”, and “Dd” indicates that “h”, “OC”, and “Dd” increase with an increase in electrolyte concentration as well as pulsed DC voltage. • ANOVA results for “h” indicate that the electrolyte concentration has more influence (86.09%) on the “h” which is statistically significant, while pulsed DC voltage (13.00%) has moderate influence on the “h” which is also statistically significant. • ANOVA results for “OC” indicate that the pulsed DC voltage has more influence (64.34%) on the “OC” which is statistically significant, while electrolyte concentration (34.26%) has moderate influence on the “OC” which is also statistically

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significant. Pulse frequency has least influence on “OC” which is statistically not significant. • ANOVA results for “Dd” indicate that pulsed DC voltage (57.08%) on the “Dd” has more influence, which are statistically significant, while the electrolyte concentration (40.59%) on the “Dd” has moderate influence, which are also statistically significant. Pulse frequency has least influence on “Dd”, which is statistically not significant. • Since the developed regression models of second order are statistically significant for “h”, “OC”, and “Dd”, hence, for further analysis, these models can be utilized. • Experimental results indicate that, there is need of development in electrolyte or development in electrolyte control system or development in electrodes to improve the defects of deep micro-holes fabrication in stainless steel through μECM process.

References 1. Geethapriyan, T., Thulasikanth, V., Singh, V., Raj, A.C.A., Lakshmanan T., Chaudhury, A.: Performance characteristics of electrochemical micro machining of tungsten carbide. Mater. Today Proc. 27(3), 2381–2384 (2020) 2. X.L. Chen, G.C. Fan, C.H. Lin, B.Y. Dong, Z.N. Guo, X.L. Fang, N.S. Qu, Investigation on the electrochemical machining of micro groove using masked porous cathode. J. Mater. Process. Technol. 276, 116406 (2020) 3. B. Bhattacharyya, M. Malapati, J. Munda, Experimental study on electrochemical micromachining. J. Mater. Process. Technol. 169, 485–492 (2005) 4. M.Z. Rahman, A.K. Das, S. Chattopadhyaya, Comparative studies in electro-physical processes (ECM & EDM) for circular micro-holes drilling. Mater Today Proc. 5, 27690–27699 (2018) 5. T. Masuzawa, State of the art of micromachining. CIRP Ann. Manuf. Technol. 49(2), 473–488 (2000) 6. M.Z. Rahman, A.K. Das, S. Chattopadhyaya, Microhole drilling through electrochemical processes. A Review. Mat. And Man. Proc. 33(13), 1379–1405 (2017) 7. Y.J. Chang, Y.C. Hung, C.L. Kuo, J.C. Hsu, C.C. Ho, Hybrid stamping and laser micromachining process for micro-scale hole drilling. Mater. Manuf. Process. 32(15), 1685–1691 (2017) 8. D. Zhu, W. Wang, X.L. Fang, N.S. Qu, Z.Y. Xu, Electrochemical drilling of multiple holes with electrolyte-extraction. CIRP Ann. Manuf. Technol. 59, 239–240 (2010) 9. S.H. Ahn, S.H. Ryu, D.K. Choi, C.N. Chu, Electro-chemical microdrilling using ultra short pulses. Precision Engineering 28, 129–134 (2004) 10. E. Kuram, B. Ozcelik, Multi-objective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill. Measurement 46(6), 1849–1864 (2013)

Chapter 15

An Iot-Based Smart Pet Food Dispenser M. V. R. Durga Prasad, M. Anita, and T. Malyadri

Abstract In the modern era, people are very fond of their pets. But having pets has become a problem for today’s working individuals as they must leave them alone at home throughout the day and must travel very frequently. Continuous monitoring of the pets is also a tedious task for the pet owners. Some pets are even overfed or underfed due to lack of proper monitoring of the feed which results in ill-health, obesity and malnutrition. The aim of our project is to develop a smart pet feeding mechanism which dispenses the food based on user requirement so that pet’s dietary plans are met. The dispenser works on the principle of IoT as it can be operated by the user from anywhere in the world.

15.1 Introduction What does a pet food dispenser or a pet feeder mean? Yes, a feeding machine or food dispensing machine for pets. What happens when you live with a pet like a cat or a dog and must go somewhere during the holidays or a vacation or when you are busy the whole day in the office? Would you take your pet with you? How are you going to feed it? A pet food dispenser essentially does that job for you. A smart pet food dispenser built using the principles of Internet of Things (IoT) has a mobile application which gives inputs or shows the output data received from the pet food dispenser through various transmission methods like WiFi, Bluetooth, Zigbee, NFC, etc. M. V. R. Durga Prasad · T. Malyadri (B) Department of Mechanical Engineering, VNRVJIET, Bachupally, TS, India e-mail: [email protected] M. V. R. Durga Prasad e-mail: [email protected] M. Anita BVRIT Hyderabad College of Engineering for Women, Nizampet, Hyderabad, TS, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_15

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In the modern era, people are very fond of their pets. But having pets has become a problem for today’s working individuals as they must leave them alone at home throughout the day and must travel very frequently. Continuous monitoring of the pets is also a tedious task for the pet owners. Some pets are even overfed or underfed due to lack of proper monitoring of the feed which results in ill-health, obesity and malnutrition. According to a survey conducted by the Department of Animal Husbandry, Government of Telangana, around 85% of the pets in the household are obese and need health care. Over 80% of the working individuals in India who own pets feel that they are not able to take good care of their pets and feed them timely because of their busy work schedule. Obesity is a problem which not only is troubling humans but also very prevalent in animals and needs an immediate solution.

15.1.1 Design and Fabrication A 250 × 250 mm and 300 mm height container has been fabricated using stainless steel sheet metal of 1.6 mm thickness (16 gauge) by performing a V-bending operation on a 1000 mm (1 m) sheet thrice after 250 mm each. After the V-bending operation is done, the two open ends of the sheet metal are welded together by arc welding. Five slits of thickness 2 mm have been provided at every 40 mm from the top on two opposite sides of the stainless steel open container so that a sheet metal of dimensions 245 mm × 300 mm used as positioners can slide through the slits. The slits at every 40 mm from the top help us to study the maximum load the motor can bear without malfunctioning and the safety limit of the load by simply changing the height at which the positioners are placed there by altering the storage volume. Holes were drilled on both sides of the positioners through the length at every 10 mm so that the positioners can be locked in the required positions by using a pin/ screw of 3 mm diameter.

15.1.2 Calculations The torque provided by a NEMA-17 type stepper motor = 0.36 N-m. We know that 1 kg-force-meter = 9.80665 N-m. 0.36 N-m / 9.80665 N-m = 3.61 kg/cm (maximum weight that can be lifted when rotated at low RPM for NEMA-17 stepper motor).

15.1.3 Observations Position of dispensing mechanism:

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Fig. 15.1 Steel container with dispensing container with centre dispensing position

Table 15.1 Observation table for the position of the dispensing mechanism Position of dispensermechanism Observation Towards one side

Food gets clogged at times and might need the use of a stirrer

At the centre

Food enters the dispensing mechanism evenly

It has been observed that the pet food gets dispensed easily without any need of stirrer and gives more space to accommodate other electrical and electronic components when positioned at the centre of the container (Fig. 15.1 and Table 15.1).

15.1.4 Storage Volume As we are rotating the motor at a slightly higher RPM, it is advisable to reduce the weight of the food in the container. Ref. [1, 2] for information. Hence, the optimum storage volume that the food dispensing mechanism or the NEMA-17 motor that is being used can handle is 2.5 kg for a rectangular container as the motor is being rotated at a slightly higher RPM. See Fig. 15.2 to understand the relationship between torque and speed.

15.2 Dispenser Mechanism A cereal dispenser essentially consists of a turbine with an enclosure/ casing whose one end is attached to the storage container and the other end is free. A wheel is provided to rotate the turbine which then dispenses the food upon rotating.

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Fig. 15.2 Top view and front view of the mechanism position towards left side

15.2.1 Auger Feed Mechanism It is a distributing mechanism consisting of an auger which causes a substance to flow evenly through an aperture at the base or on the side of hopper. An auger is basically a screw-like component that pushes the substance.

15.2.2 3d Modelling The 3D modelling has been done using SolidWorks and CATIA V5 and the parts have been shown below.

15.3 Prototyping Majority of the prototyping has been done using the rapid prototyping technology or the 3D printing technology [3] Table 15.2. Table 15.2 3D printing process parameters

Parameter

Value

Layer thickness

0.1 mm

Infill

70–100%

Supports

None

Extruder temperature

190 °C

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15.3.1 3d Printed Components Auger Feed Mechanism The auger-motor mount (refer Fig. 5.1) will be fixed to the stepper motor with the help of screws. The auger (refer Fig. 5.1) has a hole which fits the shaft of the motor, and the bottom part of the auger goes into the mount to provide fixed position. The enclosure (refer Fig. 5.3) will be fitted to the mount and will surround the auger with some clearance (Figs. 15.3, 15.4, 15.5, 15.6, 15.7, 15.8, 15.9 and 15.10). The shaft of the stepper motor goes into the hole provided to the wheel for the shaft, and the other end is inserted with a pin to fix the wheel inside the enclosure (Figs. 15.11, 15.12, 15.13, 15.14, 15.15, 15.16, 15.17, 15.18, 15.19 and 15.20). Fig. 15.3 Relationship between torque and speed

Fig. 15.4 Cereal dispenser

152 Fig. 15.5 Auger feed mechanism

Fig. 15.6 Wheel for cereal mechanism

Fig. 15.7 Rotating hand wheel

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15 An Iot-Based Smart Pet Food Dispenser Fig. 15.8 Pin dispenser

Fig. 15.9 Outer casing

Fig. 15.10 Auger

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154 Fig. 15.11 Auger-motor mount

Fig. 15.12 3D Printed auger auger mount

Fig. 15.13 3D printed motor

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15 An Iot-Based Smart Pet Food Dispenser Fig. 15.14 Auger enclosure

Fig. 15.15 Wheel enclosure of cereal dispenser

Fig. 15.16 Dispenser wheel

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Fig. 15.17 Pin and rotation Figurewheel of the dispenser

Fig. 15.18 Final setup with motor and power supply

Fig. 15.19 D1 mini

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Fig. 15.20 Internal structure of stepper motor

15.3.2 Final Prototype Most of the assembly was done using hot-glue gun. Plastic containers were used to give the body of the prototype (Figs. 15.21, 15.22, 15.23, 15.24, 15.25 and 15.26).

15.4 Electronic Hardware and Software Components Programming • Any code written in Arduino is called as ‘Sketch’. Arduino sketch can be divided into three parts: structure, values and functions. • Arduino software structure consists of two main functions Setup() function and Loop() function. The setup function is called when the sketch starts. It is used to initialize variables, pin modes, libraries, etc. This function will run only once, after powerup or resetting the board. After the Setup function initializes

Fig. 15.21 Commonly used DC stepper motor

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Fig. 15.22 Pin diagram of D1 mini

Fig. 15.23 A4988 pinout

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Fig. 15.24 A4988 pinout

Fig. 15.25 Working of Blynk app

the values, the Loop function allows the sketch to change and respond. It actively controls the Arduino board [4]. D1 Mini / NodeMCU D1 mini is a WiFi board with a 4 MB flash based on ESP-826EX module. This is useful as it serves as a very compact solution for prototyping to many small smart

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Fig. 15.26 Schematic diagram of A4988

Table 15.3 Specifications of D1 mini

Microcontroller

ESP-8266Ex

Operating voltage

3.3 V

Digital I/O pins

11

Analog input pins

1

Clock speed

80 MHz/160 MHz

Flash

4 MB

Length

34.2 mm

Width

25.6 mm

Weight

3g

objects linked to World Wide Web. This module features 4 MB flash memory, 80 Hz of system clock, 50 KB usable RAM and an on-chip WiFi transceiver (Table 15.3).

15.4.1 Technical Specifications Features: • 11 Digital input/output pins, • 1 Analog input (3.2 V max input)

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• A micro USB connection • Compatible with MicroPython, Arduino, NodeMCU

15.4.2 STEPPER MOTOR A stepper motor, which is also known as step motor or stepping motor, is a brushless DC motor which divides one complete rotation into equal number of steps. Therefore, a stepper motor usually has 12, 24, 72, 144, 180 and 200 steps per revolution and stepping angels of 30, 15, 5, 2.5, 2 and 1.8° per step. A4988 is a stepper motor driver, manufactured by POLOLU. This stepper motor driver controls one bipolar stepper motor at 2 A output current per coil. It is a simple step and direction control interface with five different step resolutions. This driver requires a supply voltage of 3–5.5 V that has to be connected across VDD and GND pins and voltage of 8–35 V for the motor to be connected across VMOT and GND. A typical stepper motor has a step size specification, which applies to full step. A4988 allows higher resolution by allowing intermediate step locations. This is achieved by energizing coils which have intermediate current levels. Each input pulse given to step corresponds to one micro step of stepper in only the direction selected by DIR pin. Also, from the table below, we can find that the step size selector inputs, i.e. MS1, MS2 and MS3, enable selections from five sizes. MS1 and MS3 have internal 100 k pull-down resistors and MS2 has an internal 50k pull-down resistor. Full step mode can be obtained disconnecting all the selection pins. Current limiting must be as low as possible; otherwise, the intermediate current levels may not be maintained. Considering the sensitivity of A4988, current limiting is highly required. The driver IC has a maximum current rating of 2 A per coil. The PCB is designed in such a way that the heat would be dispensed out by its own, but a heat sink or other cooling methods may also be required [4].

15.4.3 Blynk Blynk is a platform with Android app to control Arduino, Raspberry Pi and the likes over the Internet [5, 6].Using this, we can build a graphic interface for the projects. It is very easy to understand and very compatible to use it in the projects. It is tied to some specific board of shield. It supports to the Arduino or Raspberry Pi is linded to the Internet over WiFi, 4G, Ethernet or this new ESP8266 chip, Blynk will get you inline and ready for the Internet of Things (IoT).

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15.4.4 Working Blynk is the heart of the IoT. When the Blynk button presses the message goes to the Blynk, it may be cloud based or server based. If it is not connected to the server, then it goes back to the try to connect Blynk server. If server is connected, then it moves to calibrated load cell and then it goes to check time. Once the given time matches with check time, it opens the food pouring door for the given time period. If it does not match with the feed time, then it goes to reset time. If there is no sufficient food, then it gives message, i.e. adjust the amount of food. The entire process is run by the server. All the messages are passed to the hardware and work based on the programming given to the pet food dispenser machine. For which there is no need of computer or laptop, but it requires Internet access of Ethernet, WiFi or 4G network to run the machine [7].

15.5 Result In general, a dog can eat a daily amount of food based on age, weight and physical activity it carries out [8]. The amount of food also depends on behaviour and energy need to stay active. The measurements of food mentioned in Table 15.4 are approximate and recommended for a healthy and normal physical activity level dog. Final prototype is checked for every 6 h’ time duration with amount of food feeding set for medium dog of 10 to 15 kgs as shown in Table 15.5. According to Table 15.4 Approximate food consumption according to weight

Table 15.5 Experimental analysis

Type of dog

Weight in Kgs

Food in grams

Toy dogs

2–3

50–90

Small dogs

3–5

90–120

Small— medium dogs

5–10

120–190

Medium—small dogs

10–15

190–260

Medium dogs

15–20

260–310

Medium—large dogs

20–30

310–410

Large dogs

30–40

500–590

Giant dogs

50

800

S.No

Duration

Feeding in grams

1

6

60

2

6

60

3

6

60

4

6

60

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the weight of the dog, feeding amount can be set using Blynk app; from the mobile, we can feed the hunger dog with optimum food using the IoT-based food dispenser.

15.6 Conclusion Pet food dispensers are very new to our country; however, they are very common in the USA and other foreign nations. Most of the existing dispensers are manual where the food is stored in the reservoir (storage container) and gets dropped into the bowl due to the gravity, and the pets have access to as much ever food that they can eat which results in obesity and various other health problems. The pet food dispensing mechanism that we have designed will solve the problem once and for all. With the help of the mobile application, the user can manually give the time of feeding and the quantity in terms of cups, and the pet food dispenser will dispense the exact quantity of food at the given time. This solves the problem of not being able to feed the pets due to the pet owner’s unavailability and decreases the problem of obesity and underweight due to inappropriate and inconsistent daily diet of the pets. Hence, it can be used in residence for feeding the pets for good health.

References 1. Marine Lorin of University of Veterninary Medicine, Budapest from “The role of dog owners’ behaviour in canine obesity” in the year 2016. 2. G. A. White, K. Cobb, K. M. Miller of University of Nottigam from Journal of Small Animal Practice 2011, https://doi.org/10.1111/j.17485827.2011.01138.x 3. M. Liu, Q. Zhang, Y. Mu, Design of high precision electronic scale based on hx711. Inf. Commun. 1, 142–144 (2017) 4. A. J. Bandodkar, J. Wang, Non-invasive wearable electrochemical sesnors: a review, in an article of Trends in Biotechnology Vol. 32(7), pp. 363–371 (2017) 5. Amazon Web Services: AWS IoT Documentation, https://aws.amazon.com/de/documentatio n/iot (2016) 6. M. Aazam, I. Khan, A. A. Alsaffar, E. N. Huh, Cloud of Things: Integrating Internet of Things and Cloud Computing and the Issues Involved, in International Bhurban Conference on Applied Sciences and Technology. IEEE (2014) 7. J. Guth, U. Breitenbücher, M. Falkenthal, P. Fremantle, O. Kopp, F. Leymann, L. Reinfurt., A detailed analysis of IoT platform architectures: Concepts, similarities, and differences. From Institute of Architecture of Application Systems of title Internet of Everything (2018) 8. https://www.animalwised.com/how-much-food-should-you-give-your-dog-231.html

Chapter 16

Dynamic Performance Enhancement of Hybrid Tricycle by Design of Efficient Transmission System Amol Waddamwar, Suyog Kulkarni, and P. R. Dhamangaonkar

Abstract The revolution from gasoline-powered vehicles to electric vehicles (EVs) has been a gradual process to stabilize climate change due to global warming and maintain our standard of living. Zero emission of harmful exhaust gases is a good sign for our health. EVs have several benefits than petrol/diesel vehicles like less noise pollution, low maintenance cost, and low cost of fuel per km. Efficycle is a sustainable hybrid eco-friendly trike which can be driven by human power by pedaling and/or by power from an electric motor. It has a tadpole configuration having a capacity of two commuters. In this paper, we focused on enhancement of vehicle dynamic performance by optimum selection of transmission system components. Proper design of electric drive to overcome required torque on all terrains and comfortable drive is vital to maximize battery discharge time.

Nomenclature A GCW Fg FGrad FLevel Fr  μ

Frontal area = 2.09 (m2 ) Cd = Drag coefficient = 0.4 Gross combined weight (Kg) Fa=Aerodynamic force (N) Gradient force (N) Total resistance force on gradient Road (N) Total resistance force on level road (N) Rolling force (N) Inclination of 50 Rolling coefficient = 0.01

A. Waddamwar (B) · S. Kulkarni · P. R. Dhamangaonkar Department of Mechanical Engineering, College of Engineering, Pune, India e-mail: [email protected] S. Kulkarni e-mail: [email protected] P. R. Dhamangaonkar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_16

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Density of air = 1.225 (Kg/m3 )

16.1 Introduction Every year, pollution level is increasing due to continuous increase in the number of vehicles running on fossil fuels, a non-renewable energy source. Hence, the world is moving to electric vehicles as a mobility solution. However, there are different problems, such as low range and lower torque capacity, going with the electric vehicle. Efficycle is a hybrid version, which can run on electric motor using battery power and/or on human power using pedaling. It is a trike having tadpole configuration and carrying capacity of two commuters. This solution does not limit the range of vehicle and can be proved as a better replacement of fueled vehicles contributing toward the environment sustainability. As efficycle is a hybrid vehicle, there are three possible drives—electric, manual, and hybrid. Here, battery power decides the driving range for electric mode. On the other hand, there is a demand for human power for pedaling in manual mode. However, integration of all three modes in a single system, switching among different modes, energy consumption rate of battery and reduction in human efforts are critical parameters in the design. Considering all these factors, design of transmission system for hybrid vehicle plays a vital role. Also, while designing the transmission, it is important to consider the aspects such as simplicity in design, ease in availability of parts, weight of vehicle, cost, manufacturing feasibility, and serviceability. In this paper, detail design and validation of transmission system for efficycle is discussed.

16.2 Literature Review Deepanjan Majumdar et al. had revealed that e-rickshaws are energy efficient than other forms of motorized public road transport vehicles in the state. Proper implementation of e-rickshaws has a potential to address the issue of environmental pollution due to transportation as specific CO2 emission for e-rickshaws was found to be 19.129 gm/passenger-km [1]. Prof. S. U. Gunjal et al. have worked on delta configuration of human-powered hybrid trike. In his work, driver seats are in longitudinal direction. This configuration mainly increases the length of vehicle and power train chain. Increase in vehicle length tends to instability of vehicle during cornering and moreover, increase in weight of vehicle [2]. From the above literature work, our principal objective is to develop efficient hybrid transmission for tadpole configuration to improve its dynamic performance and validate it by performing several tests to meet design targets.

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16.3 Transmission System The main aim is to enhance vehicle dynamic performance by reducing vehicle weight and optimizing transmission system. Kerb weight of vehicle is 95 kg. This trike is a rear wheel drive vehicle, powered by electric motor drive and manual pedaling. To reduce driver fatigue and use battery power efficiently, we focused on calculating optimum gear ratio for manual as well as electric drive, wheel selection, and chain selection. We have considered three types of forces: rolling force, aerodynamic force, and gradient force in calculation to get required torque on rear wheel, optimum gear ratio, and maximum vehicle speed. Also, dynamic testing of the vehicle was done on a 100 m acceleration test track. We have targeted to achieve a maximum vehicle speed of 40 km/hr with hybrid drive (motor + pedaling).

16.3.1 Resistance Force, Torque This is a hybrid trike, which has three drives: manual, electric, and hybrid. To calculate required torque and gear ratio, forces on the rear wheel and maximum vehicle speed are calculated separately for each drive. In manual drive, the power generated by a healthy person to drive a bicycle ranges from 375 to 25 W with two hours of continuous running [3]. So, we have 375 W per driver for manual drive (total 750 W). We have BLDC motor of 400 W, 48 V, and maximum 1500 rpm. By considering 10% motor winding loses, useful motor power is 360 W and 1350 rpm. Therefore, for hybrid drive, we have a total 1110 W of power.

16.3.2 Rear Wheel Selection Wheel selection is the most important part in any vehicle design as they are the only contact between the road surface and the vehicle. Road shocks are first absorbed by tires and then transmitted to suspension. Wheel tires are chosen in such a way that it must provide traction in all kinds of surfaces without slipping. The rear wheel has been chosen precisely to improve the vehicle’s performance. Based on the targeted top distance traveled, rear wheel has been chosen as a 28” bicycle wheel. Disadvantage of selecting a 28” wheel is an increase in required torque value.

16.3.3 Resistance Force Calculation [4] For total resistance force calculation, we have considered three forces like rolling force, aerodynamic force, and gradient force. On level road, we have neglected

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Table 16.1 All drive summary

Parameter

Manual

Electric

Hybrid

Power (W)

750

360

1110

Total resistance force (N)

78.99

52.52

99.60

Torque (N-m)

28.09

18.68

35.42

Max speed (Km/hr)

34.20

24.62

40.10

Total resistance force (N)

233.50

233.50

233.50

Torque (N-m)

83.03

83.03

83.03

Max speed (Km/hr)

11.56

5.55

17.11

Level Road

Gradient Road

gradient force as no inclination, whereas on gradient road we have neglected aerodynamic force as vehicle speed is less. Using following equations, resistance forces, required torque, and maximum speed for all drives is calculated as shown in Table 16.1. GCW = 245 kg

(16.1)

Radius of rear wheel = 0.356 m

(16.2)

Rolling force = μ × m × g

(16.3)

Aerodynamic force (Fa) = ρ × A × Cd × (Cw + 1) × (V × V ) ÷ 2

(16.4)

Gradient force (Fg) = m × g × sin θ

(16.5)

Level road (FLevel) = Fr + Fa

(16.6)

Gradient road (FGrad) = Fr + Fg

(16.7)

16.3.4 Manual Drive The vehicle can be driven by the drivers in both single passenger mode and dual passenger mode as shown in Fig. 16.1. The drivers can power the vehicle using the two crank wheels (having 52 teeth, 196 mm diameter) in the front portion of the vehicle frame. These two crank wheels deliver the power to the intermediate shaft

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Hybrid Drive

Electric Drive

Fig. 16.1 Transmission system

mounted in the rear part which ultimately drives the rear wheel through freewheels (16 teeth, 72 mm diameter). The overall manual transmission has a single speed gear assembly. Roller chain and sprockets are a very efficient method (efficiency 98%) of power transmission compared to belts. The aim of the drive-train model is to deliver the power produced by the drivers to the driving wheel most efficiently. The manual drive is divided into two chain drives (front and rear) using an intermediate shaft.

16.3.4.1

Manual Gear Ratio

While selecting gear ratio, to reduce the fatigue of the driver, it is important that there should be maximum distance traveled per sprocket revolution with optimal driving effort. Sprocket and freewheel are selected of 52 and 16 teeth, respectively, to deliver maximum rpm. So, the gear ratio for front manual chain drive will be 3.25. For rear chain drive, we have selected a gear ratio of 1.0.

16.3.4.2

Manual Chain Selection [5]

From a table of power rating of simple roller chain, 8 A chain was selected for both front and rear manual chain drives. Table 16.2 shows the chain selection summary. Table 16.2 Chain selection summary

Parameter

Front chain

Rear chain

Rpm

78

255

Torque (Nm)

28.80

17.72

Power transmitted (kW)

0.235

0.458

kW chain rating

0.332

0.647

No. of chain links

142

134

Chain type

8A

8A

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16.3.5 Electric Drive The electrical transmission of the vehicle consists of a BLDC motor and freewheels. The BLDC motor of 48 volts and 400 W (1500 rpm) is powered by a lithium polymer battery of 48 volts and 35Ah. The motor is mounted on the left side of the vehicle frame and battery is on the right when viewed from the front side. The max rated torque of the selected motor is 2.7 Nm. To maximize torque, there is a need to select the proper gear ratio to get required speed reduction and torque on level road as well as on gradient road. With an electric drive, we got a maximum speed of 24.62 km/hr. Rpm of motor 1500 but considered rpm as 1350 due to 10% electric losses. Electric drive required reduction calculations are as follows: Level Road: Total resistance force = 52.52 N (Table 16.1) Torque = 18.68 Nm (Table 16.1) Maximum rpm = 184 Level road reduction = 1350 ÷ Max rpm = 1350 ÷ 184 = 7.34

(18.8)

Gradient Road: Total resistance force: 233.50 N (Table 16.1) Torque = 83.03 Nm (Table 16.1) Maximum rpm = 42 Gradient road reduction = 1350 ÷ Max rpm = 1350 ÷ 42 = 32.14

(18.9)

From calculation for electric drive, required speed reduction on level road and gradient road is 7.34 and 32.14, respectively. Available gearbox ratio options are 6.0, 10.37, 11.6, 14.3, 15.7, and 16.6. We have selected a planetary gearbox of 10.37. Output of a selected geared motor is 130 rpm and 25.2 Nm. To match required reduction on level as well as gradient road, there is a need of 12 speed (11-52 T) cassette but not available in market. So, we have selected a 9 speed (11-32 T) cassette derailleur using a shifter to get required speed reduction and torque. Cassette offers steps in gear ratios which ensure efficient use of motor. For electric drive, 8 B chain type selected by calculation as kW rating is 1.59 kW.

16.3.6 Maximum Acceleration Calculations Mass of vehicle, m = 245 kg

(16.10)

Total resistance, R = Fr + Fa

(16.11)

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Tractive force, F=(torque × Gear ratio × transmission efficiency ) ÷ r (16.12) Acceleration = (F − R) ÷ m considering F = m × a

(16.13)

Using Eqs. (16.10), (16.11), (16.12), and (16.13), calculated maximum acceleration for each drive, Manual Drive: Total resistance, R = 78.99 N Tractive force, F = (57.6 × 3.25 × 0.9) ÷ 0.356 = 473.79 N Aeleration = (473.79−78.99)÷245 = 1.61 m/s2 Acceleration with Electric Drive: Total resistance, R = 52.52 N Tractive force, F = (2.43 × 10.37 × 1.5 × 0.9) ÷ 0.356 = 95.66 N Acceleration = (95.66 − 52.52) ÷ 245 = 0.18 m/s2 Acceleration with hybrid drive: Total resistance, R = 99.60 N Tractive force, F = ((2.43 × 10.37 × 1.5) + (57.6 × 3.25)) × 0.9) ÷ 0.356 = 569.43 N Acceleration = (569.43 − 99.60) ÷ 245 = 1.91 m/s2

16.4 Validation [6] Efficycle is not a standard vehicle which is available in the open market. It is a customized version developed for competition and can be used for general or special purpose. Hence, there are no specific standard norms for testing of this vehicle. But this vehicle is developed according to the rulebook released by competition authority. Tests discussed in the validation section conform to the rulebook of the competition and results were verified with our design targets. Vehicle is tested for several tests such as acceleration test, durability test, utility test, maneuverability test, and gradient test to ensure enhancement of vehicle’s dynamic performance by selecting optimum gear ratio to overcome each terrain torque requirement.

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16.4.1 Acceleration Test Vehicle needs to travel 100 m distance as fast as possible using hybrid drive with optimized electric drive gear ratio. We have conducted five acceleration tests using hybrid drive. From results, average speed is equal to 12.92 m/s whereas calculated max speed is 11.14 m/s. It means drivers applied more power than 375 W per driver which we have considered for calculations. It has taken an average 15.48 s time to cover 100 m distance as shown in Fig. 16.2.

16.4.2 Durability Test To check the durability of a vehicle in a race condition, the track is designed to have a lot of turns, bends, gradients, and various kinds of obstacles. Total length of the circuit was around 2 km. Vehicle had run for 1.5 h along with 30 vehicles. It completed 17 laps using hybrid drive, i.e., the vehicle traveled 34 km distance in 1.5 h with average speed of 23 km/hr. After 1.5 h long run, vehicle was inspected subsystemwise and no damage or breakdown was observed.

16.4.3 Maneuverability Test To check the maneuvering capability of a vehicle in a non-race condition, vehicle is driven on a leveled surface path full of turns and bends. Vehicle has taken 54 s to cover approx. 200 m track using hybrid drive.

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16.4.4 Utility Test To check the utility capability of a vehicle in a non-race condition, a rough track is designed with full of turns, bends, gravels, potholes, and other obstacles. Vehicle was loaded with the 20 kg payload in the utility box during the test. Vehicle has taken 34 s to travel 150 m track using hybrid drive.

16.4.5 Gradient Test To check gradeability of a vehicle to climb inclination of 5°, this test was performed. Vehicle has taken 4 s to cover 25 m of length of track using hybrid drive.

16.5 Conclusion and Future Scope The vehicle has three transmission drives: manual drive, electric drive, and hybrid drive. At first, we calculated resistance forces, torques, and maximum speed for each drive. These calculated values were taken as input for selection of type of chain, gear ratio, rear wheel size, motor and battery ratings, and motor gearbox. We have selected a 9-speed cassette (11-32 T) to get required torque values which ensure the efficiency of electric drive. Maximum vehicle speed was observed as 34 km/hr, 25 km/hr, and 40 km/hr with manual drive, electric drive, and hybrid drive, respectively. Vehicle dynamic performance checked with acceleration test, durability test, utility test, maneuverability test, and gradient test to ensure enhancement of vehicle’s dynamic performance. From the acceleration test, the vehicle reached a top speed of 47 km/hr (18% more than design target) by selecting proper motor gear ratio whereas we targeted 40 km/hr speed at time of design. From durability test, vehicle had completed 17 laps (34 km) in 1.5 h with average speed of 23 km/hr using hybrid drive without any system breakdown. Vehicle successfully overcomes a 5° gradient required torque of 83 Nm using hybrid drive and covered 25 m in 4 s of time. Vehicle showed satisfactory results on all kinds of terrain surfaces. Multiple gear ratios can be used in manual drive (for driving as well as driven wheel) using gear shifter to get multiple speed options, and it will help to minimize driver initial torque requirement. Moreover, an electronic gear change sensor system can be designed to autochange of gear ratio as per torque requirement. Further vehicle dynamic performance can be improved by reducing vehicle weight by 5~1 0 kg to reduce torque requirement. This hybrid trike can be used in urban cities for daily use by office employees, students, etc. Also, this can be useful for internal movement in a company campus.

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References 1. D. Majumdar, T. Jash, Merits and Challenges of E-Rickshaw as an alternative form of public road transport system: a case study in the State of West Bengal in India. School of Energy Studies, Jadavpur University Kolkata, Energy Procedia 79, 307–314 (2015) 2. S. U. Gunjal, G. D. Sonawane, et al. Design, analysis and fabrication of efficycle: a Hybrid tricycle. Department of Mechanical Engineering, IJETT 17 (2014). ISSN: 2231- 5381 3. W. David Gordon, J. Papadopoulos, Bicycling Science Third Ed. Chap. 2 – pp. 44 4. J. Wynand, V. D. M. Steyn, J. Warnich, Comparison of Tyre Rolling Resistance For Different Mountain Bike Tyre Diameters And surface conditions. South African J. Res. Sport Phys. Educ. Recreation 36(2), 179–193. Suid- Afrikaanse Tydskrif vir Navorsing in Sport, Liggaamlike Opvoedkunde en ontspanning, 36(2), 179-193. (2014). ISBN: 0379-9069 5. V. B. Bhandari, Machine Design Data Book 2nd Ed”, Chap. 14.3—Selection of chain 6. SAENIS, “Efficycle Rulebook 2015”

Chapter 17

Pyroelectric Energy Harvesting Potential in Lead-Free BZT-BST Ceramics Satyanarayan Patel

Abstract In the present work, pyroelectric energy harvesting potential in 0.5Ba(Zr0.2 Ti0.8 )O3 -0.5(Ba0.7 Sr0.3 )TiO3 (BZT-BST) ceramics is studied. The pyroelectric coefficient was obtained by the Byer–Roundy method. The pyroelectric figures of merit for energy harvesting applications are also estimated. The pyroelectric performance in terms of measuring voltage is obtained by subjecting the material to thermal fluctuations. To increase the energy harvesting potential from waste thermal/heat energy to electrical energy, the synchronized switch harvesting on inductor (SSHI) method is used. The SSHI technique findings reveal that this concept can considerably enhance the amount of power extracted from the pyroelectric materials.

17.1 Introduction Pyroelectric materials have drawn significant attention from researchers due to their application in various fields such as sensors, thermal imaging, infrared detector, pyrocatalysis, and actuator [1]. They also explored for waste thermal energy harvesting for the autonomous and self-powered (low-power) electronic devices for example wireless sensor networks and consumable electronics [1]. In this direction, many lead-based and lead-free materials are explored; however, increasing concerns on lead poisoning have pushed the researchers to focus on the lead-free materials like BaTiO3 , (K0.5 Na0.5 )NbO3 , (Na0.5 Bi0.5 )TiO3 compositions and their solid solutions [2, 3]. Recently, the BaTiO3 -based composition got significant attention as compared to other materials due to its high dielectric, piezoelectric, and pyroelectric properties [3, 4]. Among the various BaTiO3 composition and solid solution, xBa(Zr0.2 Ti0.8 )O3 (1-x)(Ba0.7 Ca0.3 )TiO3 (x = 0.5) 50BZT-50BCT is reported with very high piezoelectric(d 33 ) and pyroelectric coefficient ( p) value of ∼620 pC/N [5, 6] and 5.84–17.14 S. Patel (B) Department of Mechanical Engineering, Indian Institute of Technology Indore, Indore 453552, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_17

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× 10–4 C/m2 .K [7], respectively, at room temperature. These enhanced properties are results of coexistence of ferroelectric rhombohedral and tetragonal phases at the morphotropic phase boundary (MPB) near room temperature [8, 9]. To further improve the pyroelectric, ferroelectric and dielectric properties, various chemical and physical modifications methods are used including site engineering at various sites (common isovalent at A-site as Ca2+ , Sr2+ and B-site Zr4+ , Sn4+ ) [10]. A systematic investigation of the effect of Sr-addition in Ba0.85 Ca0.15-x Zr0.1 Ti0.9 O3 Srx composition shows a large improvement in the p [11]. 0.5Ba(Zr0.2 Ti0.8 )O3 0.5(Ba0.7 Sr0.3 )TiO3 (BZT-BST) shows a maximum p equivalent to 25 × 10–4 C/m2 .K [11] and 7 × 10–4 C/m2 .K [12, 13] by static measurement and Byer–Roundy method, respectively. However, its Ca added compositions (x = 0.075) show constant performance in wide operating temperature [14]. To further improve the pyroelectric performance 3BaO-3TiO2 -B2 O3 glass-added in BZT-BST ceramics this increases the p from ~ 7 × 10–4 C/m2 .K to ~ 10 × 10–4 C/m2 .K [12]. In another study, BZTBST cement compositions are fabricated for pyroelectric energy harvesting [15]. Recently, various pyroelectric figures of merits (FOM) and impedance analysis are also performed on BZT-BST ceramics [13, 16]. Moreover, it is also fabricated by seed induced method to further improve the dielectric and ferroelectric properties [17]. Furthermore, the effect of sintering temperature on ferroelectric hysteresis parameters [18], electrocaloric effect [14], and Olsen cycle-based thermal energy harvesting [19] also studied in Ba0.85 Ca0.15-x Zr0.1 Ti0.9 O3 -Srx . These studies show that the BZTBST can be a potential candidate for energy harvesting application. Our previous works on the BZT-BST are limited to pyroelectric FOMs analysis [12, 13]; however, the present work shows a complete overview of it for energy harvesting. In the present work, BZT-BST ceramic is fabricated to look at the pyroelectric energy harvesting potential. The BZT-BST is synthesized by solid-state reaction route and various materials characterization is performed. The pyroelectric coefficient is obtained by Byer–Roundy method. The energy harvesting investigations were carried out by alternatively exposing the sample in front of a hot/cold source to have a temporal temperature gradient which in turn gives an electrical signal. The energy harvesting power was also estimated using different nonlinear synchronized switch harvesting on inductor (SSHI) based circuits.

17.2 Experimental Procedure Polycrystalline 0.5Ba(Zr0.2 Ti0.8 )O3 −0.5(Ba0.7 Sr0.3 )TiO3 (BZT-BST) ceramic sample was synthesized by solid-state reaction method. Reagent grade powder (purity ≥ 99%) of BaCO3 , ZrO2 , SrCO3 , and TiO2 was weighed and mixed according to stoichiometry. At first, the mixture was calcined at 1350 °C in air for 6 h and then palletized with mixed polyvinyl alcohol 2% (by wt.) as a binder with the help of uniaxial hydraulic press. The green pellet is sintered at 1450 °C for 3 h in air. The final sample size has thickness of∼ 1.0 mm and a diameter of 12 mm. The density was measured using the Archimedes principle. The single-phase formation

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and crystalline structure was confirmed by the X-ray diffractometer (XRD) (Rigaku Smart Lab, Japan) at room temperature. For the electrical characterization sample was ground and sputtered with silver electrodes on both faces. The temperaturedependent polarization–electric field (P-E) hysteresis loops were recorded by a modified Sawyer Tower circuit (Marine India) at a constant frequency of 50 Hz. The dielectric constant and loss versus temperature plots were obtained using an LCR meter (HP 4284A; Hewlett–Packard Corporation, Palo Alto, USA) in the temperature range of 25–140 °C (by a heating rate of 2 °C/min) at various frequencies. For the pyroelectric measurement, sample was poled in silicone oil at 25 °C and 3.5 kV/mm of the dc field for 1 h. Afterward, pyroelectric current is measured with the help of an electrometer (Keithley 6517B Keithley Instruments, Inc.) for a ramp rate of 3 °C/min using heating/cooling rig (Byer–Roundy method). To avoid any charge trapped on grain boundary or other effects, many heating–cooling cycles are used below phase transition temperature before the final measurement. The energy harvesting potential is obtained by measuring a voltage across the various resistances by exposing the sample to a continuous thermal gradient using hot and cold air. Furthermore, to optimize the power output various electric circuits are used.

17.3 Results and Discussion The XRD of the BZT-BST is shown in Fig. 17.1a at room temperature. The XRD pattern reveals that BZT-BST consists of a pure perovskite phase without any secondary phase within the detection limit of the instrument. The sample is indexed with the previously published data [16]. A detailed study on the structural and Rietveld refinement by Mondal et al. shows the coexistence of the tetragonal-cubic phase [16] whereas in another work Sutjarittangtham et al. suggest the rhombohedral phase [17]. In this work, XRD analysis limited to confirm the phase purity only hence detailed analysis has been not performed. Figure 17.1b shows the P − E hysteresis loop for fabricated sample at different temperature. P − E loops are well saturated with slim behavior. The hysteresis parameters such as saturation polarization (Pmax ), remnant polarization (Pr ), and coercive field (E C ) decrease as the temperature increases. It is found that as the temperature increases P-E loop become slimmer and transform to almost linear behavior at 70 °C. It suggests that BZTBST has a ferroelectric to paraelectric phase transition in the temperature range of 50–70 °C. To confirm it, the temperature dependent of dielectric constant (εr ) and loss (tanδ) is presented in Fig. 17.1c, d, respectively, at a frequency of 100 Hz to 1 MHz. It also shows that the Curie temperature TC of the BZT-BST is around the ~61 °C. As the applied frequency varies from 100 Hz to 1 MHz εr slightly decreases whereas tanδ drastically decreases. Further, εr rises as the temperature increases and attends a maximum value at TC than subsequently decreases with further increase in temperature. This kind of temperature and frequency dependent polarization and dielectric behavior is normal in ferroelectric materials which are explained in many literatures [20–22]. However, present work is limited to find out

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TC from the temperature-dependent εr and tanδ plot. The detailed discussion on the dielectric behavior of BZT-BST is presented in our previous works [12, 13]. The pyroelectric current (I ) is measured by the Byer and Roundy method and presented in Fig. 17.2a. To measure the I sample is poled (as discussed before) and many heating/cooling cycles performed below 50 °C. Afterward, the final measurement is performed as depicted in Fig. 17.2a. The dielectric study reveals that the TC of the BZT-BST is around ~61 °C. Therefore, to avoid complete depolarization of materials heating/cooling is performed below 50 °C. The I is used to estimate the pyroelectric ), where A is the surface area of sample and dT is coefficient ( p) as [1] p = I /(A dT dt dt the applied temperature gradient (as presented in Fig. 17.2a). Pr versus temperature plot is used to calculate the p via static method (depicted in inset of Fig. 17.2a). A polynomial fitting of Pr versus temperature is used to determine p = d P r /dT . The p obtained by Byer–Roundy and static methods are in good agreement. Next, focus on driving the temperature-dependent energy harvesting FOMs using the p and εr data is given. In this direction, energy harvesting FOMs are expressed as [1] 2 2 (Fe ) = εpr ε0 and (Fe∗ ) = εr εp0 c2 where cv is volume-specific heat. cv can be estimated as v cv = Cp .ρ where Cp is the heat capacity at constant pressure and ρ is the density of the material. The density is obtained equal to 5.5 g/cm3 which is ~95% of the theoretical density. The temperature-dependent C p is taken from the literature [23]. The obtained FOMs of Fe and Fe∗ is significantly higher then BaTiO3 , Ca0.15 (Sr0.5 Ba0.5 )Nb2 O6 , LiTaO3 , BZT-BCT ceramics. A comparative table of the energy harvesting and other pyroelectric FOMs with these materials is provided in previous work [11]. It shows a higher energy harvesting capacity near the room temperature 25–40 °C which is an additional advantage of BZT-BST to use for practical application. The energy harvesting potential can be altered by increasing the p and decreasing the εr and cv . However, the obtained FOMs for the BZT-BST did not provide the real energy harvesting capacity of the sample. In this direction, a real energy harvesting potential of the sample is obtained by exposing to alternate heating and cooling as depicted in Fig. 17.2c. A hot and cold air source is used in actual environmental conditions. The opencircuit voltage and temperature are measured as presented in Fig. 17.2d. The temperature is limited to ~48 °C to avoid the complete depolarization of materials. The open-circuit voltage is measured as 0.6 V which consists of all the heating/cooling cycles in positive to negative values. It is important to note that voltage signals from such a setup cannot solely be caused by a pure primary pyroelectric response due to non-uniform heating of the bulk sample along the thickness. The voltage output is a cumulative effect of thermally stimulated current, primary and secondary pyroelectric effects in such a system. However, these types of device/system are close to the real-time applications which can provide the actual energy harvesting potential. In the past, various researchers used this type of system to investigate pyroelectric energy harvesting possibilities [24–26]. To obtain the actual power, this voltage is measured across the different resistance of 1–35 M. Fig. 17.3a–c shows the various circuit that are used with resistance to measure the voltage. Three different types of circuits based on the non-switched, parallel SSHI and series SSHI are used to enhance

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the power output. The detailed discussion on the working, advantages, and disadvantages of the circuits has been already discussed by various researchers [26–28]. Briefly, the main objective of the non-switch circuit is to extract maximum power from the material by matching the impedance of the sample. The output voltage can be converted into power (W ) as (W ) = V 2 /R L and results are shown in Fig. 17.3d. The maximum power of 17.5 nW is estimated across the 30 M load resistance when the non-switched circuit is used. It is evident from Fig. 17.3d that initially power increases with the load resistance and then starts decreasing which suggests the resistance of the material is matched with the circuit for maximum power transfer. Therefore, the maximum power output is obtained when the circuit impedance matches the BZT-BST sample. In this case, a material is always connected with circuits which have high power dissipation and decreases the efficiency. Hence, the parallel and series switched SSHI circuits are designed in such a way that they are not always connected with materials. However, with the help of the inductor and flipping mechanism, they synchronize between charge extraction and temperature gradient to boost the power output. The maximum power obtained with a non-switched circuit increases from ~ 17.5 to ~23 and ~35 nW when series and parallel SSHI circuit used, respectively (shown in Fig. 17.3d). The obtained power SSHI circuit is almost

Fig. 17.1 a XRD spectra at room temperature b polarization-electric field (P-E) hysteresis loop at different temperatures c dielectric constant (ε), and d dielectric loss (tanδ) as a function of temperature at different frequency [13]

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Fig. 17.2 a Pyroelectric current and coefficient. b Energy harvesting pyroelectric figures of merit versus temperature. c Schematic of cyclic heating/cooling system by hot and cold air. d Temperature and open-circuit voltage profile as a function of time. The inset shows polarization and pyroelectric coefficient estimated by P-E loop data as a function of temperature [12]

~ 150% higher than that of the non-switched circuit. The power output also depends on the load resistance as shown in Fig. 17.3d and obtained maximum when the load impedance equals the source impedance. The obtained power is sufficient to power small sensors, batteryless devices based on microelectro-mechanical systems. Hence, it can be a potential candidate for waste thermal energy harvesting. However, one can consider chemical and physical modification to further improve the output power.

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Fig. 17.3 Pyroelectric energy harvesting interface circuits for a standard non-switched, b parallelSSHI, c series-SSHI, and d harvested electrical power with load resistance [15]

17.4 Conclusions The pyroelectric energy harvesting potential in 0.5Ba(Zr0.2 Ti0.8 )O3 −0.5(Ba0.7 Sr0.3 )TiO3 (BZT-BST) ceramics is analyzed. Large pyroelectric coefficient and open-circuit voltage equal to 3.4 × 10–4 C/m2 K and 0.6 V, respectively, are found in the wide temperature span of 303 K-340 K. Further, the pyroelectric voltage is also computed across various resistance (1–40 M) using different electric circuit to obtain maximum power output. The maximum power of ~17.5 nW is obtained when a non-switched circuit is used across the resistance of 30 M. It is further improved to ~25 nW when a parallel synchronized switch harvesting on inductor interface circuit at similar resistance is applied. The obtained results suggest that the BZT-BST ceramics can be effectively used for energy conversion (heat to electricity) at room temperature.

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References 1. C. Bowen, H. Kim, P. Weaver, S. Dunn, Piezoelectric and ferroelectric materials and structures for energy harvesting applications. Energy Environ. Sci. 7(1), 25–44 (2014) 2. T. Takenaka, H. Nagata, Current status and prospects of lead-free piezoelectric ceramics. J. Eur. Ceram. Soc. 25(12), 2693–2700 (2005) 3. J. Rödel, J.-F. Li, Lead-free piezoceramics: Status and perspectives. MRS Bull. 43(8), 576–580 (2018) 4. M. Acosta, N. Novak, V. Rojas, S. Patel, R. Vaish, J. Koruza, G. Rossetti, J. Rödel, BaTiO3 based piezoelectrics: Fundamentals, current status, and perspectives. Appl. Phys. Rev. 4(4), 041305 (2017) 5. W. Liu, X. Ren, Large piezoelectric effect in Pb-free ceramics. Phys. Rev. Lett. 103(25), 257602 (2009) 6. H. Bao, C. Zhou, D. Xue, J. Gao, X. Ren, A modified lead-free piezoelectric BZT-xBCT system with higher TC . J. Phys. D Appl. Phys. 43(46), 465401 (2010) 7. S. Yao, W. Ren, H. Ji, X. Wu, P. Shi, D. Xue, X. Ren, Z.G. Ye, High pyroelectricity in lead-free 0.5Ba(Zr0.2 Ti0.8 )O3 –0.5(Ba0.7 Ca0.3 )TiO3 ceramics. J. Phys. D Appl. Phys. 45(19), 195301 (2012) 8. I. Coondoo, N. Panwar, H. Amorin, M. Alguero, A. Kholkin, Synthesis and characterization of lead-free 0.5Ba(Zr0.2 Ti0.8 )O3 –0.5(Ba0.7 Ca0.3 )TiO3 ceramic. J. Appl. Phys. 113(21), 214107 (2013) 9. E.V Ramana, A. Mahajan, M. Graça, S. Mendiratta, J. Monteiro, M. Valente, Structure and ferroelectric studies of (Ba0.85 Ca0.15 )(Zr0.1 Ti0.9 )O3 piezoelectric ceramics. Mater. Res. Bull. 48(10), 4395–4401 (2013) 10. J.A. Dawson, D.C. Sinclair, J.H. Harding, C.L. Freeman, A-Site strain and displacement in Ba1-x Cax TiO3 and Ba1-x Srx TiO3 and the consequences for the Curie temperature. Chem. Mater. 26(21), 6104–6112 (2014) 11. S. Patel, A. Chauhan, R. Vaish, Large pyroelectric figure of merits for Sr-modified Ba0.85 Ca0.15 Zr0.1 Ti0.9 O3 ceramics. Solid State Sci. 52, 10–18 (2016). 12. K. S. Srikanth, S. Patel, S. Steiner, R. Vaish, Engineered microstructure for tailoring the pyroelectric performance of Ba0.85 Sr0.15 Zr0.1 Ti0.9 O3 ceramics by 3BaO-3TiO2 -B2 O3 glass addition. Appl. Phys. Lett. 110(23), 232901 (2017) 13. S. Patel, K. Srikanth, S. Steiner, R. Vaish, T. Froemling, Pyroelectric and impedance studies of the 0.5Ba(Zr0.2 Ti0.8 )O3 –0.5(Ba0.7 Sr0.3 )TiO3 ceramics. Ceram. Int. 44(17), 21976–21981 (2018) 14. S. Patel, A. Chauhan, R. Vaish, Large-temperature-invariant and electrocaloric performance of modified barium titanate for solid-state refrigeration. Energy Technol. 4(9), 1097–1105 (2016) 15. A. Kumar, S. Kumar, S. Patel, M. Sharma, P. Azad, R. Vaish, R. Kumar, K. S. Srikanth, Pyroelectric energy conversion using Ba0.85 Sr0.15 Zr0.1 Ti0.9 O3 ceramics and its cement-based composites. J. Intell. Mater. Syst. Struct. 30(6), 869–877 (2019) 16. T. Mondal, B. P. Majee, S. Das, T. P. Sinha, T. R. Middya, T. Badapanda, P. M. Sarun, A comparative study on electrical conduction properties of Sr-substituted Ba1-x Srx Zr0.1 Ti0.9 O3 (x = 0.00–0.15) ceramics. Ionics. 23(9), 2405–2416 (2017) 17. K. Sutjarittangtham, U. Intatha, S. Eitssayeam, Influence of seed nano-crystals on electrical properties and phase transition behaviors of Ba0.85 Sr0.15 Zr0.1 Ti0.9 O3 ceramics prepared by seed-induced method. Electron. Mater. Lett. 11(3), 374–382 (2015). 18. S. Patel, R. Vaish, Effect of sintering temperature and dwell time on electrocaloric properties of Ba0.85 Ca0.075 Sr0.075 Ti0.90 Zr0.10 O3 ceramics. Phase Transit. 90(5), 465–474 (2017) 19. S. Patel, S, D. Sharma, A. Singh, R. Vaish, Enhanced thermal energy conversion and dynamic hysteresis behavior of Sr-added Ba0.85 Ca0.15 Ti0.9 Zr0.1 O3 ferroelectric ceramics. J. Materiomics. 2(1), 75–86 (2016) 20. J.M. Ballantyne, Frequency and temperature response of the polarization of barium titanate. Phys. Rev. 136(2A), A429 (1964)

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21. F.D. Morrison, D.C. Sinclair, A.R. West, Electrical and structural characteristics of lanthanumdoped barium titanate ceramics. J. Appl. Phys. 86(11), 6355–6366 (1999) 22. P. Hansen, D. Hennings, H. Schreinemacher, High-K dielectric ceramics from donor/acceptorcodoped (Ba1-x Cax )(Ti1-y Zry )O3 (BCTZ). J. Am. Ceram. Soc. 81(5), 1369–1373 (1998) 23. M. Sanlialp, V.V. Shvartsman, M. Acosta, D.C. Lupascu, Electrocaloric effect in Ba(Zr,Ti)O3 (Ba,Ca)TiO3 ceramics measured directly. J. Am. Ceram. Soc. 99(12), 4022–4030 (2016) 24. C.-C. Hsiao, J.-W. Jhang, A.-S. Siao, Study on pyroelectric harvesters integrating solar radiation with wind power. Energies. 8(7), 7465–7477 (2015) 25. D. Guyomar, G. Sebald, E. Lefeuvre, A. Khodayari, Toward heat energy harvesting using pyroelectric material. J. Intell. Mater. Syst. Struct. 20(3), 265–271 (2009) 26. A. Kumar, R. Vaish, S. Kumar, V. Singh, M. Vaish, V. Singh Chauhan, K.S. Srikanth, Leadfree pyroelectric materials for thermal energy harvesting: a comparative study. Energy Technol. 6(5), 943–949 (2018) 27. D. Guyomar, M. Lallart, Recent progress in piezoelectric conversion and energy harvesting using nonlinear electronic interfaces and issues in small scale implementation. Micromachines. 2(2), 274–294 (2011) 28. M. Lallart, Nonlinear technique and self-powered circuit for efficient piezoelectric energy harvesting under unloaded cases. Energy Convers. Manag. 133, 444–457 (2017)

Chapter 18

Implementation of Online Self-Tuning Fuzzy-PI (STFPI) Controller for Conical Tank System M. Lakshmanan, V. Kamatchi Kannan, K. Chitra, and S. Srinivasan

Abstract In the chemical process industries, the control of liquid level of a conical tank system is a difficult task due to variation present in cross-sectional area with respect to height. In the proposed research, the conical tank is categorized into three operating regions such as lower, middle, and higher regions. The tuning of PI controller parameters using conventional method is complicated, due to the presence of the inherent nonlinearities in the plant dynamics and various uncertainties, measurement noise. In this research work, the conventional ZNT method has been utilized to tune the PI controller parameters to maintain the liquid level of conical tank system. A model free controller is required and it automatically tunes the PI controller parameters such as proportional gain Kp and integral time Ti , respectively. Hence, a self-tuning fuzzy-PI (STFPI) controller has been proposed. The property of STFPI controller has greater flexible than the conventional controllers and it is also growing very fast because of its simplicity and versatility. In servo operation, the performance of the PI controller is tested in terms of performance indices values such as IAE and ISE.

M. Lakshmanan (B) · K. Chitra Department of EEE, CMR Institute of Technology, Bengaluru, Karnataka, India e-mail: [email protected] K. Chitra e-mail: [email protected] V. Kamatchi Kannan Department of EEE, Bannari Amman Institute of Technology, Sathyamangalam, India e-mail: [email protected] S. Srinivasan Department of EEE, CMR College of Engineering and Technology, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_18

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18.1 Introduction In past two decades, it is very much essential to monitor and control the process parameters to increase the productivity and quality. The conical tank system is most commonly used nonlinear tank which finds the application in most of the chemical industries. In process industry, most preferable controller is PI because of robust performance, simplicity, and user friendly [2]. The FOSTFPID controller is being examined to control a highly nonlinear, coupled MIMO, robotic manipulator system [3, 4]. The performance of FOSTFPID has been examined for path tracking, interruption elimination, noise containment, and model improbability [3, 4]. The self-tuning fuzzy-PI controller has great capability of limiting the external interruption, and it can be offered less performances indices [5, 6]. The self-tuning fuzzy-PI (STFPI) controller has developed into an alternative to usual control algorithms to solve problems dealing with complex processes [5, 6]. Among the different approaches, the STFPI controller is designed to overcome the poor tuning problem in the proposed research [8–10]. The STFPI controller performs the tuning of the Kp and Ti based on the e and ec. The STFPI controller will automatically tune the proportional gain and integral time based on the error. The real-time experimental results of conical tank system with self-tuning fuzzy-PI (STFPI) controller and Ziegler–Nichols tuning (ZNT) are obtained by servo operations [1].

Fig. 18.1 Layout of conical tank system

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18.2 Conical Tank System The outline of conical tank system is as shown in Fig. 18.1. The experimental conical tank system consists of one inlet and one outlet. The liquid from the storage tank is pumped by variable frequency drive. The inlet pipe consists of rotameter, flow transmitter, to monitor and to regulate the flow rate. The orifice is used to convert the flow into differential pressure and flow transmitter converts differential pressure into 4 to 20 mA electrical signals. To measure and regulate the outflow rate, an outlet flow meter and pneumatic control valve are utilized. A level transmitter and digital panel meter are used to measure the tank level. The purpose of level transmitter is to measure the pressure at the bottom of the tank with respect to atmosphere and generates a 4 to 20 mA electrical signal.

18.2.1 Process Modeling Figure 18.2 is the free body drawing of the proposed conical tank system. The first order differential Eq. (18.1) [5, 6] dV = Fin − Fout dt

(18.1)

where V = volume of the tank (Cone) V=

1 1 π R 3 h = Ah 3 3

(18.2)

At any instant, the water height in the conical tank system is obtained by massbalance equation which is given by (18.3) Fig. 18.2 Free body diagram of conical tank system

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Fin − Fout = Ah

(18.3)

A = area of cross sectional, h = height of the tank (overall). From Eq. (18.1) and Eq. (18.3) we get, dV dh =A = Fin − Fout dt dt

(18.4)

The transfer function can be represented in Eq. (18.5) Fin − Fout =

1 1 AR = Ah 3 3

(18.5)

h dh + dt Rv

(18.6)

where A = π R 2 and R = h. Fin = A

By taking Laplace transform for Eq. (18.6) Fin (s) = AsH(s) +

H(s) Rv

(18.7)

Rearranging the above equation H(s) K Rv = = Fin (s) 1 + ARv s 1 + τs

(18.8)

where Rv = K = valve resistance, time constant = ARv = τ.

18.2.2 Model Identification An open-loop test on the real-time process the model parameters are identified using process reaction curve method [5, 6]. The model of the system is calculated from delay time (td ), the time taken by the level to reach 28.3 and 63.2%. The time constant (τ) is estimated by using two-point method from the response curve. The first-order model is represented in Eq. (18.9) G(s) =

Kp e−td s τs + 1

(18.9)

where process gain (Kp ) = ratio of percentage vary in output to percentage vary in input.

18 Implementation of Online Self-Tuning Fuzzy-PI (STFPI) Controller … Table 18.1 Linear models of conical tank system

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Operating regions

Process gain (Kp )

Delay time (td )

Time constant (τ)

Region 1

9.43

−6

1280

Region 2

8.1

−5.44

1050

Region 3

7.23

−4.5

980

Time constant τ=1.5(t2 -t1 ). t1 = the instant taken by the level to arrive at 28.3% t2 = the instant taken by the level to arrive at 63.2% td = delay time. Based on the process reaction curve method, three different mathematical functions are listed as shown in Table 18.1.

18.3 PI-Controller Parameter Tuning by Ziegler–Nichols Tuning (ZNT) Method This ZNT rules are very much useful in process control industries [5–7]. The ZNT method is applied to plants with step responses of the system exposed in Fig. 18.3. The suggested Ziegler–Nichols (Z-N) method to place the values of Kp and τi is used on the formula which is given in Eq. (18.10) (Table 18.2). Kp =

Fig. 18.3 Unit step response curve

0.9T L τi = L 0.3

(18.10)

190 Table 18.2 Parameter values for different operating regions

M. Laksmanan et al. Operating regions

Proportional gain (Kp )

Integral time constant (τi )

Region 1

19.18

0.35

Region 2

20.40

0.29

Region 3

9.10

0.30

18.4 Self-Tuning Fuzzy-PI (STFPI) Controller The anticipated STFPI controller is shown in Fig. 18.4. The PI controller parameters are adjusted by using fuzzy inference rules based on the e and ec while the output is the incremental change in the control output like proportional gain Kp and integral gain Ti . The performance of the controller is evaluated based on performance indices. The experienced knowledge and fuzzy set theory are utilized to regulate the PI controller parameters.

18.4.1 Fuzzification of Input and Output Variables The basic ranges of input variables are error and change in error [−1, 1], and the output variables are proportional gain Kp = [0, 21], integral time Ti = [0, 1]. Then the input and output variables are transformed into a uniform fuzzy range [−1, 1] and [0, 1] for ease of design. The membership function of input variables and output variables is shown in Fig. 18.5. The fuzzy range of input and output is divided into 7 and 5 variables, respectively, and the corresponding fuzzy subsets are e, ec = [NB, NM, NS, ZO, PS, PM, PB] and Kp , Ti = [S,MS,M,BM,B]. The NB and PB are trapezoidal membership functions and the others are triangular membership functions. Let B be trapezoid membership

Fig. 18.4 Proposed self-tuning fuzzy-PI controller (STFPI)

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Fig. 18.5 Membership function of error (e) and change in error (ec) & membership function of Kp and Ti

Table 18.3 Fuzzy rules between inputs (e, ec) and output Kp Error (e)

Change in error (ec) NB

NM

NS

ZO

PS

PM

PB M

Proportional gain (Kp) NB

B

B

BM

BM

BM

M

NM

B

BM

BM

BM

M

M

MS

NS

BM

BM

M

M

MS

MS

S

ZO

S

S

MS

M

MS

S

S

PS

S

MS

MS

M

M

BM

BM

PM

MS

M

M

BM

BM

BM

B

PB

M

M

BM

BM

BM

B

B

function and the others are triangular membership functions. The fuzzy inference rules between inputs e, and ec and outputs Kp and Ti are indicated in Tables 18.3 and 18.4. Table 18.4 Fuzzy rules between inputs (e, ec) and output Ti Error (e)

Change in error (ec) NB

NM

NS

ZO

PS

PM

PB

MS

MS

M

M

M

Integral time (Ti) NB

S

S

NM

S

S

MS

M

M

M

M

NS

MS

MS

M

M

M

M

M

ZO

MS

MS

M

M

M

BM

BM

PS

MS

MS

M

M

M

BM

B

PM

M

M

BM

M

BM

B

B

PB

M

M

BM

BM

BM

B

B

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18.5 Results and Discussions The closed loop response of conical tank system for different operating regions such as lower range (10 cm), middle range (20 cm), and higher range (32 cm) is analyzed and shown in Figs. 18.6 and 18.7. In this research work, the real-time experimental results of servo operation are carried out for 190 s. It is evident from Table 18.5 and Fig. 18.6 that the average IAE of STFPI controller is 1.16 less than that of ZNT and the average ISE of STFPI is 2.12 less than that of ZNT for lower range set point 10 cm. It is evident that, from Table 18.5 and Fig. 18.7, the average IAE of STFPI controller is 2.22 less than that of ZNT and the average ISE of STFPI controller is 7.23 less than that of ZNT for middle range set point 20 cm. The average IAE of STFPI controller is 3.47 less than that of ZNT and the average ISE of STFPI controller is 5.64 less than that of ZNT for higher range set point 32 cm.

Table 18.5 Performance indices of STFPI controller and ZNT for lower, middle, and higher range set points Performance indices

Lower range set point (10 cm)

Middle range set point (20 cm)

Higher range set point set point (32 cm)

STFPI

ZNT

STFPI

STFPI

Average IAE

0.82

1.98

2.15

4.37

5.52

8.99

Average ISE

4.61

6.73

27.35

34.58

110.57

116.21

ZNT

ZNT

Fig. 18.6 Real-time servo responses of STFPI controller and ZNT for lower range set point (10 cm)

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Fig. 18.7 Real-time servo responses of STFPI controller and ZNT for middle range set point (20 cm) and higher range set point (32 cm)

18.6 Conclusion The water level control of a conical tank system with a PI controller is implemented in real time. The entire operating region of conical tank system is categorized into three estimated linear regions and their transfer function models are derived to validate. The servo responses of the ZNT method and STFPI for PI controller tuning are compared in terms of performance indices values such as IAE and ISE. From the obtained experimental results, the proposed STFPI controller reduces performance indices values such as IAE and ISE when compared with ZNT method.

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References 1. S. Alcantara, R. Vilanova, C. Pedret, PID control in terms of robustness/performance and servo/regulator trade-offs: A unifying approach to balanced auto tuning. J. Process Control 23, 527–542 (2013) 2. K. J. Astrom, T. Hagglund, Revisiting the Ziegler–Nichols step response method for PID control. J. Process Control 14, 635–650 (2004) 3. J. Kumar, V. Kumar, K. P. S. Rana, Fractional-order self-tuned fuzzy PID controller for threelink robotic manipulator system Neural Computing and Applications 32, 7235–7257 (2020) 4. K. Nithilasaravanan, N. Thakwani, P. Mishra, V. Kumar, K. P. S. Rana, Efficient control of integrated power system using self-tuned fractional-order fuzzy PID controller. Neural Comput. Appl. 31, 4137–4155 (2019) 5. M. Lakshmanan, R. Ganesan, Implementation of proportional integral controller parameter tuning for spherical tank system using root locus technique. Asian J. Res. Soc. Sci. Human. 6(6), 85–103 (2016) 6. M. Lakshmanan, R. Ganesan, Design and implementation of PI controller tuned by Fuzzy logic for water level control of spherical tank system. J. Electr. Eng. Politehnica Publishers, Romania, 17(4), 443–452 (2016) 7. M. Lakshmanan, R. Ganesan, Design of controllers for Dual Motor Ball and Beam System. Int. Rev. Mech. Eng. (IREME), 8(4), 714–721 (2014) 8. R. K. Mudi, N. R.Pal, A self-tuning fuzzy PI controller. Fuzzy Sets and Systems 115(2), 327–338 (2000) 9. R. Jain, N. Sivakumaran, T. K. Radhakrishnan, Design of self tuning fuzzy controllers for nonlinear systems. Expert Syst. Appl. 38(4), 4466–4476 (2011) 10. M.S. Zaky, A self-tuning PI controller for the speed control of electrical motor drives’. Electric Power Syst. Res. 119, 293–303 (2015)

Chapter 19

Smart and Sustainable Shopping Cart for the Physically Challenged Prashant Kumar Soori, Kiran Mathews Abraham, and Mohamed Al-Mujtaba Ali Idris Osman

Abstract This paper discusses the design and development of a prototype working model of an automated shopping cart. The project aims to make shopping easier, especially for the elderly. In addition, this shopping cart promotes sustainability by completely eliminating the paper used for billing and plastic used as take away bags. The shopping cart is fitted with a camera connected to a Raspberry Pi module. It also consists of an inbuilt barcode scanner attached to a NodeMCU module that enables billing to be carried out by the customer and the final bill is sent to the central billing system using Wi-Fi communication technology. The trolley also has a weight tracking system that helps prevent possible theft. Upon exiting the shopping arena, the trolley follows the customer until his vehicle, and then it can be set into a mode where it returns to its designated location using a five infrared (IR) line follower array sensor mechanism.

19.1 Introduction In today’s world, the concept of in-store shopping is slowly but steadily dying out, given the convenience of online shopping. However, a recent survey conducted by researchers reported that most out of the 1,200 primary household shoppers who responded to the survey, enjoyed both in-store and online shopping. It is also reported that the “digitally native” customers (18–20 years of age) still prefer to shop in-store while the people in their 20 and 30 s would rather shop online. This is because grocery

P. K. Soori · K. M. Abraham (B) · M. A.-M. A. I. Osman Heriot Watt University, Dubai, UAE e-mail: [email protected] P. K. Soori e-mail: [email protected] M. A.-M. A. I. Osman e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_19

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shopping after a long day of work is often seen to be a tiresome process for people above the age of 20 [1]. The conventional shopping method requires the customers to push a grocery laden trolley all along the aisles of the store. Also, in many of the local grocery stores, the trolleys are poorly maintained, which has a negative impact on the shopping experience. While the process of steering a heavy trolley is a tedious task for regular people, it makes the situation even worse for people living with a physical disability. Moreover, people often have to wait in long queues to complete the billing process. This is mainly due to the fact that the products, despite the quantity, are always required to be scanned individually for the final bill to be generated. The products are then loaded into many polythene take away bags and the trolley has to be pushed all the way to the vehicle parking lot. The empty trolleys are left abandoned in the car parking lot, and it requires additional manpower to move them back to the designated areas. Many researchers have reported implementation methods for billing and tracking system of an automated trolley. Hadimani et al. has proposed a system in which a barcode scanner is fitted to the trolley and connected to a Raspberry Pi through universal serial bus (USB) connectivity [2]. The research proposed by Yewathkar et al. consists of a system in which each item in the shop is provided with a radio frequency identification (RFID) tag to be scanned by the RFID scanner. A central database also provides recommendations to the customers based on their past purchases [3]. Another group of researchers reported the use of a barcode scanner fitted to the trolley in order to scan the product barcodes [4]. Joshi et al. have designed a human follower robot using Raspberry Pi [5]. Another research conducted uses object detection and tracking using a Raspberry Pi in real time [6]. Pandita et al. discussed an automatic human follower smart shopping trolley using an infrared (IR) sensor [7]. Some researchers have also reported techniques for analyzing, designing, controlling, and developing a healthcare system using a line following robot [8]. In another study, a line following robot has been designed using five sensors [9]. Researchers have proposed the design and construction of a line following robot using Arduino [10]. In our work, the advantages and drawbacks of the systems reported in literature are studied before finalizing the prototype. This paper presents the design and fabrication of a fully functional, prototype working model of an automated shopping cart. The fabricated prototype has three sections: • The billing and anti-theft system • The user tracking system • The line follower system. The novelty of this paper lies in the ease of technology that can be used for billing from the customer’s point of view. From the shop owner’s point of view, the cart has been designed to prevent possible theft, thereby enhancing security. Moreover, sustainability aspects have also been considered in the design in terms of paper and plastic-free shopping, along with a provision to attach reusable bags to the trolley.

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The line follower mode ensures that empty trolleys are not left abandoned in the car parking lot.

19.2 Methodology The smart shopping cart operates with four distinct functions incorporated into it. The functions are: • • • •

User tracking system Billing system The anti-theft system Line follower system.

(A) User tracking system The customer, upon entering the shopping complex, will be provided with an object of unique color. This colored object will be attached to the customer’s wheelchair. The smart trolley is fitted with a camera connected to a Raspberry Pi 3 b + module. OpenCV and Python software are used to receive the image of the object tracked by the camera. Upon detection of the object, the Raspberry Pi module sends a signal to the Arduino Nano board to move the motors in the required direction. Communication with the Arduino board takes place through serial communication. (B) The billing system The trolley is equipped with a barcode scanner connected to a NodeMCU module and an LCD display. The billing system operates in two modes based on the position of the switch fitted on the trolley. When the system is switched into the “Normal/Purchase mode,” the users can purchase the required product by scanning the barcode tag of each product using the barcode scanner. The barcode scanner sends the barcode details to the NodeMCU module which is connected to the central billing system (CBS) using Wi-Fi technology. The CBS then outputs the product details, its price to the NodeMCU and it is displayed on the LCD screen. The customers can also view the bill on their mobile phone by simply accessing the designated Web page of the store. The store authorities are provided with access to the cart account by logging in using the designated cart ID, where the entire bill of all purchased goods can be viewed. When the system is switched into “Delete mode,” the customers are provided with the option to delete a product from the bill. The working is similar to that of the “Normal/Purchase mode” except that the details of the product that are deleted by the customer are subtracted from the final bill. Upon payment, the store authorities are provided with the option to clear all corresponding trolley accounts and users can keep the online bill.

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(C) The anti-theft system The trolley is also designed with an anti-theft system in which the store authorities have the added security feature of a weight tracking mechanism. Once the users scan items and place the items into the cart, the database sends the net weight of all the purchased items to the store authority screen. If the customer chooses to delete an item from the bill, the weight of the deleted item is subtracted, and the final bill is also updated simultaneously. Therefore, the added weight of all billed products is now visible on the store authority screen and can be viewed on accessing the trolley account page, using the corresponding cart ID. This enables the store authorities to keep track of the total weight of all billed products in a cart. Upon checkout, the users pass over a weighing scale and the actual weight of all the items within the trolley can be obtained by subtracting the weight of the trolley from the total weight displayed on the scale. Therefore, any mismatch between the scale readings in comparison with the storekeeper’s screen readings indicates that there is an unbilled commodity in the trolley. This feature avoids possible intentional theft or the chances of an unbilled product leaving the store unintentionally. (D) The line follower system Upon exiting the shop, the trolley continues to follow the customers up to their vehicle. Once all the purchased goods are unloaded, the trolley can then be sent back to its original allocated position at the push of a button that activates its line follower system. In the line follower mode, the trolley is designed to follow a predetermined line or a path. An IR sensor equipped with IR transmitter and receiver is placed on the base of the trolley and is programmed to trace a black line with white background or vice versa on the floor. The sensor output is fed to the Arduino board which then gives suitable commands to the motor driver to drive the motors accordingly. During the process of line following, the ultrasonic sensors fitted on the trolley would be able to detect any obstacle in its path.

19.3 Block Schematics of the Designed System The block schematics of the billing system are shown in Fig. 19.1. The billing and anti-theft system consists of the following components: Barcode: A barcode is a label which has a code represented in the form of thin black and white lines across it [11]. Barcode scanner/reader: A barcode scanner is used to scan the barcode tags. It scans the code attached to the product [12].

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Fig. 19.1 Trolley integrated to the central billing system

Barcode decoder: The barcode decoder measures the intensity of the reflected light and converts into an electrical signal which is then finally converted into data. It is a host USB that accepts any USB barcode scanner connected to its USB port and decodes the barcode [12]. NodeMCU: The ESP8266 module has an inbuilt Wi-Fi transceiver which enables it to establish Wi-Fi connectivity in order to make use of the Internet. In addition to this, it is also built with the provision to set up its own personal hotspot network thereby increasing its overall functionality. It uses the same basic Arduino programming language and is run on Arduino platform [13]. Central Billing System (CBS): The central billing system consists of a server where in all the product and cart account databases are stored. It is created using Xampp control panel software as it enables database creation using MySQL and allows easy PHP Web page development options. The block schematics of the user tracking and line follower system are depicted in Fig. 19.2. Arduino Nano:

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Fig. 19.2 User tracking and line follower system

Arduino Nano is a small board working on the AT mega328P. It operates at the 5 V input voltage which is provided by the Raspberry Pi. Raspberry Pi Module: The main microcontroller which is used to handle the tracking system is the Raspberry Pi 3. Camera is connected to Raspberry Pi using USB port. The Raspberry Pi module performs image processing using OpenCV, on the image retrieved by the camera. Camera: A camera connected to the Raspberry Pi is used to detect a particular color. IR sensor array: For line following mode, a “five infrared (IR) sensor array with obstacle detector and pump sensor” is used to determine the predetermined black path in order to send the suitable commands to Arduino Nano board. The IR sensor array is equipped with five IR transmitters and receivers. It is coded using Arduino programming language and is run on Arduino platform. Ultrasonic sensors:

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An ultrasonic sensor is a device that measures the distance between itself and an obstacle. It sends an ultrasonic pulse with a frequency above the hearing of the human ear. This frequency wave travels through the medium and it reflects when an obstacle is detected. Upon detection, the ultrasonic sensor calculates the distance with respect to the time taken to reflect it back.

19.4 Results The shopping cart is successfully fabricated and the results are presented in this section. (A) The billing and anti-theft system Figure 19.3 shows the barcode scanner connected with its circuitry to carry out the billing. The barcode scanner is connected to the NodeMCU module and LCD through a barcode decoder. Figure 19.4 shows a product database template developed using the MySQL on Xampp control panel. This database stores the details of all the products in the shopping complex to which a corresponding barcode is assigned. Figure 19.5 shows a sample of the cart database which shows the details of all billed items within the cart and is always available for the store authorities to view. It is constantly updated as the customers purchase/delete items from the bill.

Fig. 19.3 Implementation of the billing circuitry

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Fig. 19.4 Sample product database

Fig. 19.5 Continuously updated cart database

Figure 19.6 shows the barcode scanner scanning the barcode on the product which then returns the price of the scanned item. Figure 19.7 shows the LCD screen which displays the details of the scanned items. Figure 19.8 shows the central billing screen to be viewed by the store authorities. All the final cart details along with a cash balance calculator are displayed on the central billing screen. The customers can pay the required amount and the authorities can click “check out” tab in order to clear the cart database. Figure 19.9 shows the online bill that is simultaneously updated as the customers add/delete products. It can be accessed on the customers’ mobile phones by using the IP address of the network developed using PHP. (B) User tracking system

Fig. 19.6 Barcode scans the product

19 Smart and Sustainable Shopping Cart for the Physically Challenged Fig. 19.7 Cart details shown on an LCD fitted to the trolley

Fig. 19.8 Central billing screen

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Fig. 19.9 Online bill

Figure 19.10 shows the detection of an object of unique color using the camera connected to the Raspberry Pi. The camera and the motors have been programmed such that the trolley continuously maintains an optimum distance from user. Figure 19.11 is an image of the final prototype developed. Further scope of improvements includes the possible ways of charging the smart shopping cart such as: • Conventional battery charging by using charging wires. • Automatic battery charging by using a solar panel attached to the trolley while it’s parked in an outdoor space.

Fig. 19.10 Blue color detection

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Fig. 19.11 Final prototype

The research reported by various scholars [2–10] has addressed either the automated billing mechanism or the user tracking system. This paper presents a combination of the two mechanisms using suitable user-friendly and cost-effective technologies. This project also makes use of an added line follower and anti-theft mechanism. Therefore, this product serves as a one-stop solution to most of the existing problems faced by customers as it makes shopping a hassle-free experience and this product also benefits shop owners with an increased level of security.

19.5 Conclusion The outcome of this research is to make shopping a comfortable and eco-friendly process. The trolley has been designed to follow a unique color which will always be with the user. An inbuilt conventional barcode reader allows the user to bill/un-bill products based on their requirements. The barcode reader is connected to the local store database through the NodeMCU module and the bill is updated automatically as and when the products are added/deleted. Upon completion of billing, the storekeeper will be able to keep track of all the purchased items of the corresponding trolley as each trolley is provided with a unique cart ID. The billing system is designed in such a way that information, such as the total cost and the net weight of all the billed items in the trolley, along with details of each purchased product, is made available to the storekeeper. The trolley continues to follow the user till their vehicle. Upon unloading all the items, the user can switch the trolley to a line following mode. This

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mode uses an IR sensor array programmed to follow a particular line which brings the trolley back to its allocated position. The trolley is fitted with ultrasonic sensors which helps avoid any obstacle that could possibly come in its way. The paper discusses the successful development and implementation of a prototype working model of the automated shopping trolley which is ready to be deployed at local stores. It has been developed in order to aid people of determination to independently carry out their grocery shopping. In addition to this, it promotes sustainability with a paper and plastic-free shopping experience. Moreover, such an automated shopping cart which does not require customers to wait at the billing counter and makes shopping hassle-free in today’s fast-paced world. More importantly, shopping complexes can now put their knowledgeable workforce to better use rather than assigning them to fetch abandoned trolleys from the parking lots. Acknowledgements The authors would like to thank Dr. Manjula Nair for her valuable contribution in the preparation of this manuscript.

References 1. Retail Leader, FUN FACTOR: Do consumers think shopping is “fun?". (2020) [online] Available at: https://retailleader.com/fun-factor-do-consumers-think-shopping-fun. Accessed 15 Jan 2020 2. B. Hadimani, C. Kamble, Design and development of weight sensors based smart shopping cart and rack system for shopping malls. Mater. Today Proc. (2018) 3. A. Yewathkar, F. Inamdar, R. Singh, A. Bandal, Smart cart with automatic billing, product information, product recommendation using RFID and ZigBee with anti-theft. Proc. Comput. Sci. (2016) 4. S. Mane, M. Hajare, A. Arjunwadkar, Sankpal, Design and implementation of digital cart using barcode and Arduino. Int. J. Innov. Res. Comput. Commun. Eng. 6(3) 5. S. Joshi, V. Patki, P. Dixit, H. Bhaldar, Design and development of human following trolley. Int. J. Innov. Sci. Res. Technol. 4(4) 6. D. Madhekar, P. Bachute, Real time object detection and tracking using Raspberry Pi. Int. J. Eng. Sci. Comput. 7(6) (2017) 7. D. Pandita, A. Chauthe, N. Jadhav, Automatic shopping trolley using sensors. Int. Res. J. Eng. Technol.4(4) (2017) 8. D. Punetha, N. Kumar, V. Mehta, Development and applications of line following robot based health care management system. Int. J. Adv. Res. Comput. Eng. Technol.2(8) 9. A. Vamsi, B. Manasa, K. Krishna, T. Venu, A. Shashank, Design to implementation of a line follower robot using 5 sensors. Int. J. Eng. Inf. Syst.3(1) 10. K. Saw, L. Mon, Design and construction of line following robot using Arduino. Int. J. Trend Sci. Res. Dev. 3(4) 11. Barcodes, What is a barcode? (2020) [online] Available at: https://www.barcodesinc.com/art icles/what-is-a-barcode.htm. Accessed 19 Jan 2020 12. WhatIs.com, What is barcode reader (POS scanner, bar code reader, price scanner)? Definition from WhatIs.com. (2020) [online] Available at: https://whatis.techtarget.com/definition/ barcode-reader-POS-scanner-bar-code-reader-price-scanner. Accessed 19 Jan 2020 13. Last Minute Engineers. Insight Into ESP8266 NodeMCU Features & Using It With Arduino IDE (Easy Steps) (2020) [online] Available at: https://lastminuteengineers.com/esp8266-nod emcu-Arduino-tutorial/. Accessed 19 Jan 2020

Chapter 20

Investigation of Surface Roughness in MQL Aided Turning of Al/Cu/Zr Alloy Using PCD Tool Md. Rezaul Karim, Sabbir Hossain Shawon, Shah Murtoza Morshed, Abir Hasan, and Juairiya Binte Tariq Abstract Surface finish is one of the vital elements in engineering applications as it directly affects tool wear, adhesion, and friction. This paper deals with the effect of PCD insert on surface roughness under minimum quantity lubricant environment in the machining of Al/Cu/Zr alloy. The whole experiment has been designed using response surface methodology for both PCD and carbide insert. PCD tool performs better than traditional carbide tool by ensuring a substantial reduction in surface roughness under specific constraints. Cutting speed and depth of cut are found as the most dominant variable affecting surface roughness from analysis of variance. 2FI model can reasonably predict the response with a mean absolute percentage error of 3.89% for PCD tool. Moreover, the machining parameters were optimized using desirability function analysis, where it is recommended that lower level of depth of cut and higher value of cutting speed is required to induce favorable value of the response variable.

20.1 Introduction In the aerospace and marine industries, Al-based alloys are most commonly used for its high strength-to-weight ratios and strong formability owing to its superplastic nature [1]. Superalloys, modern tools, and new production processes in the form of non-traditional machining are being developed frequently to make machining more feasible for a product having sophisticated design [2, 3]. Keeping surface finish and integrity as a prime concern in the fields of tribology, it has been confirmed by various researchers that, in case of providing maximum durability, fatigue resistance, and functional interchangeability of the machined part at a minimal cost, surface roughness is a dominant parameter [3, 4]. Development of modern tools with edges of super-hard materials such as polycrystalline diamond (PCD) helps to replace the Md. Rezaul Karim (B) · S. H. Shawon · S. Murtoza Morshed · A. Hasan · J. B. Tariq Ahsanullah University of Science and Technology, Tejgaon I/A, 141-142 Love Road, Dhaka 1208, Bangladesh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_20

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traditional hard material machining method [5]. PCD comprises a sintered diamond particle layer which is bonded to a tungsten carbide substrate [6]. It has demonstrated better wear resistance and better surface finish than conventional carbide or alumina tools [7]. Jawaid et al. studied relative efficiency of PCD, cemented carbide cutting tool on multiple MMC-enhanced fabrics. They evaluated tool life with low and high cutting speeds and surface quality and reported PCD’s tool life to be two times better than cemented carbide; an aligned result was also found for surface roughness [8]. Honghua et al. used the PCD tool in their investigation process to find out the impact of tool material on surface finish. They suggested that using various cutting fluids along with polycrystalline diamond (PCD)-coated tools can reduce adhesion [9]. From the exploration of a significant number of researchers, it can be recommended that minimum quantity lubricant (MQL) cutting environment is valiantly productive for machining steels and aluminum alloys to improve tool life and surface finish [9, 10]. Davim et al. experimented with the effect of flow rate in MQL to compare it with conventional flood cooling and concluded that optimum cutting force and surface roughness could be achieved through MQL machining in turning by varying flow rate [10]. Obikawa et al. researched the effectiveness of the MQL technique in turning of X210Cr12 steel, and a comparable framework between ANN and RSM was formulated to decide the best approach [11]. Among various design techniques, response surface methodology (RSM) was employed to evaluate the cause-and-effect relationship between the control variables and recorded response, indicating accurate results to assess surface roughness in Al alloy by researchers [12, 13]. Sahin and Motorcu used RSM to develop a surface roughness model in turning of mild steel with carbide tools [14]. Al-Ahmari developed empirical surface roughness models for turning operations using two significant data mining techniques, i.e., surface response methodology and neural networks [15]. Artificial neural network (ANN) can effectively adapt multivariate inputs and outputs, ensuring acceptable precision [16]. Hasan et al. used ANN to predict the hardness of Al-Cu-based composite materials, with satisfactory results compared to experimental measurements [17]. Karim et al. analyzed the impact of MQL when turning Al alloy composite using Taguchi method that minimizes the effect of machine tool variables for surface roughness deviation [18]. Wang and Lan selected cutting conditions to achieve the lowest surface roughness in precision turning with Taguchi design [19]. Despite multiple efforts in various sector, few attempts have been made on the impact of the addition of Zr in Al alloy using PCD tool. Authors of this paper have put on their effort to understand the effect of polycrystalline diamond (PCD) tool on surface roughness in comparison with traditional-coated carbide tool. Besides, investigation of the inclusion of Zr in the Al-Cu-based alloy by designing the experiment with response surface methodology (RSM) is taken into consideration.

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20.2 Materials and Methods Turning operation was performed under MQL cutting condition by setting the pressure and flow rate at 6 bar and 100 ml/hr while machining Al-based alloy in a center lathe. Alloy was developed by stir-casting method at an RPM of 800. Acquired composition of the alloy alongside EDX analysis is shown in Fig. 20.1a. Workpiece had dimensions of length 300 mm and diameter 65 mm. Cutting tool used for the machining investigation was coated tungsten carbide insert having an ISO designation of SNMG 120,104 and PCD insert of SNMG 120,408. The experimental setup was designed as illustrated in Fig. 20.1b. Experimental runs were carried out by varying the input parameters, i.e., depth of cut, feed rate, and cutting speed. Response surface methodology was used to design the experimental run based on four levels of depth of cut, feed rate, and cutting speed. The recorded response of surface roughness alongside various combinations of input parameters is listed in Table 20.1.

Al: 51.27% Cu: 36.74% O: 9.51% Zr: 2.48%

a

b Fig. 20.1 a EDX analysis of Al/Cu/Zr alloy and b experimental diagram with MQL setup

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Table 20.1 Level and factor setting of input variables and measured responses Depth of cut, t (mm)

Feed rate, So (mm/rev)

Cutting speed, Vc (m/min)

Surface roughness Ra (µm) Coated carbide

PCD insert

0.5

0.12

132

3.11

1.955

0.5

0.14

208

2.656

1.556

0.5

0.16

264

2.329

1.264

0.5

0.18

330

1.942

0.919

1

0.12

132

3.761

2.265

1

0.14

208

3.314

1.866

1

0.16

264

2.987

1.574

1

0.18

330

2.601

1.229

1.5

0.12

132

4.420

2.575

1.5

0.14

208

3.973

2.175

1.5

0.16

264

3.646

1.884

1.5

0.18

330

3.259

1.539

2

0.12

132

5.078

2.884

2

0.14

208

4.631

2.485

2

0.16

264

4.304

2.194

2

0.18

330

3.918

1.848

20.3 Results and Discussion 20.3.1 Analysis of Variance for the Response Variable While investigating the significance of the design parameter, analysis of variance (ANOVA) was conducted for both the insert keeping significance level at 0.5 and by setting a confidence level of 95%. Sum of squares (SS), degree of freedom (df), mean square (MS), and F-value for the designed input and response variable are shown in Table 20.2. From the analysis, it can be seen that in the case of coated carbide insert t, Vc and Vc × So are significant terms owing to a p-value of less than 0.05. Apart from the partial significance of So , a similar result was observed in performing the machining operation with PCD insert.

20.3.2 2FI Model for Surface Roughness Correlation among the input variables and measured variable was built with the help of two-factor interaction regression, commonly known as 2FI model, which is shown by Eqs. 20.1 and 20.2. The correlation coefficient value is in the range of

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Table 20.2 Analysis of variance for surface roughness using different cutting insert For coated carbide insert Source

SS

df

MS

F-value

P-value

Model

11.59

6

1.93

2.387E + 05

< 0.0001

significant

t

0.0003

1

0.0003

37.96

0.0002

significant

So

2.150E-06

1

2.150E-06

0.2656

0.6187

Vc

0.0029

1

0.0029

354.76

< 0.0001

So × t

0.0000

1

0.0000

2.65

0.1381

Vc × t

0.0000

1

0.0000

2.86

0.1251

7.94

0.0201

significant

Vc × So

0.0001

1

0.0001

Residual

0.0001

9

8.095E-06

Cor. Total

11.59

15

Source

SS

df

MS

0F-value

P-value

Model

4.25

6

0.7081

1.919E + 05

< 0.0001

significant

t

1.41

1

1.41

3.808E + 05

< 0.0001

significant

So

0.0001

1

0.0001

22.24

0.0011

significant

Vc

0.0036

1

0.0036

976.76

< 0.0001

significant

So × t

8.663E-06

1

8.663E-06

2.35

0.1599

Vc × t

0.0000

1

0.0000

2.79

0.1294

Vc × So

0.0001

1

0.0001

23.71

0.0009

Residual

0.0000

9

3.690E-06

Cor. Total

4.25

15

significant

For PCD Insert

significant

98.76–99.26% for both the inserts. After analyzing the surface plots from Fig. 20.2, it can be concluded that the interaction effect of cutting speed alongside interaction between cutting speed and feed rate is pivotal to optimize surface roughness. Using these equations, the variation between the experimental and projected value of surface roughness was calculated and portrayed in Fig. 20.3. Mean absolute percentage error (MAPE) is remaining static at 5.70% and 3.89%, respectively, for coated carbide

Fig. 20.2 Variation of surface roughness for PCD insert with a different combination of input variables

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Fig. 20.3 Comparison of experimental and predicted response for carbide and PCD insert

insert tool and PCD tool, thus proving the noteworthy prediction capability of RSMbased developed equation. Ra (coated carbide insert) = 1.7815 + 2.5344 × t + 12.9286 × So − 0.008195 × Vc − 10.7429 × t × So + 0.000514286 × t × Vc + 0.0126263 × So × Vc

(20.1)

Ra(PCD insert) = 2.49823 + 0.692086 × t − 1.46964 × So − 0.00532125 × Vc − 1.02429 × t × So + 0.000342857 × t × Vc + 0.0022096 × So × Vc

(20.2)

20.3.3 Optimization Using Desirability Function Analysis Desirability function analysis shifts the focus to select the suitable condition of the input variables to generate an optimum output value of surface roughness. To provide an optimal solution, necessary optimization constraints for coated carbide insert and PCD insert are displayed in Table 20.3 with an overview of the optimized solution. Illustrated desirability contour plot from Fig. 20.4 depicts the ideal values of cutting speed, feed rate, and depth of cut. With the highest desirability value of 0.9818 for coated carbide insert and a value of 0.999 for PCD insert, as shown in Fig. 20.5, it indicates the optimum cutting parameters to induce a minimum quantity of surface roughness.

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Table 20.3 Constraints and overview of the solutions found from optimization Factor Goal

Solutions for coated carbide insert

t

Minimize

No

t

So

Minimize

1

0.574 0.120 329.999 1.940 0.982

Vc

Maximize

Solutions for PCD insert

Ra

is in range No 1

t

So

So

Vc

Vc

Ra

Ra

Desirability Selected

Desirability

0.500 0.120 330.000 0.995 0.999

Selected

*Importance of the factor t:4; So : 2; Vc : 5; Ra : 3

Fig. 20.4 Desirability contour plot for different combinations of input variable PCD insert

Fig. 20.5 Desirability bar graph for a coated carbide tool and b PCD tool

20.4 Conclusions Following conclusion can be listed based on a rigorous experimental investigation: • Incorporating PCD tool while conducting the machining operation is highly significant in comparison with coated carbide tool. It is visible that the reduction of surface roughness is substantial for the same combinations of input variables under MQL cutting condition.

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• Cutting speed and depth of cut are the most prominent factor in terms of affecting the response variable. Despite having an insignificant individual contribution, the interaction of feed rate with cutting speed is also found to have an impact on the outcome while machining the alloy. • Two-factor interaction (2FI)-based RSM generated equation is highly effective to predict surface roughness of the developed alloy. In the case of coated carbide insert, mean absolute percentage error (MAPE) is found to be at 5.70%, which itself is in the excellent range. However, MAPE value is laying at 3.89% while using the PCD tool, proving the efficacy of the model. • For attaining optimum surface roughness in the alloy, based on a desirability value of 0.982, it is recommended that machining of Al-based alloy by carbide insert should be performed at a relatively lower value of depth of cut and feed rate and at a higher value of cutting speed. • While comparing the performance of PCD tool with conventional coated insert, with the highest perceived desirability value of 0.999, PCD is proven more conducive to induce a minimum amount of surface roughness under similar conditions. Optimum setting of the input parameters at 0.5 mm, 0.12 mm/rev, and 330 m/min can generate a minimum surface roughness value of 0.995 µm.

References 1. A. Alhamidi, Z. Horita, Application of high-pressure torsion to Al-6 %Cu-0.4 %Zr alloy for ultrafine-grain refinement and superplasticity. J. Mater. Sci. 49, 6689–6695 (2014) 2. A. R. Motorcu, A. Ku¸s, I. Durgun, The evaluation of the effects of control factors on surface roughness in the drilling of Waspaloysuperalloy. Meas. J. Int. Meas. Confederation 58, 390–408 (2014) 3. S.M. Darwish, Impact of the tool material and the cutting parameters on surface roughness of 718 nickelsuperalloy. J. Mater. Process. Technol. 97, 10–18 (2000) 4. E.O. Ezugwu, S.H. Tang, Surface abuse when machining cast iron (G-17) and nickel-base superalloy (Inconel 718) with ceramic tools. J. Mater. Process. Technol. 55, 63–69 (1995) 5. R. Kowalczyk, A. Matras, W. Z˛ebala, Analysis of the surface roughness after the sintered carbides turning with PCD tools. Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments. 9290, 1–12 (2014) 6. M.W. Cook, Machining MMC engineering components with polycrystalline diamond and diamond grinding. Mater. Sci. Technol. 14, 892–895 (1998) 7. X. Ding, W.Y.H. Liew, X.D. Liu, Evaluation of machining performance of MMC with PCBN and PCD tools. Wear 259, 1225–1234 (2005) 8. A. Jawaid, A. Abdullah, Machining of Particulate 2618 Aluminium Metal Matrix Composite Using Cemented Carbide Cutting Tools. Adv. Intell. Prod. 18, 419–425 (1994) 9. H. Su, P. Liu, Y. Fu, J. Xu, Tool life and surface integrity in high-speed milling of titanium alloy TA15 with PCD/PCBN tools. Chin. J. Aeronaut. 25, 784–790 (2012) 10. J.P. Davim, P.S. Sreejith, J. Silva, Turning of brasses using minimum quantity of lubricant (MQL) and flooded lubricant conditions. Mater. Manuf. Processes 22, 45–50 (2007) 11. Y. Kamata, T. Obikawa, High speed MQL finish-turning of Inconel 718 with different coated tools. J. Mater. Process. Technol. 192–193, 281–286 (2007) 12. J.F. Kelly, M.G. Cotterell, Minimal lubrication machining of aluminium alloys. J. Mater. Process. Technol. 120, 327–334 (2002)

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13. M. Nouioua et al., Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN). Int. J. Adv. Manuf. Technol. 93, 2485–2504 (2017) 14. Y. Sahin, A.R. Motorcu, Surface roughness model for machining mild steel with coated carbide tool. Mater. Des. 26, 321–326 (2005) 15. A.M.A. Al-Ahmari, Predictive machinability models for a selected hard material in turning operations. J. Mater. Process. Technol. 190, 305–311 (2007) 16. U. Çayda¸s, S. Ekici, Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. J. Intell. Manuf. 23, 639–650 (2012) 17. A.M. Hassan, A. Alrashdan, M.T. Hayajneh, A.T. Mayyas, Prediction of density, porosity and hardness in aluminium-copper-based composite materials using artificial neural network. J. Mater. Process. Technol. 209, 894–899 (2009) 18. M.R. Karim, R.A. Siddique, F. Dilwar, Study of surface roughness and MRR in turning of SiC reinforced Al Alloy Composite using Taguchi design method, ANN and PCA approach under MQL cutting condition. Adv. Mater. Res. 1158, 115–131 (2020) 19. M.Y. Wang, T.S. Lan, Parametric optimization on multi-objective precision turning using grey relational analysis. Inf. Technol. J.. 7, 1072–1076 (2008)

Chapter 21

Comparative Analysis on the Effect of Minimum Quantity Lubrication and Chilled Air Cooling During Turning Hardened Stainless Steel Israt Sharmin, Mahjabin Moon, and Faysal Hasan Asik Abstract In order to reduce the temperature produced during machining along with better surface finish and improved tool life, flood coolant has been used for ages. However, because of its adverse effects on human health, environment, and total machining cost, finding alternative ways has become a necessity which ultimately leads toward minimum quantity lubrication (MQL), near dry and dry machining. In this study, the effects of chilled air cooling and MQL on machining performance of hardened 202 stainless steel in turning were observed in respect of surface roughness and cutting temperature. Also, machining under dry cutting condition was performed in order to carry out the relative comparison among these three. The experiment was designed using Design Expert-12, and two computational models were formulated using response surface methodology (RSM) and genetic algorithm (GA). Furthermore, separate optimization models were developed using genetic algorithm (GA) to determine the critical input parameters related to minimum surface roughness and cutting temperature.

21.1 Introduction Machining involves continuous friction of tool and workpiece material where the mechanical energy is converted to heat energy and increases the cutting zone temperature which in turns affects the surface finish of the final product as well as influences tool wear. Surface finish is often considered as one of the most important quality indicators of a finished product. So to ensure a better surface finish, reduction of this heat is required. Though the conventional flood cutting condition minimizes the heat, it has been causing problems that cannot be overlooked. It is estimated that around 85% of cutting fluids used worldwide are mainly mineral-based which require special treatment by Environment Protection Agency (EPA) before disposal I. Sharmin (B) · M. Moon · F. H. Asik Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_21

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[1]. From purchasing to disposal, the cost of mineral oil-based cutting fluid is approximately 16% of total machining cost [1]. Moreover, the use of these cutting fluids causes different health problems of the workers (respiratory diseases, lung cancer, dermatological, and genetic diseases) [2]. In order to counter these shortcomings, Dhar et al. [3] applied MQL in turning AISI-4043 steel using carbide inserts and concluded that this technique provides improved surface finish in comparison with dry and flood coolant as the tool wear is reduced to a large extent. Verma et al. [4] investigated the effects of MQL in turning EN31 steel with respect to tool wear and surface integrity using a coated tungsten carbide insert and obtained better surface finish compared to that of machining without MQL cutting conditions. Satisfactory results have been found by applying MQL in different machining processes (milling, drilling, and grinding) throughout the years [5–7]. Another alternative of flood coolant is chilled air coolant. Air is an unlimited natural resource and can be used as coolant without thinking much about the cost and it also eliminates the risk of environmental pollution and worker’s health problems [8]. Chilled air can be supplied at the cutting zone using a vortex tube, thermoelectric air cooler, or by cryogenic compressed air cooler [9]. Liu et al. [10] implemented vortex tube cooling in machining hypereutectic Al-Si alloys and concluded that temperature at the chip–tool interface could be reduced up to 7% but the increased feed and cutting speed will minimize its effectiveness on temperature reduction. Ali et al. [11] determined specific cutting energy while machining Inconel 718 under dry, MQL and vortex tube chilled air cutting conditions and observed that chilled air could reduce the cutting temperature satisfactorily. Various works have been done on different materials using MQL, chilled air coolant, and their relative comparison with conventional flood coolant. Still, as per authors’ knowledge, there is a scarce work on the comparison of MQL and chilled air coolant in turning of hardened 202 stainless steel despite having a wide range of application areas. So, in this study, the effects of MQL and chilled coolant in turning hardened 202 stainless steel were investigated with respect to surface roughness and cutting temperature. Mathematical models were also formulated using RSM and GA.

21.2 Experimental Analysis To conduct the investigation, hard turning on 202 stainless steel workpiece was carried out at different cutting velocities, feed rates, and depth of cuts under three cutting environments like dry, minimum quantity lubricantion (MQL) using vegetable oil and chilled air coolant by vortex tube. The combinations of the input parameters were developed using central composite design. After each trial with a set of inputs under one specific machining environment, cutting temperature and surface roughness were recorded. The work–tool interface temperature was captured using standard k-type thermocouple system, and roughness was obtained with Talysurf roughness checker (Surtronic 3 + , Rank Hobson, UK) using a sampling length of

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Table 21.1 Experimental conditions Machine tool

Lathe machine

Work materials

SS 202 Stainless steel (Diameter: 40 mm, Length: 80 mm)

Hardness

46 HRC

Cutting tool

Coated carbide insert (−6°, −6°, 6°, 6°, 17°, 75°, 0.8 mm)

Process parameters

Cutting velocity, Vc (m/min)

Feed rate, So (mm/rev)

Depth of cut, t (mm)

80,100,120,140

0.12, 0.14,0.16

0.5, 0.75,1

Environment

Dry, MQL, Chilled air cooling using vortex tube

4.00 mm. The detailed information of the experimental procedure has been shown in Table 21.1.

21.2.1 MQL System To deliver a minimum amount of palm oil in the cutting zone, MQL system was used and developed following the basic working principle [3] as shown in Fig. 21.1a. The design, nozzle distance from the workpiece, and air pressure were selected by using design of experiments (DOE) method. The outlet diameter of the nozzle was 2 mm, standoff distance of nozzle was 30 mm, and air pressure was kept constant at 12 bar. Experimental view of the nozzle has been shown in Fig. 21.1b.

Fig. 21.1 a Basic principle of MQL system and b experimental view of the nozzle arrangement

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21.2.2 Chilled Air Coolant System The vortex tube, also recognized as the Ranque–Hilsch vortex tube, was used to supply chilled air to the tool–work interface. The compressed air at 12 bar was injected into the vortex tube tangentially and the air got divided into two streams as hot and cold air following the basic mechanism of vortex tube [12]. The hot air was exhausted through the end of the vortex tube, and chilled air was directed to the tool–work interface using a nozzle with an outlet diameter of 2 mm, as shown in Fig. 21.2a,b.

21.3 Optimization Model To minimize cutting temperature and surface roughness, machining parameters are needed to be optimized. In this work, a multi-objective optimization model has been developed using genetic algorithm as it has been proved as a successful tool for optimizing machining parameters [13]. Basic steps of GA methodology was followed and optimization was executed using GA toolbox of MATLAB 2018a. Response Surface Methodology (RSM) was used to generate the objective functions in Design Expert 12 software. Table 21.2 shows more details about the developed GA model. Surface roughness (CA) = −0.427 + 0.00007*Vc + 8.82*So − 0.255*t + 1.33E − 06Vc2 + 1.07So2 + 0.14t2 − 0.01Vc*So − 0.0003Vc *t + 2.22So *t

(21.1)

Surface roughness (MQL) = −0.154 − 0.0019*Vc + 3.487*So + 0.217*t + 4.8E − 06Vc2 + 21.18So2 − 0.068t2

Fig. 21.2 a Basic principle and b experimental setup of vortex tube-based chilled air

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Table 21.2 Genetic algorithm parameters Constraints

Cutting speed, Vc

Feed rate, So

Depth of cut, t

Range considered

80–140 m/min

0.12–0.16 mm/rev

0.5–1 mm

Objective functions

Equations 21.1, 21.2, 21.3, and 21.4

Population size

50

Crossover rate

0.8

Mutation rate

0.1

Number of generations

300

− 0.0062Vc*So − 0.0039Vc *t + 0.98So *t

(21.2)

Cutting temperature (CA) = 314.21 + 0.179*Vc + 1546.91*So + 5.77*t + 0.00079Vc2 − 2054.32So2 + 16.85t2 − 0.377Vc*So + 0.96Vc *t − 484.019So *t

(21.3)

Cutting temperature (MQL) = 108.05 + 2.30*Vc + 2639.01*So + 124.22*t − 0.0025Vc2 − 3790.59So2 − 44.32t2 − 8.12Vc*So + 0.463Vc *t − 261.64So *t (21.4)

21.4 Results and Discussion 21.4.1 Cutting Temperature In this study, the work–tool interface temperature was investigated to evaluate the three cutting conditions (Dry, MQL, and chilled air cooling). Also, the relation of cutting speed, feed rate, and depth of cut with cutting temperature were investigated. From Fig. 21.3a–c, it can be seen that with increasing cutting parameters (cutting speed, feed rate, and depth of cut) temperature increases in all cutting conditions. Same results were observed in the previous studies [14]. Higher cutting temperature causes many problems like deformation of work material, excessive tool wear, and lower dimensional accuracy. Results show that Ranque–Hilsch vortex tube-based chilled air has significantly dropped the tool–work temperature comparative to dry and MQL system. This is due to the use of high pressurized cold air over the cutting zone, which penetrates the work–tool interface and helps to reduce the produced heat [11].

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Fig. 21.3 Cutting temperature under three different conditions a at different cutting speeds,b at different feed rates, and c at different depth of cuts

21.4.2 Surface Roughness Surface roughness is another decisive factor in this work to find out the best cutting condition for the machining process. Figure 21.4a–c reveals the same output as others that with increasing cutting speed, surface roughness increases but decreases with increasing feed rate and depth of cut [15]. On the other hand, roughness drops by highest 9% in chilled air condition than dry condition. This is because compressed air removes the chip very quickly from the cutting zone and also eliminates the formation of build-up edge. But unlike cutting temperature, lowest roughness is found in MQL-based machining. The reduction percentage of surface roughness in MQL is 11% compared to the average roughness obtained in dry condition. The oil of MQL system lubricates the contact zone of tool-workpiece, and this reduces the friction, which helps to minimize surface roughness [11]. Using a combined network of MQL–Ranque–Hilsch vortex tube-based chilled air can be helpful to obtain both minimum temperature and surface roughness. But in this study, the combined system was avoided as the objective was to find the opportunities for avoiding the MQL system because of its disadvantages [16] (Fig. 21.4).

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Fig. 21.4 Surface roughness under three different conditions a at different cutting speeds, b at different feed rates, and c at different depth of cuts

21.4.3 Results of Genetic Algorithm Here, genetic algorithm is effectively applied to optimize the output parameters, i.e., surface roughness and cutting temperature in machining 202 hardened stainless steel. Maximum 1000 generations have been used with different crossover and mutation rates (0.1–1.0). The convergences have occurred at 300 generations with a population size of 50, and the best fitness of values is found at a crossover rate of 0.8 and a mutation rate of 0.1. The optimum parameters and best validation results for both MQL and chilled air (CA) cooling conditions are concluded in Table 21.3.

Cutting speed, Vc (m/min)

89.76

84.23

Cases

CA

MQL

0.122

0.128

Feed rate, So (mm/rev)

0.519

0.565

Depth of cut, t (mm)

0.59

0.665 0.624

0.698 4.48

4.92

523.07

518.68

538.99

532.12

Measured values

Cutting temperature, 8 (°c) GA results

Error(%)

GA results

Measured values

Surface roughness, Ra (µm)

Table 21.3 Optimum process parameters for surface roughness (Ra) and cutting temperature (8) and their evaluation

2.77

2.54

Error(%)

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21.5 Conclusions In this study, turning of 202 hardened stainless steel has been done under dry condition, chilled air cooling, and MQL system to compare the effect of cooling conditions on cutting temperature and surface roughness. From all the experimental and mathematical analysis, following conclusions can be made (1) As chilled air reduced the cutting temperature by maximum 14% from the temperature in dry cooling whereas MQL by highest 10%, using Ranque–Hilsch vortex tube-based chilled air instead of dry machining and MQL system can be an excellent solution to control the high cutting temperature in turning of 202 hardened stainless steel. (2) Chilled air cooling can be easily applied where the acceptance rate of surface roughness is higher. If the surface roughness is the dominant output parameter in the machining process, the MQL system should be used. (3) The validation results proved that the developed optimization model can be employed in manufacturing industries to obtain optimal cutting conditions before actual machining, which will be helpful to reduce the production time and cost.

References 1. S. Debnath, M.M. Reddy, Q.S. Yi, Environmental friendly cutting fluids and cooling techniques in machining: a review. J. Clean. Prod. 83, 33–47 (2014) 2. H. Sato, Turning using extremely small amount of cutting fluid. Trans. JSME 62(604), 272–277 (1996) 3. N.R. Dhar, M. Kamruzzaman, M. Ahmed, Effect of minimum quantity lubrication (MQL) on tool wear and surface roughness in turning AISI-4340 steel. J. Mater. Process. Technol. 172(2), 299–304 (2006) 4. J. K. Verma, G. Bartarya, J. Bhaskar, Effect of minimum quantity lubrication on tool wear and surface integrity during hard turning of EN31 Steel, in Advances in Forming, Machining and Automation (pp. 205–218). Springer, Singapore (2019) 5. M. Rahman, A.S. Kumar, M.U. Salam, Experimental evaluation on the effect of minimal quantities of lubricant in milling. Int. J. Mach. Tools Manuf. 42(5), 539–547 (2002) 6. M.H. Sadeghi, M.J. Haddad, T. Tawakoli, M. Emami, Minimal quantity lubrication-MQL in grinding of Ti–6Al–4V titanium alloy. Int. J. Adv. Manuf. Technol. 44(5–6), 487–500 (2009) 7. E.A. Rahim, H. Sasahara, A study of the effect of palm oil as MQL lubricant on high speed drilling of titanium alloys. Tribol. Int. 44(3), 309–317 (2011) 8. T. Kostadin, G. Cukor, S. Jakovljevic, Analysis of corrosion resistance when turning martensitic stainless steel X20Cr13 under chilled air-cooling. Adv. Prod. Eng. Manage. 12(2), 105 (2017) 9. Y.R. Ginting, B. Boswell, W.K. Biswas, M.N. Islam, Environmental generation of cold air for machining. Procedia CIRP 40, 648–652 (2016) 10. J. Liu, Y.K. Chou, On temperatures and tool wear in machining hypereutectic Al–Si alloys with vortex-tube cooling. Int. J. Mach. Tools Manuf. 47(3–4), 635–645 (2007) 11. M.A.M. Ali, A.I. Azmi, A.N.M. Khalil, Specific cutting energy of Inconel 718 under dry, chilled-air and minimal quantity nanolubricants. Procedia CIRP 77, 429–432 (2018)

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12. Y. Xue, M. Arjomandi, R. Kelso, The working principle of a vortex tube. Int. J. Refrig. 36(6), 1730–1740 (2013) 13. H. Ganesan, G. Mohankumar, K. Ganesan, K. Ramesh Kumar, Optimization of machining parameters in turning process using genetic algorithm and particle swarm optimization with experimental verification. Int. J. Eng. Sci. Technol. 3(2), 1091–1102 (2011) 14. S. Masoudi, M. Sima, M.A.J.I.D. Tolouei-Rad, Comparative study of ANN and ANFIS models for predicting temperature in machining. J. Eng. Sci. Technol. 13(1), 211–225 (2018) 15. I. Sharmin, M.A. Gafur, N.R. Dhar, Preparation and evaluation of a stable CNT-water based nano cutting fluid for machining hard-to-cut material. SN Appl. Sci. 2(4), 1–18 (2020) 16. N. Boubekri, V. Shaikh, Minimum quantity lubrication (MQL) in machining: Benefits and drawbacks. J. Indus. Intell. Inf. 3(3) (2014)

Chapter 22

Deposition of Single-Layer Oxide Films with Ion Beam Sputtering Technique on Super-Polished Ceramic Glass Substrates Laxminarayana Gangalakurti, K. Venugopal Reddy, Chhabra Inder Mohan, Atchaih Naidu Varadharajula, and Radhika Kanakam Abstract A single RF ion source with plasma bridge neutralizer being used for sputtering of water cooled target materials with partial pressure of oxygen into the chamber. In the present work, single-layer Ta2 O5 and SiO2 films are grown on super polished ceramic glass substrates separately. The films are characterized with spectroscopic ellipsometer, non-contact optical profilometer, AFM, and X-ray photoelectron spectroscopy. SiO2 films are absolutely free of absorption with zero extinction coefficients. Ta2 O5 films exhibited absorption coefficient and non-stoichiometry. The process suitability is verified for the applications of laser mirrors for electro-optical sensors. The work concludes with the merits and behavior of tantala and silica films under identical single ion beam sputtering deposition process and a way forward for non-absorbing multi-layer dielectric laser mirrors.

22.1 Introduction Ion beam sputtering (IBS) deposition is an advanced deposition technique for realizing very high-quality thin films. High reflectivity and low absorption mirrors are L. Gangalakurti (B) · C. I. Mohan · A. N. Varadharajula Research Centre Imarat, DRDO, Hyderabad 500069, India e-mail: [email protected] C. I. Mohan e-mail: [email protected] A. N. Varadharajula e-mail: [email protected] K. Venugopal Reddy · R. Kanakam Department of Physics, National Institute of Technology, Warangal 506004, India e-mail: [email protected] R. Kanakam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_22

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used in high power laser, ring laser gyro and astronomical telescopes [1]. Bombardment with ions and electrons to clean the surface has been commonly used for many years. The use of ion beam rather than glow discharge will provide more control of system parameters such as ion species, flux, and energy [2]. Simple thermal evaporation of materials leads to columnar growth on substrates resulting low packing density and change in refractive index [3]. For development of optical filters, dielectric laser mirrors, and beam splitters, IBS technique is more appropriate technique owing to the requirement of achieving precise refractive index, stoichiometry, and thickness of the films [4, 5]. IBS films are having very good packing density and adhesion to the substrates. Properties of the deposited film will depend on the target material properties, ion beam parameters (flux, energy, etc.) as well as ion source targetsubstrate configuration. Besides the advantages of IBS, it has drawbacks of very low deposition rate and limited to metal oxides [6]. The low deposition rate adds to high production cost in realizing multi-layer dielectric mirrors, which may consume typically 20 hours of continuous operation. It is also a well-known fact that ion beam sputtering deposited films have very smooth surface finish and very low porosity [7– 9]. High reflectivity mirrors are coated with alternative high and low index materials of quarter-wave thickness. Silicon dioxide (SiO2 ) is widely used as low index material in multi-layer mirrors. High index dielectric materials frequently used for high reflectance mirrors in the visible region are tantalum pentoxide (Ta2 O5 ) and titanium dioxide (TiO2 ) [6]. Ta2 O5 and TiO2 are considered high refractive index materials for their amorphous and stable films in deposition process. But Ta2 O5 /SiO2 combination films show better surface smoothness than the films with TiO2 /SiO2 materials, due to poor fluidity of TiO2 during deposition on substrates [1]. Mirrors made of Ta2 O5 and SiO2 are being used in high finesse optical cavities and high sensitive gravitational wave detectors [10, 11]. In view of the advantages of Ta2 O5 and SiO2 materials, present work considered them for process investigation. Films with zero extinction coefficients are preferred to have non-absorbing mirrors on interaction with radiation. Process dependence for both Ta2 O5 and SiO2 films is independently studied here.

22.2 Experimental Work Coating chamber is equipped with two numbers of 15 cm RF ion sources. Generally, primary source is meant for sputtering the target material. Second source is to preclean the surfaces of the substrates, prior to deposition and also assist deposition during Coating. Ceramic glass substrates are thoroughly cleaned in ultrasonic bath of acetone followed by DI water. The substrates are loaded in holders of the rotation fixture. The system has planetary rotation for the substrates to improve the uniformity of coating. The chamber is evacuated with cryopump and rotary combination to attain an ultimate vacuum of 5 × 10–6 mbar. The sputtering beam is made to incident at 45° angle to the target material for maximum sputter plume toward the substrates. Figure 22.1 describes the arrangement of ion sources, sputter targets, and substrate

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Fig. 22.1 Schematic of ion beam sputtering system

rotation system. Substrate cleaning prior to deposition is extremely important for proper film adhesion. It also has influence on subsequent film growth [2]. There is a chance of deterioration of surface roughness for certain materials during ion beam interaction. Copper substrates have shown an increase in roughness when subjected to ion beam cleaning, i.e., from 25 to 50 Å [4]. Surface roughness of the substrate can be minimized by using a single crystal substrate such as silicon or super polished glass materials such as fused silica. Surface figure errors can be reduced to very low levels by means of standard polishing or ion beam figuring [5]. The amorphous or polycrystalline or single crystalline substrates such as fused silica, Zerodur, and BK-7 glass substrates, exhibited no noticeable increase in roughness of the substrates started with surface roughness of 7 Å (rms), when they were ion beam milled for several microns [6]. The secondary ion source is set with Ar gas in flow of 10 sccm for generating discharge in the ion source chamber. The flow of gas is well controlled with mass flow controller. Plasma bridge neutralizer is being used for supplying electrons to the positive Ar ion beam to avoid charge repulsions from target or substrate assembly. The neutralizer is fed with Ar gas of 5 sccm with a separate mass flow controller. Secondary ion source is kept with beam voltage of 400 V, acceleration voltage of 400 V, and beam current of 450 mA. The neutralizer draws a current of 300 mA. Ion beam cleaning of coating samples effectively desorbs water vapor, hydrocarbons, and other surface adsorbates. In special cases, the chemisorbed organic species on the surfaces can be easily removed with oxygen gas as it forms volatile compounds. However, in the present case, the substrates are cleaned with Ar ion beam only. The precleaning is done for about 10 min. For sputtering of the target material, a 15 cm RF ion source is being used. Ta2 O5 targets, of purity 99.99%, are water-cooled otherwise, the excessive heat generated during continuous ion bombardment may lead to breakage of target and detachment from copper backing plate. The sputter yield for many materials, generally, is in the range of 0.1 to 3 atoms/ion for inert gas ions of 0 to 2 keV [8]. Sputter yield increases

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with the mass of the gas ions. For Ar gas ions of 500 eV onto Ta target raises to a sputter yield of 0.57 atoms/ion [9]. The sputter yield also a function of the incident angle generally increases from 45° to 60°. At large angles of incidence, sputter yield decreases [12]. The target size is kept sufficiently larger than ion beam, so that ion beam is confined with in the target material. The primary sputtering ion source is kept at 800 V with a beam current of 100 mA. The beam acceleration voltage is kept at 400 V. The ion source is fed with Ar gas flow of 10 sccm. Oxygen gas is also fed into the chamber at 3 sccm to maintain the oxygen back ground in the chamber through the secondary source. Deposition is carried out with sputter ion source only, while the second ion source is deliberately switched off during deposition. Sputtering of Ta2 O5 target with primary source is shown in Fig. 22.2. Bare substrate and Ta2 O5 film coated samples are shown in Fig. 22.3. For the Ta2 O5 target, deposition rate achieved is 0.2 Å/s. The thickness of the film is controlled with a quartz crystal monitor (QCM) and process ended based on the set film thickness With ion beam

Fig. 22.2 Ion beam sputtering Ta2 O5 target material

Fig. 22.3 Bare substrate and Ta2 O5 thin film

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sputtering coating technique, the deposition rates are quite low when compared to ion-assisted electron beam deposition (IAEBD) method. The typical deposition rates with IAEBD technique for Ta2 O5 and SiO2 films are 2 Å/s and 4.5 Å/s, respectively, reported by Seouk-Hoon Woo et al. [3]. Similar process parameters were repeated on another set of ceramic glass substrates for deposition of SiO2 film. The film growth took less time with the increased deposition rate of 0.4 Å/s for SiO2 film. Ion-assisted deposition is generally employed for achieving improved coating uniformity at relatively reduced temperatures. The intention of the present work is to evaluate the growth properties without an assist source during deposition for Ta2 O5 and SiO2 thin films under identical process conditions.

22.3 Characterization of Thin Films 22.3.1 Optical Constants Ta2 O5 and SiO2 single layer films are subjected to testing under ellipsometer [13], M2000, Woollam make for evaluation of optical constants. The absorption coefficient α, is related to the extinction coefficient k, by the following formula [14]: α=

4π k λ

(22.1)

The best-fit data in the range 500 nm to 800 nm with n and k values of Ta2 O5 film and SiO2 film is shown in Figs. 22.4 and 22.5, respectively.

Fig. 22.4 Optical constants of Ta2 O5 film

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Fig. 22.5 Optical constants of SiO2 film

22.3.2 Film Thickness and Surface Morphology The films are inspected with a non-contact phase shift [15] interferometric optical profilometer, Nanomap 1000, to measure surface roughness and film thickness at the coating edge. Here, an area of 1 mm X 1 mm of the films is inspected at the film edge to capture thickness. Figure 22.6 shows the surface inspection of SiO2 film on ceramic glass. Surface roughness of the films is also measured with the optical profiler. SiO2 and Ta2 O5 film surfaces are characterized with Icon XR model atomic force microscope (AFM) [16, 17], and profiles of the films are shown in Figs. 22.7 and 22.8, respectively. The SiO2 film is smoother than the Ta2 O5 film on the identical super polished ceramic glass substrates. Fig. 22.6 3D view of coating edge of SiO2 film

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Fig. 22.7 AFM profile of SiO2 film

Fig. 22.8 AFM profile of Ta2 O5 film

22.3.3 Stoichiometry of the Tantala Films X-ray photoelectron spectroscopy (XPS) is a surface-sensitive quantitative spectroscopic technique that measures the elemental composition, empirical formula, chemical state, and electronic state of the elements that exist within a material. AXIS Ultra 165 model XPS equipment is utilized for film characterization. Bonding states of tantala film deposited can be deduced from the binding energies of the Ta 4d and Ta 4f levels using XPS [18]. Figure 22.9 shows 4f5/2 and 4f7/2 doublet with binding energies of Ta of the film. O-1 s peak with binding energy of tantala film is shown in Fig. 22.10.

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Fig. 22.9 4f doublet peaks of Ta2 O5 film

Fig. 22.10 O-1 s peak of Ta2 O5 film

22.4 Results and Discussion Bright et al. reported amorphous Ta2 O5 [19] films by magnetron sputtering on Si substrates demonstrated film refractive index of 2.1203, extinction coefficient of 0.00098965, and absorption coefficient of 196.52 cm−1 . Tanatalum pentoxide exhibits extinction coefficients below 10–5 in the wavelength range from 500 to 1000 nm in the work reported by Stuart Reid et al. [6]. From the ellipsometry data of the present work, Ta2 O5 films showed a considerable value of extinction coefficient in the visible wavelength region. For a wavelength of interest at 632.8 nm, the

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refractive index n of Ta2 O5 is 2.0989 and extinction coefficient k is 0.00337. Ta2 O5 films are less optically denser and exhibiting absorption coefficient of 669.22 cm−1 . The films are not purely dielectric, indicating probable presence of impurities and distortion in stoichiometry, which was verified with XPS technique. In the present work, SiO2 film exhibited a refractive index of 1.48249 at 632.8 nm. Moreover, SiO2 film showed a flat zero extinction coefficients and zero absorption coefficients from 300 to 1800 nm. The refractive index exhibited by SiO2 films deposited by ion beam sputtering is 1.45 in the work carried out by H. Demiryont et al. [14]. The results in the present study show that the SiO2 films are more optically denser than bulk fused silica and purely dielectric in nature. Surface roughness of uncoated glass substrate and Ta2 O5 film is 5.1 Å (rms) and 4.2 Å (rms), respectively. Thickness of Ta2 O5 film is 220 nm. The measured thickness is a bit higher than the expected film thickness of 200 ± 10 nm from the read out of QCM. Surface roughness of as deposited SiO2 film is 3.8 Å (rms). Thickness of SiO2 film measured is 195 nm which is very close to the set value of 200 nm. From AFM data, Ta2 O5 and SiO2 thin films showed nanoroughness of 0.13 Å (rms) and 0.1 Å (rms), respectively, with scan area of 5 μm × 5 μm. The multi-layer mirrors realized with Ta2 O5 /SiO2 quarter-wave materials deposited by Veeco Spector ion beam sputter deposition machine exhibited an RMS roughness of 3.2 Å, which is nearly half that of the 6 Å RMS measured on a fused silica substrate [20]. But, in the present work, there is slightly raise in surface roughness as there is no assist beam for further smoothening the film during deposition. Optical absorption and surface scattering add to the noise in the optical interferometers and deteriorate the performance of optical instruments [10]. Achieving surface smoothness is very important for better signal-to-noise ratio. The optical constants and surface roughness of tantala and silica films are shown in Table 22.1. Spectroscopically, Ta2 O5 compound exhibits two doublet peaks, one at 4f7/2 and other at 4f5/2 corresponding to binding energies at 26.66 eV and 28.6 eV, respectively [21]. From the XPS data, a shift in the B.E energy of about 0.6 eV has been observed corresponding to Ta-4f7/2 , 5/2 peaks compared to the standard Ta2 O5 films. The presence of elemental Ta will be witnessed by XPS peaks at 4f5/2 at 23.49 eV [22]. But there is no peak observed in the range of 22–24 eV of the XPS data, which rules out the presence of Ta elemental state. Le Yang et al. reported that tantala films deposited with ion beam sputtering of Ta target with oxygen gas exhibited well defined Ta 4f doublet without lower oxidation peaks [23]. Multi-oxidation states of “Ta” (+3, + Table 22.1 Optical constants and surface roughness of Ta2O5 and SiO2 films Optical constants @633nm

Surface roughness

n

k

RMS (Å)

Average (Å)

Substrate

1.5401

0

5.1

5.3

Ta2O5

2.0989

0.00337

4.2

4.5

SiO2

1.4824

0

3.8

4.0

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4 and + 5) have been observed which indicates that the Ta2 O5 films, in the present work are non-stoichiometric in nature. Figure 22.10 shows an asymmetry in O-1 s peak indicates the presence of the surface contamination with the atmospheric oxygen and hydroxyl groups such as moisture. This has reduced refractive index and witnessed coefficients of extinction for tantala films. The extracted O/Ta ratio is 5.6 when surface adsorbed oxygen is included and the ratio is 2.2 when surface adsorbed oxygen is excluded.

22.5 Conclusions Single-layer Ta2 O5 and SiO2 films deposited with single ion beam sputtering technique show good surface smoothness and optical constants useful for laser mirrors. SiO2 films are absolutely free of absorption with zero extinction coefficients. Refractive index of SiO2 film is optically denser than that of bulk fused silica. This work has shown that SiO2 film is purely non-absorbing, and deposition does not require additional assist beam. Oxygen gas flow of 3 sccm in the background is sufficient to keep the stoichiometry of SiO2 films intact. However, an additional assist source may be useful for bettering the surface roughness of the films. Though Ta2 O5 films are grown with identical process conditions of SiO2 film, they exhibited non-zero extinction coefficients and non-stoichiometry due to insufficiency of O2 + ions. The stoichiometry of Ta2 O5 films may improve with simultaneous O2 assist beam or with increased O2 gas flow in the background. However, the reported work concludes here highlighting the behavior and the merits of tantala and silica films fabricated under identical single ion beam sputtering deposition process.

References 1. W. C. Jr Hermann, J. R. McNeil, Proc. SPIE, 325,101 (1982) 2. N. Sidqi, C. Clark, G. S. Buller, V. V. Gopala Krishna, T. J. Mitrofanov, Y. Noblet, Opt. Mater. Express 9, 3452–3468 (2019) 3. J.H. McNeil, W.C. Jr, Hermann. J. Vac. Sci. Technol. 20, 324–326 (1982) 4. J. L. Vossen, J. J. Cuomo, Thin film Processes, pp. 11–73, Academic Press, New York (1978) 5. G. K. Wehner, Report No. 2309, General Mills, Minneapolis, MN (1962) 6. S. Reid, I.W. Martin, Coatings 6, 61 (2016) 7. D. Gangloff, M. Shi, T. Wu, A. Bylinskii, B. Braverman, M. Gutierrez, R. Nichols, J. Li, K. Aichholz, M. Cetina, L. Karpa, B. Jelenkovi´c, I. Chuang, V. Vuleti´c, Opt. Express 23, 18014–18028 (2015) 8. G. K. Wehner, G. S. Anderson, Hand book of thin film technology, McGraw-Hill, NewYork, pp. 3.1–3.8 (1970) 9. H. Zimmermann, Integrated silicon optoelectronics, Springer Series in Optical Sciences (2010) 10. J. Yi-Qin, J. Yu-Gang, L. Hua-Song, W. Li-Shuan, L. Dan-Dan, J. Cheng-Hui, F. Rong-Wei, C. De-Ying, Chin. Phys. Lett, 31, 4 (2014) 11. D. Malacara, Interferogram Analysis for Optical Testing, CRC press, 2nd edn. (2005) 12. B. Cappella, G. Dietler, Surf. Sci. Rep. 34, 1–104 (1999)

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13. D. Rugar, P. Hansma, Phys. Today 10, 23–30 (1990) 14. H. Demiryont, J. R. Sites, K. Geib, Appl. Opt. 24, 490–495 (1985) 15. T.J. Bright, J.I. Watjen, Z.M. Zhang, C. Muratore, A.A. Voevodin, D.I. Koukis, D.B. Tanner, D.J. Arenas, J. Appl. Phys. 114, 083515 (2013) 16. G.E. McGuire, G.K.K. Schweitzer, T.A. Carlson, Inorg. Chem. 12, 2451 (1973) 17. O. Yu Khyzhun, E. A. Zhurakovsky, A.K. Sinelnichenko, V. A Kolyagin, J. Electron Spectroscopy and Related Phenomena, 82, 179–192 (1996) 18. C. Yong, Z. Xiao, S. Fan, C. Boxiong, Proc. of SPIE 7283, 72830R-2 19. S.-H. Woo, C. K. Hwangbo, J. Korean Phys. Soc. 46 S187–S191 (2005) 20. D. Schiltz, D. Patel, C. Baumgarten, B.A. Reagan, J.J. Rocca, C.S. Menoni, Appl. Opt. 56, 4 (2017) 21. K. Seshan, Hand book of Thin Film deposition processes and Techniques, Applied Science Publishers (2001) 22. G. A. Al-Jumaily, N. A. Raouf, S. M. Edlou, J. C. Simons, Proc. SPIE 2262, Optical Thin Films IV: New Developments (1994) 23. L. Yang, E. Randel, G. Vajente, A. Ananyeva, E. Gustafson, A. Markosyan, R. Bassiri, M. Fejer, C. Menoni, Appl. Opt. 59, A150–A154 (2020)

Chapter 23

A Review on Latest Trends in Derived Technologies of Friction Stir Welding Maddela Narender, V. Ajay Kumar, and Aluri Manoj

Abstract Emerging manufacturing patterns such as lightweighting, improved efficiency and flexibility have increased the use of multimaterial components and therefore suggesting the need for cost-effective and robust methods of joining dissimilar materials. Several friction stir welding (FSW) derived technologies are developed for their extensive use in dissimilar material processing and joining industries. The fundamental concept and the continued evolution of the variants that are developed to progress in the field of dissimilar materials joining are studied. Friction stir extrusion (FSE), friction stir scribe welding (FSS), friction stir dovetailing (FSD), and friction stir interlocking (FSI) based on the FSW technology are the reviewed potential variants in this paper.

23.1 Introduction Joining is a crucial technology for creative and sustainable manufacturing among many manufacturing technologies. Combining several materials to form a multimaterial component is in high demand to achieve more optimum lightweighting components with high performance and also the trend of trying to integrate even more features into each component is fulfilled. Many industries such as aeronautics and automotive are in need of joining dissimilar materials to lower the weight of the end products in order to improve energy efficiency, mobility, and agility [1]. Joining of dissimilar metals using traditional jointing techniques can be an issue, owing to

M. Narender (B) · A. Manoj Department of Mechanical Engineering, RGUKT-Basar, Nirmal 504107, India e-mail: [email protected] A. Manoj e-mail: [email protected] V. Ajay Kumar Department of Metallurgical and Materials Engineering, RGUKT-Basar, Nirmal 504107, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_23

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significant variations in material’s mechanical, chemical, thermal, and physical characteristics give rise to difficulties such as metallurgical precipitation, mechanical joint properties deterioration, heat-affected zone (HAZ), intermetallic compounds (IMCs) formation, distortion and defects [2, 3]. But in recent years, various derivative technologies of friction stir welding (FSW) for dissimilar materials joining have been developed in a way to mitigate these challenges along with achieving higher joining performance. Mostly in prior reports, individual aspects of FSW are reported. In this paper, the latest potential variants of FSW such as FSE, FSS, FSD, and FSI that are adopted to join dissimilar materials are studied and reviewed the fundamental principles, research progress, and efficiency of each process. The paper ends with a conclusion and future scope of the novel derivative technologies of FSW.

23.2 Variants of FSW 23.2.1 Friction Stir Extrusion (FSE) A detailed overview of the challenges for dissimilar joining of aluminum and steel are compared to friction stir spot welding (FSSW) and standard FSW is provided by Haghshenas et al. [4]. To address the challenges, a new method is introduced for joining dissimilar materials especially steel and aluminum through friction stir extrusion (FSE) process [5, 6]. The Welding Institute (TWI) has introduced FSE back in 1993 [7] and developed very little until the patent lapsed in 2002. The FSE technique is part of the FSP technology, built after the FSW. FSE is a process of solid-state recycling (SSR) for synthesizing a material that produces extruded products in one step by transforming waste into advanced bulk materials employing thermomechanical and mechanical processing. SSR reduces energy consumption that is required for remelting in the conventional phase. The procedure includes plunging a die, which rotates in a hole chamber that contains a billet of the material extruded. The forces of friction in between the billet and the die decay in heat lead to softening the metal and the extrusion channel creates a plastic flow on the die axis of the rotation. Figure 23.1 displays a process sketch of FSE. A transition layer that heats the chip but is not homogenized like a continuum is found that is far from the tool interface. The material extruded possesses the room temperature at the end of the cycle with an air cooling. The key geometric variable of FSE procedure is the ratio of extrusion (or) extrusion ratio, i.e., the ratio of the chamber–diameter and the extruder, in a comparable way with traditional extrusion procedure. The option of a higher extrusion ratio will prevent the critical bonding of the extruded wire at the center, which leads to the formation of defects [8]. The forces of extrusion along with the rotational speed of the tool are the key factors that affect the process especially the surface quality [9]. The application of a consistent extrusion force makes it possible to adjust the plunge speed to the local material flow stress. It is evident that extrusion only happens when the raw material meets

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Fig. 23.1 Schematic diagram of FSE process [47]

the appropriate temperature and strain levels. The capacity for this approach can be used by different materials, such as Al, Mg, or steel [10–12]. Friction stir back extrusion (FSBE) is also one of the many frictions stir processing (FSP) variants which are produced via FSW, which is in the form of spiral friction stir processing (SFSP) with an aim to fabricate strong ductile tubes. Many studies also demonstrated FSBE by the fixed chamber and rotating the die and producing rod, tube, and cable. The viability and ability of the FSBE process produce tubular samples without evidence of internal defects or voids. The FSBE is also widely used to transform metal chips into wires, replacing the traditional melt cycle, as an SSR process [13]. Using this definition, a rotating tool with a defined axial feed and feed rate is plunged into a cylindrical sample. The tool’s later movement pushes the material outside as in the back extrusion, whereas friction at the interface of the sample/tool produces enough heat to suppress and deform. Furthermore, the stirring effect under significantly higher pressure forces the material to be severely deformed by plastic and refines the grain structure. The concept of FSBE is shown in Fig. 23.2. From previous studies, it is evident that FSE [9, 15, 16] and friction stir back extrusion (FSBE) [17–21] process can be used for recycling the metal chips to fabricate good surface quality wires, also with the presence of small internal voids and nonhomogeneous microstructure. In both cases, samples have a fine recrystallized grain microstructure, with grain size growing after excessive speed increase of rotation along with the increased strength when compared with the base material. For materials like magnesium alloys or even aluminum alloys, whose ductility can be smaller than steel, this is very advantageous. It is anticipated to enhance the efficiency of the tubes before and after hydroforming with improved ductility. Few researchers are therefore interested in FSBE for the production of ultra-fine meso- and microscale

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Fig. 23.2 Schematic diagram of FSBE process [14]

tubes. In a similar line of SSR investigations, researchers are enabling direct recycling into the semi-finished product of metal scraps using different processes such as FSE [22] and FSBE [23].

23.2.2 Friction Stir Scribe Welding (FSS) Processes of solid-state joining such as FSW facilitate the welding of several materials or else viewed as unweldable. Through friction-based approaches, joints across different metals are demonstrated in the past. Due to extreme variations of the melting temperatures for each component, the metallurgical immiscence of certain blends makes the joining of dissimilar materials very complicated [24]. Friction stir scribe welding (FSS) process is proposed by the Pacific Northwest National Laboratory to solve the problems caused by chemical incompatibilities and the differences in melting temperature among different material combinations. FSS enables the fusion of different materials in a lap configuration where both chemically and mechanically bonding materials allows the fitting and the improvement of the welding effect. FSS is an alteration in the process of FSW that a pin tip is inserted with a scribe [25]. The scribe is usually made of tungsten carbide (WC). The scribe works by impacting the base material sheet of a lap weld of dissimilar material as an extra tool configuration as illustrated in Fig. 23.3. The geometry of the FSS tool is carefully designed as shown in Fig. 23.4. FSS combines high melting temperature material focused matching and

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Fig. 23.3 Schematic penetration of FSS tool into a lap joint of dissimilar material (above) and tools of FSS (below) [36]

controlled and localized extrusion of the material of low melting temperature at the interface of both the materials. The simultaneous process generates a mechanically interlocked joint which is created due to the combined action at a relatively low melting point temperature of the material with a lower melting temperature. The process solid-state nature is thus maintained and the complications caused by traditional melt-solidifying weld processes are eliminated. This facilitates FSS to allow very different metals to get welded and also allows different materials like polymers to weld into metals or composites. Few studies of FSS joints for various material combinations have been reported, such as polymer to Al [26, 27], carbon fiberreinforced polyamide to Al [28], steel to Al [29–34], and a computational approach is also reported in [35]. A cross-sectional observation showed the change in the IMC layer thickness during the process of welding with a scribe trace. Steel/Al joint fractography is evident that regionally formed IMC at the interface for fracturing welds via a welded interface.

23.2.3 Friction Stir Dovetailing (FSD) While there is a wide amount of research to join steel to Al metallurgically, only limited studies have found thickness measurements for steel or Al over 6 mm [37– 39]. This is mainly because the thin sheets joining techniques are usually not well suited to thick plates. The newly evolved FSD technique [40] therefore fills a major gap in the literature published. Glue and dovetails are employed in woodworking to safely join the wood pieces, where a similar approach is adopted by FSS but in metals. Steel dovetail grooves are deformed by the Al to create a mechanical interlock with a specially designed tool. The mechanical interlocks formed by FSD between the steel and Al further enhanced by in situ metallurgical bonds while joining [42].

244 Fig. 23.4 Single (above) and double (below) scribe cutter FSS tool [46]

Fig. 23.5 FSD technique illustration and tooling demonstrating metallurgical bonding and mechanical interlocking in a dovetail groove [42]

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Simultaneously, the tool also rubs the bottom of the dovetail to form an IMC or thin metallurgical bond that “glues” the metals into the dovetail. Metallurgical bonding and mechanical interlocking combined through advanced technology in a single phase of the FSD process that creates high ductility and strength joints compared to joints produced by other friction stir techniques. Before the joint breaks, the FSD joined material can stretch over half a centimeter demonstrating ductility five times greater than aluminum and steel, combined with other friction-based techniques. FSD is demonstrated in the lap configuration on the AA6061-T651 attached to MIL-DTL-12560 J Rolled Homogeneous Armor (RHA) [41]. In the course of FSD, plastic deformed Al enters into dovetail grooves and on the underlying RHA surface, it is premachined to form a mechanical interlock, while the WC tip along with the interface of the RHA-Al produces localized heat and leads to a metallurgical bond. Figure 23.5 depicts an FSD technology of a single dovetail groove in a lap configuration, cut into the RHA. Thermocouples of type-k are incorporated in the tooltip where the WC is contained in the H13 tool. At specific locations of the tool, these thermocouples are soldered to constrain the intermetallic growth through temperature control. Pin threads are designed in a way to push the material into the dovetail while scrolling attribute to collect material on the shoulder to prevent the defects on the surface and inner wormholes from forming. FSD has been performed using an H13 steel tool that hardened to HRC from 45 to 48. A shoulder diameter of 38.1 mm, pin diameter of 15.85 mm (close to the shank), length of 11 mm with 3 flats (apart of 120°), 2.12 mm/revolution threaded, 9° frustum shaped, and a 3.18 mm/revolution of convex scrolled are included in the tool. It can be inferred from the mechanical and microstructural data that FSD is an assuring novel method for joining a thick steel-Al portion. With the aid of the FSD technique, the formation of the intermetallic compounds (IMCs) layer is controlled on the basis of localized deformation of solid state. IMCs are not unusual in processing metal at high temperatures as traditional welding but are not very often useful or controllable [42]. Later, a similar line of work AA7099 to Ni–Cr-Mo is joined using an improved FSD double-pass approach along with the previous single-pass approach. The doublepass approach is to form the metallurgical bonding of AA6061 to RHA with a silicone-enriching IMC in the dovetail and the second pass ought to be done by the FSD to establish a lap joint for the AA6061 to AA7099 [43]. Authors concluded that FSD is capable of implementing metallurgical bonding and mechanical locking and simultaneously, to extrude different alloys of aluminum (AA7099, AA6061) in dovetail grooves in RHA plates to form a lap joint. The AA6061 asymmetrical material flow on the retreating and advancing sides led to asymmetric joint results. To estimate the mechanical performance of the thick steel-Al joints that are processed using FSD, modeling and simulation approach is developed in [44]. At the Al corner of the dovetail, it predicted the failure of the FSD single-pass joints without IMCs. The dovetail neck failure location is predicted for the joints of single-pass FSD connections with IMCs. The failure position is expected at the loading side of HAZ/TMAZ (thermomechanically affected zone) of the Al for triple and double-pass joints of FSD

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with IMCs. The average predictable Al thickness, provided by the FSD joint configurations simulated for this task without driving fault, is 44.45 mm and 17.78 mm for FSD triple and double-pass joints, respectively.

23.2.4 Friction Stir Interlocking (FSI) Friction stir interlocking (FSI) is a modern, evolved solid-phase technique [45]. FSI can be adopted for joining lightweight metals to ceramics, thermoset plastics, nonmetals, and composites. There are two FSI approaches currently used to bind metal plates and sheets to non-metals. The first method is to incorporate metal inserts into non-metallic elements and then FSW of the metal plate or sheet to the metal insert directly. The second includes inserting metal pins like some kind of rivet into the non-metal and after this FSW of the metal plate or sheet to the metal insert directly to result in forming mechanical fastener. This new technology makes it possible to quickly and uniformly create numerous interlocks in one pass and tend to offer lower costs and improved efficiency in processes compared to traditional metal to non-metal fasteners, such as spot welding, riveting, and pillaring. FSI also limits the pitting and galvanic corrosion which can in composite materials between carbon fibers and metal fasteners and this can be attributed to the generation of heat during FSP resulting corrosion barriers.

23.3 Conclusions In this review, potential variants of FSW such as FSE, FSS, FSD, and FSI are investigated. Also, their fundamental principles, research progress, and efficiency of each process are studied and reviewed. Major conclusions drawn from the review include that FSE and FSBE processes can be used for recycling the metal chips to fabricate good surface quality wires, sometimes with presence of small internal voids and non-homogeneous microstructure. FSS joint fractography is evident that regionally formed IMC at the interface of steel/Al for fracturing welds via welded interface. Metallurgical bonding and mechanical interlocking combined through advanced technology in a single phase of the FSD process that creates high ductility and strength joints compared to joints produced by other friction stir techniques. Before the joint breaks, the FSD joined material can stretch over half a centimeter demonstrating ductility five times greater than aluminum and steel, combined with other friction-based techniques that made it unique and preferable. Although FSI is a recent technology that makes it possible to quickly and uniformly create numerous interlocks in one pass and tend to offer lower costs and improved efficiency in processes compared to traditional methods, from the literature, it is evident that FSD is a promising process for efficient and low cost joining and even the FSI process can be considered as a noteworthy process. In this regard, further investigations are highly

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needed for FSD and FSI technologies to explore the joining possibilities of various materials. Acknowledgements Authors are grateful to “ELSEVIER (License numbers: 4847310517057, 4847310839845, 4847311044106, 4847311305347, 4847320012736)” for providing the copyright permissions to different figures of the current paper.

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19. Y. Hangai, R. Kobayashi, R. Suzuki, M. Matsubara, N. Yoshikawa, Aluminum Foam-Filled Steel Tube Fabricated from Aluminum Burrs of Die-Castings by Friction Stir Back Extrusion. Metals. 9(2), 124 (2019) 20. I. Dinaharan, R. Sathiskumar, S.J. Vijay, N. Murugan, Microstructural characterization of pure copper tubes produced by a novel method friction stir back extrusion. Procedia Mater. Sci. 5, 1502–1508 (2014) 21. G. Jamali, S. Nourouzi, R. Jamaati, Microstructure and mechanical properties of AA6063 aluminum alloy wire fabricated by friction stir back extrusion (FSBE) process. Int. J. Min. Metall. Mater. 26(8), 1005–1012 (2019) 22. D. Baffari, A.P. Reynolds, A. Masnata, L. Fratini, G. Ingarao, Friction stir extrusion to recycle aluminum alloys scraps: Energy efficiency characterization. J. Manuf. Process. 43, 63–69 (2019) 23. M. Gelaw, P.J. Ramulu, D. Hailu, T. Desta, Manufacturing and mechanical characterization of square bar made of aluminium scraps through friction stir back extrusion process. J. Eng. Des. Technol. 16(4), 596–615 (2018) 24. Y. Hovanski, P. Upadyay, S. Kleinbaum, B. Carlson, E. Boettcher, R. Ruokolainen, Enabling dissimilar material joining using friction stir scribe technology. JOM 69(6), 1060–1064 (2017) 25. E. I. Barker, P. Upadhyay, Y. Hovanski, X. Sun, Predicting lap shear strength for friction stir scribe joining of dissimilar materials, in Friction Stir Welding and Processing IX. Springer, Cham. pp. 261–267 (2017) 26. P. Upadhyay, Y. Hovanski, L. S. Fifield, , K. L. Simmons, Friction stir lap welding of Aluminumpolymer using scribe technology, in Friction Stir Welding and Processing VIII, edited by R. S. Mishra, M. W. Mahoney, Y. Sato, Y. Hovanski, Wiley, pp. 153–160 (2015) 27. B. E. Carlson, D. Ollett, S. Kleinbaum, Friction stir scribe joining of Carbon Fiber Reinforced Polymer (CFRP) to Aluminum (No. DOE-GMLLC-07311). General Motors Co.(GM) Global R&D, Warren, MI (United States) (2018) 28. P. Upadhyay, Y. Hovanski, S. Jana, L. S. Fifield, Joining dissimilar materials using friction stir scribe technique. J. Manuf. Sci. Eng. 139(3) (2017) 29. T. Curtis, C. Widener, M. West, B. Jasthi, Y. Hovanski, B. Carlson, ... W. Bane, Friction stir scribe welding of dissimilar aluminum to steel lap joints, in Friction Stir Welding and Processing VIII. Springer, Cham. pp. 163–169 (2015) 30. T. Wang, H. Sidhar, R.S. Mishra, Y. Hovanski, P. Upadhyay, B. Carlson, Friction stir scribe welding technique for dissimilar joining of aluminium and galvanised steel. Sci. Technol. Weld. Joining 23(3), 249–255 (2018) 31. K. Wang, P. Upadhyay, Y. Wang, J. Li, X. Sun, T. Roosendaal, Investigation of interfacial layer for friction stir scribe welded Aluminum to steel joints. J. Manuf. Sci. Eng. 140(11) (2018) 32. T. Wang, H. Sidhar, R.S. Mishra, Y. Hovanski, P. Upadhyay, B. Carlson, Evaluation of intermetallic compound layer at aluminum/steel interface joined by friction stir scribe technology. Mater. Des. 174, 107795 (2019) 33. T. Wang, H. Sidhar, R.S. Mishra, Y. Hovanski, P. Upadhyay, B. Carlson, Effect of hook characteristics on the fracture behaviour of dissimilar friction stir welded aluminium alloy and mild steel sheets. Sci. Technol. Weld. Joining 24(2), 178–184 (2019) 34. P. Upadhyay, Y. Hovanski, B. Carlson, E. Boettcher, R. Ruokolainen, P. Busuttil, Joining dissimilar material using friction stir scribe technique, in Friction Stir Welding and Processing IX. Springer, Cham, pp. 147–155 (2017) 35. V. Gupta, P. Upadhyay, L.S. Fifield, T. Roosendaal, X. Sun, P. Nelaturu, B. Carlson, Linking process and structure in the friction stir scribe joining of dissimilar materials: A computational approach with experimental support. J. Manuf. Process. 32, 615–624 (2018) 36. M. Haghshenas, A.P. Gerlich, Joining of automotive sheet materials by friction-based welding methods: a review. Eng. Sci. Technol. Int. J. 21(1), 130–148 (2018) 37. E.E. Patterson, Y. Hovanski, D.P. Field, Microstructural characterization of friction stir welded aluminum-steel joints. Metall. Mater. Trans. A. 47(6), 2815–2829 (2016) 38. T. Ogura, Y. Saito, T. Nishida, H. Nishida, T. Yoshida, N. Omichi, ... & A. Hirose, Partitioning evaluation of mechanical properties and the interfacial microstructure in a friction stir welded aluminum alloy/stainless steel lap joint. ScriptaMaterialia. 66(8), 531–534 (2012)

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Chapter 24

Investigation on Hybrid Polyester Composite Comprising of Sisal and Coir as a Reinforcement and Fly Ash as Filler M. L. Darshan, Srikumar Biradar, and K. S. Ravishankar

Abstract The use of lignocellulosic fibers such as coir and sisal as reinforcements in thermoplastic and thermosetting resins for developing new material having property superior than the existing one. These biofibers have several advantages, such as good strength, flexibility, low densities, low cost, and biodegradability; over synthetic fibers, these natural fibers are made hydrophobic by 8% NaOH treatment. Alkali treatment increases wettability of fibers with resin and interfacial bond strength. This paper is highlighting about the preparation of hybrid composite and testing the prepared samples for various mechanical and tribological tests such as tensile, flexural, and slurry erosion tests conducted for coir/sisal-hybrid fibers with fly ash as a filler in unsaturated polyester resin. The obtained results are further justified by the SEM images of tested samples from different mechanical tests. This material has been used for automobile application and packing material, roofing material in construction technology.

24.1 Introduction There has been progressive research for developing value engineering, biodegradable, and less %wt carbon emission materials in product for the next generation of composite products concerning to global environmental accepts. These can be achieved by replacing synthetic reinforcement to green reinforcement in partially biodegradable and non-biodegradable polymer matrix. A natural fiber material in polymer matrix composites decreases carbon monoxide and carbon footprint of M. L. Darshan (B) · K. S. Ravishankar Department of Metallurgical and Materials Engineering, National Institute of Engineering Karnataka, Surathkal, India e-mail: [email protected] S. Biradar Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_24

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composites. Around 100,000 tons of coir and 378,000 tons of sisal fibers are produced all over the worldwide. Hand lay-up and compression molding are the popular manufacturing techniques [1]. Particle/filler reinforcements as fly ash are selected with coir/sisal hybrid fiber obtained from agricultural bio waste to improving thermosetting polyester resin mechanical properties. Fly ash is a waste disposed from thermal plants, which are rich in minerals oxides. Fly ash contain high percentage aluminosilicate minerals obtained from thermal plants. These minerals particles are hollow ceramic microspheres in nature having low density value 2.18 g/cc. More than 10%wt of fly ash leads to amalgamation of particles and decreases the mechanical properties [1, 2]. Fiber length, microfibrillar angle and alkali treatment have significant impact on mechanical properties. Fiber length of 30 mm and 6–8% NaOH treated shows high impact and tensile results [3]. Alkali-treated coir fiber shows good adhesion between the fiber and matrix. In addition, slight increases in the tensile stress, tensile modulus, and impact strength of composites reinforced with modified fibers are noted [4]. At elevated temperature, 40% sisal/coir reinforced shows 33% moisture absorption in epoxy resin [4]. NaOH treated fibers lose their crystallinity by chemical reaction. It increases the surface roughness and surface area of the fiber, which are the essential surface properties needed to enhance the mechanical gripping between the fiber and matrix interface. There was a significant improvement in the tensile strength, modulus, and microhardness of the composite samples after treatment [5, 6]. Short chopped form of glass/sisal hybrid composite are experimentally studied; tensile strength varies with fiber percentage. The alkali treatment of fibers studied for mechanical property and found its result impact on the tensile strength of laminated composite [6]. In the present paper, 5% fly ash as a particle reinforcement with equal percentages 1:1 of coir/sisal fiber is considered to take full advantage of constituents. The mechanical and tribological properties of hybrid coir/sisal composite have been explored. Cellulose, lignin content, secondary layer diameter of fiber, moisture absorption capability, and microfibrillar angle are major criterion influence properties of the fiber in composite laminate. Hybridization leads to embedding of different combination of fibers. Fiber which is weak in one property can be overcome by other. Laminated composites are suitable for the sports products, automotive panel application, packing material for cargos, and corrosion-resistant applications [7–9].

24.2 Materials, Methods and Testing Fibers were procured from local market region in Karnataka. The chemical properties of fiber is given in Table 24.1. Individually, loose coir and sisal fiber were pressed in mats form with a thickness range as per requirement. Initially, fibers were treated with 8% NaOH solution and dried in sun light for 24 h. Figure 24.1a, b illustrates the coir and sisal fiber arrangements which are kept ready for composite preparations, respectively. Since coir fiber having more hydrophobic in nature compared to sisal, we considered coir fiber as outer layer and sisal fiber in middle position between

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Table 24.1 Chemical and physical properties of fiber [1–5] Fiber

Cellulose (%wt)

Hemi cellulose

Lignin (%wt)

Pectin (%wt)

Coir

32–43

0.15–0.25

40–45

3–4

Sisal

65.8–76

8–14

10–14

0.8–10

Moisture (%wt)

Waxes (%wt)

Microfibrillar angle (°)

7

−

30–45

10–22

2

20–22

Fig. 24.1 a Preparation of coir fiber in mat form, b sisal fiber, c fly ash (F type) 420 microns size, d stirring resin with fly ash in a flask, e laminated composite in a mild steel mold.

coir, to reduce the moisture absorption effect which impact on the mechanical properties. We divided the coir fibers in equal proportions for outer layers. Commercially available unsaturated polyester resin used in industrial sector as well as domestic application with 1%wt of methyl-ethyl-ketone-peroxide as catalyst was used in laminated composite preparation. Resin procured from Carbonblack composite private Ltd, Mumbai. Unsaturated polyester resin mechanical properties are mentioned in Table 24.2. Fly ash (F type) procured from KPCL Raichur thermal power plant is Table 24.2 Properties of unsaturated polyester from supplier Viscosity@20 deg Centigrade

Density@20 deg centigrade(g/cc)

Tensile strength in MPa

Flexual strength in MPa

330

1.10–1.15

28–33

30–45

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shown in Fig. 24.1c. Fly ash is sieved to less than 500 microns, by sieving standard mesh BSS 36. Calculated amount in %wt of resin and catalyst along with calculated amount of fly ash were mixed throughout in a beaker with the help of stirring machine shown in Fig. 24.1d for respective composition. After thoroughly mixed, the resin was poured into mild steel mod cavity of dimension 300*300*5 mm3 , and the mold cavity was placed on a flat plate and thoroughly wet the corner of the cavity. Previously prepared calculated %wt amount of coir mat and sisal fiber are placed in stacking layer order of coir-sisal-coir. As showed in Fig. 24.1e, composites laminate with %wt amounts of coir/sisal fibers of 10 to 30%wt of total fibers were fabricated by considering 5%wt fixed amount of fly ash. The laminate is cured at room temperature for 48hrs. The coir fibers are placed in random orientation, and sisal fibers are placed in loading direction or axial direction in fabricated laminated composite. Sample %wt amount and their stacking sequence are noted in Table 24.3. The tensile, flexural, and slurry erosion tests are conducted as per ASTM-D-638, D-7264, and G 75–95, respectively. Tensile and flexural tests are conducted at a crosshead speed of 2 mm/min in SHIMAZDU UTM of 100kN capacity and tested samples fractography study carried by scanning electron microscopy (SEM) JOEL located at MME at NITK. Figure 24.2a–c shows tensile, flexural, and slurry erosion samples before test. Samples are tested on SHIMADZU universal testing machine of 100kN capacity at MME department NITK for tensile and flexural test. Slurry erosion test is conducted on DUCOM Tribological tester at Table 24.3 Sample preparation and its composition Sequence of fiber

Coir (%wt)

Sisal (%wt)

Fly ash (%wt)

Total reinforcement (%wt)

Polyester(resin) (%wt)

CSC*

5

5

5

15

85

10

10

5

25

75

15

15

5

35

65

* Coir-sisal-coir(CSC)

stacking

Fig. 24.2 a Tensile samples, b bending samples, and c slurry erosion samples cut pieces as per respective ASTM standards before test

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mechanical engineering department NITK. Erosion rate in %wt is calculated using Eq. 24.1 [9]. The erosion rate (%) = ((Wi − Wf ) / Wi ) × 100

(24.1)

where Wi = Initial weight in grams and Wf = Final weight in grams.

24.3 Results and Discussions Tensile tests have been conducted for varies reinforcement compositions (15–2535%wt) as per ASTM-D-638. The results highlight maximum tensile strength of 43.66 MPa and minimum tensile strength of 24.2 MPa for 25% and 35%wt of fiber composition. The tensile modulus of maximum 1848.1 MPa and minimum of 876.4 MPa are observed for 25%wt and 35%wt of reinforcement. 35%wt composition laminate shows high strain rate and can absorb high energy. Load-carrying capacity of fiber increases with weight percentage. The above variation in tensile strength and modulus is mainly due to increases in coir/sisal and filler fly ash %wt beyond the saturation or wet ability level of fiber in the composite, i.e., from 15 to 25%wt, there is uniform increases or almost double strength of the composite due to close packing of matrix and fiber, which indirectly reduces the void and hence improving the compact strength (Table 24.4). The graphs plotted for varied reinforced tensile tested hybrid composites are shown in Fig. 24.3 which also highlights the same tabulated results. From the graph, we can justify that sample failed by primary sudden catastrophic brittle failure, followed by smooth secondary failure accomplished by fibers breakage. The SEM images of failed tested sample authenticates the current outcomes. The failed samples of tensile tests is shown in Fig. 24.6a (Table. 24.5). The bending test on the hybrid composite is conducted as per ASTM-D-7264. The maximum bending strength of 87.3 MPa and minimum of 25.9 MPa has been observed, whereas flexural modulus of maximum 23.51 GPa and minimum 2.9 GPa was observed in 25%wt and 15%wt of fibers, respectively. The above variation is mainly due to simultaneous application of tension at the bottom fibers, and compression at the top fibers is balanced the overall load-carrying capacity of the sample in Table 24.4 Tensile results Property/reinforcement %wt Average of three samples max. tensile strength (MPa) Tensile modulus (MPa) Tensile strain at break (%)

(A) 15% 30.6 1191.9 2.793

(B) 25% 43.66 1848.1 6.29

(C) 35% 24.2 876.4 6.5

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a

b

Secondary Fiber Ductile Failure

c Primary Brittle failure

Fig. 24.3 a Tensile results for pure polyester resin and 15% reinforcement, b Tensile results 20% reinforcement, c Tensile results for pure polyester resin and 35% reinforcement

Table 24.5 Bending results Property/reinforcement in %wt

(A) 15%

(B) 25%

(C) 35%

Average of 3 samples, max. bending strength (MPa)

25.9

87.3

72.4

Modulus (GPa)

2.9

23.51

15.15

Strain at break (%)

1.01

0.57

0.52

25%wt category in a more proper way than 15 and 35%wt category. The specimen fails suddenly in a linear mode at the bottom surface of the specimen. As a result of the fact, that there is no inter-laminar failure at the thickness of the specimen, and shear failure mode does not occur. This balancing of load is purely due to %wt of matrix, which is responsible for load transfer from one fiber to another fiber. The graphs plotted for varied reinforced flexural tested hybrid composites are shown in Fig. 24.4 which also highlights the same tabulated results. The exact cause of these results is further explained in SEM analyses. The failed sample of bending test in Fig. 24.6b. The wet slurry erosion test is conducted in order to explore tribological properties of laminated hybrid composite as per ASTM G75-95. As the percentage of fiber increases from 15 to 35%wt, the erosion rate decreases from 2.5 to 1.33%. This is

24 Investigation on Hybrid Polyester Composite Comprising of Sisal and Coir …

257

b

a

c Primary Brittle failure

Secondary Fiber Ductile Failure

Fig. 24.4 a Bending results for 15%, b bending results 25%, and c bending results 35% reinforcement

mainly due to resistance offered by the fiber against the frictional force generated by the sand particles. As the wt% of reinforcement increases, erosion rate decreases. The slurry erosion test was conducted at a constant speed of 1500 rpm and fixed slurry concentration of 750 g with a particle size of 1400 microns. Bar chart comparison of erosion rate in percentage (Y-axis) with 15, 25, and 35%wt reinforcement (X-axis) is shown in Fig. 24.5a. The failed sample of slurry erosion test is shown in Fig. 24.6c. Fig. 24.5 a Bar chart comparison of erosion rate with %wt of reinforcement

3

a

2.5 2

1.5

erosion…

1

0.5 0 15%wt

25%wt

35%wt

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Fig. 24.6 a Tensile, b bending, and c slurry erosion samples after test

Table 24.6 Wet slurry erosion test results Sample code

Initial weight in gm

Final weight in gm

Erosion rate %

5–5-5

12.77

12.45

2.50

(15%wt

11.78

11.43

2.97

10–10-5

14.70

14.40

2.04

(25%wt

15.70

15.40

1.91

reinforcement)

reinforcement) 15–15-5

21.06

20.70

1.70

(35%wt reinforcement)

20.98

20.70

1.33

We tested two samples for each combination, and its initial, final weight and erosion rate are tabulated in Table 24.6.

24.4 SEM Analysis Figure 24.7a, b indicates tensile and bending failed sample’s micrograph. The tensile sample failed due to primary brittle catastrophic fracture and followed by fiber breakage, and the presence of void also influenced the failure of tensile samples. The bending sample mainly failed due to non-uniform packing factor between matrix and reinforcement. It can be justified by fiber pullout region. The samples failure initiates due to tension at the bottom fiber, and it varies from one sample to another sample. As %wt reinforcements increases from 15 to 25%wt, the wettability of fibers also reaches its maximum level, and hence, further addition of loose fibers leads to drop in tensile and bending strength.

24 Investigation on Hybrid Polyester Composite Comprising of Sisal and Coir …

fiber trace marks with river flow

Voids, broken fiber and fiber pullout

a

259

Fiber pullout with River flow marks

b

Fig. 24.7 Fractography SEM images of 25%wt reinforcement a tensile sample and b bending sample

24.5 Conclusion Hybrid composite laminate comprising of coir and sisal fiber with fly ash filler and unsaturated polyester resin as matrix is fabricated using hand lay technique. The mechanical and tribological properties are studied as per ASTM standard; the following conclusions were drawn as follows: (1) Fiber treated with 8% NaOH mainly resulted in an increase in the tensile and bending strengths along with strain rate of hybrid composites. (2) The mechanical properties were enhanced when the content of sisal/coir fibers increased from 10- 20%wt with the fixed 5%wt of fly ash filler. Microfibrill angle of coir fiber is higher than sisal, and stiffness of sisal fiber is more than coir fiber. This may be the reason for secondary smooth failure in tensile and bending results occurred after primary failure. When sisal fiber breaks by brittle failure, coir fiber takes the load accomplished by ductile fiber breakage. (3) A maximum tensile strength up to 43.66 MPa reached for 25% reinforcement. A maximum bending strength is up to 87.3 MPa for 25% reinforcement. Increases in the tensile and flexural modulus from 15% > 25% > 35%wt reinforcement is observed when compared with pure unsaturated polyester resin mechanical properties. Above 25%wt, reinforcement decreases in mechanical properties due to less wetablility of fiber with resin. (4) Laminated composites are tested for slurry erosion test; considerable decreases in erosion rate (2.5 to 1.3%) has been found, due to increases in the reinforcement %wt from 15 to 35. Acknowledgements I would like to thank faculty in charge of DST FIST composite laboratory of mechanical engineering department NITK for providing facilities for sample preparation, slurry erosion testing, and to materials and metallurgical department, NITK for mechanical testing facility.

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References 1. A. Gholampour, T. Ozbakkaloglu, A review of natural fiber composites: properties, modification and processing techniques, characterization, applications. J. Mater. Sci. 55, 823–892 (2020) 2. K. S. Sangamesh, S. M. Ravishankar, Kulkarni, Synthesis and comparison of mechanical behavior of fly ash-epoxy and silica fumes-epoxy composite. IOP Conf. Series Mater. Sci. Eng. 225, 012299 (2017) 3. K. Karthikeyan, Effect of alkaline treatment and fiber length on impact behavior of coir fiber reinforcement on epoxy matrix. J. Sci. Indus. Res 71, 622 (2012) 4. V.C. A. Cruz, A.G.B. de Lima, An experimental study of water absorption in polyester composites reinforced with macambira natural fiber. Mat.-wiss.u.Werkstofftech, 42(11) (2011) 5. D. K. Rajak , D. D. Pagar , P. L. Menezes, E. Linul, Fiber-reinforced polymer composites: manufacturing, properties, and applications. Polymers 11(1667) (2020) 6. S. Dixit, P. Verma, The effect of hybridization on mechanical behaviour of Coir/Sisal/Jute Fibres reinforced polyester composite material. Res. J. Chem. Sci. 2(6), 91–93 (2012) 7. C. Girisha, G. Sanjeevamurthy, G. R. Srinivas, Sisal/Coconut Coir Natural Fibers – Epoxy Composites: Water Absorption and Mechanical Properties. Int. J. Eng. Innov. Technol. (IJEIT) 2(3) (2012) 8. K. Senthil Kumar, I. Siva, N. Rajini, P. Jeyaraj, Tensile, impact, and vibration properties of coconut sheath/sisal hybrid composites: Effect of stacking sequence. J. Reinforced Plastics Compos. 33(19), 1802–1812 (2014) 9. S.M. Biradar, S. Joladarashi, S.M. Kulkarni, Tribo-mechanical and physical characterization of filament wound glass/epoxy composites” Mater. Res. Express 6(10), 105312 (2019)

Chapter 25

Thermal Performance Study of Double-Pass Solar Air Heater in Almora District Zone of Uttarakhand Divya Joshi, Satyendra Singh, and Sandeep Kandwal

Abstract The present work is based on sustainable energy source, i.e., solar energy and designs a double-pass solar air heater in ANSYS FLUENT software. It is simulated for thermal performance study at a geographic location of district Almora, Uttarakhand. The simulation of the solar device is done for the identical values of parameters such as ambient conditions, air velocity, inlet air temperature, space in the middle of the absorber and glazing, solar intensity. The influences of temperature differences and solar intensity on the outcome of double-pass solar air heater are also examined. It is observed from the study that the heat transfer rate is maximum at outlet as compared to inlet for the same mass flow rate. Thermal efficiency maximum achieved 68.8% at solar intensity 990 W/m2 .

25.1 Introduction Everyone needs energy in some forms which is necessary for social and economic growth in the planet. Solar energy is a best alternative way to overcome with global energy crises. In all the non-conventional energy systems, solar air heater is a setup of solar thermal system where air is heated inside the collector and then this heated air is transferred directly through heat transferring medium to the interior area or to some storage medium, such as a rock bin. Figure 25.1 shows a solar air heater which is framed by two glass covers on the top surface, and the airflow takes place between the glazing and the absorber plate. In a double-pass device, an absorbing plate is embedded into the air conduit to separate the box into two passes (inlet pass and outlet pass). The reason behind using double pass in place of single pass is that D. Joshi · S. Singh (B) Mechanical Engineering Department, B.T. Kumaon Institute of Technology, Dwarahat Almora, Uttarakhand 263653, India e-mail: [email protected] S. Kandwal Mechanical Engineering Department, Institute of Technology Gopeshwar, Chamoli, Uttarakhand 246424, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_25

261

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Fig. 25.1 Double-pass solar heater

the area of heat transfer increases. In this type of solar air heater, at the entrance sometimes, the incoming fresh air gets mixed with some part of heated air going outside through the outlet which results better outlet temperature as compared to conventional heaters. Many researchers done their work to improve the outcomes of solar device by modifying the model attributes and operating values, flow passes nature, number of glazing and type of absorber plate (corrugated or finned). The characteristics of solar air heater are experimentally investigated by calculating the amount of heat transfer, effects of fluid flow rate of air on the final temperature at the outlet, and thermal efficiency of a finned solar air heater with single pass for the region Biskra, Algeria [1]. From the results it is noted that an increment in airflow rate results an enhanced

Fig. 25.2 Sectional view of a double-pass solar air heater

25 Thermal Performance Study of Double-Pass Solar Air Heater …

263

value of mass flow rate but as the height of the duct decreases the efficiency increases [2]. An experimental investigation on a heater of double-pass type integrated with a thermal storage arrangement is conducted [3]. They concluded that this incorporated system delivered comparatively high temperature which results higher efficiency value. A thermo-hydraulic investigation of a solar air heater attached with number of extended surfaces is performed, which concludes that the best array of fins in the receiver in comparison to a smooth pipe arrangement was obtained by analyzing thermo-hydraulic efficiency through a test [4]. This proposed investigation results an optimum efficiency of 14% improvement in comparison to flat pipe arrangement with single glass envelope by reducing the demanded air flux. In an experimental investigation, a plate is inserted into a flat-plate channel to separate it into two sections and exterior recycling at the ends, resulting improvement in the efficiency to a large extent [5]. Experimental thermal performance study of double-pass solar air heater presented in [6, 8] and using MATLAB [7]. Recent developments over phase change materials for different solar energy systems presented in [9] and on solar water heating system in [10]. The current research deals with thermal analysis of a double-pass solar air heater in Uttarakhand state of India for the region of district Almora with the help of CFD.

25.2 Mathematical Model The fraction of the sum of useful heat and overall solar radiation striking on the surface area of the collector plate in some period of time is represented as the efficiency of solar collector. η=

Qu I0 .A c

The heat gain for the collector is given by: Q u = m  C p (Tout − Tin ) where C p is specific heat of the air and Ac denotes the collector area. So, the efficiency is thus given by η = mC p

(Tout − Tin ) I0 .Ac

The sectional view of double-pass solar air heater is shown in Fig. 25.2. The energy balance equations in favor of different parts of solar air collector are specified as [6]:     I0 τ 2 α = h 1  Tpm − T f + Ut Tpm − Ta

(25.1)

  m  (1 + R)C p dT f = h 1 B Tpm − T f B dx

(25.2)

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where

and, M =

√

   = 1 + A f η f − Afb /A p

(25.3)

η f = tanh(Mw2 )/Mw2

(25.4)

h1 (2L + 2t)/Ks Lt

from Eq. (25.1),    Tpm − T f = I0 τ 2 α − Ut T f − Ta /(h 1  + Ut )

(25.5)

substituting Eq. (25.5) into (25.2) we get     F  B Ut T f − Ta − I0 τ 2 α dT f   + =0 dz m  (1 + R)C p

(25.6)

where F =

h1θ h 1 θ + Ut

(25.7)

integrating Eq. (25.6) and applying boundary conditions T f = T i ’ at x = 0 we get.   T f − Ta − I0 τ 2 α /Ut F  Ut Bx = exp −  m  (1 + R)C p Ti − Ta − I0 τ 2 α /Ut

(25.8)

now using the condition Tf = T0 at x = L in Eq. (25.8) we get,   F  Ut A p To − Ta − I0 τ 2 α/Ut = exp −   m (1 + R)C p Ti − Ta − I0 τ 2 α/Ut

(25.9)

The useful heat gain is determined from the equation   Qu = m (1 + R)Cp To − Ti

(25.10)

Q u = m  C p (To − Ti )

(25.11)

substituting Eq. (25.9) into (25.10) we get,

25 Thermal Performance Study of Double-Pass Solar Air Heater …

265

   Q u = FR A p Io τ 2 α − Ut Ti − Ta

(25.12)

where F R is called the heat removal factor and is given as 

m  (1 + R)C p FR = Ut A p

 1 − exp −

F  Ut A p m  (1 + R)C p

(25.13)

and efficiency is given by the formula Qu I0 A p

   η = FR τ 2 α − Ut Ti − Ta /Io η=

(25.14)

For the inlet temperature Ti, we get the following energy balance equation.   m  C p (Ti − Ti ) + m  RC p (To − Ti ) = m  (1 + R)C p Ti − Ti 

Ti = Ti + [R/(1 + R)](To − Ti )

(25.15)

substituting Eq. (25.15) into (25.14) we get   η = FR τ 2 α − (Ut /Io )[R/(1 + R)](To − Ti ) + (Ti − Ta )

(25.16)

The efficiency can also be calculated by the formula η=

m  C p (To − Ti ) Io A p

(25.17)

substituting Eq. (25.17) into (25.16) we get,   FR τ 2 α − Ut (Ti − Ta )/Io

  η= F U A R 1 + mR  Ct p p (1+R)

(25.18)

The outlet temperature can be calculated by using the equation   T0 = Ti + ηI0 A p /m  C p The mean fluid temperature is

(25.19)

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Tfm =

1 L ∫ Tf (x)dx L0

(25.20)

which on substituting relevant terms gives  Tfm = Ti + [R(1 + R)]



I0 A p η mC p

 +

ηI0 Ut F R

   FR 1− F

(25.21)

Similarly, mean plate temperature is defined as. 



T pm = Ta +

  I0  2 τ α−η Ut

(25.22)

25.3 Computational Solutions CFD is a dominant tool for the prediction of the fluid action in different situations, therefore enables an appropriate design. The calculations are executed to accomplish the relation among fluids (liquid and gas) and surfaces, and the total quantity of transmitted heat. The present work is carried out with the help of ANSYS FLUENT for high-performance numerical computation and visualization. The FLUENT is applicable for the complex geometries and finite volume method is used in FLUENT to simulate a two- or three-dimensional model with input parameters like physical properties, boundary conditions, and domain characteristics. In addition, proper choice of setup models and solution process can generate valuable outputs.

25.3.1 Geometrical Description A model of a double-pass solar air heater is prepared in ANSYS software. The mechanical properties of the materials used in this model are shown in Table 25.1. Three-dimensional modeling of double-pass solar air heater is done in ANSYS software is shown in Fig. 25.3 and the dimensions of fixed geometry are presented in Table 25.2. The wireframe model of passes is depicted in Fig. 25.4. Table 25.1 Material used in this geometry Material Glass Aluminum Wood

Thermal conductivity (W/mK) 1.15 202.4 0.173

Density (kg/m3 )

Specific heat (J/kgK)

2321

750

2719

871

700

2310

25 Thermal Performance Study of Double-Pass Solar Air Heater …

267

Fig. 25.3 Model of a double-pass solar air heater

Table 25.2 Values of various input parameters and constants

S. no

Input factors

Values

1

Width of the collector (m)

0.4

2

Length of the collector (m)

0.6

3

Air velocity, (m/s)

0.5

4

Solar radiation intensity (W/m2 )

950, 1100

5

Emissivity of glazing material

0.94

6

Emissivity of absorber plate

0.95

7

Absorptivity of absorber plate

0.95

8

Transmittance of glass

0.0875

9

Stefan Boltzmann constant (W/m2 K4 )

5.67 × 10–33

10

Initial temperature (K)

283 300

25.3.2 Meshing Details A non-uniform unstructured mesh with fine relevance is generated for meshing. The smoothing is medium so that good estimate of the gradients can be obtained. The meshed model is shown in Fig. 25.5.

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Fig. 25.4 Wireframe model

Fig. 25.5 Meshed model of heater

25.4 Results and Discussion The performance analysis of the double-pass solar air heater is done computationally by considering the data for the region of Almora, 29.77 ˚N and 79.42 ˚E longitude, which is located in Uttarakhand. ANSYS FLUENT software is used for the simulation of heater. The overall heat transfer, heat transfer through radiation, and thermal efficiency of the solar device are studied for various values of solar intensity. The

25 Thermal Performance Study of Double-Pass Solar Air Heater …

269

results obtained from the analysis for different sections are shown in Table 25.3. It is observed from the results that the high value of total heat transfer rate is at the outlet as compared to inlet with constant values of mass flow rates. It is also analyzed that the radiation heat transfer rate is high on absorber plate. The temperature contours at inlet temperature 300 and 283˚K are shown in Figs. 25.6 and 25.7, respectively. From the contour it is observed that the initial temperature of the air is 300˚K at inlet and when air flows across the solar air heater it absorbs the solar radiations. That is why the temperature of air increases maximum to 330˚K at the outlet. The temperature contour scale also shows the variations in temperature of flowing air inside the solar air heater. Table 25.3 Results of total heat transfer rate, radiation heat transfer rate, and mass flow rate S. no

Total heat transfer rate (W)

Radiation heat transfer rate (W)

Mass flow rate (Kg/sec)

1

Outlet

388.06

0.738

0.0073

2

Glass

25.78

76.49

–

3

Wood wall

2.72

4.62

–

4

Ap 1

–

1.4

–

5

Ap 2

–

85.74

–

6

Back wall

1.4

3.2

–

7

Outlet

0.738

0.0073

388.06

Fig. 25.6 Temperature distribution at T = 300˚K

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Fig. 25.7 Temperature distribution at T = 283˚K

The variations in temperature of air flowing inside the solar air heater are shown in Fig. 25.7. In this case, temperature varies from 283˚K to maximum 315˚K. Due to some heat losses, the air temperature comes to outlet remains 312˚K. The velocity distribution contour for initial temperature 300˚K is also obtained (shown in Fig. 25.8) shows an airflow having velocity 0.3–0.6 m/sec inside the collector. The thermal efficiencies of a double-pass solar air heater are also determined for mass flow rate 0.0073Kg/sec and specific heat of air 1.00 5KJ/kgK and for different solar intensity 990 w/m2 and 1100 w/m2 is presented in Table 25.4 and Table 25.5, respectively. The maximum thermal efficiency for solar intensity I = 990w/m2 is 68.8% at outlet temperature 324.89˚K for solar, i.e., when the temperature difference is maximum.

Fig. 25.8 Velocity distribution at temperature 300˚K

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Table 25.4 Thermal efficiency of solar heater for m = 0.0073 kg/sec and C = 1.005KJ/kgK S. no

Io = 990 W/m2 Ti (˚K)

To (˚K)

Temperature difference, T

ï (%)

1

300

312

12

33.18

2

300

314

14

38.71

3

302.1

316

13.9

38.43

4

302.6

318

15.4

42.58

5

303

320

17

47

6

303

322

19

52.53

7

300

324.89

24.89

68.8

Table 25.5 Thermal efficiency of solar heater for m = 0.0073 kg/sec and C = 1.005KJ/kgK S. no

Io = 1100 W/m2 Ti (˚K)

To (˚K)

Temperature difference, T

ï (%)

1

300

314

14

34.84

2

302.1

316

13.9

34.59

3

302.6

318

15.4

38.32

4

303

320

17

42.30

5

303

322

19

47.28

6

300

324.89

24.89

59.72

7

300

325

25

62.22

While the maximum thermal efficiency for solar intensity I = 1100 w/m2 is 62.22% at the outlet temperature 325˚K. Hence, the efficiency is mainly depending upon the temperature difference and solar intensity for the same mass flow rate and specific heat. The variations between efficiency and temperature difference for different values of solar intensity 990 and 1100 W/m2 are shown in Figs. 25.9 and 25.10, respectively. It is noted from the figure that the variations are linear in both the cases and also observed that as the values of temperature difference increase, the efficiency also gets higher.

25.5 Conclusion The analysis for double-pass solar air heater is investigated with the help of CFD ANSYS FLUENT tool at a geographic location of Almora district of Uttarakhand. From the analysis, it is concluded that the results of the efficiency of the solar air

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80

Efficiency (%)

Fig. 25.9 Efficiency vs. temperature, for I = 990w/m2

60 40 20 0

0

10

20

30

Temperature Difference (K) Fig. 25.10 Efficiency versus temperature, for I = 1100 W/m2

collectors is based considerably on the value of solar radiation intensity and temperature difference. The efficiency of the solar air heater is maximum 68.8% for solar intensity 990w/m2 and 62.22% for solar intensity 1100 w/m2 obtained at maximum temperature difference. The solar flat-plate collector will be more beneficial for various applications for cold climatic zone.

References 1. F. Chabane, N. Moummi, S. Benramache, Experimental analysis on thermal performance of a solar air collector with longitudinal fins in a region of Biskra Algeri. J. Power Technol. 93(1), 52–58 (2013) 2. B. Gupta, J. K. Waiker, G. K. Manikpuri, B. S. Bhalavi, Experimental analysis of single and double pass smooth plate solar air collector with andwithout porous medi. American J. Eng. Res. (AJER), 2(12), 144–149 (2016)

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3. S. S. Krishnananth, K. Murugavel, K. Kalidasa, Experimental study on double pass solar air heater with thermal energy storag. J. King Saud University Eng. Sci. 25, 135–140 (2013) 4. J. Kasperski, M. Nems, Investigation of thermo-hydraulic performance of concentrated solar air heater with internal multiple fin array. Appl. Therm. Eng. 58, 411–419 (2013) 5. C.D. Ho, C.W. Yeh, S.M. Hsieh, Improvement in device performance of multi-pass flat-plate solar air heaters with external recycl. Renew. Energy 30(10), 1601–1621 (2005) 6. M. Sajawal, T. Rehman, H. M. Ali, U. Sajed, A. Raza, M. S. Bhatti, Experimental thermal performance analysis of finned tubephase change material based double pass solar air heater. Case Study Thermal Eng. 15 (2019) 7. R. Daghigh, A. Shafieian, Thermal performance of a double-pass Solar air heater. J. Renew. Energy Environ. JREE. 3(2), 35–46 (2016) 8. V. Singh, A. Singh, A. Verma, Experimental investigation of double pass solar air heater with baffled absorber plate. IJSRSET. 3(8), 163–170 (2017) 9. A. Pandey, M. S. Hossain, V. V. Tyagi, N. A. Rahim, J. A. L. Selvaraj, A. Sari, Novel approaches and recent developments on potential applications of phase change materials in solar energy. Renew. Sustain. Energy Rev. 82, 281–323 (2018) 10. A. Gautam, S. Chamoli, A. Kumar, S. Singh, A review on technical improvements, economic feasibility and world scenario ofsolar water heating system. Renew. Sustain. Energy Rev. 68, 541–562 (2017)

Chapter 26

Modeling and Optimal Control of Vehicle Air Conditioning System Nassim Khaled and Harsha Mathur

Abstract Due to the proprietary nature of automotive software development, the control logic, plant models as well as physics-based models used for simulations are closed sources. For the most part, OEMs use Simulink® to develop their control and diagnostic logic (Khaled et al. Multivariable Control of Dual Loop EGR Diesel Engine with a Variable Geometry Turbo, SAE Technical Paper 2014–01-1357, 2014 and Khaled and Pattel, Practical Design and Application of Model Predictive Control: MPC for MATLAB® and Simulink® Users” Elsevier, ISBN: 978–0,128,139,189, 2018). As for plant models of the vehicle and its various subsystems, OEMs use many simulation packages such as ANSYS, COMSOL, SimScale in addition to MathWorks. This creates significant software integration challenges and revision control complexities. In this paper, we recommend the utilization of Simscape® toolbox from Simulink as a platform for simulating the vehicle air conditioning system. Moreover, we demonstrate the development of model predictive controller based on the two-phase model of the air conditioning system. The controller relies on a model that predicts the rate of change of temperature rather than the temperature when the compressor command is manipulated. Both the controller and the plant model are available for free download. The model leverages recent improvements in the two-phase modeling in Simscape and is an extension to the basic refrigeration model provided by MathWorks. The model includes variable displacement compressor, condenser, evaporator and expansion valve components that are provided in the library of Simscape. Issues faced during development and sizing of the refrigeration model are highlighted. Lessons learned are highlighted to aid the reader to scale the model for bigger vehicles. R134a is used as the refrigerant. The linearized model is used to develop a model predictive control (MPC) to minimize energy consumption while maintaining a good temperature reference tracking in the cabin. N. Khaled (B) Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia e-mail: [email protected] H. Mathur R and D Industry, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_26

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Simulation results for the controller are provided. Future work and recommendations are provided to conclude the work.

26.1 Introduction Most OEMs develop their ECU logics, control and diagnostic software in Simulink® . These logics include combustion, transmission, emission control, diagnostics, sensor and actuators management. They rely on plant model for the vehicle. The plant models can be either a set of differential and empirical (usually causal) equations that are solved using mimicking the response of the system or physics-based plant models (usually acausal) developed using multiphysics simulation packages such as ANSYS, COMSOL, AVL Boost and SimScale. These models are then converted into dlls that are then embedded into Simulink. These dlls create integration challenges to the OEMs control developers. MathWorks® has been focusing on advancing their multiphysics toolbox to address the need to develop the control logic and the plant models in the same environment. The two-phase Simscape toolbox has undergone several iterations of improvements. In Sect. 2, we discuss the two-phase plant model for the HVAC system of the vehicle. In Sect. 3 of the paper, we revisit Simscape® toolbox after the improvements introduced by MathWorks to the toolbox. We assess its capability to model phase change pertaining to the refrigeration system of a vehicle. We also assess the difficulty of building such models. We also look into the robustness of the model. The main components of the model and how to link them are outlined. In Sect. 4, we propose a new controller design for controlling the temperature of the vehicle cabin using the compressor. There are many control strategies to handle the nonlinearity of the air conditioning system. Abdolreza et. al utilized a hybrid fuzzy and PID controller [3]. Zhang et al. used optimal PID [4]. An MPC algorithm is implemented in [5]. MPC controller design to minimize energy consumption while guaranteeing occupant’s comfort is discussed in [6] and HVAC control design for OLEVs is discussed in [7]. Occupancybased HVAC control is utilized in [8]. A fuzzy-timing Petri net method which utilizes the methodology of distributed temperature is discussed in [9]. A control strategy on segregation of non-propulsion electrical hotel load energy efficiency for an electric vehicle is discussed in [10]. Fuzzy logic for MIMO HVAC model is implemented in [11], whereas a series of machine learning rules for optimum cabin temperature are listed in [12]. Noise reduction strategy to improve occupant’s comfort is discussed in [13]. Advanced control techniques for HVAC to facilitate comfort among the occupants are presented and compared with traditional control strategies in [14]. In this paper, we propose a new technique that relies on determining the transfer function that links the compressor command to the slope of temperature instead of temperature. This transfer function is used in designing the model predictive control for the temperature. The model predictive control is developed by following the steps outlined by the author in [2].

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In Sect. 5, the performance of the developed controller is compared to the traditional proportional controller. Finally, the paper is concluded by recommendations and future work. The Simscape model and controller are available for free download on the provided website [15].

26.2 Plant Model of the Air Conditioning System The plant model of the air conditioning system consists of basic HVAC components comprising a variable displacement compressor, condenser coil, expansion valve and evaporator coil. R134a is the refrigerant used in the original model which incorporates the effects of thermodynamic properties of the refrigerant along with the dynamics of heat transfer. The compressor is variable displacement with 2.2 KW power (small vehicle). Power of the compressor is related to the flow rate by the following equation, which is part of the compressor model and is computed from every iteration of the model: ˙ out − h in ) Pcompressor = m(h

(26.1)

where Pcompressor is the power of the compressor. m˙ is the mass flow rate of refrigerant. h out is the enthalpy at the outlet of the compressor. h in is the enthalpy at the inlet of the compressor. Table 26.1 summarizes the model parameters. The evaporator model consists of evaporator pipe, evaporator pipe wall and evaporator pipe conduction. The transfer of heat is done by evaporator pipe conduction which varies with thermal conductivity, area normal to the heat flow direction, temperature difference and thickness of the material layer. Heat flows from inlet to outlet with a positive value. Table 26.1 Model parameters

Parameter description

Modified model value

Expansion valve minimum throat area (mm2 )

0.8

Expansion valve maximum throat area (mm2 )

0.8

Condenser pipe length (m)

47.8

Evaporator pipe length (m)

42.6

Pipe diameter (m)

0.07

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The condenser part of the model also consists of the same components as those of evaporator. It is responsible for rejecting the heat to the environment and converting the refrigerant into liquid phase. The expansion valve in the model consists of variable local restriction, valve control block, mass flow sensor and cycle sensors. Variable local restriction block lowers the temperature and pressure of the refrigerant by restricting the variable flow area. There is no heat rejection to the outside environment. For more details of the model, the reader can download it from MATLAB central [12].

26.3 Nonlinearity of the Air Conditioning System One of the challenges with air conditioning control problems is the nonlinearity of the system. This nonlinearity is function of ambient temperature, power of the compressor, convection conditions (stationary versus moving vehicle), as well as system sizing. Existing control strategies in production or in the literature must take into account such nonlinearity to obtain adequate temperature tracking and energy consumption results (15–21). Utilizing the model developed in Sect. 2, we highlight the nonlinearity of the system at stationary conditions (vehicle is not moving). The controller used is a hysteresis-based proportional controller. The controller commands the compressor proportionally to the temperature error. The error is set to zero if temperature error is within ± 2 degrees. It turns off the compressor when the cabin temperature is below the target by 2°. We simulate the performance of the system at two ambient temperatures, 32 and 36 °C. Plotting the cabin temperatures, we observe from Fig. 26.1 that maximum positive temperature error is 2 °C (for both ambient temperatures), while the maximum negative temperature errors are −4 and −4.77 °C; the lower the ambient temperature is, the more the magnitude of error will be. To design the MPC controller, we will need a model that properly approximates the behavior of the system. Our controller will have cabin temperature as feedback and compressor command as the output. The structure of the controller is explained in detail in the following section. Herein, we focus on conducting the experiment to obtain a reasonable model. We use ambient temperature of 36 °C as a baseline for developing a model. The model has the cabin temperature slope as an output and the compressor command as an input. This model will help MPC controller predict the behavior of the system based on compressor command. −2 CabinTemperatureSlope = G(s) = CompressorCommand 20s + 1

(26.2)

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Fig. 26.1 Simulating the system at two ambient temperatures

Note that the slope of the cabin temperature is obtained by substracting the currently measured cabin temperature from the previously measured cabin temperature (measurements are 10 s apart). Figure 26.2 shows the good performance of the model in identifying the slope of the cabin temperature as a function of compressor command. The time constant of the refrigeration system is 20 s as per Eq. 26.2.

26.4 MPC Design for Cabinet Cooling MPC controller allows embedding the constraints of both the input (temperature slope and tracking) and the output (magnitude of the compressor flow). Using the above linear model, we design the MPC controller. At each execution of the controller (which is every 10 s), the cabin temperature is measured, and then, the slope of the temperature is computed. This is provided as a feedback to the controller. At the same time, target temperature is subtracted from the cabin temperature. This constitutes the desired slope. This is provided to the controller as a reference. The structure of the controller is provided in Fig. 26.3. A discretized version of the transfer function is used in the design of the MPC controller. MPC designer tool was used. The sample time is 10 s. The prediction horizon and control horizon are 20 and 4 samples, respectively. The prediction horizon is recommended to be greater than the time constant of the system ([2]. We chose the prediction horizon to be 20 × 10seconds = 200 s.

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Fig. 26.2 Performance of the linear model in predicting the slope

Fig. 26.3 Structure of the MPC controller

26.5 Simulation Results for the Controller We set the ambient temperature to 36 °C and run the simulation with MPC as the controller. The maximum error we got was 0.6 °C (Fig. 26.4-a). To make sure that the controller can handle other ambient conditions and was not tailored for this specific ambient temperature, we repeat the simulation at ambient temperature of 32 °C. Similar tracking results were obtained (Fig. 26.4b).

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Fig. 26.4 Tracking results of the MPC controller

Table 26.2 Tracking and energy consumption results

Parameters

Hysteresis-based proportional controller

MPC controller

Tracking root mean square error (C)

0.86

0.53

Energy consumption in 2 h (KJ)

156

130

Comparison of Baseline and MPC Controllers Table 26.2 summarizes tracking and energy consumption for the hysteresis-based proportional controller and MPC controller. Both simulations were run at ambient temperature of 36 °C for 2 h. Energy consumption was reduced by 20% by reducing the undershoots of cabin temperature (overcooling). These results are simulation comparison of a baseline control approach included in the toolbox of MATLAB and a new MPC strategy. Without doing a testing on the production vehicle, we cannot generalize the advantages of MPC over production control strategy of air conditioning system.

26.6 Summary and Future Work In this paper, we developed a Simscape model for the air conditioning system of a small vehicle. The open-source cooling model was sized for a 2.2 KW compressor. We leveraged MATLAB® Simscape recently improved two-phase library to develop

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the model. We also developed a linear model for the cooling system. The model was used to design MPC controller for the cabin temperature. The MPC controller uses the slope of temperature instead of temperature as the measured output due to the nonlinearity of the cooling cycle. Future work will include sizing the model based on 2014 Chevy Cruz data. The proposed MPC controller will be retuned and tested on the vehicle. The closed-loop simulation model is available as open source for download through the author’s MATLAB central page. Acknowledgements The authors wish to acknowledge the support from Prince Mohammad Bin Fahd University.

Reference 1. N. Khaled, M. Cunningham, J. Pekar, A. Fuxman, et al.,Multivariable control of dual loop EGR diesel engine with a variable geometry turbo, in SAE Technical Paper 2014–01–1357 (2014) 2. N. Khaled, B. Pattel, Practical design and application of model predictive control: MPC for MATLAB® and Simulink® Users, Elsevier, ISBN: 978–0128139189 (2018) 3. A. Rahmati, F. Rashidi, M. Rashidi.A hybrid fuzzy logic and PID controller for control of nonlinear HVAC systems, in SMC’03 Conference Proceedings, IEEE International Conference on Systems, Man and Cybernetics, vol 3, pp. 2249–2254 (2003) 4. Z. Jun, Z. Kanyu, A particle swarm optimization approach for optimal design of PID controller for temperature control in HVAC. Third IEEE Int. Conf. Meas. Technol. Mech. Autom. 1, 230–233 (2011) 5. T. H. Lim, Y. Shin, S. Kim, C. Kwon, Predictive control of car refrigeration cycle with an electric compressor. Appl. Thermal Eng.127, 1223–1232 (2017) 6. D. Lee, M. C. Lim, L. Negash, H. Choi, EPPY based building co-simulation for model predictive control of HVAC optimization, in 2018 18th International Conference on Control, Automation and Systems (ICCAS), Daegwallyeong, pp. 1051–1055 (2018) 7. M. H. Park, E. G. Shin, H. R. Lee, I. S. Suh, Dynamic model and control algorithm of HVAC system for OLEV® application. in IEEE ICCAS 2010, pp. 1312–1317 8. D. Ardiyanto, M. Pipattanasomporn, S. Rahman, N. Hariyanto, Occupant-based HVAC set point interventions for energy savings in buildings, in 2018 IEEE International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), pp. 1–6 9. K. Watanuki, T. Murata, Fuzzy-timing Petri net model of temperature control for car air conditioning system, in IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028), vol. 4, pp. 618–622 10. N. Aziah, H. M. I. Nizam, S. Ahmad, R. Akmeliawati, S. F. Toha, B. S. K. K. Ibrahim, M. K. Hassan, Control strategy of segregation on HVAC energy efficiency as non propulsion electrical hotel load in EV.in 2012 IEEE International Conference on Power and Energy (PECon), pp. 611–615 11. Z. M. Durovic, B. D. Kovacevic, Control of heating, ventilation and air conditioning system based on neural network, in 7th Seminar on Neural Network Applications in Electrical Engineering, pp. 37 (2004) 12. D. Hintea, J. Brusey, E. Gaura, A study on several machine learning methods for estimating cabin occupant equivalent temperature. in 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO), vol. 1, pp. 629–634 13. S. Nakagawa, T. Hotehama, M. Kamiya, Assessment of auditory impression of the coolness and warmness of automotive HVAC noise, in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4171–4174

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14. C. Petrie, S. Gupta, V. Rao, B. Nutter, Energy efficient control methods of HVAC systems for smart campus, in 2018 IEEE Green Technologies Conference (GreenTech), pp. 133–136 15. https://www.mathworks.com/matlabcentral/fileexchange/73885-hvac-simscape-model-for-asmall-vehicle.

Chapter 27

Experimental and CFD Analysis of Artificial Dimples Surface Roughness by Using Application of Domestic Solar Water Heater M. Arun, Debabrata Barik, K. P. Sridhar, and G. Vignesh Abstract Solar energy is widely perceived as one of the most motivating sources of elective energy and an eco-friendly sustainable power source. The least demanding and most effective technique is the conversion of solar energy into heat energy. From the past, we can understand that in the case of solar thermal conversion, the solar-controlled electrical transformation system has a profitability of 17%. Keeping this fact in mind, we have ended up in the following research. In this research, experimental and numerical comparisons are to be done for heat enhancement devices (parallel normal plain tube versus tubes with outer dimples with water) to enhance the heat transfer rate. Introducing heat enhancement devices will create turbulence and enhance the heat transfer rate. A comparison of the two models is carried in terms of flow and heat transfer by using computational fluid dynamics (CFD) and experimental analysis. This will enable us to design a setup where we attempt to increase the efficiency from 70 to 80%.

27.1 Introduction In this article, non-concentrating and concentrating ICSSWHS with phase change material (PCM) and strategies of heat retention were reviewed. The potential of M. Arun (B) · D. Barik · G. Vignesh Department Mechanical Engineering, Karpagam Academy of Higher Education, Coimbatore 641021, India e-mail: [email protected] D. Barik e-mail: [email protected] G. Vignesh e-mail: [email protected] K. P. Sridhar Department of ECE, Karpagam Academy of Higher Education, Coimbatore 641021, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_27

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reliability of ICSSWHS systems for domestic hot water applications was found to be at lower cost after the recent developments [1]. It suffered high thermal losses during nighttime and showed the impact of utilizing nanofluids as transfer of heat liquid, the impact of changing the structure of the flat plate for better radiation retention. Furthermore, the technique for limiting thermal radiation, the utilization of polymer, the utilization of small-scale liquid stream channels, and the utilization of phase adjusting material (PCM) to supply heat during the night without tank by using CFD software [2]. Author discussed different types of solar-based water thermal heaters that are available on the business market to meet the interest of different customers, such as level flat plate specialist, absorption collector, empty pipe collector, and built-in collector storing [3]. It is proposed to build a financially qualified cum simple V-trough solar-based thermal water heater that uses a restricted course system [4]. Using the efficiently produced V-trough reflector to integrate the sunlight-based safeguard could boost the view of solar-based thermal water heater [5]. This paper shows complexities in the technical analysis, test research, and cost examination of the stagnant V-trough solar thermal-powered water system [6]. The test result showed promising results in both V-trough reflector optical efficiency and the general thermal display of the solar-based thermal water heater inspecting and addressing various types of solar energy power authorities, including both collectors for non-concentrated and collectors for concentrated [7]. These are considered as far as optical development, decrease in heat embarrassment, increase in heat recovery, and various solar following systems are concerned [8]. In both cases, the authority’s thermal exhibition was contemplated to assess the effect of stream on the gatherer’s working temperature and abilities [9]. Finally, the solar water heater problems are related to the solar panels and collectors, solar tank and pumps, including some leakages, low hot water temperature, or no hot water and low flow rate. Most frequent problems on the solar water heater are faced with the heat transfer [10]. Many problems are acted in solar water heater such as controllers, sensor, pumps, less installation, improper maintenance, and freeze damage. From this case study, the major problem of solar water heater is no hot water because of poor thermal performance throughout the flat plate collector.

27.2 Design Model and Experimental Model The parallel collector is equipped for transporting collector fluid through a network of parallel pipes from the bottom of the collector to the rim. Note that the pipes at the top and bottom are wider than vertical. Fluid mechanics endorse a higher flux rate for the tubing. That is because the intake pressure in the first tube is highest and the outtake pressure in the last tube is lowest. The pressure differential is moderated where the top and base pipes are wide and the flow rate is equal in each one of the parallel pipes. These collectors may be attached in series since the tubes on top and bottom are so growing. The parallel flow with the collector often has issues such as expense and leakage. Half inch and two inch of cotton tubing expense, not to mention

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Fig. 27.1 Parallel flow collector

the thousands and all of the solder and special T fitting by using CFD Software. A low, exposed leak may become disastrous mess on one of the T fittings as shown in Fig. 27.1. Results show that the flow of specific flat pipe and tube is contrasted to internal dimples and external dimples. Finally, to improve the performance of the solar water heater by using the CFD analysis, experimental result will be taken. Finally compare that experimental and numerical analysis of the result in this section. At inlet duct is velocity flow rate condition ranging 5, 10, 15, 20, 25 l/h at outlet duct of pressure outlet condition with specified pressure value is 101325 pa, top surface of fluid medium(water) of solar radiation heat flux applied through copper riser tube which provides a solar radiation intensity is 798, 830, 847, 840 W/M2 .

27.3 Governing Equation   ∂ ρn v¯ j =0 ∂yj    δ T¯ δ  ∀u δ(Dq,n T¯ ) δ ¯ αeff,n ρn v¯ k Dq,n T = + δyk δyk δyk σi,u δyk     ∂ v¯ j δ v¯ j 2 ∂ v¯ j δ Q¯ ∂ v¯ k ∀n − ∀n + v¯ k + + ∂ jk − ρn vj v k δyk δyk ∂ yk ∂yj 3 ∂yj

(27.1)

(27.2)

where γ , v, ρ, T, and Q indicate the thermal expansion of the coefficient, components of velocity, density, temperature and fields of pressure, respectively, where n is the water fluid mixture. The water fluid is the turbulent flow and incompressible fluid on the operating condition of fluid properties. The governed momentum equation is region of porous modeled using the Darcy–Brinkman–Forchheimer model as Eq. (27.4). ∂  ρn δ Q¯ δ v¯ j v¯ k = − + 2 ∂ yk  δy j δyk     ∀n δ v¯ j 2 ∀n ∂ v¯ j δ v¯ k ρn De − + ∂ jk − √ v j vk + (ργ )n (T − To )h  δyk δy j 3 l ∂yj l

(27.3)

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where l, Dd, and , indicate the permeability, coefficient of inertia, and porosity, respectively. The dimples pipe zone of permeability and inertia coefficient can be obtained by the correlation of Kozeny–Carmen Eqs. (27.5) and (27.6). 1.751 De = √ 150 1.5 l=

 2 E q2 150(1 − )2

(27.4)

(27.5)

where E q is the spherical dimple inside the pipe zone. Assuming the dimple pipe region in local thermal equilibrium with surroundings of the dimples tube because of the zigzag and large thermal conductivity of the dimples hollow pipe, equation of energy in dimples tube is written as Eq. (27.7)    Dq,n T = .(∝eff T ) . ρn w

(27.6)

where ∝eff is the effective of dimple pipe thermal conductivity, which is defined as Eq. (27.8) ∝eff = αeff,n + (1 − )αq

(27.7)

where αeff,n and ∝eff are thermal conductivity of fluid and dimples pipe, respectively. To calculate the Reynolds number in Eqs. (27.2) and (27.3) at the following equation is used Eq. (27.9)  −ρn v¯ j v¯ k = ∀u

∂ v¯ j ∂ v¯ k + ∂ yk ∂yj



  2 ∂ v¯l − δ jk ρn l + ∀u 3 ∂ yl

(27.8)

where l is the kinetic energy of turbulent that is given by l=

 1  2 v + w2 + x 2 2

(27.9)

In addition, the realizable l −  model is used for the turbulent flow of viscosity. It includes the addition of two equations at kinetic energy of turbulent flow l and dissipation rate of turbulent flow  as ∂ ∂ (ρn  v¯ k ) = ∂ yk ∂ yk

   2 ∀u ∂ ∀n + + ρn  D1 U − ρn C2 √ σl ∂ yk l + w

(27.10)

where Hk indicates the production of turbulent flow of kinetic energy is obtained as Hl = −ρn v j vk

∂ v¯ k ∂yj

(27.11)

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Hl = ∀u U 2

(27.12)

∀u indicates the viscosity of eddy is calculated as ∀u = ρn D∀

l2 

(27.13)

The value of constant is applied by realizable l −  model is listed below  D1 = max 0.42,



l  ,  = U , U = 2U jk U jk ,  +5

D2 = 1.9, σl = 1, σ = 1.2

(27.14) (27.15)

27.4 Punching Die Machine Tool A punching machine is a punching tool for the development of the mechanical part forming features needed. Punching is a method of shaping using a punching press to push a device, known as a strike, to produce an impact through the object. I designed and manufactured the punching die machine tool to create a dimple on the riser tube of copper upper surface. It helps to create the dimple accurately with smooth finishing. Copper has many attractive properties for heat exchangers including thermal stability and longevity. Copper is an excellent power conductor. The high thermal conductivity of copper means that heat passes fast. Other desired properties include the resistance to corrosion, resistance to bio fouling, optimum allowable stress and external strain, strength of breakup and fatigue, stiffness, thermal expansion, precise thermal power, antimicrobial properties, strength of the tensile, yield strength, melting point, alloy efficiency, facile manufacturing, and ease of contact. The thermal conductivity of copper is 60% greater than the aluminum level and 3000% greater than stainless steel as shown in Fig. 27.2. The downside of CFD analysis is that at various occasions it can show the whole flow field of the subtleties of the fluid stream, measure 7200-time steps of the dispersion of the water radiator temperature and speed vector appearing in it. Water cylinders and the structure of the water supply structure. Since the heat on the upper half of the pipe is close to the weight of the container. Furthermore, the fluid temperature is increased and thickness has been decreased for solar water heater. The fluid temperature increases rapidly, the thickness decreases, and under the gooey strength and lightness, the liquid travels downward along the splitter and afterward there will be a certain stream at the passage of a capacity tank, on the grounds that a capacity tank is not warmed up directly. There is hot stratification from the base to the top, the temperature increases bit by bit from the base to the top, the temperature inside

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Fig. 27.2 Dimension of dimples tube

the water is 24–26 °C, the base temperature is small, the liquid of amazing thickness flows into the container, rises along the upper divider after heating in the pipe, so that the liquid can be cooled.

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27.5 Experimental Results 27.5.1 Comparison of Plain Tube and Dimples Tube at 798, 830, 847, 840 W/M2 A level plate solar-based water heater with flat parallel cylinder and parallel dimple riser pipe is arranged exploratory. The test plain and dimples tube were designed and created to support the results obtained by reproducing CFD. The test performance requires the corresponding pieces like temperature sensor, U-tube manometer, rotameter, copper tubes, parabolic reflective glass, thermal imaging camera, sun meter, linear tungsten halogen lamp (300 w), punching die machine. A punching machine is a machine tool for punching to produce form-features needed as mechanical element. Punching is a method of shaping that uses a punch press to move a device called a punch to create a dimple through the piece of work. I designed and manufactured the punching die machine tool to create a dimple on the tube of copper upper surface. It helps to create the dimple accurately with smooth finishing as shown in Fig. 27.3. The channel can be represented as a biofilament by and wide. This contains two principles, the liquid falling cool and the liquid rising warmly. With a shear layer between them, these two flows continuously exist together. Limit layer flow is the smooth motion’s main thrust. The mass liquid rolling in from a tank of capacity enters the center of the cylinder and on the warmed dividers is brought into the limit layer. There is a layer of hot liquid near the sidewall base for uniform divider warming. The velocity of the surface is very close to zero, the stagnation layer is about 1/2 of the pipe, with the simultaneous existence of two kinds of streams the other way around, and the water has a relatively enormous aggravation when the tank is passed. As the water from the reservoir is pumped out at a very high pressure, it is eventually controlled using a rotameter (at the rate of 5, 10, 15, 20, and 25 LPH). The radiation of intensity is found to be 798, 830, 847, 840 W/m2 and preheating is done for 30 min. Now the temperatures (outlet and surface) are measured in both

Outlet

Parabolic Glass Inlet

Rota meter

Fig. 27.3 Experimental setup

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Table 27.1 Dimension of solar heater Plain tube (fluid medium) inner diameter

12.5 mm

Plain tube outer diameter

13.5 mm

Header plain tube diameter

25 mm

Plain tube length

12000 mm

Collector length

1800 mm

Collector width

1200 mm

Absorber plate length

1650 mm

Absorber plate width

1000 mm

Absorber plate thermal conductivity

386 W(M K)−1

Plate material density

8954 kg M−3

Thickness of plate

0.0005 m

Spacing between absorber plate and glass

300 mm

Insulating material

Glass wool

Insulating material thermal conductivity

0.044 W(M K)−1

Insulating material thickness

50 mm

Insulation material density

200 kg m−3

Absorber plate area

960 × 1200 mm

Parabolic type collector

Single glass with different intensity and different flow rate

plain tubes and dimples tubes after preheating. Hence, the results are recorded in Table 27.1. Now we found that temperatures of the dimple’s tubes are increased up to 4 °C when compared with plain tubes. This is because the Reynolds number of the plain tube is low and therefore it is a laminar flow. However, when the dimples are created in the same plain tube the Reynolds number of the dimples tube is high and therefore it is a turbulent flow. Thus, the heat transfer rate of a dimples tube is high. Although the surface temperature of both plain tube and dimples tube is seemed to be same, the water temperature is higher for the dimples tube. Thus, efficiency is higher for the dimples tube.

27.5.2 Velocity and Temperature of Different Cross Section Along with the Evacuated Dimples Tube The display of water-in-glass emptied tube. Solar water heaters are based on the cylinder cluster’s optical productivity, the cylinders’ warmth loss, the tank’s warmth misfortune, and the warmth process’s viability from the cylinder to the tanks. The optical efficiency of the cylinder display used in this analysis is moderately poor as the reflector behind the cylinder cluster was just a level finished aluminum sheet that secured only 80 percent of the cylinder’s width. A mounted reflector that

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Fig. 27.4 Temperature and velocity of plain tube

limited reflector misfortune from the hole between contiguous safeguard tubes in the exhibit could provide higher optical proficiency. Two stream confines affect the liquid exchange process between the cylinders and the tank. On a 45o wide plane, the cylinders are embedded in the tank and therefore the thermosyphon dissemination between the cylinders and the tank cannot enter the tank base in Fig. 27.4. Impact factors of the temperature field and the speed field of the sun-based water warmer for the most part have vacuum and emanation levels, width and duration, top safety, scale, tilt edge, and so on the level of vacuum and outflow rates, top heat, scale influence heat disaster to impact the temperature field, length and range, tilt edge of the structure influence the speed field. This paper studies the effect of the radiation force on the area of temperature and rate of the sun-driven air warmer water radiator. The numerical recreation of the warmth transition limit conditions is separately settled as the solar light-based radiation force 850 W/m2 , expecting a uniform conveyance of the warmth stream in the upper part of the emptied tubes or all the cylinder, and other limiting conditions are equivalent, a similar plain and dimples container of different light power, hub speed and temperature at kelvin (K).

27.5.3 Velocity and Temperature of the Junction in Dimples Tube Distinctive radiation powered by the sun influences the speed and temperature at the 45° point of the water warmer emptied cylinder slant to exceed the bearing. Since there are two requirements in the pipe at the same time, that contribute to a greater unrest at the intersection of the empty cylinder and a power tank just like the supply stream. Thus, for the most part, this work of examining the sun-oriented radiation force affects the speed and temperature at the intersection of the cleared cylinder and the water tanning to the wide bearing. The velocity and constant temperature formed at the cylinder intersection and a power tank are shown in Fig. 27.5. Above Fig. 27.6 shows flow analysis of plain tube and dimples tube, respectively. In normal tube, flow of fluid is laminar and Reynolds number is below 2000. In dimples tube, the flow of fluid is turbulent as the Reynolds number is above 4000, and the turbulence in the flow is shown in the figure of dimples tube and it is indicated

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Fig. 27.5 Temperature and velocity of dimples tube

Fig. 27.6 Flow of plain tube and dimples tube

in the yellow color. Results show that heat transfer efficiency of dimples tube is higher than plain tube. The water from the reservoir is pumped out at a very high pressure and it is eventually controlled using a rotameter (at the rate of 5, 10, 15, 20, and 25 LPH). The radiation of intensity is found to be 798, 830, 847, 840 W/m2 and preheating is done for 30 min as shown in Figs. 27.7 and 27.8. Now the temperatures (outlet and surface) are measured in both plain tubes and dimples tubes after preheating. Hence, the results are recorded in Table 27.1. Now we found that temperatures of the dimple’s tubes are increased up to 4 °C when compared with plain tubes. This is because the Reynolds number of the plain tube is low and therefore it is a laminar flow. However, when the dimples are created in the same plain tube the Reynolds number of the dimples tube is high and therefore it is a turbulent flow. Thus,

Fig. 27.7 Surface temperature of plain tube

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Fig. 27.8 Surface temperature of dimples tube

the heat transfer rate of a dimples tube is high. Although the surface temperature of both plain tube and dimples tube is seemed to be same, the water temperature is higher for the dimples tube. Thus, efficiency is higher for the dimples tube. The comparison of velocity and temperature for the base model at different mass flow rate conditions and the models with 5 mm length dimple for 3, 6, 9 relative pitch hardness is shown in Figs. 27.9, 27.10, 27.11, and 27.12. It is seen that the sunoriented copper tube water radiator fitted with dimples has higher hot performance for the entire range of mass flow rates as compared with the flat cylinder. This is because of the way dimple proximity advances disruption in the fluid stream causing the creation of whirl motion in the region of the copper tube’s upper limit layer surface. Subsequently, this results in a thorough mixing of water particles around the dimples, resulting in an increased heat transfer from the copper tube to the fluid flowing through the air. Convective comparison for base model at different mass flow rate conditions and models of 5 mm ranges dimple for 3, 6, 9 relative hardness pitch. The effect of the relative unpleasantness pitch on improved heat movement is verified Fig. 27.9 Velocity versus temperature at 798 W/m2

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Fig. 27.10 Velocity versus temperature at 830 W/m2

Fig. 27.11 Velocity versus temperature at 847 W/m2

by the Reynolds number for convective warmth movement from the copper cylinder to the liquid medium under various flow rate conditions as shown in Table 27.2, and clearly lower estimates of the relative harshness pitch result in reduced estimates of the Reynolds number due to increased choppiness rates in the fluid. Similarly, the Reynolds number increases dully with an increasing flow rate that is inferable from a higher heat movement frequency.

27 Experimental and CFD Analysis of Artificial …

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Fig. 27.12 Velocity versus temperature at 840 W/m2

Table 27.2 Comparison of plain tube and dimples tube Power in radiation intensity W/m2

Mass flow rate LPH

Velocity (m/s)

Analyzing temperature result °C

Experimental result oC

798

5 10 15 20 25

830

Surface temperature oC

Plain tube

Dimples tube

Plain tube

Dimples tube

0.18 0.342 0.488 0.637 0.786

43 44 46 48 51

46 48 50 51 52

45 46 48 50 52

47 49 51 53 56

64–65 64–69 68–70 71–72 79–82

5 10 15 20 25

0.182 0.342 0.488 0.637 0.786

45 48 50 51 54

47 49 51 53 55

46 47 49 51 53

48 49 50 53 55

72–73 75–76 76–78 78–88 86–91

840

5 10 15 20 25

0.182 0.342 0.488 0.637 0.786

48 49 53 56 55

51 53 56 57 59

46 49 51 53 55

48 50 52 54 56

80–82 83–84 86–87 87–88 95–97

847

5 10 15 20 25

0.182 0.342 0.488 0.637 0.786

49 51 53 55 57

51 52 55 58 61

48 49 52 54 55

49 50 53 55 58

88–90 90–91 91–92 93–95 99–101

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27.6 Conclusion For different power 3000, 3300, 3600, and 4500 W, the parallel value of radiation intensity is 798, 830, 840, and 847 w/m2 and mass flow rate of heat exchanger is controlled by rotameter by 10, 15, 20, 25 Lph. After 30 min, preheating process is required. Then, water inlet temperature and outlet temperature can be measured for plain tube and dimples tube and the result shows (Table 27.2) that temperature of the dimples tube is increased up to 3, 2, 1 °C due to the change in the mass flow rate. Despite the fact that the exams and procedures given are very great and include a lot of amazing assets to ensure the constant needs of the water heater system based on solar system, a few upgrades can be made at the moment. In this paper, I will review a portion of the findings that have been provided that can be further improved or extended. This field also brings out some interesting points of analysis that demand further study.

References 1. Chopade V, Patil SD (2020) Numerical analysis of heat transfer enhancement in artificially roughened solar air heater. In: Advances in energy research, vol 2, pp 297–309. Springer, Singapore 2. H.S. Arunkumar, K.V. Karanth, S. Kumar, Review on the design modifications of a solar air heater for improvement in the thermal performance. Sustain Energy Technol Assess 39, 100685 (2020) 3. Z. Guo, A review on heat transfer enhancement with nanofluids. J. Enhanc. Heat Transf. 27(1) 4. M.T. Gevari, T. Abbasiasl, S. Niazi, M. Ghorbani, A. Ko¸sar, Direct and indirect thermal applications of hydrodynamic and acoustic cavitation: a review. Appl. Therm. Eng. 171, 115065 (2020) 5. X. Sun, J. Liu, L. Ji, G. Wang, S. Zhao, J.Y. Yoon, S. Chen, A review on hydrodynamic cavitation disinfection: the current state of knowledge. Sci. Total Environ. 139606 6. T. Trzepieci´nski, Recent developments and trends in sheet metal forming. Metals 10(6), 779 (2020) 7. E.T. Akinlabi, R.M. Mahamood, Friction stir welding, in Solid-state welding: friction and friction stir welding processes 2020, pp. 39–73. Springer, Cham 8. V. Chopade, S.D. Patil, Numerical analysis of heat transfer enhancement in artificially roughened solar air heater. Adv Energy Res 2, 297–309 (2020) 9. V. Kumar, Nusselt number and friction factor correlations of three sides concave dimple roughened solar air heater. Renew. Energy 135, 355–77 (2019) 10. G.K. Chhaparwal, A. Srivastava, R. Dayal, Artificial repeated-rib roughness in a solar air heater–a review. Sol. Energy 194, 329–59 (2019)

Chapter 28

Secure Privacy Analysis of HR Analytics—A Machine Learning Approach V. Kakulapati

Abstract One of the branches of analytics is HR analytics, which is developing the system HR units in organizations function, principal to sophisticated proficiency, and improved outcomes overall—the usage of analytics in human resources for years. Though the assortment, processing, and data analysis have been generally manual and specified the nature of HR dynamics and HR KPIs, the approach has been constraining HR. Now is the prospect to effort predictive analytics in categorizing the employees furthermost likely to grow promoted. Here, we apply privacy-preserving techniques to analyze the employee information for improving his/her position in the organization. The employee data contains rewards and talent, work attitude and work culture, and service worthiness credentials. From turnover rates and workforce characteristics to payroll and employment history, never before have HR professionals had such unfettered access to personal information. In this work, we are applying homomorphic encryption, which facilitates industries a secure and informal way to execute analytics on data deprived of having to decrypt it. It is providing computation capability on the encrypted ciphertext, as a candidate to perform secure analytics and monitoring on HR sensitive data.

28.1 Introduction Every organizational human resource departments require to protect the employee’s data, talent management, the attitude of the employee, and the work atmosphere that is prospective of turnover. Without estimating, the proficiency of employees is affected by the turnover of the organization that reveals that employees are the primary resource of the industry, and productivity depends on employee performance.

V. Kakulapati (B) Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad 501301, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_28

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Consequently, employers are anticipating protection for their efforts in the form of job safety, recognizing the performance for a higher position, etc. If the HR department takes care of all the employee needs and provides benefits, it has an encouraging impact on the workers prone to work, and also employee put their maximum efforts to increase the turnover. Subsequently, the HR department protects the job security issue, which in turn, more performance can accomplish, which shows that enrichment of performance of employees through facilitating job security. Providing the security and privacy of employees is a major challenging task for the human resource department of an organization. Without the efficiency of the employees, all the other resources are in vain to fulfill the targets of the organization The main task of HR analytics is an associative method to assimilate policy for refining the employee performance analysis to progress mutually, such as employee as well as organization. Every organizational human resource department can assess the talent management of employees, workforce analytics, and people analytics. The main job of human resource management plays a crucial role in the organization for recruiting talented, employee training and development, job scheduling, employee retention, meeting, rewards, and employee benefits. These analytics encompassed quality prognostic modeling were predicting the values of changing strategies or conditions. Generally, traditional human resource management efforts based on the turnover of the organization and employee recruitment cost. The lack of many organizations is a reliable and employee point of view, which in turn HR analytics to accomplish employee’s performance enhancement. The main task of HR is to advance IT and business investigative talents and proficiencies to yield better turn over [1–3]. Additionally, the development of new technologies is developing when association with analytics exponentially enhanced HR persistence. HR analytics produces perceptions that cannot attain through traditional methods [4]. Three substantial transformations have generated starvation for prognostic analytics in HR [5]. HR management required to understanding information to adopt predictive analytics, which suitable for the organizational benefit [6]. The key functionalities of HR, such as procuring and resourcing, humankind supervision, recital and knowledge, personnel, and time management, typically usage of talent, these are exclusively assimilated with each other. The usage of analytical knowledge and accepting most extreme profits from the HR information required substantial corporate performance [7]. HE (homomorphic encryption) [8] is a form of encryption as it allows calculations on encoded data without decoding it. Generally, this encryption is used for privacy-preserving computations in this work, utilizing the homomorphic encryption to protect employee personal information. During the calculation, is it required to unscramble the information, and the outcome is creating in encoded structure? Protecting employee confidentiality is the rule that bound by what method broadly HR people can search an employee performance, employees’ activities monitoring, knowledge, or communication and their interests, especially but not entirely in the organization.

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28.2 Related Study Many researchers developed more on foreseeing the staff turnover by utilizing ML methods and also perform the comparative analysis of different ML algorithms. The main aim of HR analytics, such as statistical methods, demonstrating, and retrieving the hidden patterns of HR data, shows significant importance [9]. The main concerns of human resource information [10] are operational processing that involves the accomplishment of data into the system and store the data for further analysis purposes. Human resource management required more assessable data, and then, with this data set, HR can retrieve hidden patterns and also extract suitable patterns to discover apparent patterns. In [11], defined an overview of talent management and discovered the association of extracting the employee attitude [12] such as laziness and taking more leaves, poor performance, occupancy, and demographics on turnover in a speedily increasing segment which is software companies. In [13], predicted employee intended revenue by applying nonlinear cataloging and regression algorithms. A predictive model [14] for Swedbank employee attrition is investigating by applying the machine learning random forest, SVM, and multilayer perceptron (MLP) models with 98.6% accuracy. SVM using a kernel function to train with allocated classes by disjoining through a decision boundary [15]. For predicting employee attrition, numerous techniques are utilizing for investigation such as linear, Gaussian, and polynomial kernel [16] and random forest (RF), which utilizes multiple decision trees to train data [17], and each decision tree has a classification label for a specific dataset, and then, the RF selects which class had the maximum votes from the decision trees [18].

28.3 Methodology 28.3.1 Logistic Regression It is mainly used to predict and describe a categorical dependent variable and give better outcomes when the variable is binary. The main benefit of this regression is not limited by the normality hypothesis, which is a fundamental hypothesis in the regression analysis. Logistic regression is cooperative non-metric variables, for example, nominal or categorical variables, by coding them into dummy variables. One more benefit of this regression is that it directly envisages the likelihood of an event taking place. To ensure, the dependent variable is likely in the range of zero and one and characterizes an association between the dependent and independent variables. That can represent an S-shaped curve, which utilizes an iterative procedure to estimate the ’most probable’ values of the coefficients. These outcomes are in using a ’likelihood’ function in fitting the calculation relatively utilizing the sum of the square’s method of the regression.

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28.3.2 Homomorphic Encryption The importance of homomorphic encryption is increased along with the growth of information services that are providing on remote storages (e.g., cloud computing service) since the technique can be used to handling sensitive information without decrypting it. However, unfortunately, the computational performance of existing fully homomorphic encryptions is unrealistic to be used in real-world applications. Though many researchers put their efforts to improve the performance of existing techniques [19, 20], it seems hard to give an efficient scheme due to their structural limitations. Fortunately, there are still many valuable applications where somewhat homomorphic encryptions are still valid, and it is relatively easy to design practical, somewhat homomorphic encryption. For the above discussion, some works aim to design efficient, somewhat homomorphic encryptions that can provide adequate performance, so that they can apply to practical applications [21].

28.4 Implementation Result For implementation purposes, HR data set is taken from UCI machinery, and data set is preprocessed for removing unnecessary elements, duplicate elements, and noisy elements. Then, applying the logistic regression (Figs. 28.2 and 28.3) for better classification of HR data. Here, (Fig. 28.1) applying the sigmoid function for activation (Table 28.1). The accuracy attained in logistic regression is 91%. We are applying the homomorphism on HR data (Figs. 28.4 and 28.5).

28.5 Conclusion This paper is providing security for employee information by applying the homomorphism algorithm. Logistic regression is applied for predicting employee talent management and then HR management analyzing employee performance and workability. For career enhancement and other managerial issues, HR management uses secure employee information. In logistic regression, 91% accuracy of predicting employee talent is achieved. In this work, an applied homomorphic encryption scheme is utilized to correctness and security of the consequences when assessing functions utilized in predictive analysis such as logistic regression.

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Fig. 28.1 Sigmoid function applied to HR data where x is talent and sigma(x) is predicting the probability

Fig. 28.2 Confusion matrix of actual talent people to predicted talent employees

28.6 Future Enhancement In the future, we develop apart from the privacy of employees and also recommendation to take more consideration of standard models with various encoding techniques and scalability, by utilizing statistical methods such as descriptive data analysis and machine learning filtering techniques to facilitate more secure analysis

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Fig. 28.3 ROC curve of HR analytics of actual talent people to predicted talent employees Table 28.1 HR data Emp. number

Age

Gender

Emp. department

Business travel frequency

Distance from home

Emp. job involvement

Emp. job level

Emp. job satisfaction

E1001000

32

Male

Sales

Travel_Rarely

10

3

2

4

E1001006

47

Male

Sales

Travel_Rarely

14

3

2

1

E1001007

40

Male

Sales

Travel_Frequently

5

2

3

1

E1001009

41

Male

Human Resources

Travel_Rarely

10

2

5

4

E1001010

60

Male

Sales

Travel_Rarely

16

3

2

1

E1001011

27

Male

Development

Travel_Frequently

10

3

3

1

E1001016

50

Male

Sales

Travel_Rarely

8

3

1

2

E1001019

28

Female

Development

Travel_Rarely

1

1

1

2

E1001020

36

Female

Development

Non-Travel

8

4

3

1

E1001021

38

Female

Development

Travel_Rarely

E1001022

44

Male

Development

Non-Travel

E1001024

47

Female

Sales

Travel_Frequently

3

3

4

3

E1001025

30

Male

Sales

Travel_Rarely

27

3

2

4

E1001027

29

Male

Sales

Travel_Rarely

10

3

1

3

E1001030

42

Male

Development

Travel_Frequently

19

4

1

3

E1001035

34

Female

Development

Travel_Rarely

8

3

2

3

E1001038

39

Female

Human Resources

Travel_Rarely

3

4

2

2

E1001040

56

Male

Development

Travel_ Rarely

9

3

4

4

E1001041

40

Female

Development

Travel_Rarely

2

2

1

4

E1001042

27

Female

Development

Travel_Rarely

7

2

2

1

E1001044

29

Male

Sales

Travel_Rarely

10

3

1

2

E1001047

53

Male

Development

Travel_Rarely

6

3

2

4

1

3

3

3

24

1

1

3

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Fig. 28.4 Data after applying homomorphism encryption to the HR data

Fig. 28.5 Data after decryption to the HR data

to employee personal information. After predicting and securing the employee, information serves to provide a solution for reducing the business risk by predicting the employee churn, for predicting employee churn effectively within and organization exploiting the several modeling methods.

References 1. L. Bassi, Raging debates in HR analytics. People Strateg. 34(2) (2011) 2. M. Molefe, From Data to Insights: HR Analytics in Organizations (Gordon Institute of Business Science, University of Pretoria, 11 Nov 2013) 3. L. Bassi, D. McMurrer, A Quick Overview of HR Analytics: Why, What, How, and When? Association for Talent Development, 4 Mar 2015

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4. D. Handa, Garima, Human resource (HR) analytics: emerging trend in HRM (HRM). IJRCM 5(6) (2014), ISSN 0976-2183 5. K. Ladimeji, 5 Things that HR Predictive Analytics will Actually Predict. Recruiter, 23 Jan 2013. sec. I p. 1 6. C. Waxer, HR Executives: Analytics Role Needs Higher Profile. Data Informed, 13 Mar 2013 7. Predictive Talent Analytics the Future of HR. Press Trust of India, 28 Aug 2015. http://www. businessstandard.com/article/pti-stories/predictive-talent-analytics-thefuture-of-hr-inindia115082500643_1.html 8. J.W. Bos et al., Private predictive analysis on encrypted medical data. J. Biomed. Inf. 50, 234–243 (2014). https://doi.org/10.1016/j.jbi.2014.04.003 9. D. Connors, A. Mojsilovic, Workforce analytics for the enterprise: an IBM approach, in Chapter in Service Science Handbook, ed. by P.P. Maglio, C.A. Kieliszewski, J.C. Spohrer (Springer, Berlin 2010) 10. R. Jayanthi, D.P. Goyal, S.I. Ahson, Data Mining Techniques for Better Decisions in Human Resource Management Systems. International Journal of Business Information Systems 3(5), 464–481 (2008) 11. J. Hamidah, H. Abdul Razak, A.O. Zulaiha, Towards Applying Data Mining Techniques for Talent Managements, in 2009 International Conference on Computer Engineering and Applications, IPCSIT, vol. 2 (IACSIT Press, Singapore, 2011) 12. V. Nagadevara, V. Srinivasan, R. Valk, Establishing a link between employee turnover and withdrawal behaviours: application of data mining techniques. Res. Pract. Hum. Resour. Manage. 16(2), 81–99 (2008) 13. H. Wie-Chiang, C. Ruey-Ming, A comparative test of two employee turnover prediction models. Int. J. Manage. 24(2), 216–229 (2007) 14. M. Maisuradze, Predictive analysis on the example of employee turnover (Master’s thesis) (Tallinn University of Technology, Tallinn, 2017) 15. K.-B. Duan, S.S. Keerthi, Which is the best multiclass SVM method? An empirical study, in International Workshop on Multiple Classifier Systems (2005) 16. S. Rogers, M. Girolami, A First Course in Machine Learning (CRC Press, Boca Raton, 2016) 17. T.K. Ho, Random decision forests, in Proceedings of the Third International Conference on Document Analysis and Recognition (1995) 18. L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001) 19. J.H. Cheon, J.-S. Coron, J. Kim, M.S. Lee, T. Lepoint, M. Tibouchi, A. Yun, Batch fully homomorphic encryption over the integers, in 32nd Annual International Conference on the Theory and Applications of Cryptographic Techniques, EUROCRYPT (Springer, Berlin, 2013), LNCS 7881, pp. 315–335 20. J.-S. Coron, A. Mandal, D. Naccache, M. Tibouchi, Fully homomorphic encryption over the integers with shorter public keys, in 31st Annual Conference on Advances in Cryptology, CRYPTO (Springer, Berlin, 2011), LNCS 6841, pp. 487–504 21. K. Lauter, M. Naehrig, V. Vaikuntanathan, Can homomorphic encryption be practical?, in 3rd ACM Workshop on Cloud Computing Security Workshop, CCSW, ACM (2011), pp. 113–124

Chapter 29

Identification of Parkinson’s Disease Using Machine Learning and Neural Networks Ved Abhyankar and Rushikesh Tapdiya

Abstract Parkinson’s Disease (PD) continues to affect millions of people worldwide, with as many as a million in the US and roughly 60,000 diagnosed each year. Early detection of PD can help in better handling the finances of the treatment as well as being substantially better for the patient’s quality of life. Artificial Neural Networks and Machine Learning techniques can greatly help with the early diagnosis and prediction of PD. In this paper we have used several techniques for the classification of the test subjects as having or not having PD based on their biomedical voice samples. After comparing the techniques based on Accuracy, recall, f1 score and precision, the best performance has been obtained by using SVM along with PCA. Doctors can thus classify patients appropriately with the help of this analysis.

29.1 Introduction Parkinson’s disease (PD) is the second most common, central nervous system (CNS) neuro-degenerative disease. So far, there is no conclusive clinical test capable of diagnosing a patient with PD [1]. Patients with PD have been reported to face deterioration in handwriting, though. Therefore, various researchers in computer vision and machine learning have suggested approaches based on deep learning and computer vision. In terms of frequency, after Alzheimer’s Disease, PD is the most common neurodegenerative disorder related to one’s age. It is estimated that roughly 7–10 million people have PD worldwide, with roughly 1 million in the US and 60,000 new cases diagnosed each year [2]. Occurrence of PD is seen more commonly with increase in age. Nonetheless it is estimated that, of all the people diagnosed with PD, nearly 4% are aged less than 50. As compared to women, men are more likely (1.5 times) to V. Abhyankar (B) · R. Tapdiya Pune Institute of Computer Technology, Pune 411038, India e-mail: [email protected] R. Tapdiya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_29

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have PD [3]. It is estimated that in the US, the total cost associated with this disease, including direct and indirect costs such as, social security payments, treatment and the income that is lost, is around $52 billion per year. Our main objective is to propose how existing supervised machine learning algorithms can be effective in the identification of Parkinson Disease. Machine learning algorithms [4] help in analyzing the available data and determining the important features and characteristics from the information given in the dataset. These techniques can handle large scale data pre-processing, feature extraction / selection, model creation and testing. Researchers have applied feature selection strategies and using ML techniques have obtained high accuracies. Machine Learning algorithms have always been the effective tool in various case studies like text classification [5], disease classification [6], Stock Market Prediction [7] etc. Therefore, authors have used supervised machine learning algorithms in Parkinson’s disease Identification. To classify the subjects as healthy or having PD, we have compared classical machine learning algorithms: k-Nearest Neighbors (kNN), Decision Tree Classifier, Random Forest Classifier, SVM and Artificial Neural Network. We found that using SVM with PCA gave the highest accuracy of 98.5%. Section 29.2 describes the Related Work done on this subject. Section 29.3 explains the Proposed System. Section 29.4 presents the result and analysis. The paper ends in Sect. 29.5 by proposing concluding remarks.

29.2 Related Work Much work has been done in the classification of PD as follows. Little et al. obtained an accuracy of 91.3% using SVM [8]. Abdulhay et al. used Machine learning to investigate tremors and gait data of a person. They managed to get an accuracy of 92.7% [9]. Aich et al. compared Genetic Algorithm (GA) based feature set and PCA based feature selection with SVM. They managed to get accuracy of 97.3% [10]. Gunduz implemented a deep learning method where the framework passes feature sets to the parallel input layers which are directly connected to convolution layers. He managed to get an accuracy of 86.9% [11]. Abos et al. investigated a method using machine learning techniques that distinguished PD patients depending on their cognitive status. SVM was used as the ML algorithm and for feature selection, randomized logistic regression was used, which resulted in an accuracy of 82.6% [12]. To analyse the input features, in order to differentiate between healthy subjects and those with PD, Little et al., implemented detection of dysphonia. They used SVM on their dataset (which consisted of vowels which were recorded from the subjects) for the classification purpose and diagnosed 23 PD and 8 healthy people. They manged to obtain an accuracy of 91.4% [8].

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Xiong et al. used sparse auto-encoders to extract latent representation of the candidate features. They used the Adaptive Grey Wolf Optimization algorithm (which is a meta-heuristic global search optimization technique) to identify the predictor candidate subset from the processed vocal features . The LDA model on mRMR-sparse autoencoderY obtained an accuracy of 91.4% [13]. Alqahtani et al. proposed the identification of the Parkinson’s disease using the NNge classification algorithm along with AdaBoost ensemble algorithm [14]. The accuracy achieved was about 96%. Shu Lih Oh proposed a unique approach for PD identification using EEG Signals [15]. The author proposed CNN architecture whose accuracy was 88%. Challa et al. [16], the authors used non-motor features i.e. Rapid Eye Movement (REM), Sleep Behavior Disorder (RBD) and Olfactory Loss along with the data from PPMI. Authors implemented MLP, Boosted Random Forest and Boosted Logistic Regression with a reported accuracy of 97.16 %. Furthermore, surveys analyzed that swarm intelligence and optimization can boost the performance of many existing neural networks [17]. Moreover, the concurrent approaches can also scale up the performance by training on large scale datasets [18, 19].

29.3 Proposed System Our main objective is to survey the performance of ML algorithms on the dataset and suggest the best possible algorithm. We have implemented various standardized supervised classification algorithms and have compared their results based on several classification metrics. The algorithms implemented here are • • • • •

k Nearest Neighbours SVM Random Forest Classifier Decision Tress Artificial Neural Network.

Figure 29.1 is the gist of our proposed system. The pre-processed dataset is split into training, testing and validation set.

29.3.1 Dataset—Description and Preprocessing The UCI ML repository is a database of various survey-produced datasets and empirical theoretical studies per group research on the Machine Learning Research. In this study, we acquired the UCI Parkinson’s Dataset. Max Little (University of Oxford) and the National Centre for Voice and Speech (who recorded the speech signals) created this dataset [20]. It consists of various biomedical voice measurements of 31 people from which 23 had Parkinson’s disease (PD).The columns in the table are some given voice characteristic. The rows represent the voice recordings of 195 individuals (“name” column) [20]. This data aims to discriminate people with PD

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Fig. 29.1 Proposed system

from healthy people. The “status” column is the label column, where the value 0 indicates a healthy person and a label value of 1 indicates a person with PD [20]. In the dataset 48 instances are those of people who are healthy and don’t have PD. This constitutes 24.6% of the dataset while the remaining 147 correspond to people diagnosed with PD (75.3%). Each column corresponds to particular voice measure. The first column of the dataset includes Vocal Fundamental Frequency (Hz).The second column has the maximized value followed by two measurements of ratio of noise to tonal components in the voice in the successive columns. There are other 5 nonlinear parameters which are considered in the database i.e 2 dynamical complexity measures and 3 fundamental frequency variation measures. We normalised the dataset to bring the values of the attributes to a common scale, without distorting differences in the range of values.

29.3.2 Machine Learning Algorithms Used Various algorithms used in the implementation are as follows K Nearest Neighbours It is a classification algorithm. The labelling of the new point in the hyperspace is done on the basis of the labelled points in the neighbourhood. KNN works by calculating the distance between the unlabelled samples and all the samples in the data, only selecting the nearest K Neighbours, then vote for the

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most redundant interval (in the case of classification).The ‘K’ in the KNN algorithm denotes the number of surrounding points from whom to take vote. Support Vector Machines The SVM is classification algorithm widely used by the data scientists in the machine learning tasks. The main objective of the SVM is to find the best possible hyperplane in the space that most accurately distinguishes the training samples in space. The algorithm extracts the optimal hyperplane for classification. Here we have used SVC along with Principal Component Analysis (Fig. 29.2). Decision Trees Decision Tree uses the rudimentary concepts of tree structures for Classification and Regression tasks. The Decision tree uses continuous as well as categorical values. In a decision tree, each node represents a feature, each branch represents a decision and each leaf node represents the outcome of the feature and the decision on that feature. The decision may be based on range or the specific values (Fig. 29.3).

Fig. 29.2 Possible optimal hyperplanes [21]

Fig. 29.3 Sample decision tree

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Random Forest Classification Decision Trees are prone to overfitting. Random Forest overcomes this con of the Decision Trees. Random Forest uses the concept of ensembling i.e. It constructs various decision trees for the given dataset. For Classification, the model outputs the label or class that is the mode in the list of predicted labels by all the constructed Decision trees at training time. Random Forest is an extension of Decision Trees. Random Forest works on the basic principle that various tenuous estimators aggregate together to form a strong estimator. Artificial Neural Network ANN, also known as Feed-forward Neural Networks, are the backbone of deep learning. It consists of an Input layer followed by multiple hidden layers and an output layer. The circular entities, known as a node, takes the weighted sum of all the outputs from the previous layer and passes it through a nonlinear activation function which generates the output for that node. Similar process continues till the output node is reached. Each link in the ANN has weights associated with it. The weights are updated after each step by using back-propagation algorithm (Fig. 29.4). The ANN used here has 2 hidden layers (11 and 6 nodes respectively). The input layer has 22 nodes. The output layer uses sigmoid activation while previous layers use ReLU activation. The binary-crossentropy loss function is used. It is a custom ANN with block diagram as given below (Fig. 29.5).

Fig. 29.4 Artificial neural network [22]

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Fig. 29.5 ANN model used

29.3.3 Classification Metrics Confusion Matrix: It is used for visualization of the result in classification problems. It is a square matrix of size N * N where ‘N’ represents the number of labels in the classification problem. In this matrix, the predicted labels are represented by the rows and the authentic or genuine labels are represented by the columns. Each element in column X of the matrix represents the number of labels whose predicted label equals the row number (Fig. 29.6). Accuracy: It gives the measure of all the correct labels predicted against all the labels predicted.   TP + TN Accuracy = TP + FP + FN + TN Precision Precision is a quantitative measure of the ability of the classifier to avoid mis-classification i.e. to not label a negative example as positive. Precision scores range from 0 being the worst and 1 being the best.  Precision =

TP TP + FP



Recall: Recall tells us about the number of items, which actually are true and also predicted true by the classifier.

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Fig. 29.6 Confusion matrix [23]



TP Recall = TP + FN



F1 Score: It is the combination of both precision and recall via their harmonic mean and gives a single value by which a model can be judged. 

precision × recall F1 Score = 2 × precision + recall



29.4 Results and Analysis For implementing all the machine learning algorithms, sklearn library has been used. 70 percent of the dataset was used for Training Purpose and the remaining data was used for Testing. For implementing some algorithms, the dimensionality-reduction algorithm, PCA (Principle Component Analysis) has been applied for extraction of optimal features (Table 29.1). From the table above, SVM with PCA is the best algorithm for PD identification, but ANN was found to perform better when it was cross-validated. The ANN pipeline was more efficient in K-Fold cross validation. The results from the ANN more satisfactory than SVM and Random Forest. ANN has the power to extract deeper lower-level features than the other algorithms used, because of which ANN can serve us for the broader purpose of PD Identification.

29 Identification of Parkinson’s Disease Using Machine Learning . . . Table 29.1 Performance on various algorithms Algorithms Accuracy Precision SVM kNN Decision trees Random forest ANN ANN (k-cross validation)

98.55 94.91 93.22 98.30 97.95 92.39

0.99 0.91 0.96 0.96 0.97 0.99

315

Recall

F1 -score

0.96 0.97 0.99 0.99 1.00 0.99

0.98 0.93 0.98 0.99 0.98 0.99

29.5 Conclusion This paper talks about Parkinson’s Disease Identification using Machine Learning techniques and Neural Networks. The ANN pipeline is identified to serve a broader purpose in PD Identification. Its feature extraction capabilities, propagation strategy to optimize the weights, etc helps the ANN to achieve this purpose. But, the lack of data is a small impediment in front of this conclusion. The ANN pipeline with optimal activation functions, error functions will be efficient in handling the bulk of data in the future. Moreover, Sequential Learning Techniques may also have excellent performance in PD identification.

29.6 Declaration We have taken permission from competent authorities to use the images/data as given in the paper. In case of any dispute in the future, we shall be wholly responsible.

References 1. L. Ali, C. Zhu, N.A. Golilarz, A. Javeed, M. Zhou, Y. Liu, Reliable Parkinson’s disease detection by analyzing handwritten drawings: construction of an unbiased cascaded learning system based on feature selection and adaptive boosting model. IEEE Access 7, 116480–116489 (2019). https://doi.org/10.1109/ACCESS.2019.2932037 2. Parkinson’s Disease Statistics. https://parkinsonsnewstoday.com/parkinsons-diseasestatistics. (2020) 3. Statistics https://www.parkinson.org/Understanding-Parkinsons/Statistics (2020) 4. S. Ray, A quick review of machine learning algorithms, in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India (2019), pp. 35–39. https://doi.org/10.1109/COMITCon.2019.8862451

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5. M. Ramakrishna Murty, J. V. R. Murthy, P.V.G.D. Prasad Reddy, Text document classification based on a least square support vector machines with singular value decomposition. Int. J. Comput. Appl. (IJCA) 27(7) 21–26 (2011) 6. L. Sheng, S. Qing, H. Wenjie, C. Aize, Diseases classification using support vector machine (SVM), in Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP ’02, Singapore (2002), vol. 2, pp. 760–763. https://doi.org/10.1109/ICONIP. 2002.1198160. 7. R. Iacomin, Stock market prediction, in 2015 19th International Conference on System Theory, Control and Computing (ICSTCC), Cheile Gradistei (2015), pp. 200–205. https://doi.org/10. 1109/ICSTCC.2015.7321293 8. M.A. Little, P.E. McSharry, E.J. Hunter, J. Spielman, L.O. Ramig, Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans. Biomed. Eng. 56(4), 1015–1022 (2009) 9. E. Abdulhay, N. Arunkumar, K. Narasimhan, E. Vellaiappan, V. Venkatraman (2018) Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Future Gener. Comput. Syst 10. S. Aich, H. Kim, K. younga, K.L. Hui, A.A. Al-Absi, M. Sain, A supervised machine learning approach using different feature selection techniques on voice datasets for prediction of Parkinson’s disease, in 2019 21st International Conference on Advanced Communication Technology (ICACT), PyeongChang Kwangwoon-Do, Korea (South) (2019), pp. 1116–1121 11. H. Gunduz, Deep learning-based Parkinson’s disease classification using vocal feature sets. IEEE Access 7, 115540–115551 (2019) 12. A. Abós, H.C. Baggio, B. Segura, A.I. GarcíaDíaz, Y. Compta, M.J. Martí, F. Valldeoriola, C. Junqué, Discriminating cognitive status in Parkinson’s disease through functional connectomics and machine learning. Sci. Rep. 7, 45347 (2017) 13. Xiong, Y. Lu, Deep feature extraction from the vocal vectors using sparse autoencoders for Parkinson’s classification. IEEE Access 8, 27821–27830 (2020) 14. E.J. Alqahtani, F.H. Alshamrani, H.F. Syed, S.O. Olatunji, Classification of Parkinson’s disease using NNge classification algorithm, in 21st Saudi Computer Society National Computer Conference (NCC), vol. 2018. Riyadh (2018), pp. 1–7. https://doi.org/10.1109/NCG.2018. 8592989 15. S.L. Oh, Y. Hagiwara, U. Raghavendra, R. Yuvaraj, N. Arunkumar, M. Murugappan, U. Rajendra Acharya, A deep learning approach for Parkinson’s disease diagnosis from EEG signals, in Computer Aided Medical Diagnosis. The Natural Computing Applications Forum 2018 (2018) 16. K.N.R. Challa, V.S. Pagolu, G. Panda, B. Majhi, An improved approach for prediction of Parkinson’s disease using machine learning technique, in International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)-2016 (2016) 17. T. Ashish, S. Kapil, B. Manju, Parallel bat algorithm based clustering using mapreduce, in Networking Communication and Data Knowledge Engineering (Springer, Singapore 2018), pp. 73–82 18. A.K. Tripathi, K. Sharma, M. Bala, A novel clustering method using enhanced grey wolf optimizer and map reduce. Big Data Res. 14, 93–100 (2018) 19. A.K. Tripathi, K. Sharma, M. Bala, Dynamic frequency based parallel k-bat algorithm for massive data clustering (DFBPKBA). Int. J. Syst. Assurance Eng. Manage. 9(4), 866–874 (2018) 20. M.A. Little, P.E. McSharry, S.J. Roberts, D.A,E. Costello, I.M. Moroz, Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed. Eng. OnLine 6, 23 (2007) 21. Possible Optimal Hyperplanes. https://miro.medium.com/max/1773/ 1*ZpkLQf2FNfzfH4HXeMw4MQ.png (2020) 22. Sample ANN from Introduction to ANN. https://towardsdatascience.com/applied-deeplearning-part-1-artificial-neural-networks-d7834f67a4f6 23. Confusion matrix, performance metrics for classification problems in machine. https://medium. com/thalus-ai/performance-metrics-for-classification-problems-in-machine-learning-part-ib085d432082b (2020)

Chapter 30

Assessment of Forensics Investigation Methods Pranay Chauhan and Pratosh Bansal

Abstract Attacks and intrusion detection are common and more affecting in systems based on availability, confidentiality and integrity. Information is refined and analysed to avoid complexity by the attacker and form a more robust and efficient system. IOC is the important resource called as an indicator of compromise and information security and use of them for detecting malicious activity on the network. IOC is usually formed by URLs, domains, IP address, signature and other elements, as they are not enough to identify suspicious activities on the system. Different logs are identified through operating systems. Different operating systems like Windows and Linux perform different log operations for evidence locations. Paper presents mitigation of malicious activities by diagnosing them through their behaviour on computer systems using incident response and forensic analysis.

30.1 Introduction Process of incident response (IR) and forensic analysis determines system’s success or failure in the condition of compromise to moderate security incidents. Complexity is increased by attack vectors by making their presence unidentifiable; indicator of compromise is one of the tools in them. When talking about indicator of compromise, IOCs are found in log entries; it is a piece of forensic data, that recognize malicious activities on system and network [1]. Now coming on incident response and forensic analysis, it is required to be improved with resulting in reduced false-positive results. Recommendation of Computer Emergency Response Teams (CERTs) is important for forensic analysis in computer systems to identify intrusions and performs action accordingly by avoiding damages of infrastructure. Malicious activities are defined through security windows P. Chauhan (B) · P. Bansal Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Indore, MP, India e-mail: [email protected] P. Bansal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_30

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event logs code. They add antivirus signature, URLs, domains, IP addresses to make system more robust and precise if compromised [2]. In the IR process, the containment step is the important one as it permits to check if any type of changes was formed or not changes like change in registry keys, relocation of files or any manipulation on the computer system. It is important to know the behaviour of the attacker for better analysis of forensic and incident response (IR). Prevention from intrusion and proactive actions is essential for computer systems. Logs of events should not be isolated and should be reflected as complete [3]. To acquire an IOC, failed login is not sufficient; event should be related with some other to determine compromised system events [4, 5]. An incident of compromise is a security violation in any organization where an incident is compromised. CIRT is prepared through planning, practice and communication of incident response processes with required action of experience in a timely manner. Incident response occurs on the basis of six different steps: • • • • • •

Preparation Identification Containment Eradication Recovery Learning.

These steps are helpful in creating your own IR plan. Containment phase consists of a backup system task. Resulting in how the information is happening and activity of suspicious attack, observation of compromised systems while learning phase. For successful IR, following steps are followed by attackers in the state of system compromise, and these steps should be known by CIRT; they are as: • • • • • • •

Reconnaissance Weaponized Deliver Exploitation Installation Command and Control Action.

The process is called the Kill Chain Life Cycle, and the complete cycle consists of these seven steps. CERTs formed certain recommendations for the identification of compromised systems. The process is accomplished only with the contribution of experts [6, 7]. Whenever, if a compromised system is checked, then it is possible that the other system on the same network is also compromised. How to Catch Malicious Insider There are several methods to catch these malicious insiders in the system too. Some of the indicators along with their sources are: logon to new or odd system, new session types, accessing at unusual time of the day, doubtful sessions, shared and privileged

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Table 30.1 Indicators and source of malicious insider Indicator behavioural

Sources

Logon to a new or odd system

Cloud Servers, database applications, VPN, cloud applications File servers, cloud based file sharing applications, gateways, endpoints

New or odd login session types Unusual time of day improbable velocity Shared account usage. Privileged account usage High volume file access and unusual file accessing patterns, bandwidth usage New IP address, unusual or suspicious application usage Endpoints indicators of concession

account usage, high bandwidth, unusual file patterns, login from new or remote IP address and endpoints indicators [8]. Table 30.1 shows the detailed analysis. Log analysis can be done through various methods and algorithms for log analysis and data mining. Various methods are as: • • • • • • • •

Association Rule. Decision Tree Model. Association rule mining task. Maximal Frequency Item set. Maximal vs. closed Item set. Item set mining. Maximal Frequent item (it can be used for particular logs). Maxgen algorithm (used for identifying multiple sources).

However, in case of sources which include clouds where a big volume of data is there and data is scattered on multiple locations, big data technology can be useful.

30.2 Related Work Lock et al. In [8] elaborate the benefits of OpenIOC framework to explain malware activities and their results. Author describes tools and techniques used while analysing the report. Paper highlights on result reports, and it is as important as the result itself, which should be consistent and well-structured for humans and machines to understand. Well managed leads towards automated processes of detection, prevention and reporting of malware. IOCs support incident response capture, and it is an XML document. This editor is a free editor to capture information related to suspicious files. It provides an interface to manage data. IOC editor works as

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manipulating logical structure, applying meta-information for labels, conversion of IOS into XPath filters and management of lists. SANS distributed the logs of the list while IR. It is helpful for review of logs. Its certain approach is as [4]: • • • • • • •

Identification of log source and tools. Copy of log record location for later use. Reducing noise with eradicating routinely log entries. Focus on changes of events. Reproduction of the same scenario. Associate activities of different logs. Explore logs.

Logs This section describes the concept of log and event of log on operating systems. What is Log? Log is defined as a record of events, which comprises entries and information occurring in a network. Information can be of any type and of different kind. A. Event Logs for Windows These logs are the records of occurrence of an event on the computer system from which event entries are recorded. Events are the records happening on the computer system, and it can be the generated alert or notification. Log defined by Microsoft is an occurrence of any event in a system or program which should be notified by the user and that entry is added to log. [9] B. Categories of Windows Logs: (a) System: Windows and other windows system services sent system event log, and they are categorized as warning or error. (b) Set-up: Additional logs are displayed here, if configured. (c) Application: Warning, error, or information are the category of log in this event. Error is an important problem. Warning is not certainly important but can be added in future issues if not taken seriously. Information describes successful operations and services. (d) Security: Events related to security consist in this log and called as audit events. These logs are categorized as successful or failed. [10] (e) Forward Event: Other system forwards event to this log. (f) Other events classified by operating system are as: (g) Success Event: This shows successful operation by completing security audits. (h) Failure Event: This shows that the event is not completed successfully; e.g. if incorrect password is entered, then operation will not be performed successfully. (i) Error Event: This is an important problem and can result in loss of operations. (j) Warning Event: This is not certainly important but may create issues in future and notifies the problem to the administrator.

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(k) Information Event: Successful completion of tasks is described in this event. (l) Information consists in log entry: [11] • Event Id: Type of event is identified by the window identification system and called as event id. • Type: Is the type of event. • Source: Event caused by program or component. • Computer: Name of system. • User: Name of user who logged on at the time of event. • Date: Date on which event occurred. • Time: Time at which event occurs. Research for different events, logs and categories were selected in tables. Every category is registered in the system generating IOC. A log traces the activities of the event and secure computer system. Logs configuration manages the efficiency of log with properly diagnosing and identifying system problem [9].

30.3 Logs of Operating System For the purpose of security, computer logs are important. The most significant and important types of security data in OS are as follows: • Audit Records: It consists of information of security events such as failure of authentication or successful authentication, security policies and privileges. • System Event: Operating system components perform actions of operations; these operational actions are called as system events. OS permits the administrator to identify the type of log and details like status, error codes and associations of user and system with each other. Log created in the operating system is favourable in identifying or inspecting distrustful activity involving hosts. After malicious activities like attacks, frauds and inappropriate practices, logs are consulted to attain information of activities and situations. Normally, it consists of information about activity. Other logs contain lesser information detail and help in associating events and records. Various Events and Event Ids There are various events and event id which are helpful for event management. Table 30.2 lists some of the event ids on logon and sessions summary [10]. Process plays a crucial role in assessment and analysis. Creation and expiration of a new process indicate many activities, and it can also indicate malicious or untrusted processes which can cause trouble. Table 30.3 represents the event id along with its summary [10]. If susceptible privileges are assigned to a new logon session, event 4672 will generate for that simulating new logon. 4673 will be used to check whether privilege service was called. Table 30.4 represents the event id and summary for privilege use.

322 Table 30.2 Events log on sessions events

Table 30.3 Events of process tracking category

Table 30.4 Events of privilege use category

P. Chauhan and P. Bansal Logon session event 4624

Successful logon

4625

Logon failure (also check logon failure code)

4778

Remote desktop session connected

4779

A Remote desktop session disconnected.

4800

Workstation locked

4801

Workstation unlocked

Event id

Event summary

4688

Creation of new process

4689

Process expired

Event id

Event summary

4672

Privilege to new login

4673

Privilege service was called

Security logs event ids such as 4665, 4664 are used for tracking success and failure of file and windows objects while 4698, 4700 are used for scheduled tasks information and 5051 is used to check whether a file was virtualized. Table 30.5 shows the detailed summary of object access category [10]. Table 30.6 shows various hexadecimal code related to the logon failure activities. This can be used to identify suspicious account activities, and this indicator can help in assessment of detailed system information such as login with incorrect passwords, user locked out, account disabling, account and password expiration, change of password after next logon could help in assessment of unknown entity trying to access system [10]. Table 30.5 Events of object access category

Event id

Event summary

4665

Attempt to create application context

4664

Attempt to create hard link

4668

Attempt was initialized

5051

File was virtualized

4691

Indirect access to object

4698

Scheduled task created

4700

Scheduled task was enabled

4657

Registry value modified

4660

Deletion of object

30 Assessment of Forensics Investigation Methods Table 30.6 Logon failure code

323

Logon failure code Oxcooooo64

User name does not exist

Oxcooooo6A

User name is correct but the password is wrong

Oxcoooo234

User is currently locked out

Oxcooooo72

Account is currently disabled

Oxcooooo70

Workstation restriction

Oxcooooo193

Account expiration

Oxcooooo1

Expired password

Oxcoooo224

User is required to change password at next logon

Oxcoooo225

Bug in window but no risk

Configuring these logs properly can help to manage the logs efficiently to identify and diagnose the system. [11]. Log Correlation Event • Logs are mainly collected from various end points (computes, laptop, mobile, system). • Logs are collected from many servers (database server, email servers, domain servers) [12]. • Logs are collected from various firewalls such as packet filtering, application level gateway, circuit level gateway. • Logs are collected from various IDS systems (intrusion-based detection such as host-based intrusion, network based intrusion by seeing its anomaly-based behaviour such as pattern behaviour or heuristic behaviour) [13]. After collecting various logs from different sources, log correlation is performed. Also, a dashboard is there to perform event monitoring; various events are being monitored as each event is having its own event id. So various events are being monitored and alert is generated for the same. [14] For example, for windows-based machine following security event can be monitored • • • • • •

Log on events Account management Object access Policy change Privilege use Security event audit.

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30.4 Conclusion Malicious activity is a planned and projected program, which is performed by malware. Malicious intent can be easily installed on any system. Many consequences opt in targeting financial industries by attacking their software like the banking sector. Author reports the increase of malware activity by using tools and methods. Summarization describes the combination of different windows event logs labelling the possibilities of malicious activities on a system and is added with different techniques to generate IOC.

References 1. Margaret Rouse: Indicator of Compromise. http://searchsecurity.techtarget.com/definition/Ind icators-of-Compromise: Accessed 21 Mar 2020 2. IOC: Indicator of Compromise and Role. https://www.auscert.org.au/resources/glossary/. Accessed 20 Feb 2020 3. Monitoring Active Directory for Signs of Compromise. https://docs.microsoft.com/en-us/ windows-server/identity/ad-ds/plan/security-best-practices/monitoring-active-directory-forsigns-of-compromise. Accessed 24 Feb 2020 4. P. Kral, The Incident Handlers Handbook (SANS Institute InfoSec Reading Room, 2020). Accessed 2 Mar 2020 5. MandiantFireEye Security: Consulting IOC User Guide Version. https://www.fireeye.com/con tent/dam/fireeye-www/services/freeware/ug-ioc-editor.pdf. Accessed 25 Mar 2020 6. Chuvakin, L. Zeltser, Critical Log Review Checklist for Security Incidents: Cheat Sheet Version 1.0 (SANS 2020) 7. T. Sager, Killing Advanced Threats in Their Tracks: An Intelligent Approach to Attack Prevention (SANS Institute InfoSec Reading Room, 2017) 8. H. Lock, Using IOC: Indicators of Compromise in Malware Forensics (SANS Institute InfoSec Reading Room, 2013) 9. K. Kent, M. Souppaya, Guide to Computer Security Log Management, NIST Special Publication 800–92 (Recommendations of the National Institute of Standards and Technology, USA, 2006) 10. A. Fortuna, Windows Event Id. https://www.andreafortuna.org. Accessed 24 Mar 2020 11. A. Howell, Performing windows event log search with power shell. https://searchitoperati ons.techtarget.com/answer/Perform-a-Windows-event-log-search-with-PowerShell. Accessed on 26 Mar 2020 12. P.K. Sahoo, C. Pattanai, Research issues on windows event log. Int. J. Comput. Appl. 41(19) (2012) 13. E. Beam, Event log events and end point security. https://www.exabeam.com/siem-guide/siemconcepts/event-log. Accessed Mar 2020 14. T. Dwyer John, Finding analomly in windows event logs using standard deviation, in 9th IEEE International Conference (2013)

Chapter 31

Smart Tourism Development in a Smart City: Mangaluru A. N. Parameswaran, K. S. Shivaprakasha, and Rekha Bhandarkar

Abstract The concept of ‘Smart Cities’ is becoming increasingly popular in India. The main idea behind the development of smart cities is to assure the overall development of the region which in turn would contribute towards nation building. The Government of India has initiated project on Smart Cities Mission (SCM) in the year 2015 and has identified an ample number of cities across different states as the possible candidates for the project. Six cities from Karnataka have been chosen for this opportunity and Mangaluru is one of them. Tourism development in smart city environment could be a possibility towards the economical growth and thereby enhancing the standard of living. In this paper, an attempt has been made in analysing the scope for the tourism development in smart city environment and the application of technology towards the same. The discussions in the paper are limited to Mangaluru city. Nevertheless the same analysis may be applicable to other cities as well.

31.1 Introduction Migration of rural people towards urban areas is increasing over the years and a significant shift can be witnessed in the last decade. Increase in the urban population also necessitates proper management of the infrastructure and other service-specific issues in cities. The concept of ‘Smart Cities’ could be one possible solution to this issue. The major objective is to deal with these issues in a more sophisticated and strategic manner. ‘Smart Cities’ are feasible with the advancements in information A. N. Parameswaran · K. S. Shivaprakasha (B) · R. Bhandarkar N. M. A. M. Institute of Technology, Nitte, Udupi District, India e-mail: [email protected] A. N. Parameswaran e-mail: [email protected] R. Bhandarkar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_31

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and communication technology (ICT) in the recent past. The concept of Smart Cities is a strategic application of the technology towards the overall upliftment of the city which in turn would lead the betterment in the quality of the life of the citizens [1]. In the recent past, many countries have successfully developed smart cities in terms of infrastructure, productivity, standard of living of people, economical stability and many more. Inspired by these initiatives, India has also explored this perspective towards the overall development within a country [2]. As a result, the Ministry of Urban Development has conceptualised Smart Cities Mission (SCM) in the FY 2015–2016 with an aim of developing 100 smart cities across different parts of the country by 2019–2020 [2]. The Ministry of Urban Development defines ‘Smart City’ as ‘building and promoting cities that provide core infrastructure and give a decent quality of life to its citizens, a clean and sustainable environment, and the application of “smart” solutions’. The SCM emphasises on the following characteristics: • • • • •

Living: standard of living of people, tourism development Mobility: connectivity, local accessibility, transportation system Environment: pollution control, sustainability Economy: entrepreneurship, developing economic conditions Governance: political strategies, transparency in policies, people participation in decision making [2].

Most of the smart city concepts have been evolved from bottom-up approach wherein the citizens themselves are involved in the implementation of the smart projects [3]. However, the involvement of universities, industries and Government in this process would result in a more systematic implementation of the projects. In [2], authors have defined the key parameters towards the defining of vision statements used to describe smart cities in India. Tourism is considered to be as one of the top 10 most frequent characteristics included in the vision statement. Also, SCM emphasises on economic growth that enable enhancing standard of living of people and tourism development is one such possibility to achieve this [4]. Thus, we present the scope of tourism development in Smart City perspective in Mangaluru, Karnataka state, India. Mangaluru is one of the cities selected under SCM along with five other cities in Karnataka state, India. In this paper, we propose a few smart solutions for tourism development pertaining to Mangaluru city. Although enough work has been carried out in the area of the Smart City development, there is still a wide scope for the research in the area of tourism development in smart cities [5]. The rest of the paper is organised as follows: Sect. 2 gives a perspective on tourism development in Mangaluru. Section 3 gives an insight into possible smart solutions to encourage the tourism. Section 4 presents the architecture for the tourism development in Mangaluru city. Finally, Sect. 5 gives the concluding remarks.

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31.2 Tourism Development in Mangaluru City Mangaluru is a beautiful city located amidst Western Ghats at one side and Arabian Sea at the other. The city got its name after the Goddess Mangaladevi, presiding deity. The city enjoys an annual rainfall of around 400 cm. Mangaluru is well connected by road, water and air. It is one of the important ports of the nation that handles majority of the cashew export of the country. It has a population of 4.85 lakh as per 2011 census. Many articles have discussed various aspects of tourism in Mangaluru. From the statistics presented in [4], it is inferred that around 85% of the people opined that Mangaluru has tourism potential in Karnataka due to beaches, scenic beauty, religious places and cultural interest. Some of the popular tourist places located in and around Mangaluru are: • • • • • • • • • • • • • • •

Mangaladevi Temple Shree Gokarnatha Kshetra, Kudroli Panambur Beach St. Aloysius Chapel NITK Light House, Surathkal Summar Sand Beach, Ullal Pilikula Nisargadhama Tannirbavi Beach Kadri Manjunatha Temple Sultan Battery Milagres Church Srimanthi Bai Government Museum Kadri Park Someshwar Beach Sharavu Mahaganapathi Temple.

Around 59% of the respondents have opined that major drawbacks of Mangaluru tourism are traffic and infrastructure [4]. This calls for the smart solutions for traffic, water and waste management in the city environment [6].

31.3 Smart Solutions to Tourism Tourism development in a smart city like Mangaluru is becoming crucial in terms of economic growth of the city. It would create jobs, enhance the income to the Government through taxes and thereby leading to a self-sustaining economical model for the ‘Smart City’. However, as the number of visitors increases, it requires management of traffic, water, energy, waste in a smarter manner. Various architectures have been proposed in this regard. In this section, we propose a few smart solutions towards enhancing tourism in Mangaluru city.

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31.3.1 Smart Traffic Management Majority of the tourists visit Mangaluru in their own vehicles. This would certainly add to the traffic congestion in the city. Thus, a mechanism for traffic management is essential. The following are a few solutions that can be considered for the implementation of smart traffic system: • Vehicle detection and tracking can be achieved. Using vehicular ad hoc networks (VANETs), we can not only track tourist vehicles, but also can guide them to visit all tourist destinations in optimal time. An Android application can be developed that can be used by the tourist. The app can guide tourist in covering all destinations in minimal time. The same app can also give information about the traffic intensity and congestions for a selected route and can even suggest alternate paths. • Parking of tourist vehicles is one of the bottlenecks to cater to a large tourist count. Multi-stage/level parking can be devised with the help of ICT, so as to achieve better parking management in the available space. • Getting entry tickets at tourist spots is a time-consuming process, especially during vacations. The existing system of getting the entry tickets separately at each place can be replaced with one-time payment at the time of entering the city. An appassisted online payment would make the process even simpler. However, tourist help desks can be set up at prominent places in the city to assist tourists in this regard. • Tourists/people attracted to Mangaluru for: beaches and scenic beauty, religious places, health tourism and cultural interest. The tourist assistance app can pop up the exhaustive list of tourist places pertaining to each of the above streams. Tourist can make their choice by clicking onto the places they wish to visit, based on which a plan can be created by the app. This will not only enable the tourist to make all sort of payments (entry fee, parking fee, etc.) online but also will give an optimal schedule to cover all places selected. The app could also serve as a tourist guide through audio information about the places. • Local vendors can post their advertisements on the app. This would certainly enhance their visibility and thus achieving a better economical growth of Mangaluru.

31.3.2 Smart Water Management Water is a basic necessity for any human being. Water management is very important for any city and its role becomes more crucial especially during summer in cities like Mangaluru. • Rainwater harvesting is the optimal solution to cater to water requirement at the city. Mangalore being surrounded with Western Ghats receives an annual rainfall of around 400 cm. Thus, rainwater harvesting would be the most feasible solution. • Smart metering can also serve the purpose of efficient usage of water in the city.

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31.3.3 Smart Waste Management With the increase in floating population in a city, managing waste becomes a vital issue. The following points can be considered towards the smart waste management in a city. • Providing smart bins at houses and tourist destinations. The status of dustbin (full/not full) can be sent to the cloud on regular basis. This can be easily implemented using the existing IoT technologies. Based on the status of the waste bins, the Mangaluru City Corporation can take appropriate action. • Detect odour using gas sensor. The same information can be conveyed to the central station (city corporation) to take necessary action. • Segregation of wet and dry waste is an important step which most of the people do not perform. Developing a system that would automatically separate wet and dry wastes could solve this issue in an efficient manner.

31.3.4 Smart Energy Management Energy management also becomes crucial if the number of tourists increases at a place. The following points can be considered in this regard: • Mangaluru being a coastal place, usage of solar energy can be encouraged during majority of the months in a year. Hotels and lodges can be encouraged to use solar energy as the major source of energy. • An energy-efficient system for the switching of street lights based on the natural light intensity can be developed. Relay controlling by cloud can be achieved using long range (LoRa) technology. • Smart metering system can be installed.

31.4 Architecture for Smart Tourism In the previous section, various aspects involved in smart solutions for tourism development in Mangaluru city have been presented. The architecture for the same is to be developed for the successful implementation of the project. A simple layout of the architecture for the implementation of smart city in Mangaluru is given in Fig. 31.1. Sensors: Sensors are the brain of the architecture for smart tourism in Mangaluru city. All solutions detailed in Sect. 3 use sensors. Sensors are required in traffic management, parking management, water, energy and waste management. Most of the smart cities choose to place sensors on public light poles [7]. This is applicable for the cases of smart lighting and traffic congestion detection in the proposed model. Connectivity: Physical parameters sensed by sensors are to be conveyed to the cloud for storage and further processing. This requires for a wireless connectivity

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Fig. 31.1 Smart city architecture for tourism development in Mangaluru—a simple layout

to be established [7]. We can propose to have star or mesh networks for the sensor networks. Cloud and IoT Platform: Cloud and IoT are the backbone in the implementation of smart city projects [8]. Smart solutions proposed in Sect. 3 generate a huge amount of data that is to be properly stored and processed. Cloud computing solutions would be the only preferred way to achieve this in an efficient manner. Analytics: Cloud computing can be used to analyse the data gathered from sensors. Presentation: Based on the processing of the data gathered at the cloud, proper decisions are to be taken at the administrative levels [9]. Tourists are to be informed whenever necessary through mobile apps. For instance, if the highway to a particular tourist destination is overcrowded, either the vehicles are to be suggested with an alternative path to reach the same destination or they can be advised to appropriately reorder the schedule for places they are visiting on that day. Based on the discussions carried out, a more detailed schematic layout of the smart city architecture for Mangaluru is shown in Fig. 31.2. From technical perspective, a detailed layered architecture can be drawn for the smart tourism in Mangaluru city. It consists of three basic layers, namely perception layer, network layer and application layer. The same is represented in Fig. 31.3. Perception layer basically deals with the collection of physical information through sensors. Network layer is responsible for the establishment of connections between the sensors and from sensors to the cloud environment. Finally, application layer deals with the processing of the information gathered and making appropriate decisions.

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Fig. 31.2 Schematic layout of smart tourism in Mangaluru City

Fig. 31.3 Layered architecture

31.5 Conclusion Rapid development of technologies is enabling the introduction of smartness in all aspects of our lives. Development of smart cities in India is one such aspect that was conceptualised in the year 2015. The implementation of SCM at the city level will be done by a special purpose vehicle (SPV). This SPV is a public limited company incorporated under companies act 2013 that will implement smart city projects in

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public-private partnership in a self-sustainable revenue model. As economic development is one of the major objectives of SCM, promoting tourisms in smart cities could be a possibility to achieve the same. In this paper, an attempt has been made in proposing a few ideas for promoting tourism with smart solutions in Mangaluru. A detailed architecture to achieve the same is also presented. Although there are a few challenges like citizen involvement in the process, delay in Government policies, lack of synergy amongst stakeholder that hinder the implementation of the proposed solutions, it can be overcome through policies at higher level and people involvement in making their hometown a better place to live.

References 1. K. Boes, D. Buhalis, A. Inversini, Conceptualising Smart Tourism Destination Dimensions (Springer International Publishing, Switzerland, 2013), pp. 391–403 2. K. Gupta, R.P. Hall, The Indian perspective of smart cities, in Smart City Symposium Prague (2017) 3. R.P. Dameri, R. Pavola, The conceptual idea of smart city: university, industry, and government vision, in Smart City Implementation (Springer, Cham, 2017), pp. 23–43 4. A. Kumar, S. Crasta, Bhagyashree, Smart city implementation, tourism opportunities in Mangalore as a smart city. Int J Comput Res Dev 2, 96–101 (2017) 5. M. Jeet Kaur, P. Maheshwari, Smart tourist for Dubai city, in 2nd International Conference on Next Generation Computing Technologies (NGCT-2016) (Dehradun, India, 2016), pp. 30–34 6. D. Buhalis, A. Amaranggana, Smart tourism destinations, in Information and Communication Technologies in Tourism (Springer, Cham, 2013), pp. 553–564 7. J. Antonio Rodriguez, F. Javier Fernandez, P. Arboleya, Study of the architecture of a smart city, in Proceedings of Multidisciplinary Digital Publishing Institute, vol. 2 (2017) 8. G. Maji, S. Mandal, S. Sen, N.C. Debnath, A conceptual model to implement smart bus system using internet of things (IoT) (CATA 2017, Honolulu, Hawaii, USA, 2017) 9. Smart Cities need to be on the cloud to speed up sustainable development. https://www.smartc ity.press/cloud-computing-benefits/ Accessed on 19 Nov 2019

Chapter 32

Big Data Analytics and Internet of Things in Health Informatics Pawan Singh Gangwar and Yasha Hasija

Abstract The increasing amount of data in healthcare industry has necessitated the adoption of big data techniques to deliver quality health services. As the healthcare and technology industries are intensely entwined, an accelerated expansion is seen in the area of Internet of Things (IoT) and biomedical big data. Hence, a number of technologies like health devices and mobile applications are being integrated with telehealth and telemedicine via the biomedical IoT which constantly monitors autoadminister therapy-based devices, health indicators, or devices which keep real-time track of patient’s data of a self-administered therapy. Nowadays, due to increased Internet and smartphone access, patients have started using wearable biosensors, mobile apps for personalized mHealth and eHealth technologies managing their daily health needs. This paper reviews healthcare big data analytics and biomedical IoT and analyzes growing concerns in IoT technology pertaining to smarter ways of healthcare applications underlining the big data privacy and security challenges.

32.1 Introduction In past few years, the association between technology and healthcare has seen a big rise all over the world. Since then, big data analytics and Internet of Things (IoT) have progressively gained attention for eHealth and mHealth next-generation services. Data amount and its generation speed in different fields have taken a big leap in recent years. Science and Nature have published special issues to uncover big data opportunities and to overcome its challenges. Nowadays, healthcare data producing sources are improved quickly, like wearable medical devices, high-throughput instruments, sensor systems, which produce large amounts of data. Big data has been playing a crucial role in a variety of sectors, viz. scientific researches, health care, P. S. Gangwar · Y. Hasija (B) Department of Biotechnology, Delhi Technological University, Delhi 110042, India e-mail: [email protected] P. S. Gangwar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_32

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Omics

EHR

BIG DATA

ANALYTICS

Imaging

V I S U A L I S A T I O N

Fig. 32.1 Big data flow from its sources to storage, analytics, and visualization

industry, social networking, natural resource management, and public administration. Big data consists of large unstructured data which becomes tricky for analyses using traditional data processing techniques and platforms and requires advanced real-time analysis [1]. Characteristics of big data are defined by 6 V’s of volume (lots of data), variety (data obtained from different sources exists in different forms), velocity (data is accumulated speedily), variability (consistency of data over time), veracity (uncertainty of the data), and value (data relevance). It is more interesting to see big data as it relates to sources, repositories, and its analysis (Fig. 32.1). Internet of Things (IoT) is a system of devices and different equipments, integrated with sensor, software, electronics, and network connection, enabling such devices to gather and perform data exchange [2]. Its effect on medication would be perhaps more personalized and most significant. The amalgamation of healthcare and information technology, like biomedical informatics, would surely transform healthcare in several ways as reducing costs, minimizing inefficiencies and saving lives.

32.2 Biomedical Big Data Data types in biomedical sciences are majorly clinical and scientific data. Patient data and data pertaining to patient care like health surveys and epidemiological data are clinical data, whereas scientific data consists of bench science data. The data sources are broadly categorized into primary and secondary data source. In primary data analysis, an individual or group of researchers design, collect, and analyze the data. Primary data comes with the advantages of guaranteed data quality with minimum number of missing values and consistency of the instrument. The secondary data source depends on the existing data or the data collected already for some other purpose. Secondary data sources have several advantages as low cost,

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less time-consuming data collection and large data samples. Several famous big data biomedical projects which generated large-scale sequencing data include 1,000 Genome Project, The Cancer Genome Atlas (TCGA), etc. The types of healthcare data sources on which big data analytics is applied are Electronic Health Records (EHRs), Omics, medical imaging, clinical texts, etc.

32.2.1 Big EHR Data The principal source of big data in human health is the electronic health records (EHRs), since conversion from handwritten charts to EHRs. EHR stores each patient’s information, like research facility tests and results, statistic data, analysis, medications, clinical notes, and radiological images [3]. To analyze such data, it needs to be converted from text to a much-structured form, with or without natural language processing (NLP). EHR data types are unstructured (clinical notes) or structured (clinical data, imaging data, administrative data, charts, and medication).

32.2.2 Medical Imaging Data The image data consists of X-rays and CT scans. Picture Archival and Communication Systems (PACS) is a medical imaging data storage system which is used for image storage and retrieval [4]. Medical images are stored in database of biomedical images. These images are much complex and occupy more space. It is a two-phase system, i.e., training and testing. During the training phase, features are extracted by applying several algorithms using 80% data. In testing phase, rest 20% data is tested to check if the system gives true results of the input query image.

32.2.3 Clinical Text Mining Data Healthcare data are structured, unstructured and might have textual records. Text/Data mining is a method by which high-quality information is extracted from unstructured data. Text mining when applied electronic medical records (EMRs) helps to find patient stratification, unknown diseases, better drug targeting, and distinct side effects of the drug. Natural language processing (NLP), a machine learning method, is generally used to extract information from clinical texts. Several data mining tools are used to analyze textual records of health domain, e.g., clinical Text Analysis and Knowledge Extraction System (cTAKES).

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32.2.4 Big OMICs Data Omics data contains catalog of molecular profiles (e.g., genomics, proteomics, transcriptomics, metabolomics, and epigenomics) which provides the base for personalized medicine. The genomes, transcriptomes, and epigenomes are in upstream as compared to the metabolome and proteome. Genomics. Complete set of DNA of an organism. A genome information is contained in frameshift mutation (insertion/deletion), single nucleotide polymorphism (SNP) and four copy number variations (CNVs); Transcriptomic. All the gene present in a cell. Transcriptomic knowledge is carried in gene expression, transcripts expression and alternative splicing; Epigenomics. A large number of chemical compounds which direct the genome; Proteomics. The total protein which are encoded by the genome; Metabolomics. A well-rounded catalog of metabolites in the cell of an organism.

32.3 Healthcare Internet of Things (IoT) A decade prior just individuals were associated with the Internet, however, now things/devices are additionally associated with the Internet which is due to the sensors embedded in them. Hence, the core of IoT is the wireless sensor network, which senses the events and shares the data [5]. It can be understood by an example of a typical IoT hospital in practice where a diabetic patient has an ID card, which when scanned, links to a secure cloud that stores his/her EHR information, lab results, medical and prescription history. These records can be then used on smartphone, tablet, or computer by physician and nurses [6].

32.3.1 IoT Architecture IoT produces huge amounts of data for real-time processing; however, there is a delay in data transfer between cloud and end user. Hence, to diminish this delay, a fog assisted real-time alert generation and remote monitoring system architecture was proposed [7]. It has three layers: device layer (where user information is collected by IoT devices and medical sensors), fog layer (here patient information is analyzed using classification rules), and cloud layer (informs cautioning alerts to the relatives enabling them to check the patients critical signs from anyplace anytime). Figure 32.2 shows the generalized healthcare IoT system architecture. Using machine learning and advanced inference algorithms, the healthcare IoT system itself learns from patient history and sensor data to give feedback about the present and predicted health in future of the patient, and could even generate cautions if vital.

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DEVICE LAYER

IoT Connected Thing Wearable devices/sensors Temperature sensors

FOG LAYER

IoT Gateway Data preprocessing Data filtering & mining

CLOUD LAYER

IoT Big Data Analytics Data storage Data analytics

MOBILE APP

IoT Application Hospitals/Doctors Emergency alerts

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Fig. 32.2 Healthcare IoT system architecture [8]

32.3.2 IoT Data Source Health and behavioral data of patients in IoT framework is recorded by distinct types of sensors (wearable or implanted) equipped on patient for personal monitoring on multiple parameters and to transfer real-time data to medical personnel. Source of IoT data depends on the type of IoT application. The data is broadly branched in three classes: passive, active, and dynamic data. Passive data is from active response lacking device. Active data is collected from the IoT device which is actively reacting and sending the response. Dynamic data is collected and helps making the self-decision to do a better performance [9]. These types of data are sourced from Industrial Control Systems (Cortana Analytics and IBM Watson), Business Applications, Wearables (wearable devices are embedded with the sensors, e.g., Mi Smart Band 4), Agriculture Assistant (agri analytics), Open and Web Data (publicly available social network data like Facebook, Twitter to deduce the current trends among a group of people), GPS data (Global Positioning System).

32.4 Studies Related to Big Data Analytics in Healthcare IoT IoT and big data in healthcare has been a very hot topic recently. Some of the recent trends and developments in light of big data and IoT eHealth are mentioned.

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Rahmani et al. [10] proposed a smart eHealth Gateway in a fog computing-assisted system architecture. The gateway offers many features like local storage, real-time data processing, and integrated data mining. Additionally, the described architecture is powerful enough to deal several rising issues in omnipresent healthcare frameworks such as scalability, mobility, reliability, and energy efficiency. At last, using a prototype, authors have described some of the high-level qualities of the gateway like IoT-based Early Warning Score (EWS) patient health monitoring. Woo et al. [11] focused on very crucial matter of fault-tolerant healthcare data services. Therefore, authors presented a fault-tolerant algorithm reliable for the IoT network. Gateways are linked forming a daisy chain for fault tolerance in this architecture. Additionally, one gateway stores the backup copy of the previous gateway located immediately before of the gateway in the daisy chain. Using this approach, two gateway faults can be recovered which occurred simultaneously. Ammae et al. [12] described body movements sensing method by measuring changes in signal strength of Wi-Fi among two Wi-Fi enabled devices which allows an unnoticeable method to measure quality sleep. Based on maximum likelihood linear regression (MLLR), they adapted a person’s model of body movement detection by using training data of other users. This allowed to adapt to a movement detection model which is user independent. Their method was assessed by using real data of 60 sessions collected from six participants and the model achieved high detection accuracy. Farahani et al. [13] provided eHealth and mHealth IoT architecture systematic review. The ongoing present healthcare challenges all over the world are thoroughly described by the authors. The review suggests to move health services from hospital centric model to individual centric by IoT support. A multi-layer, holistic IoT environment is put forward which is driven by three layers of health devices, fog gateway/computing and cloud computing. It points IoT environment present challenges and suggests possible solutions. Smart textiles and smart eyeglasses case studies are also described demonstrating the competence of the proposed eHealth IoT ecosystem. Several studies have been performed on biomedical IoT in health informatics. Table 32.1 illustrates some studies with description, methodology, or algorithm of Table 32.1 Reviewed IoT healthcare system Author

Description

Methodology

Kalid et al. [14]

Prioritization of telemedicine patient remote health monitoring using big data analytics

Remote monitoring real-time system

Prajapati et al. [15]

Proposed for continuous ICU patients monitoring

Intelligent real-time IoT-based system

Rani et al. [16]

Early diagnosis and preventive Fuzzy K-nearest neighbor measures to control chikungunya virus algorithm

Sood et al. [17]

Identify and control chikungunya virus Fuzzy C-means algorithm by wearable IoT device

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study. Health Devices and Mobile Apps In the current era of information, enriched with data and knowledge, apps and devices are seen as a “health buddy.” Myo, a game controller, is presently being used in the orthopedic patients advised for post-fracture exercise. Patients monitor self-progress and doctors could measure angle of movement with Myo’s help. Zio Patch is for measuring heart/pulse rate and it is approved by FDA (US Food and Drug Administration) [18]. DarioHealth delivers evidencebased personalized digital therapeutic interventions for diabetes [19]. SleepBot is a mobile and web application, which has a “smart alarm” feature and tracks sleep [20]. RANKED Health is a project which critically evaluates and ranks health apps and connected devices is run by the Hacking Medicine Institute [21].

32.5 Challenges for Medical IoT and Big Data in Healthcare IoT leading platforms have to provide powerful, yet simple application access for IoT data and devices for fast development of mIoT apps and analytics application. Leading IoT platforms must enable: (1) Easy device management: Enables improved resource accessibility, expanded throughput and decreased maintenance costs. (2) Simple connectivity: It is simple for devices to connect and carryout management functions in a better IoT platform. (3) Information ingestion: Store and transform IoT data intelligently. APIs connect the gap between cloud and data, making it simple to draw the data which is required. (4) Informative analytics: Gain IoT platform from large bulk of IoT data for making better decisions and real-time analytics application to monitor present status and react accordingly. Leverage cognitive analytics to realize situations and learn as conditions change. (5) Reduced risk: Act on notifications and isolate incidents created anywhere from a single console. Security and privacy of the patient/individual are the two major issues of big data in healthcare [22]. Medical data is very sensitive and several countries consider it as legally possessed by the patient. Big data analytics software solutions should use advanced encryption algorithms and pseudo-anonymization of personal data to address the security and privacy challenges. These software solutions must give security on system level and authenticate for every associated user, ensure privacy and security, and also set up better governance norms and practices.

32.6 Conclusion Technology and health have consistently been associated, and this relation has fundamentally transformed because of fast development of IoT and the dominance of

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wearable devices lately. Smart IoT sensor devices, fog and cloud computing and smartphones collectively function as an efficient and flexible architecture for IoT in health care. Wearable health devices like fitness bands, smartwatch, smartphone, smart shirts, and headband detect user’s body temperature, heart/pulse rate, blood pressure, and different activities leading to personalized healthcare by increased healthcare IoT access. Today, the best quality of IoTs in health industry is remote health monitoring system and real-time location feature, in which patients can be guided and monitored in real-time from anyplace. As medical IoT is depended on patient’s data for quality health monitoring, healthcare IoT devices require efficient security algorithms to provide secure communication between server and devices, so that individual privacy is maintained along with data security to prevent misuse of data by an unauthorized entity. This review addresses major aspects of eHealth and mHealth IoT technologies which include wearable smart sensors, pervasive advanced health systems, and big healthcare data analytics furnishing better eHealth services leading to healthy society.

References 1. Y. Wang, L.A. Kung, T.A. Byrd, Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Change (2018). https://doi.org/ 10.1016/j.techfore.2015.12.019 2. A. Zanella, N. Bui, A. Castellani, L. Vangelista, M. Zorzi, Internet of things for smart cities. IEEE Internet Things J. (2014). https://doi.org/10.1109/JIOT.2014.2306328 3. P.S. Gangwar, Y. Hasija, Deep Learning for Analysis of Electronic Health Records (EHR) (2020). https://doi.org/10.1007/978-3-030-33966-1_8 4. J. Sun, C.K. Reddy, Big data analytics for healthcare (2013). https://doi.org/10.1145/2487575. 2506178 5. N. Nalini, P. Suvithavani, A study on data analytics: internet of things & health-care. Int. J. Comput. Sci. Mob. Comput. 6, 20–27 (2017) 6. D.V. Dimitrov, Medical internet of things and big data in healthcare. Healthc. Inform. Res. (2016). https://doi.org/10.4258/hir.2016.22.3.156 7. P. Verma, S.K. Sood, Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet Things J. (2018). https://doi.org/10.1109/JIOT.2018.2803201 8. V. Jagadeeswari, V. Subramaniyaswamy, R. Logesh et al., A study on medical Internet of Things and Big Data in personalized healthcare system. Health Inf. Sci. Syst. 6, 14 (2018). https://doi. org/10.1007/s13755-018-0049-x 9. S. Patil, A.R. Kokate, D.D. Kadam, Precision agriculture: a survey. Int. J. Sci. Res. (2013). https://doi.org/10.4172/2157 10. A.M. Rahmani et al., Exploiting smart e-Health gateways at the edge of healthcare Internetof-Things: a fog computing approach. Futur. Gener. Comput. Syst. (2018). https://doi.org/10. 1016/j.future.2017.02.014 11. M.W. Woo, J.W. Lee, K.H. Park, A reliable IoT system for personal healthcare devices. Futur. Gener. Comput. Syst. (2018). https://doi.org/10.1016/j.future.2017.04.004 12. O. Ammae, J. Korpela, T. Maekawa, Unobtrusive detection of body movements during sleep using Wi-Fi received signal strength with model adaptation technique. Futur. Gener. Comput. Syst. (2018). https://doi.org/10.1016/j.future.2017.03.022 13. B. Farahani et al., Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare. Futur. Gener. Comput. Syst. (2018). https://doi.org/10.1016/j.future.2017. 04.036

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14. N. Kalid et al., Based real time remote health monitoring systems: a review on patients prioritization and related ‘big data’ using body sensors information and communication technology. J. Med. Syst. (2018). https://doi.org/10.1007/s10916-017-0883-4 15. B. Prajapati, S. Parikh, J. Patel, An intelligent real time IoT based system (IRTBS) for monitoring ICU patient, in Smart Innovation, Systems and Technologies (2018). https://doi.org/10. 1007/978-3-319-63645-0_44 16. S. Rani, S.H. Ahmed, S.C. Shah, Smart health: a novel paradigm to control the chickungunya virus. IEEE Internet Things J. (2019). https://doi.org/10.1109/JIOT.2018.2802898 17. S.K. Sood, I. Mahajan, Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus. Comput. Ind. (2017). https://doi.org/10.1016/j.compind.2017. 05.006 18. C.E. Tung, D. Su, M.P. Turakhia, M.G. Lansberg, Diagnostic yield of extended cardiac patch monitoring in patients with stroke or TIA. Front. Neurol. (2015). https://doi.org/10.3389/fneur. 2014.00266 19. Dario Health, Blood Sugar Meter & Diabetes Tracker by Dario (Version 4.5.0.0.18) [Mobile app] (2020). Retrieved from https://play.google.com 20. Insomaniapps, SleepBot. [Sleep Tracking Device] (2010). Retrieved from: https://mysleepbot. com 21. RANKED Health, Retrieved from: http://www.rankedhealth.com (2016) 22. J. Archenaa, E.A.M. Anita, A survey of big data analytics in healthcare and government, in Procedia Computer Science (2015). https://doi.org/10.1016/j.procs.2015.04.021

Chapter 33

Medicinal Leaves Recognition Using Contour-Based Segmentation B. R. Pushpa, K. B. Amaljith, and N. Megha

Abstract Classification of medicinal plants is a challenging process through the automated system and achieving proper result is a rigorous work. India is well known for its prosperity of medicinal plants and its medicinal practice. In this modern world a person may know few plants which are common in place. There are wide varieties of plants which are unaware. To come out of this problem and to make use of all the medicinal plants an automated system is useful. The system can be used by researchers, students and in the medicinal production sector. The plant has its own properties and uses, preserving such plants makes very helpful for the future. The existing system also helps in classifying the plants and our proposed study helps in classifying the plants with lower quality images whereas the existing asks for the high-resolution images. One of the major goals of the study is to create native dataset using low cost capturing efforts. Proposed work contains contourbased segmentation which deeply considers leaf morphology, feature extraction using Local Binary Pattern and Wavelet methods and classification using supervised K-NN classifier.

33.1 Introduction Plants are the lungs of earth and plays a vital role in earth ecosystem. Plant species are used for preparing medicines, for research purpose and also it provides oxygen for the living beings on earth. Human beings are depended on plants for most of the things. There are many plants which are used in medicinal field. Few plants which B. R. Pushpa · K. B. Amaljith (B) · N. Megha Department of Computer Science, Amrita School of Arts and Science, Amrita Vishwa Vidyapeetham, Mysore, India e-mail: [email protected] B. R. Pushpa e-mail: [email protected] N. Megha e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_33

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are surrounded near the houses or which are common can be easily identified but there are many numerous plants which are unaware. Those plant species before it goes to extinction stage the identification of those species and classification of those species should be taken place. In the proposed system the automated classification of plant leaf is done by using several digital image processing methods, where the input is image of leaf and the classification of that particular leaf to its category is the result. The images are captured using mobile phones. There are ten different plants are used in the systems which are native plants from Kerala and Karnataka in India and dataset of these plants do not exist till now. Mainly three fundamental processes are there in implementation, they are segmentation, feature extraction and classification. Images in the entire dataset are of standard quality and good enough to extract the desired feature. So, segmentation is an inevitable process to do and considered the morphology of every leaf image. In order to achieve proper leaf segmentation contour-based techniques are implemented in the study. Segmented images are subjected to extract desired texture features through two methods wavelet and local binary pattern and performance of these methods are evaluated using supervised k-nearest neighbour classifier.

33.2 Literature Review Ehsanirad et al. [1] had proposed a work of leaf recognition using two methods gray level co-occurrence matrix and principle component analysis. Total 390 leaves images are collected from 13 different plant species to train the algorithms and tested with 65 new deformed leaves images. Classification is based on Eigen space approach. PCA features gives good accuracy of 94.46% and efficient performance but slower compared to GLCM which gives an accuracy of 78.46%. Kebapci et al. [2], The methodologies used in this study are max-flow min-cut (MFMC), augmenting path algorithm of Ford and Fulkerson, augmenting path method by Boykov and Kolmogorov, push-relabel method, SIFT features, Gabor wavelets, color histograms (color spaces used. RGB, normalized RGB (nRGB) and HSI color spaces), color co-occurrence matrices. Dataset includes 380 images of 78 different species. The investigation results with 50–55% accuracy. Singh et al. [3], Probabilistic Neural Network with key component analysis, Support Vector Machine using Binary Decision Tree and Fourier Moment have been used. SVM-BDT strategies focused on machine learning compared to the Artificial neural network classifier Probabilistic Neural Network and Fourier moment classification techniques are applied to characterize the form of the leaf and it is concluded that SVM-BDT performs very well compared to other methods. Kumar et al. [4], Novel-based feature extraction is applied. A new multi-resolution and multidirectional Curvelet transform is applied on subdivided leaf images to extract leaf information. Support Vector Machine classification algorithm is used. High classification accuracy is obtained on the dataset using this method. Al-Hiary et al. [5], The system deals with detection and classification of plant diseases. In this study

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an automatic detection and classification of leaf diseases has been proposed, this method is based on K-means as a clustering procedure and ANNs as a classifier tool using some texture feature set. With neural network classifier an accuracy of 94% is obtained. Mareta et al. [6] This approach proposed a strategy in which leaf identification is done in natural background.Images are collected with natural environment and hence complex segmentation techniques are used. Nine types of morphological features are extracted using local binary pattern algorithm on 30 images per each plant species. Proposed study results high accuracy of 96.07% with the multilayer perceptron algorithm. Revathi et al. [7] have done a study to identify cotton plant’s diseases through homogeneous segmentation depended on edge detection and neural network. The preprocessing takes place using automatic pre-processing methods, the ultimate goal of the study is implementing homogeneity based edge detector segmentation and recognize diseases in the cotton leaf and the images classified using neural network. Valliammal et al. [8], In this paper the high-resolution leaf image is taken and the segmentation is carried out using nonlinear k means clustering and the result is compared with the traditional approach. segmentation methods are applied and achieved the better result than other method. Gopal et al. [9], In this paper moment features, color and boundary-based features of the leaf. Ten different plant species are used for training and testing the dataset and the 50 leaves of different species are trained and 50 leaves are used for testing. This automated classification system is very efficient in classifying the leaf and as achieved 92% of accuracy. Ehsanirad et al. [10] texture feature is used to achieve the more accuracy as texture is considered as the powerful feature of the leaf. Proposed study depicts an efficiency comparison of gray level co-occurrence method and principle component analysis. The dataset contains 13 different kinds of plants of 390 images for training and testing. Both methods achieved the better result of 98% using PCA method and 78% using GLCM method.

33.3 Problem Definition The overall work can be divided into four stages: image collection, segmentation, extracting fundamental feature set and classifying obtained feature set. Figure 33.1 indicates the work flow of our proposed methodology.

33.4 Methodology 33.4.1 Image Collection Images are collected by capturing different leaves from each plant. All images are captured using normal smartphones of standard quality. Each image has same size

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Fig. 33.1 Operational flow of proposed study

and numbers of images in each species is different. Total 10 species of leaves are considered for the study; it is depicted in Table 33.1.

33.4.2 Segmentation To extract the leaf part from the background images should go through some segmentation process. It is inevitable to eliminate the background part according to the varying morphological of different leaves. The most of the leaves used in our study includes circular morphological structure. The methods used are given below in Fig. 33.2. Figure 33.3 is a pictorial representation of a single leaf and each process of segmentation is described in followed titles.

33.4.2.1

RGB to GRAY Level Image

RGB image contains colour pixels, making it gray helps to minimize the colour content, that is the image should be of pixels with lower intensity and higher intensity. Pixels with higher gray value depict the foreground and lower gray value depicts the background.

Botanical name

Piper betle

Chromolaena

Flectranthus

crispa

Psidium guajava

Sl. No

1

2

3

4

5

Guava (GV)

Gooseberries (GB)

Doddapathra (DP)

Communist pacha (CP)

Betel (BT)

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Table 33.1 Plant species used for experiment

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Azadirachta indica

Lawsonia inermis

Capsicum Frutescences

Hibiscus Rosa Sinensis

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Neem (NM)

Mehandi (MD)

Kanthari (KN)

Hibiscus (HB)

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Fig. 33.2 Stages of segmentation

Fig. 33.3 Depicts the outcome for each procedures of segmentation

33.4.2.2

Gaussian Blurring

The gaussian filter is applied to smooth or blur image and helps to reduce the noise. Blurring results with a lower pixelated image. Less pixelated image is best suitable for thresholding and contour analysis. It is easy to calculate pixel gray level with the blurred image and deference in gray value makes foreground and background identification easier. Because most of the images are captured with lighter background and thicker foreground. Hence blurring is a catalyst to contour-based segmentation.

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Thresholding

To manipulate the leaf region the blurred image is subjected to Otsu thresholding. Thresholding techniques are used to whether a pixel is fall in background or foreground. After the thresholding results a pixel classes of two levels. One is foreground and another one is background. So, the pixels intraclass intensity variances are minimized and maximize the interclass variance. Gaussian blurring results with a blurred image which is suitable to calculate pixel gray levels. Most of the images used in the study includes lighter background and thicker foreground hence the gray level image makes the foreground background extraction easier. The following figure shows the Otsu-threshold image.

33.4.2.4

Contour Analysis and Masking

Background elimination is dependent on the morphological features of the leaf image. Through contour analysis the circular shape of the leaf can be modelled and pixels that represent the leaf part can be extracted from the background. The images collected for the study contained milled background and there is a high variation of contract between background and foreground leaf, which enables the extraction easier. By applying black mask, regions other than the leaf structure can be eliminated. Mask created according to the leaf morphology obtained. Following figure shows a simple demo of contour study of RGB image.

33.4.3 Methodologies Used for Feature Extraction The segmented images are subjected to extract particular set of features from it. The methodologies used are Local binary pattern and wavelet. These methods are concentrated on texture features, because texture properties are the most important identification factor of leaves.

33.4.3.1

2D Discrete Wavelet

Wavelet is an efficient texture feature extraction method used in the field of image processing. A matrix of characteristics is collected from each applied image by using wavelet method. It is a function having 2D structure. ϕ(x, y) = ϕ(x)ϕ(y) H, V and D are the 3 types of wavelets, depicted in Eq. 33.2

(33.1)

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ψ H (x, y), ψ V (x, y), ψ D (x, y)

(33.2)

Varying intensity levels are tested horizontally, vertically and diagonally by using these wavelets. Using Eqs. (33.3)–33.5 energy coefficients are determined.  E Coeff

    = (x, y) 2/255 ψCoeff 

 2 V ψCoeff (x, y) /255

(33.4)

 D 2 D E Coeff =  ψCoeff (x, y) /255

(33.5)

V E Coeff

33.4.3.2

(33.3)

=

LBP

LBP (Local Binary Pattern) is an efficient texture descriptor used in the field of digital image processing. This local representation is generated by comparing each pixel to its surroundming pixel neighbourhood. LBP investigates points around a central point and checks if the surrounding points are larger than or smaller than the central point (i.e. calculate a threshold). For every pixel in the grayscale image, we pick a region of size R around central pixel. If the pixel value is greater than or equal to central pixel value, the cells are coded with 0 else 1 is added. Equation (33.7) is used to perform the comparison. To calculate grayscale invariance of image different labels are assigned to each pixel. And the Local Binary Pattern descriptor for every pixel is given as, LBP P,R =

P−1    s g p − gc 2 P

(33.6)

p=0

P is the count of pixels in radius R. The two variables gc and gp represents intencity values. Central pixel element gc represents graylevel value of central pixel and gp represents graylevel value of neighbouring pixels. where, 



s g p − gc =



  1,  g p − gc  ≥ 0 0, g p − gc < 0

(33.7)

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33.4.4 Classification Using KNN Algorithm K-Nearest Neighbour technique is used as the classifier. KNN classifier is applied on varying percentage of input data, which belongs to different classes. After feature extraction the KNN algorithm is trained with feature vectors of images. While training the corresponding class label of data is also assigned. The training percentage is predefined in the algorithm and in each run, it can be changed. While testing the input feature vector is compared with the trained classes using a distance measure called Euclidian distance. Based on the shortest distance it classifies input data to a particular class. Equation (33.8) depicts distance measure.

n

 (xi − yi ) dEuclidean (x, y) = 

(33.8)

i=1

33.5 Result and Observations Local Binary Pattern and wavelet features are applied to compare the classification accuracy on the created dataset. Based on KNN classifier LBP achieves the accuracy of 69% and wavelet achieves 57%. Accuracy is calculated by varying testing and training percentage of image set. Table 33.4 represents the accuracy of each method respectively which contains the information about training and testing and the correct classification of leaf, accuracy is represented in percentage. The accuracy is denoted in Figs. 33.4 and 33.5. The graph contains the information of training dataset and testing dataset and the accuracy obtained in each step of wavelet transform method and Local binary pattern method. Tables 33.2 and 33.3 depicts the confusion matrix obtained after testing all test samples.

33.6 Conclusion Developing an automated system for segmentation and recognition of medicinal leaves is a complicated process. In the proposed study two methods are used LBP and wavelet. After comparing the results LBP obtained highest accuracy 69.16%. That is LBP shows more unique identical feature for used dataset. Hence dataset quality is poor comparing to other standard datasets like Swedish, so in future better results can be obtained by using sophisticated technologies for dataset collection.

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Fig. 33.4 Results of local binary pattern

Fig. 33.5 Results of wavelet transform

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Table 33.2 Represents the Wavelet’s confusion matrix 1

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70

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Training (%)

Correctly classified

Testing (%)

Training (%)

Testing samples

Wavelet

LBP

Table 33.4 Shows the accuracy of Wavelet and LBP with varying percentage of dataset

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201

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68

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50.24

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References 1. A. Ehsanirad, Y.H. Sarath Kumar, Leaf recognition for plant classification using GLCM and PCA methods. Orient. J. Comput. Sci. Technol. 3(1), 31–36 (2010) 2. H. Kebapci, B. Yanikoglu, G. Unal, Plant image retrieval using color, shape and texture features. Comput. J. 54(9), 1475–1490 (2011) 3. K. Singh, I. Gupta, S. Gupta, Svm-bdt pnn and fourier moment technique for classification of leaf shape. Int. J. Signal Proces. Image Proces. Pattern Recogn. 3(4), 67–78 (2010) 4. S. Prasad, P. Kumar, R.C. Tripathi, Plant leaf species identification using curvelet transform, in 2011 2nd International Conference on Computer and Communication Technology (ICCCT2011), pp. 646–652. IEEE (2011, September) 5. H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik, Z. Alrahamneh, Fast and accurate detection and classification of plant diseases. Int. J. Comput. Appl. 17(1), 31–38 (2011) 6. A. Mareta, I. Soesanti, O. Wahyunggoro, Herbal leaf classification using images in natural background, in 2018 International Conference on Information and Communications Technology (ICOIACT), pp. 612–616. IEEE (2018, March) 7. P. Revathi, M. Hemalatha, Homogenous segmentation-based edge detection techniques for proficient identification of the cotton leaf spot diseases. Int. J. Comput. Appl. 47(2), 875–888 (2012) 8. T. Munisami, M. Ramsurn, S. Kishnah, S. Pudaruth, Plant leaf recognition using shape features and colour histogram with K-nearest neighbour classifiers. Proced. Comput. Sci. 58, 740–747 (2015) 9. A. Gopal, S.P. Reddy, V. Gayatri, Classification of selected medicinal plants leaf using image processing, in 2012 International Conference on Machine Vision and Image Processing (MVIP), pp. 5–8. IEEE (2012, December) 10. A. Ehsanirad, Plant classification based on leaf recognition. Int. J. Comput. Sci. Inform. Secur. 8(4), 78–81 (2010)

Chapter 34

Deep Learning for Robot Vision Mamilla Keerthikeshwar and S. Anto

Abstract Deep learning comes under a class of machine learning where we use it for extremely high-level output, like recognition of images, etc. It has been used in pattern recognition over a vast area such as handmade crafts to extract the data from learning procedures. At present, it has gained a great significance in robot vision. In this paper, we show how neural networks play a vital role in robot vision. Image segmentation, which is the initial step, is used to preprocess the images and videos. The multilayered artificial neural networks have a lot more applications. It can be applied in drug detection, military bases, and many more. The main objective of this paper is to review how deep learning algorithms and deep nets can be used in various areas of robot vision. There are some predefined deep learning algorithms that are available in the market, which are used here to perform this comparative study. These will help us to have a clear insight while building vision systems using deep learning.

34.1 Introduction Deep learning is the booming topic in the area of research, and it has gained a lot of attention in the last couple of years. It is involved in machine learning and robotics. A lot of conferences and workshops are being conducted on this [1–3]. The convolutional neural network has a lot of applications in robot vision. Many more algorithms have been developed for robot vision. The main intention of this paper is it acts as a guide for new developers who are keenly interested in robot vision. In this, convolutional neural network plays an important role, and mostly convolutional neural networks are employed and in M. Keerthikeshwar (B) · S. Anto School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India e-mail: [email protected] S. Anto e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_34

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some cases for pedestrian detection or to find fast-moving objects using semantic segmentation and how different datasets are used.

34.2 Deep Learning Deep learning is a subset of machine learning that uses the artificial networks for the preprocessing of the data, and it also creates patterns that are used for decision making. It is a sequential process that takes step by step. Deep learning that includes AI can learn the unstructured data. Deep learning is used to detect fraud. Deep learning has many applications. It is used in the fields of the research area, military base camps to detect the human movements, and pose estimation, and it is also used in the image processing where it is used to segregate the image by pixel is used to predict the genes.

34.3 Convolutional Neural Networks The convolutional neural network comes under a class of neural networks. It is used for analyzing visual imagery. It is in the form of layers where one neuron in one layer is connected to all the neurons in the next layer. Image representation is done by convolutional neural networks. Deep neural networks using convolutional temporal architecture and ordered LSTM cells can be used for classifying video. The final layer uses the temporal feature and late pooling for second convolutional and smaller temporal uses for slow pooling [4]. Human pose estimation is one of the applications that uses a convolutional neural network. This pose is estimated which results in deviated pose prediction. This uses novel structure convolutional networks for training deep networks. It has three methods to estimate pose (i) design novel networks, (ii) multi-task network is designed, and (iii) human pose is evaluated [5]. Another convolutional neural network is the residual attention network which incorporates the state-of-the-art bottom-up top-down structure; it has two branches (i) Mask branch, it has four convolutional layers, and it is employed in the image segmentation for robots. (ii) Trunk branch, it is employed for video classification and uses an end-to-end approach to classify the videos, and it classifies video by layer approach [6]. The fully convolutional network is a convolutional network used for the detection of objects regionally. It uses the sensitive position of maps and ROI. The regional proposal came out from RCNN with deep networks [2]. Bayesian convolutional neural network shows six DOF camera pose from a single RDB image [7]. Using convolutional neural networks, one can detect the pedestrians, and LIDAR is used to detect the light and sense the objects. The very popular framework in this is the Caffe framework [8]. Pedestrian detection makes use of support vector machine [SVM], and Kalman filter is used for tracking the pedestrian [9, 10]. Convolutional

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neural networks have a lot many more applications such as it is used to build 3D scenes from a single image, and this uses machine learning and uses CRF for refining. It also uses a database source, but that does not display 3D, and this type of convolutional network is fully convolutional networks [11]. Different scale-specific detectors are combined to produce strong object detector [12]. The accurate detection of the simple stage is by rolling convolutional. It is a two convolutional neural network used for object detection [1]. Shared convolutional neural networks is another neural network used for object discovery, where the speed is about 100 frames per second on GPU [13]. ADE20K is a dataset used for perspective adaptive convolutions. For this, an efficient algorithm for online training of network trajectories is used, and this analyzes problems and uses XNOR and SqueezeNet as detectors [14]. Image is segmented pixel-wise by encoding and decoding the image at SegNet. It uses the VGG16 network which contains 13 convolutional layers, and it is specially designed for the robotic vision. It takes input picture and differentiate the image into several colors, for the image which is of the same type, and then, they have the same color and the images which are different have other colors [15]. The 3D scene uses an NYU depth V2 dataset and SUNRGBD dataset [11]. Segmentation using convolution networks uses PASCAL [16]. VOC, NYUDv2, and SIFT as its datasets [10, 17]. CNN can be also used for playing games such as soccer using back propagation through time (BPTT) and real-time recurrent learning (RTRL) [18].

34.3.1 Fast RCNN We mainly focus on the fast RCNN for the object detection. In this, convolutional feature maps are generated by taking in the input images. These images are then extracted, and then these are again reshaped into fixed size. Algorithm: Step 1: Image is taken as the input, and these are processed. Step 2: ConvNet generates the regions which we wanted to extract. The image is sent to ConvNet for extraction. Step 3: For resizing the extracted image, Rol pooling layer is used for the images generated from the ConvNet, and later, this is passed to fully connected network. Step 4: After getting the result from the fully connected layer, these are further passed on to the Softmax layer and linear regression layer.

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34.4 Generative Adversarial Networks Generative adversarial networks are used for semi-supervised learning. It helps the robot to interact with objects. It is a minimax game between a generator (G) and a discriminator (D) by mapping noise z from p(z), where p(z) is noise distribution. The function of the discriminator is that it differentiates between real date and generator sample. For robots like self-driving or warehouse robots, it uses depth perception that also makes use of 3D object recognition and path planning. So to use this, the robot should also know the depth of the ground, to know the depth of the ground it makes use of some highly expensive hardware tools, and to overcome this problem, YGAN is used, that uses data from cameras from all the sides to estimate the depth [19, 20]. GAN has a lot of applications, where image resolution and classification is one of them. To classify GAN’s accuracy, we take datasets consisting of repeated results approximately 7000 series with 1000 series in each material. It got the result of 7000 by sixfold cross-checking, with each fold being 1000 samples [21].

34.5 Restricted Boltzmann Machine Restricted Boltzmann machine is an unsupervised model that produces never seen data from the original data. It is of the form of layers with one visible layer and several hidden layers. But Boltzmann machine is different from restricted Boltzmann machine, in restricted Boltzmann machine the visible node and the hidden nodes are not linked to one another, whereas in Boltzmann machine, the nodes are linked to each other. A deep belief network is a process where multiple RBMs are stacked together can be fine-tuned by process and back propagation. In the restricted Boltzmann machine, all the neurons behave individually [22]. Restricted Boltzmann machine has a lot many applications, and automatic hand sign language is one among them. Input is taken by RGB and depth. These inputs are sent to the RBM. The output RBM is simplified to another RBM, and this model is trained by datasets [23].

34.6 Recurrent Neural Networks Recurrent neural networks are used to join images that are drawn. Kazuma Sasaki stated that they have conducted two experiments. First experiment tells that model can learn 15 drawing shapes by the bottom-up process. In the second experiment, four images are trained with four deformed variations per each type, and the images are segregated based on their type using drawings and image classification [24]. Recurrent neural network can also be a part to develop a 3D scene layout. A robotic camera is installed, and it captures images, and these images are filtered using RGB,

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depth, and foreground, and later after that, the image is converted into a 3D tensor. To convert it into a 3D scene layout, it is passed through recurrent neural network [25]. Recurrent neural networks can be also used planners for the bio-inspired robotic motion, it uses long-term memory networks of sequential data, and it also makes use of the simulated fish trajectories. Using this, it can be implemented in the robots without even knowing their position. This work is related to animal behavior and then used to operate the robots [26]. Recurrent neural networks works effectively for path planning and also in object avoidance which is generally represented in the form of neurons [27].

34.7 CNN Architectures In the last couple of years, we have witnessed a numerous increase in the CNN architectures. These architectures are used by giving input datasets. In this paper, we have taken the most used CNN architectures and described them how are they useful in the robot vision.

34.7.1 AlexNet AlexNet is one of the convolutional neural networks. It has over eight layers out of which five are convolutional layers, and the remaining three layers are fully connected layers. Some networks use tanh function, but AlexNet uses rectified linear units. It also has multiple GPU training where the time is reduced and makes it run faster. AlexNet checks that it is overfitting [4]. To print a real-time 3D scene fully, convolutional neural network uses AlexNet instead of VGG [11]. Image segmentation by a convolutional network uses AlexNet [10]. This has the same architecture like that of the LeNet. AlexNet uses rectified linear units instead of tanh functions since this accelerates the speed six times at the same accuracy and uses dropout as it overcomes the overfitting but doubles the time for 0.5.

34.7.2 GoogLeNet GoogLeNet is another convolutional neural network that is pretrained. Similar to AlexNet, GoogLeNet also has 22 layers. The image in this can be trained using an image net or place S65 dataset. It allows only one unique video to be processed by multiple image processing [4]. Semantic segmentation uses GoogLeNet as one of the datasets [10]. The performance of GoogLeNet is similar to that of humanlevel performance, and it requires human training to beat the GoogLeNet. This is

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the combination of a group of small convolutions which is to reduce the parameters. AlexNet has over 60 million parameters, but GoogLeNet has over 4 million parameters, which is quite small. GoogLeNet is more accurate.

34.7.3 RGB-D RGB-D is the mixture of depth with the RGB image. In this, depth image is an image in which distance is calculated from plane to RGB image. It is used for object detection. The architecture for RGB-D has three steps: (i) input image is processed, (ii) train network, (iii) classifying depth images [28, 29]. RGB-D dataset is the largest dataset that holds household objects. The object recorded in a video sequence and the objects is rotated in a circular plane, so that the object is captured from all the sides. The video is recorded using a Kinect style 3D camera. This even has other indoor and outdoor environments such as garden, kitchen, living room, and it can capture these scenes from long distances even though they are partially included or fully involved in the frame.

34.7.4 KITTI KITTI is the dataset used for the detection of moving objects. As pedestrians move from one place to another there, KITTI dataset is used to detect them [8, 9]. This KITTI dataset also uses Velodyne LIDAR. Fast object detection is done using KITTI and Caltech dataset [15]. Single-stage detectors RRC using novel recurrent rolling has achieved a benchmark in KITTI. Scale-dependent, pooling, and cascaded classifiers make use of the PASCAL object detection challenge and KITTI. It has two colored cameras and a gray-scale camera installed which are used to detect the objects [16].

34.7.5 ImageNet ImageNet is a database that contains hundreds of images per a single node. The performance of residual attention is done by the ImageNet [5]. ImageNet was created for educators and researchers for those who use a lot of images for training. To make it easy, a large database of images is created, and this database is termed as ImageNet. It does not own any of the copyrights for images, but instead, it only holds URLs and images all together.

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34.7.6 CIFAR-100 CIFAR-100 is the same as CIFAR-10 but with 100 classes and over 500 images in each class, and these 100 classes are divided into 20 superclasses. It contains thousands of images per class. It has five training batches and one test batch. They may contain images from any class. It is used in residual attention network evaluation [6].

34.8 Discussion As mentioned earlier, deep neural networks are suitable for robotics applications to deal with the limited camera resolution and human pose. For the biomedical image to be segmented, we provide elastic deformation with fully based implementation [30]. As said, convolutional neural networks are used to estimate human pose, and PoseNet is used to discriminate the original pose and fake pose [5]. Image classification is done by residual attention network which can capture attention and can be extended to convolutional network [6]. ResNet is used for image classification in regionbased fully convolutional network [2]. Trajectory-centric RL algorithm will able to learn different type of skills, and these are used to autoencoders [31]. For pedestrian detection, we employ LIDAR and fusion methods, where fusion performs well [8]. A convolutional neural network is also employed in the real-time 3D scene and uses CRF to refine the boundary and remove extra groups [11]. For scene parsing, we employ perspective adaptive convolutions in parallel GPU and this improves accuracy [32]. Signet is another architecture that is smaller, faster, and more efficient [15]. Noise-aware training is accurate, and it also improves recognition accuracy [28]. Shared convolutional neural networks employed in object detection and have better performance than a single model [13]. Gradient-based algorithm for online uses XNOR, SqueezeNet [14]. GTX.TITAN X GPU is used for testing grasp detection [13]. Image description in the wild (IDW) is used to improve segmentation accuracy using weak supervisions [33].

34.9 Conclusion This paper has addressed the use of convolutional networks and image segmentation in the area of robot vision. It mainly focused on object detection, pedestrian detection, and showed how different a networks have been used in robot vision development [9]. This paper will help and provide a valuable guide for developers and researchers who are working in the robot vision since it gives them the basic idea of all the algorithms used and different datasets that have been used in it. Training with more sets in RGB-D object detection does not show any better results [28].

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It is expected that the robot vision using deep learning will increase in the next few years and thanks for better thinking and adapting the DNN for robot vision. We all know that robots should interact with the environment and human beings. It should be adapted to the surroundings, and to do this, it should be trained properly. Geometry-based and deep-based methods will be a part of state-of-the-art vision systems which leads to the increase of robotic autonomy and training of DNNs. Different CNN architectures are made use of for robot vision. There are many other architectures of CNN and other networks. GoogLeNet and AlexNet are the most used CNN architectures. When a single model architecture for original images is considered, ResNet has top accuracy than other architectures and followed by VGG architecture. AlexNet and GoogLeNet have the least accuracy. When the error rate is considered, GoogLeNet has more error rate than other architectures. When the preprocessed images are taken into the consideration, AlexNet tops the list.

References 1. J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y.-W. Tai, L. Xu, Accurate single stage detector using recurrent rolling convolution, in CVPR (2017) 2. J. Dai, Y. Li, K. He, J. Sun, R-FCN: object detection via region-based fully convolutional networks. arXiv:1605.06409 3. G. Wang, P. Luo, L. Lin, X. Wang, Learning object interactions and descriptions for semantic image segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 5859–5867 4. Y.H. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, G. Toderici, Beyond short snippets: deep networks for video classification, in IEEE Conference on Computer Vision and Pattern Recognition[CVPR] (2015), pp. 4694–4702 5. Y. Chen, C. Shen, X.-S. Wei, L. Liu, J. Yang, Adversarial PoseNet: a structure-aware convolutional network for human pose estimation. arXiv:1705.00389 6. F. Wang, M. Jiang, C. Qian, et al. Residual attention network for image classification (2017). arXiv preprint arXiv:1704.06904 7. A. Kendall, R. Cipolla, Modelling uncertainty in deep learning for camera relocalization, in IEEE International Conference on Robotics and Automation [ICRA] (May 2016) 8. J. Schlosser, C.K. Chow, Z. Kira. Fusing LIDAR and images for pedestrian detection using convolutional neural networks, in IEEE International Conference on Robotics and Automation [ICRA] (May 2016) 9. M. Szarvas, A. Yoshizawa, M. Yamamoto, J. Ogata, Pedestrian detection with convolutional neural networks. IEEE Proc. Intel. Veh. Sympos. 2005, 224–229 (2005). https://doi.org/10. 1109/IVS.2005.1505106 10. J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in CVPR 2015 [best paper honorable mention] 11. S. Yang, D. Maturana, S. Scherer, Real-time 3D scene layout from a single image using convolutional neural networks, in IEEE International Conference on Robotics and Automation [ICRA] (2016) 12. Z. Cai, Q. Fan, R.S. Feris, N. Vasconcelos, A unified multi-scale deep convolutional neural network for fast object detection, in European Conference on Computer Vision (Springer International Publishing, 2016), pp. 354–370 13. D. Guo, T. Kong, F. Sun, H. Liu, Object discovery and grasp detection with a shared convolutional neural network, in IEEE International Conference on Robotics and Automation [ICRA] (2016)

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14. N. Cruz, K. Lobos-Tsunekawa, J. Ruiz-del-Solar, Using convolutional neural networks in robots with limited computational resources: detecting NAO robots while playing soccer (2017). arXiv:1706.06702 15. V. Badrinarayanan, A. Kendall, R. Cipolla, SegNet: a deep convolutional encoder-decoder architecture for image segmentation (2015). arXiv:1511.00561 16. F. Yang, W. Choi, Y. Lin, Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers, in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2016) 17. J. Fu, J. Liu, Y. Wang, H. Lu, Stacked deconvolutional network for semantic segmentation. arXiv preprint arXiv:1708.04943 [2017] 18. R.J. Williams, J. Peng, An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Comput. 2(4), 490–501 (1990) 19. M. Alonso Jr, Y-GAN: a generative adversarial network for depthmap estimation from multicamera stereo images, 3 Jun 2019. arXiv preprint arXiv:1906.00932 20. A. Pronobis, R.P. Rao, Learning deep generative spatial models for mobile robots, in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 24 Sep 2017. IEEE, pp. 755–762 21. Z. Erickson, S. Chernova, C.C. Kemp, Semi-supervised haptic material recognition for robots using generative adversarial networks (2017). arXiv:1707.02796v2 22. A. Al, M. Zain Amin, A briefly explanation of restricted boltzmann machine with practical implementation in pytorch 23. R. Rastgoo, K. Kiani, S. Escalera, Multi-modal deep hand sign language recognition in still images using restricted boltzmann machine, in Entropy 23 Oct 2018 24. K. Sasaki, K. Noda, T. Ogata, Visual motor integration of robot’s drawing behavior using recurrent neural network. Rob. Auton. Syst. 86, 184–195 (2016) 25. R. Cheng, Z. Wang, K. Fragkiadaki, Geometry-aware recurrent neural networks for active visual recognition, in 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada 26. A. Khan, F. Zhang, Using recurrent neural networks (RNNs) as planners for bio-inspired robotic motion, in 2017 IEEE Conference on Control Technology and Applications (CCTA), 27 Aug 2017. IEEE, pp. 1025–1030 27. N. Bin, C. Xiong, Z. Liming, X. Wendong, Recurrent neural network for robot path planning, in International Conference on Parallel and Distributed Computing: Applications and Technologies, 8 Dec 2004 (Springer, Berlin, Heidelberg, 2004), pp. 188–191 28. A. Eitel, J.T. Springenberg, L. Spinello, M. Riedmiller, W. Burgard, Multimodal deep learning for robust RGB-D object recognition, in IEEE/RSJ International Conference on Intelligent Robots and Systems [IROS], Hamburg, Germany (2015) 29. M. Schwarz, H. Schulz, S. Behnke, RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features, in ICRA (2015) 30. O. Ronneberger, P. Fischer, T. Brox. U-net: convolutional networks for biomedical image segmentation (2015). arXiv preprint arXiv:1505.04597 31. C. Finn, X.Y. Tan, Y. Duan, T. Darrell, S. Levine, P. Abbeel, Deep spatial autoencoders for visuomotor learning, in IEEE International Conference on Robotics and Automation [ICRA] (2016) 32. R. Zhang, S. Tang, Y. Zhang, J. Li, S. Yan, Perspective-adaptive convolutions for scene parsing, in IEEE Transaction on Pattern Analysis and Machine Intelligence [Early Access] 33. P. Luo, G. Wang, L. Lin, X. Wang, Deep dual learning for semantic image segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2718–2726

Chapter 35

Deep Learning Approach for Prediction of Handwritten Telugu Vowels Ch. Prathima and Naresh Babu Muppalaneni

Abstract Telugu is an ancient language in which many historical and valuable scripts are written. Understanding and digitalization of theses scripts is difficult task. Hence, here we are proposing a machine learning approach which recognizes the handwritten Telugu vowels (Achulu). We employed convolutional neural network (CNN) approach to find the features automatically than the handicraft features. Dataset has been built and deployed in IEEE dataport. The results are impressive and interesting. Handwritten character recognition could be a part of Optical Character Recognition System (OCRS). OCRS can be applied to each printed text and handwritten documents. This paper describes the handwritten Telugu vowels recognition by employing a CNN approach. The dataset is pre-processed and also extracted the features using deep neural network system for training. The model is validated on test dataset, and ~98% of training accuracy ~92% of test accuracy are observed.

35.1 Introduction Optical Character Recognition System (OCRS) technology is being used for digitization by reading the characters and text from printed documents. The results are not impressive while reading the handwritten characters form printed documents using OCRS. There are other techniques for handwritten characters in combination with OCRS like Intelligent Character Recognition System (ICRS), machine learning, and combination of machine learning with multiple engines of OCRS. The OCRS process mentioned in Fig. 35.1 includes preprocessing, segmentation, extraction, and classification. Segmentation can additionally be isolated in line, word, and character division. Isolating the lines from other is done in line division, and at that point, Ch. Prathima Sree Vidyanikethan Engineering College (Autonomous), Tirupathi, India e-mail: [email protected] N. B. Muppalaneni (B) National Institute of Technology Silchar, Silchar, Assam, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_35

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Fig. 35.1 Architecture for character recognition process

words are portioned, and lastly characters are confined from each other [1, 2]. When characters are disconnected, a few highlights are figured out which recognize one character from another. A character vector is set up to train the classifier, and the numbers of classes are dictated by number of characters in that content. Feature extraction [3] is a basic method, as the order process exceptionally relies on feature. The feature precision relies on the uniqueness of the character vectors contemplated [4, 5]. Figure 35.1 shows every step involved in OCRS framework. The main issue of OCRS varies from character to character [6], as all the characters have various qualities. It works well on text data rather than handwritten characters, the characters features should be trained through the CN network to the computer, OCRS systems are expensive and consumes more memory space, and the main part of Indian characters is having header lines, which is a one of a kind feature. In this way, segments or feature extraction calculation of this factor should be considered carefully. Compatability is the situation of Telugu Achulu characters [7], where the characters are 3000 years of age. Telugu script characters are altogether same as the greater part of the significant Indian characters [8], the content utilized for composing Telugu territorial language has 52 characters (Aksharamulu), including vowels 16

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Fig. 35.2 Telugu vowels (Achulu)

(Achulu) and consonants 36 (Hallulu) which are familiar with extra phonetic sounds. The Telugu vowels are shown in Fig. 35.2.

35.2 Methodology 35.2.1 ANNs, ML, and DL Nowadays, artificial neural networks (ANNs) has become prevalent and supportive model for characterization, grouping, design recognition, and expectation in all disciplines. ANNs are one sort of model for AI (ML) and has gotten moderately aggressive to traditional and regression models. As of now, man-made consciousness (AI, neural system, deep learning, and robots), data security, enormous information, distributed computing, Web, and scientific science are on the whole hotspots and energizing themes of data and correspondence innovation (ICT). ANNs full applications can be assessed regarding information examination factors, for example, exactness, handling speed, dormancy, execution, adaptation to non-critical failure, volume, adaptability, and combination [2, 4]. The extraordinary capability of ANNs is the fast preparing gave in a monstrous parallel usage, and this has increased the requirement for inquire about in this space [5]. ANNs can be created and utilized for picture recognition, normal language handling, etc. These days, ANNs are for the most part utilized for general capacity guess in numerical ideal models as a result of their incredible properties of self-learning, adaptivity, adaptation to non-critical failure, nonlinearity, and progression in contribution to a yield mapping [9]. A best advantages of ANNs is that it can make models simple to utilize and progressively precise from complex normal frameworks with huge inputs. The ANN is seen as an exceptionally novel and

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Fig. 35.3 General architecture of convolutional neural network

helpful model applied to critical thinking and AI. ANNs has seen enormous use in explicit areas, for example, discourse acknowledgment, translation of multi-language messages, three-dimensional article acknowledgment, surface investigation, facial acknowledgment, and manually written word acknowledgment. Classification viewed as a type of troublesome enhancement challenge. Most specialists apply machine learning methods for taking care of classification issue. ANNs with more than two hidden layers are called deep systems on the grounds that the system has gotten unpredictable with more than one hidden layer. DL is a part of AI, the term deep learning refers (ANN) with complex multilayers. Deep learning has increasingly complex methods for associating layers, likewise has a larger number of neurons. Consequently, DL is characterized as a NN with an expansive factors and layers with an isolated essential system engineering of trained systems, convolutional NNs, recursive NNs, and recurrent NNs. One of the most widely recognized deep NNs is the convolutional NN called CNN [10]. A CNN is a standard NN that stretches out crosswise over space by means of shared loads. CNN is intended to perceive pictures by having convolutions inside that can perceive the picture of an item. CNN has numerous layers [11]; including completely associated layer, pooling layer, convolutional, and nonlinearity layers as shown in Fig. 35.3. The completely associated layers and convolutional layers have parameters; anyway nonlinearity layers and pooling do not have parameters. Though the number of layers is more in deep neural networks, training is fast because of sharing of parameters. Study has indicated that CNN has a great execution in ML issues. Especially, in the applications to picture information, similar to the most broad picture order dataset, normal language handling, and PC vision.

35.2.2 Preprocessing Stage In this section, we described the stages prior to recognition stage. Figure 35.2 shows the Telugu alphabets (Achulu)

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(i) Dataset Details For performing the test, we have gathered manually written Telugu characters (Achulu) from various individuals with various foundation of different age gathering 1116 pictures through handwritten app developed and 241 pictures are utilized for testing reason and 875 are used for training reason. Analysis is done on all 16 Telugu alphabets (Achulu). https://ieee-dataport.org/documents/telugu-handwrittenvowels. (ii) Image Acquisition and Converting In this stage, the raw data is created. First, handwritten text samples were collected from a group of people with varying handwriting style through the paint app created by us. Secondly, the samples are saved as image file and converted into gray-scale image of 30 × 30 size.

35.2.3 CNN Architecture for Telugu Alphabet Recognition The CNN model utilizes 96 filters as the primary conv layer, explicitly with the size of 30 × 30 pixels and yields. In the wake of Max pooling (called a subsampling layer), another convolutional layer has 64 filters with an explicit size of 15 × 15 pixels and yields, again with a littler size, i.e., 8 × 8 pixel yields with earlier convolutional layer, again pursued by pooling. In the reiteration of these two squares of convolution and pooling layers, the pattern is an expansion in the quantity of filters. Contrasted with present day applications, the quantity of filters is likewise little; however, the pattern of expanding the quantity of filters with depth of the model additionally stays a typical example in current utilization of the method. The flattern of the component maps and elucidation and characterization of the feature by FC layers. The last segment of the design is a classifier, while the convolutional and pooling layers prior in the model are referred as the feature extractor, i.e., 16 classes. Figure 35.4 shows where each class falls into one Telugu character (Achulu) 0–15 classes.

Fig. 35.4 Our CNN model for handwritten Telugu vowels recognition

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Fig. 35.5 Training accuracy versus test accuracy

35.3 Results and Discussions In this paper, a deep learning-based acknowledgment of written by hand Telugu characters is proposed. They have utilized probabilistic and fuzzy element and CNN based order. The analysis was done on 16 Telugu vowels, and 1116 samples of filtered pictures have been utilized for preparing and 241 for testing reason. With our deep learning model, we achieved ~ 98% of training accuracy and 91.28% test accuracy, with 10 epochs recognition process represented in Fig. 35.5. They have utilized worked in by structuring a model for this reason and back propagation-based ANN learning method. The acknowledgment rate differs from digit to digit in the work distributed. The results are presented as confusion matrix in Table 35.1. It is observed that our model has misclassified some characters, because of with different handwriting styles or similarity of characters. Five samples of class label 14 is misclassified as with class label 15.

35.4 Conclusions The handwritten Telugu character letter set model is exhibited in this paper. The character acknowledgment framework is considered as classifier drawback, so deep learning method, i.e., CNN, is the model arranged for acknowledgment. It is seen that the accomplishment of the framework relies upon the features used to recognize the character just as on the division phase of the test picture for example line division, word division, and character division. Characters gathered for even an ideal human client thinks that it is troublesome to separate these characters physically. The large execution of the framework that we accomplished may not be acceptable from the client’s perspective; however, as indicated by overview, for human like acknowledgment, an exactness of at the very least 99% is required. Along these lines, regardless we have to improve the exhibition of our framework. The framework can additionally be improved by thinking about a progressively strong feature, and other

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Table 35.1 Confusion matrix of prediction on test dataset Prediction

Actual

16

0

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0 0

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0 15

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0 13

1

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0 0

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0 14

0

0

0

0

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0 0

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0 15

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0 0

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14

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0 0

0

0

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14

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15

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0 0

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1 10

2

0 0

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0

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0 0

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0

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1

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0

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0 14 0

0

0

0

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0

0

0

0

0

0

0

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1

0 9

5

0

0

0

1

0

0

0

0

0

0

0

0

0

0 1

13

example acknowledgment framework like help vector machine (SVM) may likewise be considered in the acknowledgment stage.

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References 1. N.B. Muppalaneni, Handwritten Telugu compound character prediction using convolutional neural network, in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 2020, pp. 1–4 2. L. Vasantha, C. Patvardhan, An optical character recognition system for printed Telugu text. Pattern. Anal. Appl. 7,190–204 (2004) 3. Z. Majid, F. Karim, F. Farhad, Language based feature exraction using template matching in Farsi/Arabic handwritten numeral recognition, in Ninth International Conference on Document Analysis and Recognition, vol. 1, pp. 297–301 (2007) 4. M. Wenying, D. Zuchun, A digital character recognition algorithm based on the template weighted match degree. Int. J. Smart Home 7(3), 53–60 (2013) 5. R. Singh, M. Kaur, OCR for Telugu script using back-propagation based classifier. Int. J. Inform. Technol. Knowl. Manage. 2(2), 639–643 (2010) 6. C.V. Jawahar, K. Pavan, S.S. Ravi Kiran, A Bilingual OCR for Hindi-Telugu documents and its applications, in Proceedings of Seventh International Conference on Document Analysis and Recognition, pp. 408–412 (2003) 7. E. Kavallieratou, N. Fakotakis, G. Kokkinakis, Handwritten character recognition based on structural characteristics, in 16th International Conference on Pattern Recognition, pp. 139–142 (2002) 8. Ch. Prathima, Ch. Sreenu Babu, N.B. Muppalaneni, C. Kishore, Using large scale deep learning method to predict punctuations in Telugu language 2017. Helix 7(5), 2025–2028. https://doi. org/10.29042/2017-2025-2028 9. S. Arora, D. Bhattacharjee, M. Nasipuri, D.K. Basu, M. Kundu, Application of statistical features in handwritten Devanagari character recognition. Int. J. Recent Trends Eng. 2, 40–42 (2009) 10. P.H. Surya, Handwritten Telugu character recognition using convolutional neural networks, in Project Report, 2017. Available: https://github.com/Harathi123/Telugu-Character-Recogn ition-using-CNN 11. S.T. Soman, A. Nandigam, V.S. Chakravarthy, An efficient multiclassifier system based on convolutional neural network for offline handwritten Telugu character recognition, in 2013 National Conference on Communications (NCC), pp. 1–5 (2013)

Chapter 36

Literature Review of Lean Methodology and Research Issues for Identifying and Eliminating Waste in Software Development Mona Deshmukh and Prateek Srivastava Abstract Lean principles is a methodology that focuses on identifying and eliminating activities or tasks that are not considered as important by the customer and do not add value, whether it is a manufacturing or software process. Be it manufacturing or a service industry, there are some components which can be identified as waste. Lean is a customer-centric concept; hence, activities which do not add value to the customer are considered as waste. An activity in a process which does not add value to customer consumes resources and adds cost or time can be can be called as waste or useless and hence can be eliminated. Lean is a widespread concept of manufacturing but seldom used in software industry. Literature contribution on Lean methodology in software and manufacturing industry are fragmented and show some significant limitations. Aim of this paper is to promote Lean concept in software industry.

36.1 Introduction Lean is a unique idea for providing better service for the user by means of removing items which are considered as waste. A task in a process that tends to consume time or resources without increasing the price is considered as a target for removal. Lean concept is widely practiced in manufacturing industries but is not popular in the software sector. In a production context, the products and production methods are closely observed; hence, waste is simple to perceive, whereas waste in software sectors are not as same as in the manufacturing/production. Many industries do not realize the importance of Lean methodology hence retaliate to change the organizational culture that suits implementation of Lean methodology rather they flow the conventional way. But the constantly changing market situation put more demand to pay greater attention on client which without a doubt adds value. The motivating factor behind this study is to analyze the Lean concepts for IT industry taking Indian IT sector as domain due to the reason that Indian IT sectors are not achieving that M. Deshmukh (B) · P. Srivastava Department of Computer Engineering, SPSU, Udaipur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_36

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much profit ratio as compared to the foreign IT sector. Main reasons are wastes produced during the business process and less quality product. This research aims to introduce the Lean mindset and its requirements in the software industry.

36.2 Review of Literature Word “Lean” was first used by Krafcik in 1988 to elucidate the Toyota manufacturing system. However, this became a widely used concept after the release of a book entitled “The Machine that changed the world” (Womack et al. 1991). Another e-book entitled, “Lean Software Development: An Agile Toolkit,” authored by using Poppendieck and Poppendieck particularly been a bedrock of many Lean initiatives in software development domain. Poppendieck and Poppendieck [13] laid the foundation of Lean tasks. The authors provide justification as to why the Lean principle works in improvising the software devices. They also provide seven concept of Lean software development. According to Economic Times, when one of India’s eminent software exporter Wipro, was searching solutions increased inflation and outsourcing complexities, Wipro, alongside many other Indian software industries possessed all the quality certifications viz ISO 9001 to SEI CMM. But like most initiatives and certificates, even that they had become mere tags and did not help to form a difference as a result of which Wipro thought of adapting the Lean methodology. Unlike conventional methods of improving performance and quality of software projects, Lean focused on identifying and cutting waste to enhance ROI. In a software sector, waste could include duplication of efforts, and time utilized in preparing for a new project. Wipro has almost 1600 projects which are Lean and save an average of almost 20% yearly. In 2004, the organization launched a pilot “Lean” initiative: an attempt that attempted to translate the Lean manufacturing concepts from production to S/W development, and operational performances were highly impacted by the Lean initiative at Wipro. Its observed that Lean initiatives have higher performance rate, reduced efforts, and overall performance than non-Lean projects. The idea of “going for short wins” is intuitively famous, although he understood the concept of Lean as an ongoing development tool. Every industries or manufacturer should learn to implement the concept of Lean. Lean is not just a thing they ought to usher in practice, but they ought to also attempt to bring various other Lean concepts into account. Lean should not be implemented by itself to achieve maximum output, and it should be implemented strategically. Industries like Timberline Inc. adapted the Lean subculture. Here, Lean principles are employed by the software development agency so as to extend productivity and to attenuate waste. Timberline extensively uses the concept of takt and an everyday stand up assembly acquainted from their plans and also to motivate individual. After adapting Lean, the time required to review defect from the entire improvement cycle dropped by 65–80%, among other improvement. Alvarez et al. observed that the implementation of Lean methodology results in operational excellence, continuous development and elimination of non-value-added activities.

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Taylor et al. provided a Lean framework to analyze the impact of Lean. With comparison within Lean culture, Lean layout, Lean supply chain management with the traits, and factors of various ways of innovation various strategies for various industries to attend stability while implementing Lean and innovation at the same time are also discussed. Behrauzi et al. (2010) analyzed the idea of Lean production system which was initiated from Toyota, a Japanese automating company, which has been continuously growing within the international market from years. Ohno in 1988 discussed and reviewed the Toyota production system which improved business to deal with the production difficulties the company had to face as consequences of the World War II. TPS was compelled to pick the waste discount to attend strategic aim. Bhim et al. (2010) talked about the benefits of value stream mapping (VSM), a concept of Lean manufacturing, to improve the assembly line for production companies. Gadre et al. [9] studied that the Lean is considered to be a system for financial savings, reduction of inefficiency, and to increase customer satisfaction. In a Lean approach, the mixture of change control and an integrated method approach, throughout all elements that impacts worker behavior in an economic services agency, is the most effective manner to work successfully to achieve sustainable results. As Lean is being widely used in manufacturing industries, it has also stared to mark its presence in software industries too, Wipro is one of the best example for the same. Unlike manufacturing industries, software industries have their own challenges to implement Lean, since the processes type of outcomes in software are completely different; hence, the Lean principles also vary. Table 36.1 represents the Lean principles in software industry. Waste elimination is a key principle of Lean methodology. Waste is considered as anything, a process or a task which does not add value to the customer. In context of manufacturing industry, inventory is considered as waste. One reason of it may be the undiscovered defects in inventory. These defects later tend to be more expensive to fix in the process. In software industry, an unfinished product can be considered as waste or the waiting time required for products for testing or any other type of approval can be considered as waste (Table 36.2). Table 36.1 Key principles of Lean software development Principle

Description

Waste elimination

Lean strives to eliminate waste all through. Activities that do not add value to the customer are eliminated

Amplify learning

Eliminating waste requires constant learning and knowledge creation

Deliver fast

Reduce development time without compromising the quality

Build quality in

Identifying the root cause of bugs and eliminating them will result in building a bug-free system

Optimize

Optimize the whole system rather than individual components

Respect people

Empowering the team allows those that are experienced make the decisions to avoid delays due to overheads. It helps to motivate the team

Defer commitment Do not commit until sufficient information is acquired. This lessens the probability developing useless work

378 Table 36.2 Lean software wastes from literature

M. Deshmukh and P. Srivastava Waste

Description

Partially done work Unfinished code or features Extra processes

Unneeded processes that do not add value to customer

Over production

Extra features not needed by the user

Motion

Cost of task switching

Delays

Delays occurred while waiting for others

Defect

Unidentified bugs

Transportation

Hands-off caused while waiting for others

The worldwide organization, BBC, introduced Lean practices during a nine person team working for BBC with different roles and responsibilities within the organization. Middleton et al. (2012) main target was to review the lead time. By minimizing variance and decreasing dimension of units of work, they were ready to limit the work to capacity. However, switching from agility to Lean was described as “an advanced improvement that agility is not abandoned when Lean is accepted.” This is often natural, because agile practices focus more on project development, whereas Lean focuses on the entire value chain. Wang et al. (2012) the “Agile Manifesto” emerged in early 2001 to provide a solution to the problems of current software program improvement strategies and hastily changing surroundings. Lately, agile framework has begun to seem closer to Lean software improvement tactics. Software products are not as tangible as manufacturing products; hence, the Lean principles of manufacturing process differ to that of the software processes. Table 36.3 represents variety of the Lean methods and their results from literature. If one controls the enterprise and merchandise, it could be much easy to attain Lean. Table 36.3 Lean methods and their outcomes Author and year

Lean method

Result type achieved

Adam et al. (1991)

Continuous improvement (Kaizen)

Redesigned assembly line resulted in reduced WIP, reduced delays, and reduced waiting time

Kanban

Reduction of work in progress, optimized floor layout; visual information

Value stream mapping(VSM); total productive maintenance (TPM); setup time

VSM optimizes the flow process resulting in reduced flow time, inventory and lead time

Malek and Rajgopal (2007)

36 Literature Review of Lean Methodology and Research Issues …

379

36.3 Research Gap A thorough study of lean methodology is performed to perceive the scope of adapting lean in software development. Lean ideas from various researches are studied and presented a couple of authors have provided more educational frameworks [13], while others have focused on case studies. Middleton et al. (2012) mainly focus on lean application on Indian IT zone because agile has already been implemented and also studied in software process improvement initiatives (Dyba et al. 2008). Cawley et al. [5] did a survey on the adaption of Lean and agile methodology, wherein they mentioned that Lean methods have the potential to enhance and improve safety critical systems. Shah et al. evolved an operational degree of lean production and presented a framework that identifies its most salient dimensions. Although very useful, the tool is not always commonly applicable, and it is especially designed for manufacturing sector and hence cannot be applied in software development process. There is intrinsically no definitive listing of Lean software development ideas. Identified wastes in a software product customization process using the value stream map with the intention to reduce lead time: waiting time, extra features, and motion which were the identified wastes. The value stream map helped in identifying the non-valueadded activities in the process. Table 36.4 gives an in-depth survey and review of research papers from various domains to spot research gaps scope of improvement while implementing lean.

36.4 Conclusion Lean principles from literature have been studied and presented here, some authors designed frameworks [13] (Poppendieck et al. 2009, 2010), whereas some of them have focused on case studies (Middleton et al. 2012). This research mainly emphasizes on the application of Lean in the software development since agile methods have already been studied and adapted in many software projects (Dyba et al. 2008). Lean approaches seem to be better than agile (Wang et al. 2012), because of which Lean and Lean principles are the basis of this research. Dyba et al. (2008) organized a systematic literature review on agile software development till 2005, and from the identified 36 studies, they identified that only one applied Lean practice to software development. Shah et al. developed an operational measure of Lean production and provided a framework that identifies its most salient dimensions. Although very useful, the instrument is not generally applicable because it was developed for a production environment and not for other environment. Software industries especially in India need to adopt the Lean culture to improve investments and customer satisfaction. Lean focuses on reducing waste, whereas agile focuses on flexibility and adapting to customers’ requirements. There is no specific well-defined list of Lean principles. To achieve “Lean,” a software sector needs to identify certain Lean factors like types of waste and Lean principles relevant for the adaption of Lean

Name of paper

“Lean Performance Evaluation”

Application Authors of mahalanobis distance as a Lean assessment metric: Jayanth Srinivasaraghavan and Allada (2006)

“Redesigning an assembly line through Lean manufacturing tools,” Alvarez et al. (2009)

“A survey on Lean practices in Indian machine tool industries,” Eswaramoorthi et al. (2011)

Sr. No.

1

2

3

4

The primary aim of this study is to find out the needs and examine the degree to which the concepts of Lean manufacturing are put into practice within Indian machine tool industries

Authors have redesigned an assembly line in a manufacturing unit using a Lean tool (VSM) with an objective to identify and eliminate non-value-added activities

Authors propose a quantitative Leanness assessment tool

Paper introduces an AI based quantitative model to measure Lean performances for a manufacturing company

Major idea

Table 36.4 Literature review and identified research gap

The survey result revealed that 31.6% of the companies have implemented different Lean tools and techniques in selected areas. The remaining 68.4% of the companies have not yet taken up the Lean initiatives

Inventories have been reduced. This provokes the reduction of idle times, from initial 32–10. Nine improvement objectives were twofold: reducing stocks while avoiding idle times or movements of worker due to accumulated material. Both have been reached

The calculation of MD by the Gram–Schmidt orthogonalization process is more informative and useful when compared to the inverse correlation matrix method

A Lean performance measure would help the managers and decisions makers to get a better understanding of Lean performance

Findings

(continued)

Proposed taxonomy appears widely applicable, organizations with different software development cultures may experience different waste types

Response to the questionnaire was limited by respondent’s knowledge about Lean practices. Hence, employees need to be educated with the Lean principles before implementing Lean or else the outcome will be unreliable input data/poor response rate

According to Kaizen Institute, value stream mapping cannot and should not be applied as it is something like a software product

This model is restricted to manufacturing domain as it cannot be directly applied to software domain

Research gap

380 M. Deshmukh and P. Srivastava

Defining and developing measures Adapted the Lean measurement of Lean production, Shah and method developed by Shah and Ward (2007) Ward (2007) for a logistic service environment

Model Development of a Virtual Learning Environment to enhance Lean Education, Gadre A

Zhuravskaya O., Michajlec M., Mach P. “Success Case–Study Lean Production Electronics Manufacturing Workshop,” IEEE

7

8

9

Designed a virtual laboratory which provided a simulation platform to perform production line experiments

Developed an instrument to measure Lean production and presented a framework that could highlight the most important features

The purpose of this paper is to identify and describe different types of waste in software development

”Software Development Waste,” Sedano T, Ralph P, Peraire C

5

Major idea

Name of paper

Sr. No.

Table 36.4 (continued)

The platform provided students a real-world experience and an opportunity to visualize and improve their work

This paper introduced the first empirical waste taxonomy. It identifies nine wastes and explores their causes, underlying tensions, and overall relationship to the waste taxonomy found in Lean software development

Findings

(continued)

Focused on electronic industry and uses the VSM method of Lean

Needs to be implemented in real environment. Limited to production/manufacturing domain. Does not talk about the other domains

Although very useful, the instrument is not generally applicable because it was developed for a production environment and not for another environment

Not used the insights from shop floor employees to validate the instrument and was developed for service environment

Research gap

36 Literature Review of Lean Methodology and Research Issues … 381

Name of paper

Materials flow improvement in a Lean assembly line: a case study, Alvarez and Melodià (2014)

An exploratory study of waste in software development organizations using agile or Lean approaches: A multiple case study at 14 organizations, Hiva, A.

Sr. No.

10

11

Table 36.4 (continued)

The paper analyzes the flow of material in an assembly line of Bosh industries with the objective to identify the bottlenecks and optimize the assembly line

The paper describes the core idea of Lean production as a methodology with series of tools for continuous improvement in the electronics manufacturing.

Major idea

The empirical results drawn from the case study serve to demonstrate that an operating decision has helped to improve the Lean metrics, particularly the transportation time, increased learning. Reduction in waste in terms of excessive inventory. All of this without making major changes in the process

By implementing Lean, production line was able to: increase output for a key product group, from 1162 units per day on one operator to 1912 units per day on one operator—more than 60% increase of operator productivity; use more than 60% less floor space than in their previous assembly area; improve product quality and reduce rework; increase employee awareness about

Findings

(continued)

To identify and eliminate waste, a Lean mindset is needed which is agreed upon by the entire organization

The findings are limited due to the focused nature of the case study. Although the solution is designed for a particular plant, the methodology is fully exportable. The paper shows a real case study illustrative for systems management

Research gap

382 M. Deshmukh and P. Srivastava

Lean Software Management: BBC This paper explores the Worldwide Case Study, Middleton application of Lean in agile and David (2012) software development

Empirical studies of agile software development: A systematic review Dingsøyr

Lean/Agile Software Development Methodologies in Regulated Environments—State of the Art Cawley, O., Wang, X.

Using metrics in Agile and Lean Software Development—A systematic literature review of industrial studies, Kupiainen, E., Mika, V.

13

14

15

16

Authors conducted a SLR on the adaption of Lean and agile methodology by safety critical system developments

The review investigates what is currently known about the benefits and limitations of, and the strength of evidence for, agile methods

“Leagile” software development: Authors investigate the concept of An experience report waste in agile/Lean software Analysis of the application of Lean development organizations and how it is defined, used, prioritized, reduced, or eliminated in practice

12

Major idea

Name of paper

Sr. No.

Table 36.4 (continued) Research gap

Authors identified that most of them have adopted agile practices along with traditional plan driven development methods

Conducted a systematic literature review (SLR) of empirical studies of agile software development and LSD and identified 36 relevant empirical studies

Lean approaches seem to scale better than agile

(continued)

This study could have been improved by studying the reference list of the primary studies

The study was focused on safety critical systems. Although they mentioned that Lean methods have the potential for improving the development of safety critical systems. Authors point out the need of further investigations in

Agile methods are mainly applied to and studied in smaller scale software development projects

The framework used would not necessarily be adapted by other organizations as this was a specific organization

Various wastes, categorized in 10 The Lean aglile concept is yet not different categories were identified widely adapted in software industry by the respondents. It was concluded that task switching was considered as the most important waste and extra features as the least important one

Findings

36 Literature Review of Lean Methodology and Research Issues … 383

Name of paper

Adapting the Lean Enterprise SelfAssessment Tool for the Software Development Rodríguez and Kuvaja (2012)

Agile to Lean Software Development Transformation: a Systematic Literature Review

An exploratory study of waste in software development organizations using agile or Lean approaches: A multiple case study at 14 organizations (Hiva et al. 2019)

Sr. No.

17

18

19

Table 36.4 (continued)

Authors have tried to identify drivers, barriers, and metrics required for Agile to Lean transformation

This paper presents a proposal for adapting the Lean enterprise self-assessment tool (LESAT) to guide the transformation of software development companies toward Lean

This paper presents a systematic literature review (SLR) on using metrics in industrial agile software development. Authors identified 774 papers, which we reduced to 30 primary studies through our paper selection process

Major idea

Outcomes: reduced cycle time, improved learning, optimized flow

In this study, concepts and expressions of LESAT were analyzed and mapped to software development following the ISO/IEC 12207 standard. Seven assessment items concerning life-cycle processes were modified from the original LESAT

Results show that although agile teams use many metrics suggested in the agile literature, they also use many custom metrics. Finally, the most influential metrics in the primary studies are velocity and effort estimate

Findings

(continued)

No distinction is made between waste and overhead, especially when it comes to identification and measurements of wastes. In addition, another issue h is the identification of domain-specific vs. common wastes

Challenges faced: Inculcating the Lean mind set and Lean thinking, identifying non-value-added activities

This paper presents an initial proposal for adapting LESAT for software. However, existing evaluation is still limited and more empirical studies, in which LESAT for software is applied in individual company cases, are needed to validate the tool and make a more comprehensive

Research gap

384 M. Deshmukh and P. Srivastava

Name of paper

Lean Software Development Domain (Udo et al. 2008)

LEAN Software Development is Feasible? Sowmyan Raman, The Boeing CO, Seattle, WA 981240-7803-5086-3/98/$10.00 01998 iEEE

The Combination of Agile and Lean in Software Development: An Experience Report Analysis, Wang (2011)

Sr. No.

20

21

22

Table 36.4 (continued)

Authors advocated that the Kanban Lean tool will help in achieving desired results in planning and scheduling project development

Various wastes categorized in ten different categories that were identified by the respondents. From the identified wastes, not all were necessarily waste but could be symptoms caused by wastes. Task-switching and extra features the former was identified as one of the most crucial waste

Findings

This paper discusses the feasibility Question project by the authors of Lean adaption in software resulted to be positive. Hence, it is development feasible to use lean in S/W development

This paper presented the Lean S/W Dev domain, which is an important approach in the software development

This paper investigates the concept of waste in Lean software development organizations. They studied how waste is identified, prioritized, and eliminated in practice. The data were collected using semi-structured open-interviews. Two practitioners from 14 embedded software development organizations

Major idea

Research gap

36 Literature Review of Lean Methodology and Research Issues … 385

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approach. This investigation aims to identify the Lean requirements which can be adaptable in the software development domain. The research study gives an idea on how to apply Lean concepts for the software industry sectors and make it as a Lean organization. With Lean concepts, IT sectors will have the following advantages: – More disciplined process

– Decreased cycle time less inventory

– Improved productivity

– Increase capital utilization

– Improved quality of the product and process

– Improved efficiency

– Reduction in 5 m’s (men, machine, material, money, and management)

– Improved quality of the product and process – Customer satisfaction

– Reduced staff turnover

References 1. A. Akarte, M. Chaple, B. Narkhede, R. Raut, Interpretive framework for analyzing lean implementation using ISM and IRP modeling. Benchmarking: Int. J. 25 (2018). https://doi.org/10. 1108/bij-07-2017-0177 2. P. Arnout, J. Durk, W. Jacob, Lean planning in the semi- process industry, a case study. J. Prod. Econ. 131(1), 194–203 (2011) 3. D. Aurelio, A. Grilo, C. Machado, A framework for evaluating Lean implementation appropriateness, in Proceedings of Industrial Engineering and Engineering Management, Singapore December (2011), pp. 6–9 4. R. Carandente, M. Gallo, Murino, G. Naviglio, A strategic—Operative Lean integrated model for small companies. Proc. SoMeT, BudRoot, Hungary, Sept 22–24 (2013) 5. O. Cawley, I. Richardson, X. Wang, Lean/agile software development methodologies in regulated environments—state of the art, in Proceedings of the Business Information, Berlin (2010) 6. T. Dyba, D. Torgeir, Empirical studies of agile software development: a systematic review. Elsevier J. Inform. Softw. Technol. 50(9), 833–859 (2008) 7. M. Eswarmoorthi, G. Kthiresan, P. Prasad, P. Mohanram, A survey on Lean practices in Indian Machine tool industries. Int. J. Adv. Manuf. Technol. 52, 1101–1091 (2011) 8. K. Filip, B. Rossi, Agile to lean software development transformation. Syst Liter. Rev. 15, 969–973 (2018). https://doi.org/10.15439/2018f53 9. A. Gadre, C. Elizabeth, C. Steven, Model development of a virtual learning environment enhance lean education. Proc. Comput. Sci. 6, 100–105 (2011) 10. P. Middleton, D. Joyce, Lean software management: BBC worldwide case study. IEEE Trans. Eng. Manage. 59(1) (2010) 11. Y. Min-Chun, G. Mark, Hung-Chung Li, Fuzzy multi- objective vendor selection under Lean procurement. Elsevier Eur. J. Oper. Res. 219(2), 305–311 (2012) 12. M. Overboom, J. Haan, F. Naus, Measuring the degree of Leanness in logistics service providers, in Proceedings of International Annual EurOMA Conference, Porto (2010) 13. M. Poppendieck, Poppendieck Lean Software Development: An Agile Toolkit (Addison Wesley, 2003) 14. M. Poppendieck, T. Poppendieck, Introduction to Lean Software Development, in Proceedings of Extreme Programming and Agile Processes in Software Engineering. Springer Conference, Berlin, Heidelberg, vol. 3556, p. 280 (2005)

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15. M. Poppendieck, C. Michael, Lean software development: a tutorial. IEEE Trans. Softw. 29(5), 26–32 (2012) 16. A. Rehman, U. Usama, A multi criteria approach to measure leanness of a manufacturing organization. IEEE Access 6, (2018). https://doi.org/10.1109/access.2018.2825344 17. P. Rodríguez, J. Partanen, P. Kuvaja, Combining Lean thinking and agile methods in software development: a case study of a Finnish Provider of wireless embedded systems, in Proceedings of System Sciences Conference, Waikoloa, January 6–9 (2014) 18. J. P. Womack, D. T. Jones, D. Roos, The machine that changed the world. Simon & Schuster (2007) 19. B. R. Staats, D. M. Upton, Lean Principles, Learning, and Software Production: Evidence from Indian Software services. Working Paper. Harvard Business School (2009) 20. S. Bhim, Sharma. S Garg, G. Chandandeep, Lean implementation and its benefits to Production Industry of Emeralds group (2010) 21. Xiaofeng Wang, Kieran Conboy, Oisin Cawley “Leagile” software development: An experience report analysis of the application of lean approaches in agile software development. Journal of Systems and Software 85(6):1287–1299 (2012) 22. R. Alvarez, R. Calvo, M. Pena and R. Domingo, Redesigning an assembly line through lean manufacturing tools. The International Journal of Advanced Manufacturing Technology, 43,949–958 (2009) 23. Tore Dybå, Torgeir Dingsøyr, Empirical studies of agile software development: A systematic review. Information and Software Technology 50(9-10):833–859 (2008) 24. Abdulmalek, A. Fawaz, Rajgopal, Jayant, Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study, International Journal of Production Economics, Elsevier, 107(1), 223–236, May. (2007) 25. M. Adams, B. J. Schroer, S. K. Stewart, “QuickstepTM Process Improvement: TimeCompression as a Management Strategy,” Engineering Management Journal. 9(2), 21–32 (1997) 26. M. Adams, B. J. Schroer, S. K. Stewart, “QuickstepTM Process Improvement: TimeCompression as a Management Strategy,” Engineering Management Journal, Vol. 9 No. 2, pp. 21–32. 27. Adams, M., Schroer, B.J., & Stewart, S.K. (1997), “QuickstepTM Process Improvement: TimeCompression as a Management Strategy,” Engineering Management Journal, Vol. 9 No. 2, pp. 21–32. 28. Adams, M., Schroer, B.J., & Stewart, S.K. (1997), “QuickstepTM Process Improvement: TimeCompression as a Management Strategy,” Engineering Management Journal, Vol. 9 No. 2, pp. 21–32. 29. Adams, M., Schroer, B.J., & Stewart, S.K. (1997), “QuickstepTM Process Improvement: TimeCompression as a Management Strategy,” Engineering Management Journal, Vol. 9 No. 2, pp. 21–32. 30. Rosario Domingo, Roberto Alvarez Fernandez, Marta Peña, Roque Calvo, Materials flow improvement in a lean assembly line: A case study. Assembly Automation. 27, 141–147 (2007). https://doi.org/10.1108/01445150710733379 31. Peter Middleton, David Joyce, Lean Software Management: BBC Worldwide Case Study. Engineering Management, IEEE Transactions on. 59, 20–32 (2012). https://doi.org/10.1109/ TEM.2010.2081675 32. Dybå, Tore & Dingsøyr, Torgeir. (2008). Empirical studies of agile software development: A systematic review. Information and Software Technology. 50. 833–859. 10.1016/j.infsof.2008.01.006 33. Dybå, Tore & Dingsøyr, Torgeir. (2008). Empirical studies of agile software development: A systematic review. Information and Software Technology. 50. 833–859. 10.1016/j.infsof.2008.01.006 34. Hiva Alahyari, Tony Gorschek, Richard Berntsson Svensson, An Exploratory Study of Waste in Software Development Organizations using Agile or Lean approaches: A Multiple Case Study at 14 Organizations. Information and Software Technology. 105, (2018). https://doi.org/ 10.1016/j.infsof.2018.08.006

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Chapter 37

IQINN: Improve the Quality of Image by Neural Network Priyanka Birajdar and Bashirahamad Momin

Abstract The proposed paper deals with image quality improvement. In many applications, images play noteworthy roles. In the application of pathology, satellite imaging better excellence of the image is very vital. In recent days, there are numerous methods that are used to increase the quality of the image. There are different noise removing algorithm are available such as homogeneous filter, Gaussian filter, median filter, and bilateral filter. Such kinds of algorithms improve the excellence of the image. The proposed work is done with the help of neural network architecture. The ideal is to work on seven-layer neural network architecture. The model is light weighted. The model gets analyzed by changing the parameters. The performance of image quality is evaluated through peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index (SSIM). It is noticed that the proposed model works well.

37.1 Introduction The digital image is denoted by a two-dimensional matrix. Pixel is the minimum component of the digital image. Each pixel can be signified by a numerical value. The image cannot be zoomed outside pixel resolution. After the increase of the image, the fineness of the image degrades outcomes blurred image. To increase the excellence low-resolution image (LRI), different noise removal algorithms are presented such as a median filter. A medium filter performs best to remove noise present in the image. In the field of investigation, forensics, medical imaging, satellite imaging, the quality of the image plays a main role. Deep learning knowledge is useful in computer vision practices. Image grouping, item detection, expression recognition, P. Birajdar (B) · B. Momin Department of Computer Science and Engineering, Walchand College of Engineering, Sangli, Maharashtra, India e-mail: [email protected] B. Momin e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_37

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image denoising applications had been operated by deep learning knowledge. Deep learning is built via neural network architecture as well as it is encouraged using human intelligence. It involves a diverse reference library used for execution. The proposed ideal is practical through deep learning concepts. A simple convolutional neural network is planned by succeeding steps that are convolution, max pooling, flattening, and full connection. The number of super-resolution algorithms [1–4] motivated on gray-scale image super-resolution. The proposed model named “IQINN: Improve the Quality of Image by Neural Network.” Deep learning technology benefits to increase the superiority of the images. The architecture of the model rests simply. A model works very fast. The model takes less time to train. The model is planned in seven-layer network architecture with an appropriate number of kernels and kernel sizes. Overall, Contribution of this study is mainly created in the following aspects: 1. To improve the quality of an image by convolutional neural network architecture. 2. To work on the flower dataset. 3. To analyze the visual assessment of the image by varying parameters in the neural network architecture. 4. To evaluate the image quality by evaluation matrix of image.

37.2 Literature Survey CNN grows fame in the arena of image classification. CNN model works now wellorganized way plus gives expected results. Zhang et al. [5] recommended an ideal for pictorial recognition. The writer discussed how the model gives well image recognition accuracy than the previous archetypal. The previous CNN model took a fixedsized input image which lessens image recognition accurateness. The network architecture is very proper for control of diverse sizes, scales, and aspect ratios. The writer verified SPP-net growths the rightness of a difference of CNN architecture. Sutskever et al. [6] recommended a model that is built on deep learning systems. Image classification is finished with the assistance of a deep neural network. The writer planned the network architecture it covers five convolutional layers followed by a max-pooling layer plus three fully connected layers. To train network fast, author used nonsaturating neurons. To reduction overfitting in a completely connected layer writer applied the technique of “dropout.” Item detection is one of the vital researches in computer vision. Ouyang et al. [7] effort on item detection. Object detection is completed with the largest item detection dataset which progresses item detection performance. The Def-pooling layer is used in network structure instead of the max-pooling layer. It improves deep learning perfectly. Szeged et al. [8] established a process for high-quality object detection which is simple, effective, and applied to use. Object detection presentation increases by the technique of multi-scale convolutional MultiBox.

37 IQINN: Improve the Quality of Image by Neural Network

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Sun et al. [9] recommended a technique that is made of face recognition and face verification. The demonstration is ended over with the help of deep learning technology. To recognize the face is an interesting task. The Deep Identificationverification features are well-educated by wisely planned deep convolutional networks. By the evolution of inter-personal variation in addition decreases of intrapersonal variation which monitors vital face verification accuracy. In current ages, face recognition has been completed over deep learning [10–15]. Image rebuilding is the procedure of taking corrupt images in addition to approximating the clean, original image. Burger et al. [16] worked on image denoising. An image denoising method takings a blaring image as input then outputs an image where the noise has been compressed. The number of image resolution concepts [17–20] is discussed in a very efficient way.

37.3 Methodology To increase the quality of the image, one original image has been taken from the test dataset. Degrade it with a bicubic interpolation method with the desired size. In a further step, give that degraded image (LRI) to the seven-layer neural network model (IQINN) model then it is noted that image quality is improved when the image is processed through a neural network. As mentioned in Fig. 37.1, the L1, L2, L3, L4, L5, L6, and L7 are the layers used in the architecture.

Fig. 37.1 System architecture—seven-layer neural network

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Table 37.1 Methodology detail Serial number

Layer number

Number of filters

Filter size

Activation function

1

Layer 1

128

9*9

Relu

2

Layer 2

64

1*1

Relu

3

Layer 3

64

1*1

Relu

4

Layer 4

64

1*1

Relu

5

Layer 5

64

1*1

Relu

6

Layer 6

64

1*1

Relu

7

Layer 7

1

5*5

Linear

37.3.1 Methodology Detail The planned model implements on the flower dataset. The random flowers are chosen from the flower_102 dataset. 225 pictures are in use of the training dataset and 35 pictures are taken for the test dataset. Pictures are processed with patch size 32. All the processed data is saved in hdf5 file format. Table 37.1 shows the number of layers, number of filters, filter size, activation function used in the methodology.

37.4 Experiments The hardware configuration used in implementation with processor Intel(R) Core(TM) i7. The RAM size is 8.00 (7.89 GB usable) in addition to System Type stands 64 operating system. Implantation is done with Python 3.7 software. The packages like Keras, NumPy, cv2, in addition math are used.

37.4.1 Result The result is calculated on taking one test image from the test dataset. The comparison of two images is done with original image to degraded image as well as the comparison of two images is done with the original image to a processed image that is processed through the IQINN model. The image quality is improved by the IQINN model as found by the evaluation matrix of the image. Figure 37.2 shows the result of image with the number of epoch is 50. Figure 37.3 shows the number of epochs used in the IQINN model is 75. It is found that image quality is improved in the term of MSE, PSNR, and SSIM.

37 IQINN: Improve the Quality of Image by Neural Network

Fig. 37.2 Visual observation of degraded image and IQINN image with epoch 50

Fig. 37.3 Visual observation of degraded image and IQINN image with epoch 75

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Table 37.2 Result analysis Sr. No.

No. of Epoch

PSNR of degraded image

PSNR of IQINN image

SSIM of degraded image

SSIM of IQINN image

MSE of degraded image

MSE of IQINN image

1

50

33.79

38.04

0.96

0.977

10.14

6.77

2

75

33.79

38.35

0.96

0.978

10.14

6.73

37.4.2 Analysis Analysis of results has been done with the change of epoch and image quality evaluation matrix. Table 37.2 is an analysis of the result with epoch 50 and epoch 75.

37.5 Conclusion In this paper, the suggested model IQINN operated on the image resolution concept. The model applied through neural network architecture as well as it is of sevenlayer neural network architecture. It has constraints like the number of the epoch, number of the kernel, kernel size, activation function, learning rate, and some other. The model examined the performance of the image. The superiority of the image is considered by PSNR, SSIM, MSE mathematical formulae. Training and testing of the image are done on the flower dataset. This model gives well accuracy even it took less time for training.

References 1. M. Bevilacqua, A. Roumy, C. Guillemot, M. Morel, Low-complexity single-image superresolution based on nonnegative neighbor embedding, in British Machine Vision Conference (2012) 2. H. Chang, D. Yeung, Y. Xiong, Super-resolution through neighbor embedding, in IEEE Conference on Computer Vision and Pattern Recognition (2004) 3. Y. Yang, Z. Wang, Z. Lin, S. Cohen, T. Huang, Coupled dictionary training for image superresolution. IEEE Trans. Image Process. 21(8), 3467–3478 (2012) 4. R. Timofte, V. Smet, L. Gool, Anchored neighborhood regression for fast example-based superresolution, in IEEE International Conference on Computer Vision, pp. 1920–1927 (2013) 5. K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, in European Conference on Computer Vision, pp. 346–361 (2014) 6. A. Krizhevsky, I. Sutskever, G. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, pp. 1097–1105 (2012) 7. W. Ouyang, P. Luo, X. Zeng, S. Qiu, Y. Tian, H. Li, S. Yang, Z. Wang, Y. Xiong, C. Qian, et al., Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection (2014). arXiv preprint arXiv: 1409.3505

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8. C. Szegedy, S. Reed, D. Erhan, D. Anguelov, S. Ioffe, Scalable, high quality object detection (2014). arXiv preprint arXiv: 1412.1441 9. Y. Sun, Y. Chen, X. Wang, X. Tang, Deep learning face representation by joint identificationverification, in Advances in Neural Information Processing Systems, pp. 1988–19 (2014) 10. Y. Taigman, M. Yang, M. Ranzato, L. Wolf, DeepFace: closing the gap to human-level performance in face verification, in Proceedings of CVPR (2014) 11. S. Chopra, R. Hadsell, Y. LeCun, Learning a similarity metric discriminatively, with application to face verification, in Proceedings of CVPR (2005) 12. G. Huang, H. Lee, E. Learned-Miller, Learning hierarchical representations for face verification with convolutional deep belief networks, in Proceedings of CVPR (2012) 13. Y. Sun, X. Wang, X. Tang, Hybrid deep learning for face verification, in Proceedings of ICCV (2013) 14. Z. Zhu, P. Luo, X. Wang, X. Tang, Deep learning identity-preserving face space, in Proceedings of ICCV (2013) 15. J. Hu, J. Lu, Y. Tan, Discriminative deep metric learning for face verification in the wild, in Proceedings of CVPR (2014) 16. H. Burger, C. Schuler, S. Harmeling, Image denoising: can plain neural networks compete with BM3D?, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 2392–2399, 2012 17. C. Dong, C. Loy, K. He, X. Tang, Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016) 18. J. Kim, J. Lee, K. Lee, Deeply-recursive convolutional network for image super-resolution, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637–1645 (2016) 19. N. Kumar, A. Sethi, Fast learning-based single image super resolution. IEEE Trans. Multimed. 18(8), 1504–1515 (2016) 20. J. Kim, J. Lee, K. Lee, Accurate image super-resolution using very deep convolutional networks, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)

Chapter 38

Traffic Monitoring System in Smart Cities Using Image Processing Syed Qamrul Kazmi, Munindra Kumar Singh, and Saurabh Pal

Abstract The problem of traffic control is rapidly increasing as the number of vehicles is increasing day to day. There are several ways already implemented to control the traffic. These methods include timer-based traffic control and others that uses sensors in order to detect the number of vehicles passing and hence produce signal that cycles. These systems are effective but still there are areas in which the traditional system does not produce effective control over the traffic. We introduce a system for traffic monitoring by implementing image-based control in traffic lights instead of the timer or sensor-based systems. This paper discusses a novel approach toward traffic monitoring that can be applied in smart cities and prevent traffic congestions that may happen due to ineffective traffic signal lights. The image of real-time traffic is captured and edge detection technique is used to find the number of vehicles present at a time at traffic light stops. The edge detected image is compared with a reference image for matching purpose. Calculating the number of vehicles effectively can be used to decide the amount of time given to each side of traffic.

38.1 Introduction With the increasing demand of intelligent traffic control systems, the traditional systems seem to be updated with the new technology-based systems for controlling the traffic lights. Many people suffer huge traffic jams in their daily life and their precious time and fuel are wasted in the way. In urban areas, the situation is worse and average time to travel is increasing day by day. This can be controlled if some efficient S. Q. Kazmi (B) · M. K. Singh · S. Pal Department of Computer Applications, V.B.S. Purvanchal University, Jaunpur, UP, India e-mail: [email protected] M. K. Singh e-mail: [email protected] S. Pal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_38

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technique can be followed so that the people can get to their destination in time. Realtime counting of vehicles is to be done in order to get the exact amount of time given to each traffic light at crossings for turning green or red. Image processing involves techniques that are applied on the images obtained from the cameras or sensors that are installed at various places or images taken on normal day life for various applications. The image is a usually a two-dimensional picture that is similar to a physical object or of a person. Image processing is to deal with images and modify or improve according to the requirements for a particular purpose which may be useful for further analysis or predictions. The things that come under image processing [1] is to enhance the image such as adjusting the sharpness, blur, increasing the contrast, size, and color variations. Image processing may be considered as processing of signals where the image serves as an input. Usually, all image processing techniques involve treating images into 2D signals and using standard signal processing techniques. In general, image processing often means Digital Image Processing (DIP) but there can be two aspects of optical and analog image processing. As the number of vehicles is increasing more efficient and robust algorithms are required [2, 3] to collect the image data and hence computed for smooth traffic flow. Most of the algorithms that are developed for image capturing and processing such as edge detection, background subtraction, and dual method are efficient but still lot of work is to be done in order to make more reliable and cost-effective solutions for the traffic control systems. In this paper, we have discussed how the concepts of AI and image processing can be applied in order to gain the desired goal. The paper is organized in the following way. Image processing and related work are discussed in Sect. 38.2. The proposed system is discussed in detail in Sect. 38.3. Results and Conclusion are discussed in Sect. 38.4.

38.2 Image Processing Image processing is nothing but enhancing raw images received from sensors or cameras placed at a place for capturing or photos captured normally in everyday life for different uses. Image processing constitutes various terms related to representing an image, techniques that involve compression and other complicated operations [4], which can be applied on the data of an image [5]. The operations included in image processing for enhancement are sharpness, blur, brightness, edge enhancement, etc. We will use OpenCV for collecting and manipulating the data of roads and traffic conditions and train our system to handle the traffic according to the present situation. OpenCV is a library with open source for image processing and computer vision. Therefore, it is not mandatory for your OpenCV communication to open for free. It contains several inbuilt functions that focus on real-time image processing. There are hundreds of algorithms related to image processing and computer vision that make development of advanced computer vision applications simple, easy, and efficient. It is optimized for real-time image processing. It has python interface which can be

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used for machine learning features and runs on various platforms such as Windows, Mac, and Linux. Thakkar et al. [5] focused on the various tasks related to how an image can be represented, the techniques of compression and other operations, that can be applied on the image data.

38.3 Proposed System There are various recent developments in image processing during the past years. Generally, all the methods are developed for image enhancement. The systems used for image processing are getting huge popularity as the computing becomes easy in terms of powerful PC’s, huge storage capacity, GUI interfaces and many other facilities becoming cheap and readily available. Digital cameras are used to capture the images of traffic as well as the roads so that a comparison can be made by using image processing techniques [6]. The number of vehicles or in more general the traffic can be visualized in a better way as cameras are very much cheaper than other devices. Also the installation of cameras near traffic lights is not a big task and can be used for the purpose of traffic monitoring [7]. The proposed system will have the following steps as shown in Fig. 38.1. Firstly, the image is captured by the live video camera and a reference image of the empty road. Both images will undergo image scaling process [8, 9]. Then it is passed for RGB to Gray Conversion followed by the image quality improvement process. Edge detection is carried out and comparison of empty road image and live video frame is done. According to the match, the time is allocated. At present, there is a provision in the traffic lights only that they can allow the traffic to halt or go as per the color red or green. We have introduced a novel approach in the vehicle also with the Engine Cut Off feature for the vehicles violating the signal when it is red. If the person tries to move even if the signal is red, then the vehicle will automatically be shut down and thus preventing the accidents that may happen due to traffic violation and can also save the fuel. Our system consists of mechanism for the traffic lights to change the color and also for the vehicles to get their engine on or off according to the scenario.

38.3.1 Acquisition of an Image The beginning of an image processing operation usually starts with the image acquisition. When the image is captured, various operations can be carried out on the image and the tasks associated with a particular image can be done. The future tasks on which the image is processed successfully [10] can be done only if the image is captured and acquired with precision. The image enhancements methods will even

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Image of empty road as reference

Image taken from live Video frame

Image Scaling

Image Scaling

RGB to Gray Conversion

RGB to Gray Conversion

Image Quality Improvisation

Image Quality Improvisation

Edge Detection

Edge Detection

Comparision of both Images Time Allocation Fig. 38.1 Layout of proposed system [5]

fail if the image is not captured up to the mark [11]. So a lot of satisfactory results depend on the very crucial stage of image acquisition. In our proposed system, the input image is acquired with the help of cameras mounted on the traffic light poles. There is a reference image of the empty road and another image when there is traffic and is compared for the decision making of time allotment.

38.3.2 Image Scaling The scaling of an image is usually the resizing of an image which can help in reducing the complexities that may arise due to the larger number of pixels that are unwanted in our system. By reducing the size, the pixels and the area on which the various image processing features are applied can be limited to achieve good results [12]. The complexity of an image increases as the number of pixels increase [13]. We can use the three types of scaling suited to our need as linear, cubic, and area interpolation methods.

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38.3.3 RGB to Gray Conversion The digital image which is RGB (red, blue, and green) or generally known as the colored image is converted to a grayscale digital image. A grayscale image is an image which consists of shades of gray color and scientifically it carries only the intensity information which varies along the whole image. These images are different from black-and-white images or one bit bi-tonal images sometimes known as binary images [14]. These types of images consist of many shades of gray but the color is single called as monochromatic, i.e., single (mono) color (chrome). The conversion is done using the function provided in the OpenCV, and the result is then passed for further processing.

38.3.4 Image Quality Improvisation It is to improve the image quality making it better for readability and performing further complex operations which consist of increasing the signal-to-noise ratio and/or intensity or color modification of an image [14]. The image can be modified for the brightness and contrast of desired level so that it becomes easy to access clearly the number of vehicles present at a time. Brightness and contrast adjustments can be done by the following processes with product and sum with a constant: q(x) = αp(x) + β

(1)

We have α > 0 and β as two parameters and these parameters are used to control contrast and brightness, respectively. You can think of p(x) as the source image pixels and q(x) as the output image pixels. In other way, we can write q(i, j) = α · p(i, j) + β

(2)

where i and j indicates that the pixel is located in the ith row and jth column.

38.3.5 Detection of Edges Edge detection is a concept in image processing in which we study about the areas of a particular image in which the intensity changes at once. This is particularly found at the corners and thus we can detect and extract the features of an image [15]. There are various edge detection techniques available as given in Table 38.1 such as (a) Roberts edge detection (b) Sobel edge detection (c) Prewitt edge detection

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Table 38.1 Comparison of various edge detection techniques Technique

Advantages

Limitations

Roberts

Cross operator performs 2D spatial gradient and the spatial frequency is highlighted

It is very susceptible to noise

Sobel

Simple and approximate to gradient magnitude, can detect edges and their orientations

Signal to noise ratio

Prewitt

Prewitt detection is slightly simpler to implement computationally than the Sobel

It tends to produce somewhat noisier results

Canny

Gaussian filter allows removal of noise in Time-consuming and sensitive to the image. Detects edges in noisy state environmental variations. Lacks Robustness

(d) Canny edge detection technique. The techniques, advantages, and limitations for each of the algorithm discussed above are given in Table 38.1. For our system, we have used the canny edge detection technique as this gives the best result out of the various techniques. The canny edge detection technique works in the following way (a) (b) (c) (d)

Reduction of Noise Calculating the intensity gradient Non-maximum suppression Hysteresis Thresholding.

The edge detection used is found suitable to count the number of vehicles when the captured image is processed with scaling and enhancements already done. We have done various edge detection procedures and compared the results. The result shown in Fig. 38.2 clearly indicates that canny edge detection outperforms all other methods used for edge detection. It is then compared with the reference image. The percentage of matching decides the time for which the traffic signal can be turned green or red and gives satisfactory result in controlling the traffic in smart cities.

38.3.6 Comparison of Images The result of successful detection of edges in our captured image and reference image is then passed for matching. The percentage of match decides the number of seconds the light will be kept on green as given in Table 38.2. For situation when the following match occurs, the time allotted is given below.

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Fig. 38.2 Comparison of various edge detection techniques through results obtained by OpenCV [16]

Table 38.2 Allocation of time according to results of image matching

Percentage of match

Color of traffic light

No. of seconds allotted

5–40

Green

50

40–70

Green

30

70–90

Green

20

90–100

Red

50

38.4 Results and Conclusion The paper discussed a method for traffic light control which is done by the image processing technique whereas the traditional systems or earlier systems consist of timer-based controls and metal detectors used in the pavements for calculating the density of the vehicles passing by. We have used image processing techniques such as image acquisition, image enhancement, and image matching to detect the number of vehicles passing and thus can get the real-time situation of the traffic in order to

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smoothly control it. The number of vehicles is found by matching the image of an empty road with the image having vehicles and comparing them. The percentage of match decides the time for which a particular light can be set red or green. The canny edge detection method gave the better result in comparison with other edge detection techniques. The application is a GUI interface which will match the real time situation with the reference image already stored and can decide the allocation of time which will help in maintaining the traffic as well as can save time. Further, improvements can be done by implementing machine learning and deep learning techniques.

References 1. I. Gulati, R. Srinivasan, Image processing in intelligent traffic management international. J. Recent Technol. Eng. (IJRTE) 8, 2S4 (2019) 2. W.A. Okaishi, I. Atouf, M. Benrabh, Real-time traffic light control system based on background updating and edge detection, in 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Fez, Morocco, pp. 1–5 (2019). https://doi.org/ 10.1109/wits.2019.872375 3. V. Mishra, S. Kumar, N. Shukla, Image acquisition and techniquesto perform image acquisition. SAMRIDDHI: J. Phys. Sci. Eng. Technol. 9 (2017). https://doi.org/10.18090/samriddhi.v9i01. 8333 4. S. Francis, K. Sunitha Beevi, SM intelligent traffic control using raspberry PI international. J. Electron. Electr. Comput. Syst. IJEECS 5(6), (2016) 5. C. Thakkar, R. Patil, Smart traffic control system based on image processing. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET) 5(VIII) (2017) 6. L. Vasu, An effective step to real-time implementation of accident detection system using image processing. Signal & Image Process. Int. J. (SIPIJ) 5(1) (2014) 7. L. Wouters, J. Van den Herrewegen, F.D. Garcia, D. Oswald, B. Gierlichs, B. Preneel, Dismantling DST80-based immobiliser systems, in IACR Transactions on Cryptographic Hardware and Embedded Systems, vol. 2020, no. 2, pp. 99–127 8. S. Bono, M. Green, A. Stubblefield, A. Juels, A.D. Rubin, M. Szydlo, Security analysis of a cryptographically-enabled RFID device, in USENIX security symposium, vol. 31, pp. 1–16 (2005) 9. R.D. Kühne, R.-P. Schäfer, J. Mikat, K.-U. Thiessenhusen, U. Böttger, S. Lorkowski, New approaches for traffic management in metropolitan areas, in Proceedings of the 10th IFAC (International Federation of Automatic Control) Symposium on Control in Transportation Systems. Tokyo, Japan (2003) 10. R.A. Hadi, G. Sulong, L.E. George, Signal & image processing: vehicle detection and tracking techniques: a concise review. Int. J. (SIPIJ) 5(1) (2014). doi: https://doi.org/10.5121/sipij.2013. 5101 11. G. Salvi, An Automated Vehicle Counting System Based on Blob Analysis for Traffic Surveillance (Department of Economics Studies, University of Naples “Parthenope”, Naples, Italy) 12. D. Beymer, P. McLauchlan, B. Coifman, J. Malik, A real-time computer vision system for measuring traffic parameters. in IEEE Conference on Computer Vision and Pattern Recognition, pp 495–501 (1997) 13. M. Fathy, M.Y. Siyal, An image detection technique based on morphological edge detection and background differencing for real-time traffic analysis. Pattern Recogn. Lett. 16, 1321–1330 (1995)

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14. A. Francillon, B. Danev, S. Capkun, Relay attacks on passive keyless entry and start systems in modern cars, in Proceedings of the Network and Distributed System Security Symposium, NDSS 2011, San Diego, California, USA, 6th February–9th February 2011 (The Internet Society, 2011) 15. Z. Jie et al., Moving vehicle detection for automatic traffic monitoring. IEEE Trans. Vehic. Technol. 56, 51–59 (2007) 16. Retrieved from “Cars looks very beautiful while running on the roads”. May 10, 2020. https:// www.youtube.com/watch?v=aXeO-WpKpkU

Chapter 39

Sensitivity Context-Aware PrivacyPreserving Sentiment Analysis A. N. Ramya Shree, P. Kiran, and Sharan Chhibber

Abstract Sentiment analysis has been a prominent research domain under data analytics whose outcomes have immensely contributed to business growth. Consumers being an integral part of any business, their data along with feedback are exposed and are prone to privacy breach. Although many researchers have contributed extensively towards privacy preservation,some plentiful gaps and challenges still exist. This paper proposes an approach Sensitivity Context-Awareness (SCA) based privacy preserving sentiment analysis to preserve the privacy of consumer data which is acquired with their consent. Experiments are conducted on synthesized dataset about healthcare services and sentiments including positive, negative, and neutral are obtained while preserving the consumer’s privacy.

39.1 Introduction The data in the current world are very crucial and gained more importance in the growth of the business. In the data analytics process, data provider and data scientist are the two major roles involved in knowledge discovery. . The data provider collects data from different sources and provides it to data analyzers. The data scientist is the one who performs data analytics to extract useful knowledge from data. When data gathered from data provider’s, i.e., by consumers for analysis the main risk involved here is how this data published to data scientists such that the privacy of the consumer and utility of data is maintained for analysis. We propose a novel approach called sensitivity context-awarenessbased anonymization to preserve privacy and A. N. Ramya Shree (B) · P. Kiran · S. Chhibber RNS Institute of Technology, Bengaluru-98, Karnataka, India e-mail: [email protected] P. Kiran e-mail: [email protected] S. Chhibber e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_39

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retain the utility of data in sentiment analysis. The privacy of the individual is the most concerning aspect of the data analytics [1]. The sentiment analysis is a category of text mining, where it requires opinions/feedback from the individuals. When data collected from the individuals, it contains the personal details of individuals who provide their opinion. The personal details include name, age, phone number, DOB, etc., which represents the unique identity of an individual. The main issue here is the consumer’s privacy breach ,which might occur when their data collected from an organization to analyze the reviews related to the service offered by them. The data publisher is the organization that publishes data for analysis. The data scientists may be analytics experts within an organization or third party. When data subjected to the data analytics process there is a possibility of consumer’s privacy violation or data leak. To overcome this problem, privacy-preserving approaches are deployed [2]. In the proposed approach, we consider the healthcare organization as data publisher who wishes to perform sentiment analysis on their healthcare services based on consumer’s feedback/opinions.

39.2 Related Work The data publisher receives data from disease victims and the attributes are divided into explicit, quasi, sensitive, and not sensitive. In healthcare services, data explicit identifier is a collection of attributes, like patient name, patient unique ID, mobile or phone number, that directly identifies the data owners. The quasi attributes may indirectly recognize data providers. The sensitive attributes are specific to a patient like disease and opinion/feedback. The non- sensitive group contains attributes which do notdisclose patient identity. Each row in the table is related to a unique record holder. The major and popular privacy-preserving approaches are randomization, data swapping, anonymization, and cryptography. These approaches mainly concentrate on numeric or discrete data. Data anonymization is a privacypreserving approach where personal identifiable information (PII) will be removed, quasi-attributes and sensitive attributes are modified so that the resultant data able to preserve privacy and retain utility. The resultant data will be safe from the attackers and analytics operation can be performed on data. The privacy models are classified into two groups based on their attack ideas, here, it assumes the attacker has the background knowledge. Group 1 considers a privacy flaw that occurs when the data attacker is capable enough to relate a data provider to a particular row in the published tabular data referred to as linkage attacks. Related to linkage attacks, it assumed that the attacker knows victim quasi attribute. Group 2 considers that the released table should provide the attacker with minimum extra details more and above the background knowledge, where attacker pre- and postbelief are considered here, and if there is a variation with these beliefs is called probabilistic attacks. Generalization and suppression are the major essences of anonymization where it replaces values of the quasi-attributes and sensitive attributes, with less specific details [3–5]. The sentiment analysis mainly deals with individual opinions,

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which is of type text. The text data is highly unstructured in nature. Among the individuals, some of them are ready to disclose or reveal their details and some of them are not. When suitable privacy-preserving approaches are not deployed, then there is a possibility of a personal data breach of individuals who are not ready to reveal their identity. The data can be accessed from individuals in a secured manner and the main risk involved here is personal privacy breach when data subjected to the analytics process. In the paper, we consider the healthcare organization as data publisher who wishes to perform opinion mining or sentiment analysis on their healthcare services based on feedback/opinions [6].

39.3 Proposed Methodology The healthcare services are treated as one of the major sectors of a country economy because it generates revenue and also it involves consumers from different parts of the country as well as the world. It is a highly competitive field where consumer opinion plays a crucial role in organizational growth. It involves a collection of opinions about the services offered by the organization. The disease victims are the consumers who provide their feedback about the healthcare services offered [7]. In this paper, we considered the major healthcare service domain-specific attributes. They are name, date of birth, phone, origin, sex, disease, duration, doctor name, availability, expert, environment, staff, facilities, food, opinion or feedback, and IRP. The Web page is created to receive consumer opinions. The attributes like doctor availability, expertise, environment, staff, facilities, food ratings are been assigned in the numeric scale 1. avearge, 2. medium, and 3. best. The patient will select the value based on their service received at the hospital.

39.3.1 SCA Anonymizationbased Sentiment Analysis To preserve the privacy of the data providers or owners and to perform privacypreserving sentiment analysis, the data is anonymized using our proposed technique called sensitivity context-aware anonymization. In the proposed approach, the feedback provider permissions related to data disclosure are used. The proposed model is described in Fig. 39.1. Let consider Table T is made of tuples with different categories of attributes and they are quasi-identifiers as Q, sensitive attributes as S, non-sensitive attributes as NS, and person identification attributes as PA. We consider table for model development after removal of PA such that the published Table T consists n tuples T = {t l , t 2 …, t n }. The sensitive attributes are described in word cloud representation as shown in Fig. 39.2. We consider table T’ for model development after removal of PA such that the published Table T consists n tuples T = {t l , t 2 …, t n }. The entity information can be referred like t.s, t.q. The requirement for SCAbased personal privacy is that the

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Fig. 39.1 Sensitivity context-aware privacypreserving sentiment analysis workflow

Fig. 39.2 Word cloud representation of sensitive attributes

IRP attribute set for each tuple t. We used the classifier model to categorize the tuples into a sensitive class or not sensitive class based on the identity reveal permission (IRP) attribute value. We use a unique attribute called IRP which is the key attribute whose value received from the consumers at the time when they provide data for the organization to perform the evaluation of service offered by the organization. The data collected from consumers are broadly categorized into sensitive or not sensitive prior to sentiment analysis by using the key field identity reveal permission attribute which is associated with two values 0 and 1. The value 1 indicates not agreed to reveal personal details and 0 means agreed to reveal personal details. The classification is the task of assigning a Boolean value to each pair t i , cj  ∈ T  X C, where T  is a collection of tuples and C = {C j , C k } is a collection of predefined categories. The IRP value 1 is with t i , cj  indicates that the tuple ti belongs to the sensitive category C j , and IRP 0 means that tuple ti belongs to the non-sensitive category C k . Where C j indicates sensitive category tuple and C k indicates non-sensitive category tuple. The task of identifying the category of each tuple is generally executed through a classifier that can be defined as a function F: T  X C . In the sentiment analysis, the opinions collected from disease victims which are of type text data are subjected to preprocessing [8, 9]. The natural language processing approach provides several methods to preprocess text data. The Natural Language Toolkit (NLTK) module offers several techniques

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for preprocessing text data. Tokenization—a process of dividing longer strings into smaller strings where the document converted to a collection of paragraphs, paragraphs converted to sentences, and sentences converted to phrases. Stemming eliminates affixes from a word to obtain a stem word, for example, dancing, danced to dance. Lemmatization—it is a type of stemming which removes suffix or postfix of a token and takes into consideration of a list of highly preferred prefix and suffix, and it also takes into consideration of morphemes which is the morphological unit of a language that cannot be further divided. Stemmer is easier when compared to lemmatization because it requires in-depth linguistic knowledge to look for suitable form of word. Stop words removal—the words like a, an, the, be, etc., not add any additional information w.r.t to sentence or statement. It may create noise while model building is called stop words, which can be removed prior to text analysis. The Porter stemmer for English language used in the proposed approach to perform stop words removal [10]. After preprocessing of opinions, healthcare domain-specific features related to the sentiment analysis are explored. To achieve it, the words which describe the goodness are treated as positive opinions like best, good, excellent, unique treatment, field specialist, and expert doctor are labelled as positive sentiment with value 1 and words which describe negativity like treatment not good, not hygienic treatment ,very poor facility, etc., are treated as negative sentiment with value 0. To build the classifier model which classifies the opinions by disease victims into positive or negative, every token occurrence frequency related to positive or negative sentiment is treated as a feature. The vector of token frequencies is build using a tf-idf vectorizer. The tf-idf vectorizer builds a matrix which contains tf-idf weight with respect to the positive sentiment and negative sentiment specific to the healthcare domain used for sentiment analysis. To measure the usefulness of term t tf-idf weightage is used. It is calculated based on feature words occurrence in the opinions collected from consumers. The formulae 1,2 and 3 are used to calculate tf-idf weightage where t is feature term, d is the set of words in the column, and N is the total count of rows. The weightage of each term is used for building the classifier model. The frequency counts of opinions used in sentiment analysis described in Fig. 39.3. The extremely randomized tree classifier as an ensemble classifier used to build the sentiment analysis model which

Fig. 39.3 Frequency count of opinions used in the sentiment analysis

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classifies the given feedback/review by the patient into positive or negative [11–13]. t f (t, d) = No. times term(t) occured in document /Total No. of words in document (1) document frequency(t) = No. of rows which contain t/Total number of tuples(N ) (2) id f is the inverse of d f, where id f (t) = N /document frequency(t)

(3)

t f id f (t, d) = t f (t, d) × log(N /(d f + 1))

(4)

The sentiment analysis or opinion mining is a category of text mining approach mainly involves mining opinions by consumers on service by service providers. The consumer posts review which are available in raw text format. The text data is highly unstructured, i.e., it does not have predefined format. It first subjected to preprocessing prior to opinion extraction. The sensitivity context-awareness based anonymization is used prior to the sentiment analysis. The table used for analysis where we suppressed the PII by giving ‘*’ symbol and quasi-attributes are modified according to anonymization and sensitive attributes are retained for analytical operations. The DOB attribute is a QA such that using it the patient age determined and put into bins such that sensitive rows age value replaced by bin average so that the attacker cannot easily approximate the age and not sensitive rows values are retained same to support utility. The origin is a QA such that it subjected to generalization based on concept hierarchy such way that the only sensitive row origin value is anonymized like India to Asia, Germany to Europe, etc. The non- sensitive rows are retained same to support utility. The sex attribute is subjected to one hot encoding so that 0 represents female and 1 represents the male. All the other sensitive attributes and non- sensitive attributes are retained same for further data analysis. In the proposed approach, we assumed data publisher is trustworthy and data scientist inside or outside the organization treated as compromised/attacker [,14–16]. The results obtained after model evaluation describe the given feedback by patient toward healthcare facility is positive or negative, and same time patient privacy is preserved using our proposed approach SCAbased anonymization to perform privacypreserving sentiment analysis.

39.4 Results and Discussion The model developed is used to perform prediction which predicts the sentiment for the test review. The multinomial Naive Bayes (MNB) model considers word or term prior and posterior probability of occurrences related to positive label and negative label related to data frame. The probability of words that occurred in feedback belongs

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to positive and negative class are calculated and the class with the highest probability is used to assign a sentiment label as 1 for positive class or a sentiment label as zero for negative class. The logistic regression is another model used to build the classifier which estimates the connection between categorical dependent variable and independent variables. It works with dependent variables of binary values. The linear regression considers the probability scores as the predicted values related to dependent variable, i.e., feedback terms which lie in the range 0 to 1 and take natural logs of the odds of the dependent variable to create a modified version of dependent variable using logit function. The extremely randomized tree classifier, which is an ensemble technique used to perform sentiment analysis and obtained better results when compared to LR and MNB methods. The accuracy metric used to measure classification model accuracy. It calculated using the confusion matrix which contains the summary of classification model evaluation measures.[17].The accuracy of the multinomial Naive Bayes (MNB) classifier and logistic regression (LR) models are compared with the test data. The MNB accuracy score on testing data is 0.80 and the LR accuracy score w.r.t testing data is 0.77. The MNB preforms well compared to LR. The extremely randomized tree classifier ensemble method performs better and the result described in Table 39.1. The classifiers’ performance comparison based on the accuracy metric described in Fig. 39.4. The metrics are used to calculate the loss of information of generalization related to actual data and the significance of attributes which vary in data analysis also be considered. The normalized certainty penalty is used for calculation of the utilization w.r.t anonymized data. The numeric attributes are subjected to anonymization [18]. Consider the relational table RT which contains QA (a1 , …, an ) with constraint all attributes are of type numeric. If row T = (p1 , … pn ) is generalized to a row T  = ((q1 , r 1 ), …, (qn , r n )) such that qi ≤ pi ≤ r i (1 ≤ i ≤ n). The NCP defined for ai described in equation 5. NCP(ai ) (RT) = ri − qi /|ai |

(5)

The |ai | = max t ∈ RT{t.ai } − min t ∈ RT{t.ai } is the boundary of all rows w.r.t attribute ai . In our published table, age is a numeric QA which subjected to Table 39.1 Classification model evaluation measures

Holdout score Cross-validation score Macro-precision

0.875

0.813

Accuracy

0.857

0.818

Macro-recall

0.875

0.784

Weighted precision

0.893

0.833

Macro-F1 measure

0.857

0.785

Weighted F1 measure 0.857

0.816

Weighted recall

0.857

0.818

Log loss

0.524

0.559

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0.857 Accuracy

0.801 0.77

0.72

0.74

0.76

0.78

Classifiers ERT

0.8

0.82

Classifiers MNB

0.84

0.86

0.88

LR

Fig. 39.4 Classifier models comparison w.r.t accuracy metric

anonymization. We consider 200 tuples ,among them 89 tuples are sensitive and their age attribute value modified related to bin ranges 1–25, 26–50, and 51–100 , then according to age attribute NCP is equal to 0.79. The origin attribute of row t associated with value v on the attribute a. Consider x 1 , …, x n as a gathering of leaf nodes in the concept hierarchy tree structure, where value will be altered by the collection values {x 1 , …, x n }, where x 1 , …, x n are the values of rows on the attribute with the considered generalized category. Consider node u in the concept hierarchy tree structure, in that u is an ascendant of x 1 , …, x n , also u does not have descendant remains same as ascendant of x 1 , …, x n . The descendants of u, i.e., size of u referred as leaf nodes, called as size (u). The NCP of row t describedin equation 6. NCPa(RT) = size(u)/|a|

(6)

The origin attribute is related to patient’s country citizenship and it can be generalized to continent. The number of descendent is one such that size of u is 1 and thus NCP of origin QA is 0.011. It indicates the minimal loss for origin QA [19, 20].

39.5 Conclusion and Future Work The sensitivity context-awareness anonymization is used to perform privacy preserving sentiment analysis. The proposed method is used by data publisher of the healthcare facility to publish data to the organization data scientist or third party data scientist. It ensures her/him that the data are used only for analysis of the feedback provided by the patients who used the service offered by healthcare service providers. The PII attributes are removed and Q attributes related to sensitive category are subjected to anonymization. It leads to an increase in the data utility, i.e., the attributes are retained as provided by disease victims helps in increasing the analytics

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efficiency and effectiveness which will be very useful in healthcare service providers decision-making process. The proposed approach mainly concentrates on disease victims personal privacy. The disease victim personal privacy is very crucial and has to be protected from an inside organization compromised analyzer and third party compromised analyzer or attacker. The patient details only made available as same, only if he/she agreed to disclose their details by setting the identity reveal permission value as 0 else patient set IRP value as 1, if he/she not ready to disclose their identity. The approach takes the privacy concern from the data provider or disease victims who provide data for analysis which leads to privacy preserved disease victims feedback analysis. The approach can be further enhanced with the usage of cryptography in the secure transmission of data and hybrid approaches to perform anonymization of quasi identifiers and sensitive attributes.

References 1. X. Wu, et al., Privacy preserving data mining research: current status and key issues, in International Conferece on Computational Science (Springer, Berlin, Heidelberg, 2007) 2. R. Agrawal, R. Srikant, Privacy-preserving data mining. ACM SIGMOD Record N. Y. 29(2), 439–450 (2000) 3. L. Sweeney, Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 588–6571 (2002) 4. X.K. Xiao, Y.F. Tao, Personalized privacy preservation, in Proceedings of the ACM Conference on Management of Data (SIGMOD), pp. 229–240 (2006) 5. C.C. Aggarwal, S.Y. Philip, A general survey of privacy-preserving data mining models and algorithms, in Privacy-Preserving Data Mining (Springer, Berlin, 2008), pp. 11–52 6. R. Bhargava, Abstractive text summarization using sentiment infusion. Proc. Comput. Sci. 89, 404–411 (2016) 7. S.W. Chen, Confidentiality protection of digital health records in cloud computing. J. Med. Syst. 40(5), 124 (2016) 8. B.C.M. Fung, K. Wang, P.S. Yu, Anonymizing classification data for privacy preservation. IEEE Trans. Knowl. Data Eng. 711-725 (2007) 9. A.N., Ramya Shree, P. Kiran, Privacy preserving unstructured data publishing (PPUDP) approach for big data. Int. J. Comput. Appl. 975, 8887 (2019) 10. F. Popowich, Using text mining and natural language processing for health care claims processing. SIGKDD Explor. 7(1), 59–66 (2005) 11. S. Matwin, Privacy-preserving Data Mining Techniques: Survey and Challenges. Discrimination and Privacy in the Information Society (Springer, Berlin, Heidelberg, 2013), pp. 209–221 12. H. Jiawei, M. Kamber, Data Mining Concepts and Techniques (China Machine Press, Beijing, 2006), pp. 1–40 13. S. Comas, Improving the utility of differentially private data releases via k-anonymity, in 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (2013) 14. S.K. Adusumalli, V.V. Kumari, An efficient and dynamic concept hierarchy generation for data anonymization, in International Conference on Distributed Computing and Internet Technology (Springer, 2013), pp. 488–499 15. N. Hamza, Attacks on anonymization-based privacy-preserving: a survey for data mining and data publishing. J. Inform. Secur. 4, 101–112 (2013) 16. V. Ayala-Rivera, Enhancing the utility of anonymized data by improving the quality of generalization hierarchies. Trans. Data Priv. 10, 27–59 (2017)

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17. A. N. R. Shree and P. Kiran, "Sensitivity Context Aware Privacy Preserving Text Document Summarization," 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, 2020, pp. 1517-1523, doi: 10.1109/ICECA49313.2020.9297415. 18. Kiran, P., and N. P. Kavya. "A survey on methods, attacks and metric for privacy preserving data publishing." International Journal of Computer Applications 53.18 (2012). 19. Shree, Ms AN Ramya, and P. Kiran. "A Survey on Privacy-Preserving Mining on Unstructured Data." Proceedings of IEEE International Conference of Science, Technology, Engineering and Management (ICSTEM’ 17) 20. Shree, AN Ramya, and P. Kiran. "Quasi Attribute Utility Enhancement (QAUE)-A Hybrid Method for PPDP."IJITEE(2019)

Chapter 40

Analysis of Heart Disease Data Using K-Means Clustering Algorithm in Orange Tool Sarangam Kodati, Kumbala Pradeep Reddy, G. Ravi, and Nara Sreekanth

Abstract Data mining are a strategy of handling models or design in huge amount of data. Every 12 months, 19 million peoples around expire from heart disease around the world. Heart patients show a few manifestations, and it is very difficult to ascribe them to the heart disease in such countless strides of disease development. Data mining to explain a disguised model from the medical heart disease data set are applied to a database in this assessment. Each and every available estimation in grouping procedure is appeared different in relation to each other to get the highest accuracy orange tool apparatus to analysis, representation, and concentrate data using data mining. Orange tool is perfect to perform all analysis operations. In this paper, an experimental analysis is done in orange tool to cluster the heart disease data sets with different distance measures and thereby observing the variation of the performances in k-means clustering algorithm.

40.1 Introduction Data mining are a methodology for discovering potential, novel, interesting, and an already obscure example from measure of data. It insinuates use for mining learning S. Kodati (B) Department of CSE, Teegala Krishna Reddy Engineering College, Hyderabad, Telangana, India e-mail: [email protected] K. P. Reddy CSE Department, CMR Institute of Technology (Autonomous), Hyderabad, Telangana, India e-mail: [email protected] G. Ravi Department of CSE, MRCET, Hyderabad, Telangana, India e-mail: [email protected] N. Sreekanth Department of CSE, BVRIT Hyderabad College of Engineering for Women, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_40

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from a huge measure of information [1]. Data mining is in like manner called as “knowledge discovery from data” (KDD) [2]. There are a number over various terms like data mining like learning extraction, data mining, and data prehistoric studies. Orange tool is a python based for data mining life created in the bioinformatics laboratory as regards the faculty of computer science at the University of Ljubljana. It is performed maintain used either thru Python scripting as a Python module or thru visible programming. Orange canvas programming interface provides a structured view on supported functionalities [3]. A short report over every widget is available inside the interface. Programs are performed by putting widgets on the canvass and connecting theirs data inputs and outputs. The interface is extremely clean and visually appealing, offering a pleasant user experience. One apparent report about orange is that the number of accessible widgets appears to be restricted when compared according to different tools, for example, KNIME, particularly due to the need about the combination with weka. The coverage of data mining procedures is very acceptable, as much can lie visible from table [4]. Moreover, there are a number about interesting widgets as of now in progress that can lie found of the “Prototype” category, so that it is reasonable according to expect that the feature set will be extended within the future.

40.2 Heart Disease Dataset and Methodologies The data set used in this analysis of heart disease is available at https://archive.ics. uci.edu/ml/datasets/heart+disease. The data set has seventy-six raw attributes. The data set consists of 303 rows of which 297 are complete. Six rows have missing values, and they are eliminated from the experiment [5]. Figure 40.1 given below is the flowchart that explains the process of k-means clustering algorithm. Data Understanding It starts including a preliminary data collection, to get familiar including the information, to identify data excellence problems, according to discovered first insights within the data or in accordance with detect interesting subsets after form hypothesis for hidden information. Data Preparation Covers all things to do according to construct the final data set beyond raw data. Modeling Various model techniques are selected and utilized, and their parameters are calibrated in accordance to best values. Evaluation In this stage, the model is completely evaluated and reviewed. The steps executed according to construct the model to keep certain it correctly achieves the business objectives. Deployment The deployment phase performed is as simple as generating a report or so complex namely enforcing a repeatable data boring technique throughout the enterprise.

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Fig. 40.1 Methodology of the proposed system for k-means clustering algorithm

40.3 K-means Clustering Algorithm Using in Orange Tool Data mining orange tool is a machine learning technology. Orange tool can stay using for investigation data evaluation and representation [6]. It offers a stage since analyzes determination, prescient demonstrating, and suggestion systems to be utilized between gnomic research, biomedicinal drug and bioinformatics. It is

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meant because both skilled users and analysts concerning machine learning any need to model new algorithms at the same time as reusing as similar to a significant part of the code as much conceivable due to those honestly getting into the field who performs also write python contents in view of the analysis of data and appreciate into the powerful as easy to utilize the visible python programming [7]. Orange uses for useful of the heart disease data sets implementation. Data instances are in line and theirs attribute values in columns. The data set is organized through making use of the trait “attribute length, information concerning heart disease data set size, and number and types on attributes” [4]. Values about continuous attributes can stay visualized together with bars; different colors might also be attributed according to a number of classes [8].

40.4 Heart Disease k-means Clustering in Scatter Plot and Visualization of Data Implementation and Results The scatter plot widget as shown in Fig. 40.2 shows a two-dimensional scatter plot portrayal by virtue of each persistent and discrete regarded attributes. Figures 40.2 and 40.3 are scatter plots after applying k-means cluster analysis of heart disease with cholesterol levels and rest levels. The data is appeared as lot of an assortment of points of a combination of focusing, each having the value concerning the x-axis property choosing the position concerning the level axis and the incentive about the y-axis attribute determines the vertical axis. Different properties of the diagram in heart disease, cholesterol, and rest SBP. Parameters old enough, cholesterol and rest SBP utilizing in k-means clustering, like color shading, size, and state of the focuses, axle titles, greatest point

Fig. 40.2 Heart disease cholesterols k-means cluster in scatterplot

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Fig. 40.3 Heart disease rest SBP k-means cluster in scatterplot

size, and jittering will be balanced in regards to one side concerning the widget. A preview beneath shows the scatter plot in regards to the heart disease data that accept with the diverse coloring shading machine of the class attribute. Save picture saves the graphs according to the computer in a.png or.svg group. The number of interim is set through precision scale [9]. The orange tool k-means clustering algorithm is to generate cholesterol different clusters (C) like a cluster C1, C2, and C3, Clustering C1 level very low range like 200 cholesterol, C2 level from 200 to 239 high cholesterol, and C3 range 240 or higher level. The orange data mining tool in heart disease data sets of rest systolic blood pressure (SBP) k-means clustering algorithm analysis of age (28–78) and range of rest SBP (0–200), different clusters like C1 age from 28 to 40 years very low range of rest SBP, C2 age from 40 to 60 years high range of rest SBP and C3 age from 60 to 78 years very high range of rest SBP (Fig. 40.4). Orange tool using k-means clustering algorithm using in heart disease data sets analysis of visualization cholesterol level ranges from female and male 509, cholesterol level of frequency from 0 to 70, and cholesterol level of probability from 0 to 0.7 (Fig. 40.5). Orange tool using k-means clustering algorithm using in heart disease data sets analysis visualization of rest SBP level ranges from female and male 187, rest SBP level of frequency from 0 to 60, and rest SBP level of probability from 0 to 0.7.

40.5 Conclusion In this paper of data analysis heart disease using data mining orange tool k-means clustering, orange tool makes analysis or investigation of k-means clustering work

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Fig. 40.4 Heart disease cholesterol data visualization orange data mining tool

Fig. 40.5 Heart disease rest SBP data visualization data mining orange tool

simple. Performed evaluation of heart disease data sets only according to watch the performance of k-means clustering algorithm analysis using different clusters and colors along respect to cholesterol and rest SBP data visualization heart disease data sets with various numbers about attributes while choosing a proper distance measure, contrasting their parameters with data representation and Scatterplot k-means cluster each other. Therefore, orange tool has performed well and simple to used. A preview below shows the scatterplot on the heart disease data that acknowledge with the distinctive orange tool of the class quality. Moreover, after performing useful usage, orange has done everything as its feature said. In our future work, we will consider some other specific measures for distance in K-means algorithm, including respect to a huge data set, try according to propose a

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better one for the assignment of clustering heart disease data set. Also, we will try according to the extent of our evaluation for other partitions of K-medoids clustering algorithms.

References 1. K. Rajalakshmi, Dr. S.S. Dhenakaran, Analysis of data mining prediction techniques in healthcare management system. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(4) (2015) ISSN 2277 128X 2. N. Rikhi, Data mining and knowledge discovery in database. Int. J. Eng. Trends Technol. 23(2), 64–70 (2015) 3. S. Kodati, Dr. R. Vivekanandam,Data mining tools and applications in bioinformatics. Int. J. Eng. Res. Online 5(1), 231–235 (2017).ISSN 2321-7758 4. A. Rajkumar, G.S. Reena, Diagnosis of heart disease using data mining algorithm. Glob. J. Comput. Sci. Technol. 10(10), 38–43 5. S. Kodati, Dr. R. Vivekanandam,Dr. R.P. Singh, Comparative analysis in diagnosis of heart disease with data mining orange tool. J. Adv. Res. Dyn. Control Syst. 10(09), 2229-2236 (2018) 6. M. Umamaheswari, Dr. P. Isakki@Devi, Myocardial infarction prediction using K-means clustering algorithm. Int. J. Innov. Res. Comput. Commun. 5(1) (2017) 7. S. Balasubramanian, B. Subramani, Symptom’s based diseases prediction in medical system by using K-means algorithm. Int. J. Adv. Comput. Sci. Technol. 3(2), 123–128 (2014). ISSN 2320-2602 8. K. Joshi, H. Gupta, P. Chaudhary, P. Sharma, Survey on different enhanced K-means clustering algorithm. Int. J. Eng. Trends Technol. 27(4) (2015) 9. A.K. Pandey, P. Pandey, K.L. Jaiswal, A.K. Sen, Data mining clustering techniques in the prediction of heart disease using attribute selection method. Int. J. Sci. Eng. Technol. Res. (IJSETR) 2(10), 2003–2008 (2013)

Chapter 41

Development of Biomass Green Champo Leaf DRAM Memory Cell Gaurang K. Patel, Jitendra P. Chaudhari, and S. P. Kosta

Abstract An experimental study to develop a DRA memory cell in a live green Champo leaf was successfully carried out. One fresh experimental biomass green Champo leaf was plucked on which the transistor, restoring capacitor, and parasitic bit line capacitor was realized by inserting proper wire harness terminals through which appropriate EMF potentials (in volts) were applied. Measurements were made to realize essential conventional parameters of a DRAM memory cell. The leaf memory cell functioned well for a substantial time period.

41.1 Introduction A memory cell (computing) is fundamental building block of memory. Conventionally, it is implemented using different technologies such as bipolar, MOS, and other semiconductor devices. It can also be fabricated form ferrite cores or magnetic materials. Regardless of the implementation, the purpose of binary memory cell is to store one bit of binary information that can be accessed by reading the cell and it must be set to store one and rest reset to store to zero. Dr. Kosta [1–4, 7, 8] and his team, during 1985–2005, with blessing of god, developed electronics components (R, L, C, diode, transistors, etc.) and electronic circuits like amplifier, oscillator, multivibrator using live biomass green leaves and branches. Dr. Kosta also demonstrated TV reception through green papaya, banana plant/tree leaves. G. K. Patel (B) Devang Patel Institute of Advance Technology and Research, Changa, India e-mail: [email protected] J. P. Chaudhari · S. P. Kosta Chandubhai S Patel Institute of Technology, Changa, India e-mail: [email protected] S. P. Kosta e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_41

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In this communication, we for the first-time report successful development of a biomass DRAM cell made from the green biomass Champo leaf. This development may lead to utilize a green biomass tree as warehouse of memory (information storage device).

41.2 The Experiment A theoretical circuit depicted in Fig. 41.1 was realized in a green Champo leaf as shown in Fig. 41.2. The storing capacitor C was realized by inserting two wire probes on the middle rib nerve with distance around 4 mm. Later on, this distance was varied to get appropriate value of capacitor. The transistor was realized using 3 wire terminals pinned along mid-rib nerve as shown in Fig. 41.2. The parasitic capacitor was similarly located on a mid-rib nerve [5]. Appropriate voltages were applied to various components of the experimental circuit as required in theoretically. The storing capacitor, on application of 5 V or so stored the charge which was measured using digital multimeter. The stored charged with time degraded which was measured using digital electronic method (reading and writing).

41.2.1 Realization of DRAM Cell on Green Biomass Circuit 41.2.1.1

Theoretical Circuit

DRAM is a main memory used in all the computers. Conceptually, DRAM cell is made from MOSFET and storage capacitor. Each cell used to store one bit at a time.

Fig. 41.1 Theoretical circuit of single DRAM cell [2]

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Fig. 41.2 DRAM cells in green biomass

Each time memory is refreshed because of leakage in a capacitor [6]. This is theoretically as shown in Fig. 41.1. For the experimental setup, the value of capacitances was approximately 69.4 µF and N type MOSFET is NTR4501NT1G.

41.2.1.2

Practical Biomass Circuit

On green Champo leaf, the multivibrator circuit elements like resistors, capacitors, and transistors were realized as shown in Fig. 41.2. Champo leaf was subjected to appropriate varying EMF and transmission characteristics were measured using conventional frequency generator. DRAM circuit is generally used to store a one bit and act as a memory element. DRAM circuit was developed by inserting a wires in the leaf. Wires were inserted along center nerve and side nerves. This circuit realization indicates four cell of DRAM circuit, such that one bit is stored by each cell. Experimental setup for biomass DRAM cell demonstrated in Fig. 41.3. Different colors of wires are pinned into the green Champo leaf to design MOSFET and capacitors. Capacitors charging voltage provided by the function generator via bradboard which are shown in Fig. 41.3. Here, DMM is used to measure the capacity of capacitor in terms of voltage. Operational Realization To store a data using DRAM circuit involve a write and read operation. DRAM Write Operation 1. Write “1”:

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Fig. 41.3 Experimental setup for green biomass DRAM cell

• BL = 1, WL = 1 then capacitor C1 charges to “1”. • Apply 0 at WL line. 2. Write “0”: • WL = 1, BL = 0 then capacitor C1 discharges to “0”. • Apply 0 to the word line. DRAM Read Operation • Precharge bit line to half of its input voltage. • Select Word line to turn on transistor. • Measure voltage across capacitor C1.

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41.3 Observations The measured parameters of the experiment are depicted in Tables 41.1, 41.2, 41.3, and 41.4. Table 41.1 depicts a storing capacity of capacitor and variations in a storage and distance between two probes of capacitor. In this, we observe that the storage capacity Table 41.1 Storing capacity and distance characteristics Distance for capacitor 6 mm

10 mm

14 mm

Time (s) Voltage (V) Time(s) Voltage (V) Time (s) Voltage (V) 1

1

3.27

1

3.36

1

3.4

2

2

3.28

2

3.37

2

3.41

3

3

3.29

3

3.37

3

3.42

4

4

3.29

4

3.38

4

3.44

5

5

3.29

5

3.39

5

3.44

6

6

3.3

6

3.4

6

3.46

7

7

3.29

7

3.42

7

3.48

8

8

3.3

8

3.44

8

3.49

9

9

3.3

9

3.45

9

3.52

10

10

3.31

10

3.46

10

3.55

Table 41.2 Write ‘1’ operation value for time

Write ‘1’ operation Time (s)

Voltage (V)

Logic level

1

2.741

1

2

2.736

1

3

2.737

1

4

2.748

1

5

2.692

1

6

2.699

1

7

2.719

1

8

2.716

1

9

2.713

1

10

2.704

1

11

2.724

1

12

2.706

1

13

2.694

1

14

2.703

1

15

2.714

1

430 Table 41.3 Read ‘1’ operation value for time

Table 41.4 Write ‘0’ operation value for time

G. Patel et al. Read ‘1’ operation Time (s)

Voltage (V)

Logic level

1

2.734

1

2

2.743

1

3

2.735

1

4

2.729

1

5

2.716

1

6

2.713

1

7

2.711

1

8

2.709

1

9

2.711

1

10

2.709

1

11

2.718

1

12

2.719

1

13

2.72

1

14

2.731

1

15

2.735

1

Time (s)

Voltage (V)

Logic level

1

0.104

0

2

0.097

0

3

0.083

0

4

0.076

0

5

0.075

0

6

0.074

0

7

0.073

0

8

0.064

0

9

0.06

0

10

0.056

0

11

0.054

0

12

0.051

0

13

0.048

0

14

0.0456

0

15

0.044

0

Write ‘0’ operation

41 Development of Biomass Green Champo Leaf DRAM Memory Cell Table 41.5 Read ‘0’ operation value for time

431

Read ‘0’ operation Time (s)

Voltage (V)

Logic level

1

0.67

0

2

0.656

0

3

0.651

0

4

0.635

0

5

0.623

0

6

0.615

0

7

0.591

0

8

0.572

0

9

0.568

0

10

0.539

0

11

0.519

0

12

0.515

0

13

0.505

0

14

0.496

0

15

0.482

0

of the DRAM depends on the distance between two capacitor plates. The storage capacity of capacitor is generally indicated by voltage across the plates. In Table 41.1, the voltage across capacitor increases as the distance increase between two plates of capacitor with respect to time. Data taken from the DMM measurement for memory write and memory read operation for bit ‘1’ and bit ‘0’ is shown in Tables 41.2, 41.3, 41.4 and 41.5. In all table, output voltage across Cs is measured using DMM. To set logic level of the bit. Logic ‘1’ = V c > V cc /2 and Logic ‘0’ = V c < V cc /2. Where V c is voltage across capacitor C s . V cc = 5 V. Tables 41.2, 41.3, 41.4 and 41.5 depict a write and read operation characteristics of the green biomass circuit. Tables 41.2 and 41.3 describe measurement data of write and read operation for bit ‘1’. Same as Tables 41.4 and 41.5 depict a measurement data for bit ‘0’ at given time. It is also shown from the tables that as the distance increasing between plates the storing capacity of capacitor are also increases. For this setup, we take total 15 sample for 6, 10, and 14 mm. For the experimental setup, the value of capacitors is approximately 69.4 µF.

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41.4 Conclusion From this experimental study, it is clear that biomass can be made to function as a DRAM. Potential dream application is to realize green tree as wave house of memory. The circuit gives a good performance for more than 18 h as long as the leaves retained good greenness.

References 1. K. Pandya, S. Kosta, Synthetic plasma and silicon tubular harness-based pure biological transistor amplifier circuit. J. Biomed. Res. 31(5), 466–467 (2017). https://doi.org/10.7555/jbr.31. 20160054 2. S.P. Kosta, Y.M. Dubey, A.G. Yogesh, P. Kosta, S. Kosta, Wet soil electronic device-transistor and its application circuit. J. Eng. Appl. Sci. 3, 798–802. https://medwelljournals.com/abstra ctdoi=jeasci.2008.798.802 (2008) 3. S.P. Kosta, A. Dubey, P. Gupta et al., First physical model of human tissue skin based memoristor and their network. Int. J. Med. Inf. 5(1), 5–19 (2013) 4. S.P. Kosta, V. Patel, S. Kosta, Y.P. Kosta, Green biomass bistable transistor multivibrator. Int. J. Electron. 90(2), 117–120 (2003). https://doi.org/10.1080/0020721031000147624 5. S.P. Kosta, V. Patel et al., Green biomass bistable multivibrator. Int. J. Electron. 90, 117–121 (2003) 6. S.P. Kosta et al., Wet soil electronics device transistor. Medwell J. Eng. Appl. Sci. 3(10), 798–802 7. S.P. Kosta et al., Application of human blood in disease healing. IJAR J. Med. Biomed. Sci. (2010). ISSN 2078-0273 (Accepted for publication) 8. S.P. Kosta, S. Kosta et al., Novel electronic device diode made from human blood. Int. J. Biomed. Eng. Consumer Health Inf. 2(1), 7–13 (2010)

Chapter 42

An Unscented Kalman Filter Approach for High-Precision Indoor Localization Yashwant Yerra, D. Ram Kumar Reddy, and P. Sudheesh

Abstract An indoor localized system is a network of sensors used to track people or objects that cannot be tracked completely using GPS in areas, such as multistory buildings, airports, tunnels, and many more. A wide variety of techniques and technologies are implemented to provide indoor positioning ranging from already installed reconfigured devices such as smart phones, Wi-Fi and Bluetooth antennas, digital cameras, and clocks to specifically designed installations with strategically placed relays and beacons throughout a specified space. IPS has broad applications in the industrial, defense, retail, and inventory monitoring industries. In this paper, we implement the localization concept which receives a phased-radio signal which is fed into unscented Kalman filter (UKF) without any prior processing to subjugate. For this process, standard preprocessing concepts, like angle-of-arrival prediction, beam forming, and time-of-flight or time-difference-of-arrival predictions were never necessary. It completely nullifies the essential difficulties of other phase related, highly accurate techniques of localization similar to synthetic aperture methods. To prove this above procedure, let us employ a desirable setup with 24 Ghz frequencymodulated continuous-wave single-input-multiple-output (S.I.M.O) along with a secondary radar with bandwidth of 250 MHz. The ideal trajectory of the transmitter is assumed as helical in nature. Apart from the challenging conditions like interference due to noise and low bandwidth, the results produce good localization with a minimum RMSE of around a few centimeters. The suggested process could be utilized for almost any form of C.W-carrier E.M signal and provides an interesting unorthodox to traditional multi-purpose approaches.

Y. Yerra (B) · D. Ram Kumar Reddy · P. Sudheesh Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] D. Ram Kumar Reddy e-mail: [email protected] P. Sudheesh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_42

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42.1 Introduction Indoor localization is a widely growing field of technology, especially at a place where we encounter an unavailability of GPS [1]. The sensible factor for the wireless local positioning system would be the microwave radar, let us consider an instance where the applications in industrial field like tracking objects or humans and many more [9]. Depending on the circumstances and constraints presented, we might need very accurate localization and more iterative rates along with the attempts to compensate the difficulty and expense of infrastructure at the same time [7]. The implementation done by the conventional techniques such as angle-of-arrival (A.O.A), round-trip-time-offlight (R.T.O.F), time-difference-of-arrival (T.D.O.A), or received-signal-strength (R.S.S) are used [1]. The applications approach would rule out these techniques due to the challenges and drawbacks faced in indoor environment. The localization based on RSS depends on the attained strength of the signal from the tags of radio frequency or Wi-Fi hotspots [1, 2]. This approach is too sensitive in industrial applications as we have to monitor the circumstances by maintaining rigidity to alter the locality, despite the fact that the inertial sensors might improve the location accuracy [1]. The parallel proposition might be transit-time-based method which requires highly precise synchronization. Innovations utilizing this fundamental postulation of multi-lateral are diverse [12]. This can be explained using an example where the self-localization is implemented by utilizing a cluster of available landmarks with rigid points or counter by tracking the tag binded to the entity. So, the precision of the range is determined by the bandwidth, ultra-wide band (U.W.B) systems has a preference for accurate multi-angulation modules, but we need to use non-synchronized [6], non-coherent beacons, and we rule out the T.O.F and R.T.O.F dependent algorithms that are omitted [3]. The suggested solution depends only with deviations of phase within the signals received on array antennas as opposed to these technologies. The conventional approach is a beam-former, for example, obstruct and M.U.S.I.C algorithm to assess the A.O.A [5]. The inclination is measured for each of the local stations, and the direction is determined by the use of the combined information from several base stations. A mobile device measures the angle to several transponders in landmark scenarios and calculates its position and angle [11]. The transmitter will be positioned at the far end of the established station for the purpose of finalizing AOA. The curvature due to wave front should be considered in order to achieve high accuracy for close ranges. This would require distance knowledge. In addition, as indicated, angulation and lateration can be combined [1]. The best indicator of the idea is the limited localization infrastructure, and therefore, it cannot use for nonsynchronized systems from the inconveniences of AOA and TOF as already stated. The synthetic aperture principles seen in few applications are other solutions to highprecision localization using modest bandwidth [4]. However, at the expense of large effects of computer functionality, the high precision achieved with these methods is achieved. These problems and difficulties demand that we suggest a different indication that executes the phase differences that are computed straight away using a robust effective unscented Kalman filter (U.K.F) [10]. The perspective remedies

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eqivocacy and allows the vulnerability of high phase at high frequencies of carrier wave to be exploited. A few other difficulties are the complexity of unknown phases of non-correlative systems and are compatible for different technologies, whereas low computer power is needed. These following chapters are organized in this document as follows: Initially, we need to present the basic model of the signal and speculations for the nozzle. The proposed UKF is then defined, and the individual matrix’s compositions are specific modified. Finally, the algorithm is converted into an indoor position scenario by means of a frequency continuous-wave modulated (F.M.C.W) with a frequency of 24 Ghz radar and tests that are obtained [1, 12].

42.2 System Model The ongoing process of extracting the position from a positioning system based on wireless technology, where we require the relation between the transmitter and the input data given to the system. So, let the object be emitting a radio signal. sTX,RF (t) = sTX,BB (t) e jω0 t+φ0

(42.1)

where this radio signal is observed at frequency of 2π f 0 which is nothing but carrier frequency, and φ of unknown phase offset. The above Eq. (42.1) is representing the slowly varying baseband signal. Since we make no prediction of the signal source, the slowly varying baseband signal might retrace back, by the entity or sent by resolution due to the wireless transmitter [7]. Henceforth, the estimation of any wave structure is never certain, so we need to make sure any signal sent by the arbitrary transmitter that should be localized [8]. On the obliging resolution, we consider M established stations at rigid localities that were selected, which were each installed with an arbitrary number Im of cogently acquiring antennas are convenient, and therefore, this wave form is shown in Eq. (42.2)   sim = aim ∗ sT X,B B t − τim e jω0 t+φ0 +m

(42.2)

where every antenna receives, the magnitude part conveys the attenuation factor, and φm represents unknown phase offset of mth—base station and signal which is sent needs τ time to travel the distance in Eq. (42.3) ri m =



xim − px

2

2  2  + yim − p y + z im − pz

(42.3)

at an object’s location of [ px , p y , pz ] to receivers antenna i m at [xim ,yim ,z im ]. Due to prioritizing the position of object, the phase difference within each pair of coherent receiving antenna is computed by correlating the signals received which are sim (t) and s jm (t) of the ith and jth antenna, respectively, at the mth station [1].

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 =

si∗m (t)s jm (t)dt

(42.4)

  ∗ t − τim e− j (ωo (t−τim )+φ0 +φm ) aim sTX,BB   a jm sTX,BB t − τ jm e j (ω0 (t−τ jm )+φ0 +φm )

(42.5)

∗ aim sTX,BB (t − τim ) sTX,BB (t − τ jm ) e j (ωo (τim −τ jm )) dt

(42.6)

   2 2  ∗ sTX,BB t − τim  e jω0 (τim −τ jm )

(42.7)

   2   ∗ sTX,BB t − τim dt

(42.8)

≈a

= ae j (τim −τ jm )

The above Eqs. (42.4), (42.5), (42.6), (42.7), (42.8) hold similar understanding of a modulation signal sTX,BB (t) which is predicted as a quasi-statically changing [1, 6]. Therefore, this location-reliant phase difference within every pair of antennas is emerged (the appropriate comparison of localization is found). In common discussion, there are in numerous ways of evaluating the phase difference, and particularly, there are particular types of radar systems, such as F.M.C.W or O.F.D.M, which are used. The general dependency is explained by Eq. (42.9).   ϕm,i− j = ω0 τim − τ jm

(42.9)

There exists a phase difference between related acquiring i m th and jm th antenna at the mth stations that stands, irrespective of any situations. This distinctiveness might be due to the equivocacy reliant on the positions of antenna which was modeled to the range [−π, π ] by the Eq. (42.10).  ϕm,i j =

    mod2π ϕm,i− j , for mod2π ϕm,i− j < π     mod2π ϕm,i− j − 2π, for mod2π ϕm,i− j > π

(42.10)

and few other discrepancies in hardware have been updated [9]. Henceforth, the location of every independent antenna is known. Kalman filters are justified to show better results even against non-idealities.

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42.3 Unscented Kalman Filter The suggested U.K.F is a convenient method for the task at hand, which is explained in the following procedure. Nevertheless, the above-mentioned algorithm is computationally very efficient, there exist many convenient methods of iteratively positioning a non-rational transmitter dependent with computation of phase differences of receiver passage which are coherent. The Kalman filter comprises of two steps.

42.3.1 Prediction Step In an optimal condition, the final state of the function which is X k−1 along with the estimate latest state xk must be function with linear characteristics along with appended noise which has Gaussian distribution. xk = F xk−1 + n k

(42.11)

where X k , the condition of sample is position k, F the transition matrix, and n k can be modeled as a stray vector which is distributed by Gaussian elements which is explained by the covariance matrix Q.

42.3.2 Update Step z k = Hk xk + wk

(42.12)

where z k are the measurements contained by a matrix, Hk explains the conversion of measurement domain to state domain in the form of a matrix, and wk is an unwanted signal which is characterized by Gaussian distribution entries detailed by the covariance matrix R.

42.3.3 Unscented Transformation The statistics of a random variable has a routine for assessing which experiences a nonlinear transformation of x (dimension L) along with a nonlinear function, y = g(x). In the process of quantifying statistics of y, we obtain the vectors of sigma points χ (with corresponding weights) in terms of matrix, which could be conveyed by the following Eqs. (42.13)–(42.18). χ0k = X k , i = 0(mean)

(42.13)

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χik = X k + χik = X k −

(L + λ) i

for i = 1 to n

 (L + λ) i−L for i = L + 1 to 2L

W0(m) =

(42.14) (42.15)

λ , i = 1, 2, . . . , 2L L +λ

(42.16)

λ 2(L + λ)

(42.17)

  λ + 1 − α2 + β L +λ

(42.18)

Wi(m) = Wi(c) =

This cluster of sigma vectors were communicated along with a function with nonlinear characteristics as shown in Eq. (42.19). yi = g(χi ) i = 0, . . . , 2L

(42.19)

Proximity computation of mean and covariance of y could be achieved by utilizing a fewer weighted sample mean and covariance of the posteriori cluster of points (sigma) in Eqs. (42.20) and (42.21). 2L

Wi(m) Yi

(42.20)

Wi(c) {Yi − y˜ }{Yi − y˜ }T

(42.21)

y˜ ≈

i=0

Py ≈

2L i=0

42.4 Results The suggested U.K.F could be utilized to compute the position of a transmitter which then moves in a helical trajectory as manifested in Fig. 42.1 and which displays the ideal and estimated values obtained from the filter. The trajectory points are equally spaced with a distance of 17 mm, and 200 points of instances have been used to check how efficiently the UKF filter estimates the location [9]. Also the RMSE is then calculated using the formula shown in Eq. (42.22).

RMSEx,y,z =

L

I =1

px,y,z (k)2 L

(42.22)

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Fig. 42.1 Plot between ideal (with added noise) and estimated values by the UKF for helical trajectory

With x,y,z as the elements of Cartesian coordinate that contains an error which is then calculated and L represents the number of data points or instances. The complete precision of RMSEx = 0.15 cm, RMSEy = 0.15 cm, and RMSEz = 0.09 cm has been achieved for the trajectory decided upon which filter estimated in Fig. 42.1. Varying the values between 0.1 and 0.5 in increments, we could observe that the trajectory estimation was best at 0.25. Furthermore, beyond Fig. 42.2, the RMSE was considerable, and distorted response for the trajectory was obtained which required changes in various inputs of the filter which then could give a response similar to the plot shown above. The RMSE for the various measurement noise in x, y, z is given in Table 42.1.

Fig. 42.2 Plot between ideal (with added noise) (in green) and estimated values by the UKF (in red) for elliptical trajectory

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Y. Yerra al. Measurement noise (R) in filter

RMSE (in cm) x

y

z

0.1

0.67

0.74

0.58

0.15

0.45

0.55

0.32

0.2

0.32

0.25

0.14

0.25

0.15

0.15

0.09

0.3

0.22

0.19

0.13

0.35

0.34

0.27

0.25

0.4

0.50

0.53

0.31

0.5

0.66

0.71

0.48

42.5 Conclusion In this paper, a methodology related to the new concept called localization capable of moderately accurate 3D positioning related to phase differences is conveyed. Furthermore, the unscented Kalman filters employment offers fewer calculative efforts with respect to the conventional methods such as beam formers or SAR approaches. Simulations are supervised to assess the capabilities of the suggested algorithm. The outcome displays that the proposed UKF based algorithm catches up with the upcoming random inputs.

References 1. M. Lipka, E. Sippel, M. Vossiek, An extended Kalman filter for direct, real-time, phase-based high precision indoor localization. IEEE Access 7, 25288–25297 (2019). https://doi.org/10. 1109/ACCESS.2019.2900799 2. P. Sudheesh, M. Jayakumar, Nonlinear signal processing applications of variants of particle filter: a survey, in Lecture Notes in Electrical Engineering (accepted for publication), vol. 521 (2017), pp. 91–97 3. J. Ramnarayan, J.P. Anita, P. Sudheesh, Estimation and tracking of a ballistic target using sequential importance sampling method. Commun. Comput. Inf. Sci. 746, 387–398 (2017) 4. F. Gustafsson, G. Hendeby, Some relations between extended and unscented Kalman filters. IEEE Trans. Signal Process. 60(2), 545–555 (2012). https://doi.org/10.1109/TSP.2011.217 2431 5. H.F. Khazraj, F. da Silva, C.L. Bak, A performance comparison between extended Kalman filter and unscented Kalman filter in power system dynamic state estimation,in 2016 51st International Universities Power Engineering Conference (UPEC) (IEEE, 2016), pp. 1–6 6. R.P. Tripathi, S. Ghosh, J.O. Chandle,Tracking of object using optimal adaptive Kalman filter, in 2016 IEEE International Conference on Engineering and Technology (ICETECH) (Coimbatore, 2016), pp. 1128–1131 7. Q. Zhang, L. Zhao, J. Zhou,A novel weighting approach for variance component estimation in GPS/BDS PPP. IEEE Sensors J. 19(10), 3763–3771 (2019). https://doi.org/10.1109/JSEN. 2019.2895041

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8. K. Reif, R. Unbehauen, The extended Kalman filter as an exponential observer for nonlinear systems. IEEE Trans. Signal Process. 47(8), 2324–2328 (1999). https://doi.org/10.1109/78. 774779 9. C. Liu, P. Shui, S. Li, Unscented extended Kalman filter for target tracking. J. Syst. Eng. Electron. 22(2), 188–192 (2011). https://doi.org/10.3969/j.issn.1004-4132.2011.02.002 10. A. Konstantinidis, G. Chatzimilioudis, D. Zeinalipour-Yazti, P. Mpeis, N. Pelekis, Y. Theodoridis,Privacy-preserving indoor localization on smartphones. IEEE Trans. Knowl. Data Eng. 27(11), 3042–3055 (2015). https://doi.org/10.1109/TKDE.2015.2441724 11. H. Abdelnasser et al.,Semantic SLAM: using environment landmarks for unsupervised indoor localization. IEEE Trans. Mob. Comput. 15(7), 1770–1782 (2016). https://doi.org/10.1109/ TMC.2015.2478451 12. W. Zhou, J. Hou, A new adaptive robust unscented Kalman filter for improving the accuracy of target tracking. IEEE Access 7, 77476–77489 (2019). https://doi.org/10.1109/ACCESS.2019. 2921794

Chapter 43

Implementation of Energy Detection Technique for Spread Spectrum Systems T. Anjali, T. S. Aparna, M. Meera, A. Parvathy, and Gayathri Narayanan

Abstract Spectrum spreading is a communication technique by which a signal produced by a bandwidth given is intentionally spread over the frequency domain, resulting in a broader signal bandwidth. This helps to give channels immunity by not allowing interference or disruption of any sort, thus ensuring security in communication systems. The Inefficient spectrum use contributes even to the invention of new methods which enable users not allowed to utilize the radio spectrum each time a hole in the spectrum or free space is available. The technology which pinpoints the presence of a licensed radio spectrum user using complex spectrum access techniques is called cognitive radio. The paper’s key contribution is the study and comparison of various spreading techniques implemented in a signal for transmission and further implements the technique for energy detection of spectrum sensing.

43.1 Introduction In the modern world, wireless and telecommunication play a central role in the human race. Through the wireless and telecommunication technology, one can easily communicate with anyone located in whichever part of the world. By this technology, T. Anjali · T. S. Aparna (B) · M. Meera · A. Parvathy · G. Narayanan Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] T. Anjali e-mail: [email protected] M. Meera e-mail: [email protected] A. Parvathy e-mail: [email protected] G. Narayanan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_43

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utilization of electromagnetic waves such as Infrared (IR), Radio Frequency (RF), satellite, etc. data is transferred through air. Nowadays, wireless communication is a ubiquitous technology since it has higher speed, adaptability, and network efficiency. However, the implementation of this technology has two risks. The first risk can be solved by proper security measures and the other risk is to prevent interference. To prevent interference, a spread spectrum communication method is adopted in which signals modulated by these techniques are hard to interfere and cannot be jammed. Through this technique, signals having a certain bandwidth are distributed in the domain of frequency and it evolves to a signal with a wider bandwidth. Spectrum scarcity is a serious issue faced by us due to conventional spectrum allotment policies. For methodical usage of radio spectrum, FCC proposed a method to allow sharing of the spectrum among unlicensed users. That idea paves the way for technique of complex spectrum exposure focused on the cognitive radio. Spectrum sensing is the major purpose of cognitive radio. The spectrum sensing is the process of detecting primary user presence (PU). Licensed users (PU) have access to the spectrum license. Cognitive radio makes consumers without a license to operate the spectrum when not used by the primary user for efficient spectrum utilization. Due to extensive growth of wireless technology, the radio spectrum is becoming crowded day by day. As a consequence of fixed spectrum allocation of limited radio spectrum, the spectrum scarcity problem is becoming more severe. Spectrum policy task report of FCC reveals that the usage of radio spectrum covers a range of 15– 85% in the below 3 GHz frequency band [1]. From the report, it is obvious that most of that radio spectrum is underused. To resolve this spectrum underutilization, a method is introduced to divide the spectrum between licensed and unlicensed users without any harmful interference. The legal rights the spectrum to be used is owned by primary users whereas unlicensed users do not have any legal rights to make use of the spectrum but spectrum of PU can be utilized without causing any undesired interference. One of the important terms which are frequently associated with spectrum sensing is spectrum hole. Spectrum holes or White space is a band of spectrum reserved to primary users but may not be utilized throughout. The spectrum utilization is increased by cognitive radio by assigning the white spaces to unlicensed users. Perfect sharing of the secondary user (SU) spectrum requires dynamic acquiring of the spectrum rather than static distribution of spectrum [2]. The main challenge faced in detection of dynamic spectrum access is the spectrum hole productively for effective sensing of the radio spectrum. Continuous detection of spectrum holes helps to share the underutilized spectrum with unlicensed users. Whenever a licensed user needs to utilize the spectrum, the SU should empty the spectrum band to prevent harmful interference. Cognitive radio is the technology to address all these issues. The concept of radio technology arises from software defined radio [3] which changes the radio operating parameters by sensing the surroundings. The primary functionality in cognitive radio is spectrum sensing which identifies holes by refining the signal received, and deciding if the main signal is present or not. The motive is to combine the advantages of the concept of spreading for detection in communication systems. The paper is set out as given below. Section 43.1 gives a

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basic introduction to spread spectrum and cognitive radio technology. In Sect. 43.2, relevant literature survey is discussed and Sect. 43.3 provides the theoretical background of the work. Section 43.4 discusses the method for sensing the spectrum and in that energy detection was treated for the detailed study. Section 43.5 focuses on hardware implementation of combining spectrum spreading and spectrum sensing techniques. Section 43.6 shows the results obtained while simulating both spectrum spreading and sensing using MATLAB. Finally, Sect. 43.7 gives the conclusion and future scope in this field.

43.2 Related Works Various researches are done and papers are published in the field of spread spectrum communication as well as spectrum sensing techniques. Our study was mainly on the two different techniques used in wireless communication and how to interlink both these techniques for better performance.

43.2.1 Sensing Time In spectral sensing technique, sensing time is defined as the total time consumed by the user of cognitive radio to find the primary user signal present. For this particular duration, cognitive radio cannot interfere with increasing the sensing time and hence, spectral efficiency may be increased. Detection requires more time for sensing and for transmission it requires less sensing time. This results in decreasing the throughput of CR. They are called the sensing efficiency problem [4, 5] in spectrum sensing.

43.2.2 Spread Spectrum Techniques in Wireless Communications The paper “Spread spectrum techniques for wireless communications” provides a concise introduction to the use of spread spectrum in wireless communications. At first, paper presents a simple communication system that operates in discrete time. Then a model is build up on to show basic concepts and boons of spread spectrum techniques [6].Then introduction of two basic spectrum spreading techniques direct sequence spread spectrum (DSSS) and frequency hopping spread spectrum (FHSS) is given.

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Fig. 43.1 Schematic representation of DSSS transmitter and receiver

43.3 Theoretical Background The transmitted signals are disseminated over a broad frequency band in the distributed spectrum scheme, bigger than the base data transmission needed for transmitting the data. Spreading is aced by a signal or code that is autonomous of the information. De-spreading is completed by a correlation between the received spread signal and a spread signal replica used to spread the information. The two common forms of techniques for spreading spectrum are DSSS and the FHSS.

43.3.1 Direct Sequence Spread Spectrum (DSSS) The DSSS transmitter design is relatively simple. Figure 43.1 shows the direct multiplication of information bits with a pseudo-noise (PN) sequence generated by a generative PN code, which can produce different types of spreading code, and spreads the direct sequence spread spectrum baseband information. Direct multiplication of binary data with PN sequence performed for producing a transmitted baseband signal. At the receiver, the RF signal is first transformed into a baseband signal, and a de-spreading operation is performed by multiplying this baseband signal with the same PN sequence. This returns to its actual level after the de-spreading phase, well above the level of noise, since the interference is dispersed and no longer blocks receiving signal.

43.3.2 Frequency Hopping Spread Spectrum As shown in Fig. 43.2, FHSS devices spread the basic information signal by hopping its carrier over a wideband spectrum. It is a complicated frequency hopper, with a frequency synthesizer and a code generator. The spreading factor in an FHSS method refers to the amount of frequencies used in the hopping series. The duration

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Fig. 43.2 Schematic representation of FHSS transmitter and receiver

this frequency will last is called the “dwell time.” Dwell times in slow frequency hopping (SFH) systems last more than a duration of symbols. For example, a system of 2 symbols per hop as a hopping rate is a hopping system with a slow frequency. On the other hand, hopping of frequency inside an image period more than once, at that point such frameworks are called Fast Frequency hopping (FFH) frameworks. An obvious advantage of fast frequency hopping is its “Inherent Diversity of Frequencies.” Various parts of an information image or bit are transmitted in FFH frameworks over various frequencies. FFH is more desirable but this does not mean a lack of interest in the slow frequency. A SFH system over DSSS systems can be preferred only to hold away interference, without the hassle of FFH.

43.4 Spectrum Sensing Technique The general spectrum sensing model could be represented as: r (n) = k(n) H0 :PU absent r (n) = h ∗ s(n) + k(n) H1 :PU present, where n = 1, 2 . . . N where N is defined as a sample number, r(n) is the received signal at secondary user, k(n) is the AWGN with variance σ 2 and mean zero, h is the channel gain. H 0 hypothesis reveals lack of primary consumer and H 1 hypothesis indicates the PU presence. For each technique used for identification output of the detector when compared to a predefined threshold value making the decision whether or not the primary consumer is present. If detector output is greater than threshold then we choose the H 0 hypothesis. If detector output is less than threshold, then the H 1 hypothesis is selected. If we infer that the primary signal is missing, then SU can use the corresponding channel and

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can start to transmit whatever they wish to. If the opposite condition occurs, i.e. PU is present, then secondary has to stop its transmission. The method of detecting energy is more of a blind detection system where there is not a need of prior understanding of primary user signals [7]. Here, we only require knowledge of the variance of the noise. The test statistics of energy detection was calculated by N sample averaging of the squared magnitude of FFT [8]. The test statistics represents the signal energy received and is given by TED =



N r (n)2

(43.1)

n = 1, 2, 3, …, N, r(n) is the SU received signal. Pd and Pf is given by    √ Pd = Q λ − N (1 + γ ) / 2N (1 + γ )2

(43.2)

  √  2 4 P Pf = Q λ − N δW / 2N δW

(43.3)

Q denotes the Q function, the threshold value is λ, SNR is γ , λ0 is the average value of threshold which is given by  2 λ = λ/ δW

(43.4)

For one fixed PFA, the sensing threshold can be given as  2  √ λ = Q (−1) (P f ) 2N + N δW

(43.5)

It is clear from the above equation that the threshold for sensing depends on the power of noise.

43.5 Methodology Figure 43.3 displays a general simulation model for the implementation of spectrum spreading and spectrum sensing. Here input sine wave signal is spread by either DSSS or FHSS spreading technique and passed through an AWGN channel where it gets added with noise. These noisy signal test statistics are computed after applying spectrum sensing method. These test statistics were contrasted with an estimated threshold value to make the sensing decision. The threshold estimation block will dynamically create a threshold at each iteration and multiply with some threshold factors to evaluate the threshold’s effect on spectrum sensing.

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Fig. 43.3 Simulation model for implementing spectrum spreading and spectrum sensing

43.6 Simulation Results 43.6.1 MATLAB Simulation of DSSS Figure 43.4 shows a random input bit sequence of ones and zeros is generated and then bitwise XOR operation is done on the input bits with a generated PN sequence. To get the signal, result is multiplied with the carrier and output is plotted to get a DSSS-spreaded signal as shown in Fig. 43.5. DSSS extends the spectrum of data wider than is appropriate for its actual transmission. Spreading work reduces efficient power to the limit by keeping signal level below level of noise of AWGN. That ability to stay buried in noise gives low interception probability.

Fig. 43.4 Input and spreaded signal

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Fig. 43.5 FFT of spread signal

43.6.2 MATLAB Simulation of FHSS Figure 43.6 displays a random input bit sequence of ones and zeros and then a set of frequencies are assigned as a PN sequence. Each input bit was multiplied with two different frequencies of the PN sequence and finally the output is plotted in Fig. 43.7 to get the FHSS spread signal.

Fig. 43.6 Input and spreaded signal

43 Implementation of Energy Detection Technique …

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Fig. 43.7 FFT of spreaded signal

For FHSS systems, bandwidth spread equals the frequency band the system can hop over. So an FHSS signal has no low probability of interception (LPI) capability and prevents hopping interference from one frequency to another, rather than eliminating it.

43.6.3 MATLAB Simulation of Energy Detection-Based Spectrum Sensing The efficiency of spectrum sensing depends on the parameters such as detection probability (PD ) and false alarm probability (PFA ). Using the operating features of the receiver curve, the energy detector performance is characterized. ROC curve is the graph showing the variation of PD and PFA . Figure 43.8 shows the PD versus signal to noise ratio (SNR) curve for sine waves having a frequency of 100 Hz and carrier frequency F c of 1000 Hz. Sampling frequency of the signal is 8192 Hz and 512 numbers of samples are considered. Number of iterations is 1000. The graph clearly depicts the detection variance probability at different SNR ranges, and it shows that lower the SNR, the detection performance for energy detection is very poor. Figure 43.9 displays the variation of PD for different counts of samples values. It is clear from the graph that the PD increases as sample count increases. Also at low SNR values, the number of values needed to get a given PD increases. Whereas the number of samples needed for the sensing of a given probability decreases at high SNR. It shows that SNR of −20 db requires about 1000 plus samples to achieve a moderate detection. SNR of −5 db requires almost 300 samples for the identification of probability of 1.

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Fig. 43.8 PD versus SNR for energy detection

Fig. 43.9 PD versus N for different SNR values

PD versus SNR curve with different PFA is shown in Fig. 43.10. This diagram demonstrates that as PFA decreases, the detection performance also decreases. But PFA should be as low as possible to increase the spectral occupancy. Therefore, we choose a trade-off in selecting the detection and false alarm probability. A decrease in PFA implies that there is more chance to identify CR the unused locations in the spectrum thereby increasing the spectral occupancy. Figure 43.11 shows the ROC curve for different SNR values. It is obvious from the graph that higher the SNR higher is the PD and lower the SNR, lower is the detection performance. It shows that for a given PFA , the PD increases as the ratio of SNR increases. From the graph, it reveals that for signal-to-noise ratio of −15 db and for a PFA of 0.1, the PD is 0.2 and for SNR of −5 db and for the same PFA ,R the PD

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Fig. 43.10 PD versus SNR for different PFA values

Fig. 43.11 PD versus PFA for different SNR values

is 0.8. The ability of algorithms to achieve a high value of probability of detection can reduce the chance of wrong decisions. An effective energy detector is proposed in which the parameters are calculated and those are used to determine the threshold for this device in a realistic scenario. The threshold will be where both the probability parameters complement one another.

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43.7 Conclusion In this project, using spreading codes in MATLAB the two key spreading techniques of DSSS and of FHSS are implemented and are compared. Further, the energy detection method is implemented as a detector for spread spectrum systems. On comparing the two spread signals, it is evident that spreading has taken place in both the cases DSSS and FHSS but more spreading is observed in the FHSS combination. It is observed that the frequency hopping rate is more in the FHSS system than in the DSSS system. Along with that, since the rate of hopping is more in FHSS the probability of getting interfered will also be low. Hence from our understanding, we can conclude that FHSS is a much more advantageous option than DSSS. As sample size increases, our uncertainty decreases and we have greater accuracy. Thus, the rise in the number of values raises the chances for the identification of probability of one for all ranges of SNR values. Earlier detection techniques use a fixed threshold approach which results in inefficient sensing decisions due to noise uncertainty. This work deals with improving the spectrum sensing performance using dynamic threshold approach instead of static threshold. The method clearly shows the improvement compared to earlier techniques. Our findings show that the dynamic threshold selection based on calculation of the noise rates in the signal obtained during the detection process increases the probability of detection and decreases the likelihood of false alarms compared to that of static threshold energy detection. In the future, we should consider different threshold setting techniques by considering some threshold selection optimization methods, in order to define the optimal energy detector decision law.

References 1. F.C.C.S.P.T. Force, Report of the Spectrum Efficiency Working Group, 15 Nov 2002 2. Q. Zhao, B.M. Sadler, A survey of dynamic spectrum access, May 2007 3. J. Mitola, The Software Radio Architecture (IEEE Communications Magazine, May 1995), pp. 26–38 4. W.Y. Lee, I.F. Akyildiz, Optimal spectrum sensing frame-work for cognitive radio networks. IEEE Trans. Wirel. Commun. 7(10), 38453857 (2008) 5. Y.C. Liang, Y. Zeng, E. Peh, A.T. Hoang, Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans. Wirel. Commun. 7(4), 13261337 (2008) 6. P.G. Fikkema, Spread Spectrum Techniques for Wireless Communications, vol. 14, issue 3 (IEEE Signal Processing Magazine, May 1997) 7. J. Dalai, VLSI implementation of energy detection algorithm for WLAN and WiMAX applications. M.Tech thesis (2013) 8. A. Chandran, R. Anantha Karthik, A. Kumar, R.C. Naidu, M. Subramania Siva, U.S. Iyer, R. Dr. Ramanathan, Evaluation of energy detector based spectrum sensing for OFDM based cognitive radio, in Proceedings of 2010 International Conference on Communication and Computational Intelligence, INCOCCI-2010 (2010)

Chapter 44

Implementation of Low Area ALU Using Reversible Logic Formulations Niveditha Duggi and Swaminadhan Rajula

Abstract The adders, multipliers are the essential building blocks for every integrated circuit (IC). Thus, the design of adders and multipliers must inhibit the area, delay, and power-efficient properties. But most of the CMOS-based logic gates are failed to provide these properties in adders, multipliers implementation. To solve this problem, reversible logic gates have been developed at nanotechnology level using the quantum-dot cellular automata properties. The quantum cost for this reversible logic gates very low, thus in this paper reversible logic gates based N-bit adder, N-bit subtractor, N-bit multiplier, and N-bit ALU developed with reconfigurable properties. The effective utilization of these gates provides more flexible nature for ICs. The implementations are conducted in Xilinx ISE environment, the simulation results shows that proposed method is area, power, and delay efficient compared to the conventional approaches.

44.1 Introduction The digital computer performs operations that seem to discard data in computer’s history. In this, the machine state will be ambiguous [1]. The operations of computers incorporated to overwrite/erase the data and also consist of a section which addresses a bit of data addressed at distinctive transfer instructions. Hence, the system is logically irreversible—its transition work lacks a single-esteemed inverse [2]. In the development of nanotechnology, which is speed enhanced, less sized, and composed of highly convoluted engineering design than existing systems. The improvement in the technology has introduced a system considering the parameters like power and heat dissipation and clock frequency [3]. Almost in all the total logic gates for logical N. Duggi (B) · S. Rajula Department of Electronics & Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India e-mail: [email protected] S. Rajula e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_44

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operations in old computer are irreversible. Hence, in every time a logical operation is performed to know input lost and it is dissipated as heat. For any digital design, the power loss must be considered for desired parameter [4]. The current technology improvement in the computer design is increased and also the power utilization also optimized by using the Reversible logic (RL). As per literature [5] with exponential development of heat produced because of information loss is major issue. The heat dissipation causes the reduction in the circuit’s execution time and lifetime. Thus, the use of reversibility can give low power consumption and heat dissipation system. In the work of literature [6] indicated how the reversibility can be achieved with zero power dissipation. Also, reversibility cannot cause information loss as like in irreversible. For reversibility circuit design, we need set of reversible gates (RGs) and these kind of gates are available since last decades. As per literature [7] focused on logical irreversibility which causes more heat losses. Accordingly, a computer must dissipate entropy (kTln 2) of energy for each data loss. An irreversible computer can be made reversible by conserving the information. The reversible machine additional unit is used to store the every operation performed. In this machine, it controls both the input and output information. Thus, as discussed in [7], this will prevent the information loss as it can be reused. Thus reversible computer halts the information, can be erased in middle results, the output can be reused as input.

44.2 Literature Survey The work of Yugandhar et al. [8] gives a new design method for array multiplier which uses more garbage outputs. Authors considered the 4-bit reversible high performance array multiplier (RHPAM) with reversible high performance adder (RHPA) synthesizing by utilizing the advanced bi-directional synthesis mechanism. Hence, for multiplier synthesis, the optimization of input bits and also the delay are not yet addressed except in the recent works which discuss the post-synthesis mechanism to reduce the quantum bits in the reversible multiplier. Shukla et al. [9] described a design and implementation of reversible ALU of N-bit through low power addition (LPA). The ALU reduces the propagation delay and the power dissipation also reduced through clock gating but cost ineffective. Rahim et al. [10] described a novel nonprogrammable logic gate and verified its implementation in ALU design using reversible multipliers. With this work, author has found that the delay and area of ALU using RG should be low. Amirthalakshmi and Selvakumar Raja [11] have described concepts of 8-bit Reversible ALU (RALU). Also, designed and implemented high cost, efficient, fault-tolerant, reversible ALU. In this, more garbage outputs were compensated with fewer operations. The author concluded that the ALU performs all the logical operations better than existing methods not arithmetic operations. To solve these problems, the paper is contributed as follows: Sect. 44.3 describes about the Fredkin reversible gate and Feynman reversible gate. Section 44.4 describes

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Fig. 44.1 Feynman gate

a novel full adder which is designed with Fredkin gate and Feynman gate combinations; this reduces the delay and area requirement. A N-bit reversible RCA is developed utilizing the reversible full adder. A N-bit reversible array multiplier is been developed by utilizing the Fredkin gates and N-bit reversible RCA. A N-bit reversible adder-subtractor is designed, using this reversible adder-subtractor and array multiplier an N-bit reversible ALU is developed. Section 44.5 gives the detailed analysis of results in comparison with the existing method.The results show that proposed method is area, power, and delay efficient. Section 44.6 discusses the conclusion and future work.

44.3 Reversible Logic Gates Reversible logic gates are developed with the pass transistor technology using quantum-dot cellular automata.

44.3.1 Feynman Gate Figure 44.1 shows the Feynman gate, it acts as both buffer and exclusive or gate.

44.3.2 Fredkin Gate Fredkin gate is a universal gate, any arithmetical and logical operation can be implemented very effectively with low area, delay, and power consumption compared to basic gates. Thus, it is effectively used in the ALU operation. The Fredkin Gate is shown in Fig. 44.2. The main applications of Fredkin gate are: it acts as both AND gate and OR gate by controlling the input pins. If C input of Fredkin gate is 1 b0, then R output of Fredkin gate acts as AND operation. C = 0 → R = A&B + A&1 b0 = A&B

(44.1)

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Fig. 44.2 Fredkin gate

If B input of Fredkin gate is 1 b1, then R output of Fredkin gate acts as OR operation respectively.   B = 1 → R = A& 1 b1 + A&C = A + A&C = A + C

(44.2)

44.4 Proposed Method The ALU is the essential building block in the every DSP processor DIP processor, Intel processors and all the types of integrated circuits. Thus, the efficient design of ALU makes the design to area, delay, and power efficient. The proposed method majorly focuses on design of the N-bit ALU with combinations of Feynman gate and Fredkin gate, The N-bit ALU also utilizes the N-bit adder, N-bit subtractor and N-bit multiplier, thus the operation of each arithmetic unit explained in detail.

44.4.1 Proposed Reversible Full Adder Figure 44.3 shows the architecture of proposed reversible full adder (RFA), and it is designed based on the fundamentals of full adder using two half adders.

44.4.2 Proposed N-Bit Reversible Ripple Carry Adder The design of proposed N-bit Reversible Ripple Carry Adder (RRCA) is shown in above Fig. 44.4. For performing N-bit addition, it requires N number of reversible full adders. This N-bit RRCA exhibits the reconfigurable property because the designer can be able to change the size of the adder as per the design requirements. Thus, RRCA perfectly suits for the N-bit arithmetic operations efficiently like ALUs, multipliers, and DSP processors. So, the proposed RRCA is used in the N-bit array multiplier.

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Fig. 44.3 Proposed reversible full adder

Fig. 44.4 Proposed N-bit reversible ripple carry adder

44.4.3 Proposed N-Bit Reversible Array Multiplier The multiplier is the essential building block in the DSP processor, DIP processor, ALUs and all the types of integrated circuits. Figure 44.5 represents the Proposed N-bit Reversible Array Multiplier (R-AM) utilizing these Fredkin gates and N-bit proposed RCA. For implementing N-bit multiplier, we require N 2 number of AND operations. Here, the Fredkin gate will be utilized to perform the AND operation.

460

Fig. 44.5 Proposed N-bit reversible array multiplier

Fig. 44.6 Proposed reversible full adder-subtractor

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44.4.4 Proposed Reversible Full Adder-Subtractor The design of proposed reversible full adder-subtractor (RFAS) is shown in Fig. 44.6, will be effectively used for the purpose of ALU by utilizing the single architecture for both operations. From Fig. 44.6, it is observed that if Ctrl input is zero, the design acts as reversible full adder. If ctrl input is one, the design acts as reversible full subtractor and subtraction operation developed based on the twos complement addition. Ctrl = 0 → out = A + B = A + B + Ctrl

(44.3)

Ctrl = 1 → out = A − B = A + B + 1 = A + B + Ctrl

(44.4)

44.4.5 Proposed N-Bit Reversible Adder-Subtractor Figure 44.7 shows the proposed N-bit reversible adder-subtractor (RAS) consisting of N-number numbers of RAS, it acts as both N-bit reversible adder and N-bit reversible subtractor depending on the ctrl input. If ctrl is zero, it acts as N-bit reversible adder and, if ctrl is one it acts as N-bit reversible subtractor, respectively.

Fig. 44.7 Architecture of N-bit RAS

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Fig. 44.8 Proposed N-bit reversible ALU

Table 44.1 ALU operations

s[1]

s[0]

Operation

0

0

Addition

0

1

Subtraction

1

0

Multiplication

1

1

Bitwise AND using Fredkin gate

44.4.6 Proposed N-Bit Reversible ALU Figure 44.8 shows the proposed N-bit Reversible ALU (RALU) which consist of NBit RAS, N-bit R-AM and N-bit reversible logical operations. Table 44.1 shows the ALU operations. When selection lines of multiplexer are 2 b00, then control input for N-Bit RAS becomes zero and multiplexer selects the adder output, if selection lines of multiplexer are 2 b01, then control input for N-Bit RAS becomes one and multiplexer selects the subtractor output, respectively. When selection lines of multiplexer are 2 b10, then multiplexer selects the N-bit R-AM output such as multiplication. When selection lines of multiplexer are 2 b11, then multiplexer selects outputs of bitwise AND operation generated using Fredkin gate correspondingly.

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44.5 Results and Discussions All the proposed designs have been programmed and designed using Xilinx ISE software this software tool provides the two categories of outputs named as simulation and synthesis. Table 44.2 shows the comparison of LPA [9] and proposed RCA. For 8-bit, the power is reduced to 43 µW and delay to 11 ns. For 16-bit, the power is reduced to 54 µW and delay to 17 ns. For 32-bit, the power is reduced to 71 µW and delay to 36 ns. Table 44.3 shows the of RM[10]and proposed R-AM with 8-bit, 16-bit, and 32-bit. For 8-bit, the power is reduced to 54 µW and delay to 14 ns. For 16-bit, the power is reduced to 69 µW and delay to 21 ns. For 32-bit, the power is reduced to 90 µW and delay to 45 ns. Table 44.4 shows the comparison of RALU [11] and proposed RALU with 8-bit, 16-bit and 32-bit. For 8-bit, the power is reduced to 82.37 µW and delay to 22.326 ns. For 16-bit, the power is reduced to 104.28 µW and delay to 32.936 ns. For 32-bit, the power is reduced to 136.28 µW and delay to 68.842 ns. The comparison tables show huge improvement in the proposed design in terms of area, power, and delay. In this paper, an area-efficient reversible full adder is designed, utilizing this full adder an N-bit ripple carry adder is developed. And utilizing this N-bit RCA and fredkin gate an N-bit array multiplier is developed. Table 44.2 Comparison of LPA [9] and proposed RCAwith 8-bit, 16-bit and 32-bit Adder bit length

Parameter/Method

8-bit

LPA [9]

16-bit

LPA [9] Proposed RCA

Proposed RCA

32-bit

LPA [9] Proposed RCA

Slice registers 254

LUTs

LUT-FF

Delay (ns)

Power (µW) 90

471

28

38

55

99

15

11

43

262

1011

47

53

115

142

252

18

17

54

1017

2592

63

180

221

443

790

36

36

71

Table 44.3 Comparison of RM [10] and Proposed R-AM with 8-bit, 16-bit, and 32-bit Multiplier bit length

Parameter/Method

Slice registers

8-bit

RM [10]

322

596

36

48

114

70

126

19

14

54

RM [10]

332

1281

60

68

146

Proposed R-AM

180

320

24

21

69

Proposed R-AM 16-bit 32-bit

LUTs

LUT-FF

Delay (ns)

Power (µW)

RM [10]

289

3283

80

229

280

Proposed R-AM

562

1001

46

45

90

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Table 44.4 Comparison of RALU [11] and Proposed RALU with 8-bit, 16-bit, and 32-bit ALU bit length

Parameter/Method

8-bit

RALU [11]

483

895

54

72.49

Proposed RALU

106

189

29

22.326

16-bit

RALU [11]

499

1922

91

32-bit

RALU [11]

Proposed RALU Proposed RALU

Slice register

LUTs

LUT-FF

270

480

36

1934

4925

121

843

1502

69

Delay (ns)

102.37 32.936 343.73 68.842

Power (µW) 172.29 82.37 220.2 104.28 421.2 136.28

Finally, common architecture for both adder and subtractor is developed, using this adder-subtractor, array multiplier and fredkin gate an N-bit ALU is designed with low hardware resources utilization. The simulation and synthesis results show the proposed architectures are area, power, and delay efficient compared to the existing architectures.

44.6 Conclusion In this paper, four modules are proposed namely N-bit array multiplier, N-bit ripple carry adder, N-bit adder-subtractor and N-bit ALU. The proposed N-bit ALU using adder-subtractor, array multiplier and Fredkin gate is designed with low hardware resources utilization. The simulation and synthesis results show that proposed methods are area, power and delay efficient compared to the conventional approaches. This work can be extended to implement the N-bit sequential logical units such as shift register, counters, etc. Acknowledgements We have taken permission from competent authorities to use the images/data as given in the paper. In case of any dispute in the future, we shall be wholly responsible.

References 1. A. Zulehner, R. Wille, One-pass design of reversible circuits: Combining embedding and synthesis for reversible logic. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 37(5), 996– 1008 (2017) 2. S. Raveendran, et al.,Design and implementation of reversible logic based RGB to gray scale color space converter, in TENCON 2018-2018 IEEE Region 10 Conference (IEEE, 2018) 3. J. Qian, J. Wang, A 4-bit array multiplier design by reversible logic, in Information Technology (CRC Press, 2015), pp 21–24 4. A. Ghosh, S.K. Sarkar, performance investigation of nanoscale reversible logic gates designed with SE-TLG approach.In. J. Electron. (2020) (Just-accepted)

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5. H.M. Gaur, A.K. Singh, U. Ghanekar, In-depth comparative analysis of reversible gates for designing logic circuits. Procedia Comput. Sci. 125, 810–817 (2018) 6. T.N. Sasamal, A.K. Singh, A. Mohan, Reversible logic circuit synthesis and optimization using adaptive genetic algorithm.Procedia Comput. Sci. 70, 407–413 (2015) 7. P.Z. Ahmad, et al.,A novel reversible logic gate and its systematic approach to implement cost-efficient arithmetic logic circuits using QCA.Data Brief 15, 701–708 (2017) 8. K. Yugandhar, et al.,High performance array multiplier using reversible logic structure, in 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) (IEEE, 2018) 9. V. Shukla, et al.,Reversible realization of N-bit arithmetic circuit for low power loss ALU applications.Procedia Comput. Sci. 125, 847–854 (2018) 10. B.A. Rahim, et al.,Design of a power efficient ALU using reversible logic gates, in International Conference on Communications and Cyber Physical Engineering 2018 (Springer, Singapore, 2018) 11. T.M. Amirthalakshmi, S. Selvakumar Raja, Design and analysis of low power 8-bit ALU on reversible logic for nanoprocessors.J. Ambient Intell. Hum. Comput. 1–19 (2018)

Chapter 45

Evaluation of Transfer Learning Model for Mango Recognition Chanki Pandey, Prabira Kumar Sethy, Santi Kumari Behera, Sharad Chandra Rajpoot, Bitti Pandey, Preesat Biswas, and Millee Panigrahi Abstract In this research article, we have performed an evaluation of transfer learning model for recognition of mango fruits. The dataset used for the experiment is generated by collecting new high-quality images that comprise of the 15 varieties of most popular mango of India. The experimentation is carried out with nearly 1850 mango images. Here, we use transfer learning approach which includes four CNN models namely AlexNet, GoogLeNet, ResNet50, and VGG16. The GoogLeNet performed better compared to the other three models, with 87.62% of F 1 score and 0.80% of the false positive rate.

C. Pandey Department of ET&T Engineering, GEC, Jagdalpur, CG 494001, India e-mail: [email protected] P. K. Sethy Department of Electronics, Sambalpur University, Sambalpur, Odisha, India e-mail: [email protected] S. K. Behera Department of Computer Science and Engineering, VSSUT, Burla, Odisha, India e-mail: [email protected] S. C. Rajpoot (B) Department of Electrical Engineering, GEC, Jagdalpur, CG 494001, India e-mail: [email protected] B. Pandey Department of Agriculture, IGKV, Raipur, CG, India e-mail: [email protected] P. Biswas Department of ECE, CVRU, Bilaspur, CG, India e-mail: [email protected] M. Panigrahi Department of ECE, Trident Academy of Technology, Bhubaneswar, Odisha, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_45

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45.1 Introduction India is a well-known leading exporter of mangoes to the world. “The nation has sent out 46,510.27 MT of new mangoes to the world for the value of Rs. 406.45 crores/60.26 USD Millions during the year 2018–19. The significant mangodeveloping states are Andhra Pradesh, Uttar Pradesh, Karnataka, Bihar, Gujarat, and Tamil Nadu. Uttar Pradesh positions first in mango creation with a portion of 23.47% and most elevated profitability [1].” Other than India, the major countries where the mango is produced can be Philippines, Mexico, Pakistan, Indonesia, and China [2]. “In India, more than 1000 varieties of mangoes are cultivated; however, only 3% of these have predominance in the trade and export business. Totapuri, Alphonso, Dasheri, Kesar, Banganpalli, Langra, and Chausa are few to list [3].” The fruit grouping and quality evaluation by visual investigation cause blunder because of outer impacts, for example, weariness, retribution, and predisposition. Despite expert administrators, the characterization of fruits in the fruit industry drives irregularities due to varieties in visual observation. Thus, a mechanized framework is required for dissect the products to provide reliable information. Hence, quick, canny, and nondamaging methods are necessary for this application area [4]. In this examination, the mango fruits are recognized as per their variety. Numerous researchers have been accounted for in their work related to mango classification. For automatic identification and recognition of mango Vyas et al. [5], had proposed a deep learning approach using the support of convolutional neural networks (CNN) with high accuracy of 99% for both the class of Badami and Totapuri. To classify mangoes, Borianne et al. [6] had proposed an algorithm with an accuracy of 94.97%, and grading of mango was done within a second using color and size features. Momin et al. [7] presented a faster R-CNN network for the detection and identification of mango fruits from color images of trees. The study result was about 90% for fruit detection as a network accuracy (F 1 -score) but fell to 56% for fruit cultivar identification. To automate the grading of mangos, Tamilselvi et al. [8] had successfully developed image acquisition and processing system with an accuracy of 79% for perimeter, 36% for roundness features, and 97% for the projected area. For classification of mangos into one of the three mass grade, i.e., small, medium, and large, an image processing algorithm was adopted. Ullagaddi et al. [9] had proposed a mango grading algorithm and grades mango into four different grade namely G1, G2, G3, and G4 based on image processing techniques especially support vector regression (SVR), multi-attribute decision making (MADM), and fuzzy model. The result of the study was nearly 87% as performance accuracy along with less than 3% as size error. The time required by the proposed model for grading of one mango was approximately 0.4 s. Disease recognition in mango crop using modified rotational kernel transform features was carried out by. Al Ohali [10] with accuracy up to 98%. In the last decade, a vast number of research work have been reported on different aspects related to fruit industry like grading [11], fruit quality assessment [12, 13], prediction of volume and mass [14], and defect detection [15]. Modern technologies

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such as image processing [16–19], computer vision [20–23], and machine learning [24] have enabled wide application for classification and grading of various fruit especially mango, apple, and papaya. Algorithms for detecting fruit size [25], color, shape or ripeness [26–28], and defects [29–31] were successfully applied in those grading systems. In the fruit industry as well as in the agriculture sector, the utilization of computer vision [32], machine learning [33], and deep learning [34–37] is increasing with a rapid rate. In the last few years, many work have been done for on-tree detection [38–40], classification and grading. With this background, the proposed work aims to an evaluation of transfer learning models for recognition, classification, and grading of mango fruits. The material and proposed method are discussed in Sect. 45.2. The results and discussion are in Sect. 45.3. Finally, we summarize our work and conclude this research paper in Sect. 45.4.

45.2 Material and Proposed Method This section comprises the collection of data and the methodology adopted.

45.2.1 Collection of Sample Data The mango fruits are collected from the various vegetable market from the different city across India and placed on white paper one by one to capture their images. Then, images are captured using a smartphone that has a 13-megapixel resolution in natural light of the day, between 9 A.M. to 4 P.M. With the help of ELV aluminum, adjustable mobile phone foldable holder stand, the process of image acquisition was carried out while maintaining a distance of 30 cm. To avoid reflection and shading on the background, we use the white background that enhances clarity and remove visual obstacles and clutter along beside taking care of natural light and no direct light on the object. Dimension 250 × 250 are used to resize all the images. We took about 1850 sample image of 15 varieties, 100–200 samples for each variety of mango fruits out of which some samples of all 15 varieties are shown in Fig. 45.1 from Sl.no. 1– 15 in Table 45.1 which shows Alphonso, Ambika, Amrapali, Banganpalli, Chausa, Dasheri, Himsagar, Kesar, Langra, Malgova, Mallika, Neelam, Raspuri, Totapuri, and Vanraj, respectively.

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Fig. 45.1 Samples of 15 kinds of mango fruit

45.2.2 Transfer Learning Approach The transfer learning approach is a subset of machine learning that utilize precollected knowledge from the established model. In this study, we adopt the transfer learningbased pretrained CNN model, as shown in Fig. 45.2. Here, four pretrained networks are considered for the evaluation of transfer learning model for recognition, classification, and grading of mango fruits. The pretrained networks are namely AlexNet, GoogLeNet, ResNet50, and VGG16. The following steps summarize the transfer learning: Step-1 Collection of mango images. Step-2 Preprocessed the image, i.e., resize to 227 × 227 × 3 dimension. Again, augmentation is used to fit the image size with the input size of the network. Step-3 Load a pretrained network. Replace the classification layers for the new task and train the network on the data for the new task. Step-4 Classification is performed using the newly created deep model and measures the performance of the new network. The similar approach is repeated for all CNN model.

45 Evaluation of Transfer Learning Model for Mango Recognition Table 45.1 Total dataset used for performing the experiments

471

Sl. No.

Variety of mango

1

Alphonso

203

2

Ambika

100

3

Amrapali

150

4

Banganpalli

100

5

Chausa

100

6

Dasheri

100

7

Himsagar

150

8

Kesar

100

9

Langra

100

10

Malgova

150

11

Mallika

200

12

Neelam

100

13

Raspuri

100

14

Totapuri

100

15

Vanraj

Total samples

No. of images

100 1853

Fig. 45.2 Transfer learning based pretrained CNN model for mango recognition

45.3 Results and Discussion In this examination, we analyzed the presentation of four transfer learning models for recognition of mango fruits. The exploration study was actualized utilizing the MATLAB 2019a. All the applications were run on a laptop, i.e., HP Pavilion Core i5 5th Generation with basic NVIDIA GEFORCE. The measurement of performance of each model is measured in terms of F 1 Score and FPR. The F 1 score and FPR are calculated in Eqs. (45.1)–(45.4).

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Table 45.2 Confusion matrix measures of transfer learning models Transfer learning models

F 1 score Mean

FPR Minimum

Maximum

Mean

Minimum

Maximum

AlexNet

0.858882

0.7749

0.9254

0.008873

0.004894

0.013606

GoogLeNet

0.876266

0.795077

0.921287

0.008018

0.005042

0.01322

ResNet50

0.866101

0.7688

0.918935

0.008754

0.005332

0.014961

VGG16

0.862333

0.752998

0.922563

0.0088

0.005131

0.016951

The best results are indicated in bold font

precision = recall = F1 Score = 2 × FPR =

TP TP + FP

TP TP + FN

(45.1) (45.2)

precision × recall precision + recall

(45.3)

FP FP + TN

(45.4)

where TP = true positive, TN = true negative, FP = false positive, and FN = false negative. The mean, minimum, and maximum values of F 1 score and FPR of four transfer learning models of 20 independent runs are recorded in Table 45.2. It is observed from Table 45.2 that GoogLeNet resulted in highest F 1 score and lowest false positive rate among four pretrained transfer learning models. The highest F 1 score is 87.62% achieved by GoogLeNet model. The lowest F 1 score, i.e., 86.23%, is resulted by VGG16. With concerning both minima and mean value of F 1 score and mean and maximum value of FPR, GoogLeNet is the best convolutional neural network (CNN) model for recognition of mango in the transfer learning approach.

45.4 Conclusion The manuscript mainly focused on the evaluation of four transfer learning model for recognition of mango fruits. Here, 15 variety of Indian mango fruits are considered for experimentation. The transfer learning model, i.e., GoogLeNet, performs well for recognition of mango. This research may be extended with more variety of mango and quality evaluation of internal flesh based on skin information.

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Chapter 46

An Inter-Comparative Survey on State-of-the-Art Detectors—R-CNN, YOLO, and SSD B. Bhavya Sree, V. Yashwanth Bharadwaj, and N. Neelima

Abstract In recent years, breakthrough enhancements in computer hardware and supercomputers made object detection a significant topic of research. Accurate object detection models are computationally expensive and are inefficient on simpler and limited configuration settings while faster models achieve real-time speed, work well on simpler configurations but fail to be accurate. There is always a trade-off between speed and accuracy. There is no clear-cut answer on which detector performs the best. The user will have to make a choice based on the requirement. This paper aims at analyzing numerous CNN-based object detection algorithms—R-CNN, Fast R-CNN, Faster R-CNN, You Only Look Once (YOLO), and Single Shot MutliBox Detector (SSD)—and make comparisons concerning performance, precision and speed and state as to which algorithm performs better under certain constraints. This enables the user to pick an object detector of his/her choice that better addresses the demands of an application.

46.1 Introduction It is trivial for a human eye to distinguish, recognize, and classify objects in its view. However, it is hard for a machine to comprehend objects in real-world scenarios because they are highly adaptable and take in a variety of shapes, sizes, colors, and textures. Recent developments in computer vision and image processing, however, simplified the task of object detection. Object detection and tracking technology are

B. Bhavya Sree (B) · V. Yashwanth Bharadwaj · N. Neelima Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India e-mail: [email protected] V. Yashwanth Bharadwaj e-mail: [email protected] N. Neelima e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_46

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quite effective and finds numerous applications in self-driving cars, medical diagnosis, ball tracking in sports, video surveillance systems [1], etc. Object detection algorithms identify objects in a digital image or a video frame and fit a bounding box around with a label stating as to which class the object belongs. However, some objects may go undetected by sensors and this may be critical in the case of autonomous vehicles as they are to be working with 100 percent accuracy. For instance, there has been a case of death relating to a self-driving vehicle. Failing to sense, An Uber self-driving vehicle hit a pedestrian. Perception is, therefore, a life-or-death issue and cares for a lot more attention. The advancements in deep learning and neural networks led to the discovery of state-of-the-art models like R-CNN, YOLO, SSD, etc. The fundamental duty of these detectors is to generate bounding boxes, estimate class probabilities, and assign a confidence score based on these probabilities. This paper gives an overview of how various object detection algorithms—R-CNN, Fast R-CNN, Faster R-CNN, YOLOv1, YOLOv2, YOLOv3, and SSD—work and differ on various grounds. These models are evaluated based on the mean Average Precision (mAP), test time, and memory specifications. The model that best fits the demands of your application can, henceforth, be selected and used in accordance.

46.2 Literature Review 46.2.1 R-CNN Region-based CNN (R-CNN) [2, 3] is one of the various CNN-based object detection methods. To perform object detection, we need to know the class to which an object belongs, coordinates, and offset values of a bounding box. In R-CNN, a selective search (SS) algorithm employs a segmentation method to group adjoining pixels by color, texture, intensity, etc., and generate about 2k region proposals. Each proposal is warped into a fixed square size (227 × 227) and is fed into a CNN, AlexNet that makes use of five convolutional layers and two fully connected layers to extract a feature vector of dimension 4096 × 1. SVM makes use of this feature vector to classify objects in an image. It also outputs 4 offset values to enhance the exactness of a bounding box.

46.2.2 Fast R-CNN R-CNN [2] works quite convincingly. However, it is tedious to compute a feature vector for every region proposal and this in turn accounts for a lot more memory space—2000 feature vectors for 2000 region proposals. Furthermore, three models— CNN, SVM, and a bounding box regressor—are to be trained separately. The author

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of R-CNN, therefore, has come up with a better algorithm nullifying the constraints on time and memory. Fast R-CNN [4] seems like a better solution. This algorithm inputs an entire image and processes it through a set of convolutional and max-pooling layers to generate a convolutional feature map. This is where the difference lies. Fast R-CNN computes a feature map on an entire image, unlike R-CNN. A corresponding part of the feature map is extracted for each region proposal. The region of interest (RoI) pooling layer warps the region proposals into fixed-length feature vectors. The feature vectors are loaded into a sequence of fully connected (FC) layers that bifurcate into two output layers. One that uses a softmax classifier to predict the probability estimates over the complete set of object classes and the other to output four offset values for refined bounding box aspect ratio.

46.2.3 Faster R-CNN However, R-CNN and Fast R-CNN are found ineffective in real time because they employ a selective search [5] technique, which being a tedious process hinders the network’s efficiency. Hence, Shaoqing Ren et al. have come up with an improvised algorithm [6] that uses a Region Proposal Network (RPN) in estimating the object proposals. In Faster R-CNN, an entire image is fed into a deep layered network to generate a convolutional feature map. A mini-network inputs an n × n block of this convolutional feature map to generate region proposals as illustrated in Fig. 46.1. This feature is led into a pair of FC’s—a regression layer (Reg.) and a classification layer (Class.). A maximum of ‘k’ proposals are generated at every sliding window’s

Fig. 46.1 Region proposal network. Source Ren et al. (2016)

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location. The Reg. layer outputs 4 × k encoded coordinates of k boxes and the Class. layer outputs 2 × k probability estimates for the proposal constituting an object or not. The k proposals are parametrized correlative to k anchor boxes. This architecture is administered with an n × n convolutional layer succeeded by a pair of 1 × 1 convolutional layers meant for regression and classification.

46.2.4 YOLOv1 You Only Look Once (YOLO) is one of the most efficient algorithms in the history of object detection. R-CNN, Fast R-CNN, and Faster R-CNN employ region proposal methods to first generate region proposals and then load these proposed boxes into a classifier. These complex pipelines are time-consuming and so, they are not efficient in real-time. However, YOLO [7] is simpler and is extremely fast. You Only Look Once (YOLO) at an image to detect and classify an object. Its base network runs at a speed of 45 fps. Furthermore, its mean average precision (mAP) is twice more than other real-time systems. The foremost step in YOLOv1 is to split an input image into an s × s grid and bring about s2 grid cells. If an object’s centroid falls within a grid cell, then that grid cell takes control in detecting that object. Each grid cell got a task of estimating the bounding boxes (BB), confidence parameters, and class probabilities (Pc ). Mathematically, the output size happens to be s × s × (BB × 5 + Pc ). Higher the confidence scores, more confident is the model in predicting that an object exists. What if the confidence score is zero? Then it is clear that the grid cell holds no object. A confidence score is computed by enacting an Intersection over Union (IoU) technique on the predicted box relative to a ground truth and if the value falls under a threshold, the detection would be straight away stated as a false negative (FN) detection. Even after threshold filtering, many boxes with higher objective scores and yet no sign of target objects are left out. To eliminate duplicate detection, a second filter called Non-Maximum Suppression (NMS) is used. YOLOv1’s architecture emulates GoogleNet’s model constituting a series of twenty-four convolutional layers adjoined by two fully connected layers. 1 × 1 layers are used in between to lessen the feature space.

46.2.5 YOLOv2 YOLOv1 performs fairly well. However, it will have to put up with a few shortcomings concerning localization and recall. Recall can be understood as the number of correct hits, and it is relatively low in comparison with other region proposal methods. YOLOv2 [8] is an improvised version over YOLOv1 and aims at enhancing object localization and recall.

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Batch normalization would help enhance mAP by 2%. Furthermore, the network is trained on ImageNet images for 10 epochs at a higher resolution of 448 × 448. This in turn improves the mAP by 4%. The use of anchor boxes to localize objects enhances recall rate by 7%. However, there is a decrease in mAP by 0.3%. YOLOv2 [9] employs k-means clustering to instinctively discover good anchors rather than hand-picked anchor boxes as used in YOLOv1. This enhances the mAP by about 5%. Opting new image proportions arbitrarily for every 10 batches trains the network competently across various image dimensions.

46.2.6 YOLOv3 YOLOv3 is a modified version of YOLOv2. YOLOv2’s 30 layered model architecture (Darknet-19 and 11 layers on top for object detection) often struggle to detect smaller objects as a result of downsampling the fine-grained features by the layers. To overcome this crisis, YOLOv3 [10] concatenates the existing feature map with the preceding layer’s feature map and captures low-level features. YOLOv3 makes use of the model, Darknet-53 that originally has 53 layers with an additional 53 layers stacked onto the existing layers to perform object detection, attributing to its efficiency in locating smaller objects. However, this makes v3 slower in comparison with its other versions. As depicted in Fig. 46.2, YOLOv3’s object detection at three different output layers (8,294,106) and three different scales enhances its ability in detecting smaller objects. Logistic classifiers are used as an alternative to softmax for predicting the class and bounding box priors are constructed by employing a k-means clustering algorithm on the training dataset.

Fig. 46.2 Architecture of YOLOv3. Source Hossain and Lee (2019, p. 10)

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46.2.7 SSD Unlike other region proposal algorithms like Faster R-CNN that requires two distinct steps in detecting objects—one for estimating region proposals, other for detecting objects in each region proposal, SSD [11] takes one shot in detecting multiple objects in an image. It uses VGG-16 model architecture to extract feature maps. 3 × 3 convolutional filter is used on these feature maps to find the bounding boxes and class scores of objects. SSD makes use of distinctly scaled feature maps to precisely detect larger and smaller objects in an image. Low-resolution feature maps would work sufficiently well on larger objects. However, high-resolution feature maps would be necessary for detecting smaller objects. Default bounding boxes are chosen manually beforehand to enclose a broad range of real-time objects. Different resolution filters use boxes with various aspect ratios (1, 2, 3, 1/2, 1/3). To find a perfect bounding box for an object, a technique called matching strategy is used, which states that a default box with IoU higher than a threshold (say 0.5) concerning the ground truth is a positive match. NMS is used on top of this to remove possible duplicates.

46.3 Experimental Analysis The proposed algorithm, YOLOv3 is trained on COCO and PASCAL VOC datasets, and the experimental results are compared among various object detectors—R-CNN, Fast R-CNN, Faster R-CNN, YOLOv1, YOLOv2, and SSD.

46.3.1 R-CNN, Fast R-CNN, and Faster R-CNN R-CNN generates about 2000 region proposals for a single image and the detection time is about 49 s as inferred from Table 46.1. This is not effective for real-world examples. Fast R-CNN is, therefore, devised to take in an entire image as input. This saves up a lot of training time and is quite efficient in a way. However, selective search Table 46.1 Comparison among region-based neural network detectors R-CNN

Fast R-CNN

Faster R-CNN

Method

Training data

SS

SS

RPN

mAP (%)

07

58.5

66.9

69.9

Detection time (s)

12

53.3

65.7

67.0

07 + 12

–

68.4

73.2

07 + 12 + COCO

–

–

75.9

49

2.32

0.2

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Table 46.2 Performance measure on Pascal VOC dataset. Source Redmon and Farhadi (2018, p. 4) [10] Method

Input proportions

# Boxes

Mean average precision (%)

Frame rate (FPS)

Faster R-CNN (VGG-16)

1000 × 600

6000

73.20

7

YOLO (VGG-16)

448 × 448

98

66.40

21

SSD

300 × 300

8732

74.30

46

SSD

512 × 512

24,564

76.80

19

[5] constitutes a major part of the training period. Hence, R-CNN and Fast R-CNN are ineffective in real-time. Faster R-CNN replaces selective search and instead uses a region proposal network (RPN) that will help process an image in just 0.2 s and is, therefore, worthy of usage in real time.

46.3.2 Faster R-CNN, YOLO, and SSD Faster R-CNN works well. However, its FPS is pretty low in comparison with normal standards. If we care for real-time speed, SSD and YOLO are at the rescue. YOLO and SSD are state of the art models that are capable of achieving a higher frame rate. As inferred from Table 46.2, YOLOv1 s mAP is the least and it fails in detecting minor objects. However, SSD300 outruns all other detectors while maintaining a fair FPS—real-time speed. Table 46.3 depicts how YOLOv2 outperforms prior object detection methods concerning accuracy and speed. Look at the way it runs for different resolutions of input images for an effortless exchange between accuracy and speed. At low resolution, i.e., at 288 × 288, YOLOv2 runs at almost 90 FPS. This feature makes it worthy for usage in high FPS video streams and compact GPU’s. At higher resolution, YOLOv2 is very much accurate with an mAP, 78.6, and an acceptable real-time speed, 40 FPS (Table 46.4). If a comparison is to be made between SSD and YOLOv3 on COCO, at an input resolution, 320 × 320, YOLOv3 runs about three times faster than SSD achieving almost the same mAP (28%).

46.4 Conclusion This paper makes a comparative analysis among various CNN-based object detection algorithms from the very basic R-CNN to the very recent path-breaking SSD putting forth various aspects on speed, accuracy, and memory. R-CNN and Fast R-CNN

482 Table 46.3 Performance w.r.to Pascal VOC 2007

Table 46.4 Performance on COCO dataset

B. B. Sree et al. Method

Training data

Mean average precision (%)

Frame rate (FPS)

Fast R-CNN

07 + 12

70.00

0.5

Faster R-CNN VGG16

07 + 12

73.20

7

Faster R-CNN residual net

07 + 12

76.40

5

YOLO

07 + 12

63.40

45

SSD-300

07 + 12

74.30

46

SSD-500

07 + 12

76.80

19

YOLOv2 (288 × 288)

07 + 12

69.00

91

YOLOv2 (352 × 352)

07 + 12

73.70

81

YOLOv2 (416 × 416)

07 + 12

76.80

67

YOLOv2 (480 × 480)

07 + 12

77.80

59

YOLOv2 (544 × 544)

07 + 12

78.60

40

Algorithm

Mean average precision Time (in ms) (%)

SSD (321 × 321)

28.00

61

SSD (513 × 513)

31.20

125

YOLOv3 (320 × 320) 28.20

22

YOLOv3 (416 × 416) 31.00

29

YOLOv3 (608 × 608) 33.00

51

take about 49 s and 2.3 s, respectively, to process an image. It’s quite high for realworld applications. Faster R-CNN is comparatively better in respect of detection time. However, YOLO and SSD are state-of-the-art models and work efficiently with real-time speeds. SSD300 runs at 59FPS exceeding the existing state-of-the-art YOLOv1’s 45FPS. YOLOv2 is faster and can run over different image proportions providing a smooth barter between speed and accuracy. YOLOv3 and SSD can work well on smaller objects. Various constraints stated in this study might help the user choose an algorithm that better serves the application.

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References 1. J.V.V.S.N. Raju, P. Rakesh, Neelima. N, Driver drowsiness monitoring system, in Smart Innovation, Systems and Technologies (SIST) vol. 169 (2019), pp. 675–683 2. R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014). ISBN 978-1-4799–5118-5 3. K. Madan, K. Bhanu Anusha, P. Pavan Kalyan, N. Neelima, Research on different classifiers for early detection of lung nodules. Int. J. Recent Technol. Eng. 1037–1040 (2019) 4. R. Girshick, Fast R-CNN, in IEEE International Conference on Computer Vision (ICCV) (2015). ISSN 2380-7504 5. J.R. Uijlings, K.E. van de Sande, T. Gevers, A.W. Smeulders, Selective search for object recognition. Int. J. Comput. Vision (IJCV) 6. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks, in Advances in Neural Information Processing Systems (NIPS 2015), vol. 28 (2015), pp. 91–99 7. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real-time object detection (2016), pp. 1–10 8. J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), pp. 6517–6525 9. D. Foley, R. O’Reilly, An evaluation of convolutional neural network models for object detection in images on low-end devices, in AICS, 2018, vol. 2259 (2018), pp. 1–12 10. J. Redmon, A. Farhadi, YOLOv3: an incremental improvement (2018) pp. 1–6 11. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, A.C. Berg, SSD: single shot multibox detector, in EECV 2016 (2016), pp. 21–37

Chapter 47

Diabetes Patients Hospital Re-admission Prediction Using Machine Learning Algorithms Sneha Grampurohit

Abstract The excess amount of blood glucose in the body leads to a chronic disease called diabetes. It causes severe damage to the eyes, kidneys, nerves, and other parts of body. Hospital re-admission is a scenario in which the patient gets re-admitted to the hospital after a certain duration of time. Diabetes patient’s hospital re-admission majorly impacts on the healthcare cost reduction as diabetic patients are more likely to get re-admitted than those without diabetes. The proposed work aims to predict the re-admission of diabetic patients and highlight the factors that lead to re-admission within 30 days of their discharge considering the database of 10-year administrative patients’ record using decision tree and AdaBoost classifiers. With all the preprocessing and feature selection techniques, the proposed approach has obtained an accuracy of 95%.

47.1 Introduction When the blood glucose also called blood sugar gets high in the body, it leads to a chronic disease called diabetes. In India, as a result of less attention paid to diabetic patients, diabetes is listed as the primary diagnosis by less that 2% of hospital discharges annually [1]. The global health expenditure on diabetes is expected to total at least 376 USD billion in 2010 and is estimated to go up to 490 USD in 2030 [2]. About 9% of the current US population is represented by diabetic patients [3], but they account to approximately 25% of hospitalization [1, 4, 5]. The re-admission rates of a diabetic patient within 30 days are found to be around 14.4–22.7% [4, 6–9]. In USA (2012), $124 billion was spent on hospitalization of diabetic patients out of which $25 billion was attributable to 30-day re-admission with the assumption of 20% re-admission rate [2, 4]. S. Grampurohit (B) Department of Electronics and Communication, K.L.E Institute of Technology, Hubballi 580027, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_47

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A scenario in which a patient gets re-admitted again to the hospital within a particular duration of time after his/her discharge refers to the hospital re-admission (RA). Hospital RA can be one of the strong measures to judge the quality of service provided by the hospitals. The tremendous growth in big data has generated opportunities for greater patient insights that can help the healthcare department to reduce its cost while providing a better healthcare service. The main objective of the proposed solution is to build a predictive model to identify diabetic patients who are more likely to get re-admitted to the hospital within 30 days of their discharge based on the administrative data of the patients collected by the hospital and to highlight the important factors leading to the RA. The dataset considered is a real-world dataset and it represents 10 years of clinical care at 130 US hospitals and integrated delivery network. It has been taken from the Kaggle repository [10]. The dataset consists of 101,766 diabetic patients’ records with 50 features/factors which contribute to diabetic hospital RA. Information was extracted from the database for encounters that satisfied the following criteria. (1) It is an inpatient encounter (a hospital admission). (2) It is a diabetic encounter, that is, one during which any kind of diabetes was entered into the system as a diagnosis. (3) Laboratory tests were performed and medications were administrated during the encounter. The factors or the independent variables include number of laboratory tests performed during the encounter; number of diagnoses entered into the system; and so on. The dependent variables have the value “30” if the patient was re-admitted after 30 days of his discharge, and “No” if the hospital does not hold any record of re-admission. The remaining part of the paper consists of four sections: Sect. 47.2 highlights the literature survey done prior to the beginning of the work. Section 47.3 gives the detailed explanation of the methodology which includes the steps such as data preprocessing, feature engineering, data balancing, normalization, and lastly investigation of machine learning algorithms on the considered dataset. The proposed approach makes use of the data mining algorithm such as decision tree classifier, and boosting algorithm such as AdaBoost algorithm which was further tuned using GridSearchCV function. Section 47.4 comprises results and discussions which give a comparative performance of the algorithms along with the risk factors. The evaluation methods considered are the accuracy rate, precision rate, recall rate, and confusion matrix. Section 47.5 concludes the presented work.

47.2 Literature Survey The previous studies which were analyzed have highlighted the risk factors that predict the hospital RA rates of diabetic patients [11–22]. Duggal et al. [19] have put forth the key factors leading to RA of diabetes as number of inpatient visits and LOS and have also inspected the cost reduction using five different machine learning algorithms. However, they have obtained an accuracy of up to 87%. Hanan et al. [20]

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have addressed the problems of patient RA and have obtained maximum accuracy of 93% using SVM algorithm. Hammoudeh et al. [22] have proposed an approach using deep learning models to predict the hospital RA of diabetic patients. The work yields an accuracy of 93% and does not focus on the key features that correspond to hospital Jiang [11] has explained about the demographic and socioeconomic factors that have major impact on the hospital RA rates. More than 52,000 patients’ records were examined by Eby et al. [14] to predict the RA risks. Rubin et al. [16] have reported the strategies that can help to overcome the RA problem along with the barriers to reduce RA risk of patients with diabetes. Bhuvan et al. [18] have investigated different machine learning algorithms on public dataset for short-term and long-term diabetic RA. This work mainly focusses on the cost analysis due to hospital RA. In contrast to any previous works, the presented paper highlights risk factors that majorly impact on the RA of diabetic patients and predict whether the patient with the related factors is likely to get re-admitted to the hospital within the period of 30 days with a highest accuracy compared to previous works, i.e., up to 95%.

47.3 Proposed Methodology The proposed methodology presents the brief explanation of the work from data preprocessing up to the investigation of algorithms.

47.3.1 Data Exploration The distribution of data of some of the important features in the raw dataset collected has been presented below [11–16]. Based on the data distributions, the pre-processing (data cleaning), feature engineering, and data balancing techniques were applied (Figs. 47.1, 47.2, 47.3, 47.4, 47.5 and 47.6).

47.3.2 Data Preprocessing and Feature Engineering While we are dealing with the real-world data which is often inconsistent, incomplete, and noisy, pre-processing them is necessary to convert them to an interpretable form. The pre-processing techniques that were implemented are as follows: • Removal of factors with large missing values. • In order to measure how much of hospital services a person has utilized in a year, a feature column was created called “Service_utilization” which is the resultant column of the addition of two columns, namely number of inpatient(admissions) and outpatient visits for a particular patient in a year.

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Fig. 47.1 Distribution of RA

Fig. 47.2 Time in hospital versus RA

• The dataset considered consists of 23 features for 23 drugs which indicate for each of these, whether the hospital had made the change in the medications or not during the entire course of treatment, as some of the research works suggest that change in medication for diabetes leads to low admission rates [23]. Hence, another column “num_of_changes” was added which consists of the count of how many medication changes were made for a particular person during the entire treatment.

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Fig. 47.3 Age of patient versus RA

Fig. 47.4 Number of medications versus RA

• Label encoding was performed on the dataset for columns such as gender; categorical encoding was performed on attributes such as tests and age, collapsing of multiple encounters for same patient. • Categorization of diagnosis: Three diagnosis levels were present in the considered dataset, i.e., primary, secondary, and addition; each of these diagnoses had 700– 900 ICD codes. Henceforth, the above three diagnoses were clubbed into nine disease categories, namely circulatory, respiratory, digestive, diabetes, injury, etc., as clubbed in [15]. • The confidence intervals cannot be reliably calculated if the data is not normally distributed; hence, moment-based measures such as skewness and kurtosis are used on data to investigate how much data is deviated from the normality. Further, log transformations have been applied to normalize the data.

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Fig. 47.5 Change in medication versus RA

Fig. 47.6 Diabetes medication prescribed versus RA

• The number of non-re-admitted was found to be very large compared to the number of re-admitted. Hence, the model will not be able to effectively learn the decision boundary, in case of imbalanced data. To prevent this, the Synthetic Minority Oversampling Technique (SMOTE) has been applied [21].

47.3.3 Decision Tree Classifier Decision tree classifiers are well known for their classification techniques they possess in character recognition, image classification, etc. They can efficiently solve the classification problems due to their high adaptability and computationally effective features. One of the most important features of decision tree is the capability of

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capturing descriptive decision-making knowledge from the supplied data. Decision tree classifiers comprise of different decision rules or learning models. There are different learning models, and one of the most notable models is the CART model, which is a binary recursive practicing technique [24]. It involves constructing a suitable tree by selecting the input variables and split points on those variables based on the algorithm used. The selection of the specific split or cut point or the input variable to use is decided based on a greedy algorithm we use to minimize the cost function. Gini index has been used as the cost function to evaluate the splits in the dataset. It gives an idea of how good a split is, by how mixed the classes are in the considered two groups (30 days) created by the split. The target variable is the “re-admission” feature whose values: • 0 → the patient may not get re-admitted to the hospital within 30 days of his discharge or his record of re-admission is not found. • 1 → the patient is more likely to get re-admitted to the hospital within 30 days of his discharge. The Gini index for each feature can be calculated by the below formula: G=

     n h1     n h2  + 1 − (h 20 )2 + (h 21 )2 ∗ 1 − (h 10 )2 + (h 11 )2 ∗ n n (47.1)

where h10 , h11 h20 , h21 nh1 , nh2 n

proportion of instances in group 1 of class “0” and “1” proportion of instances in group 2 of class “0” and “1” total number of instances in group 1 and group 2 total number of instances we are trying to group from parent node.

47.3.4 Feature Importance Feature importance is checking how relevant each feature is or how much the feature contributes toward the final output result. It is calculated as the decrease or amount of reduction in node impurity weighted by the probability of reaching that particular node. The node probability can be calculated by the ratio of number of instances that reach the node to the total number of instances. The greater the value, the more important the feature will be. For each decision tree, the nodes’ importance is calculated using Gini importance. The importance of each feature in a decision tree with help of node probability is calculated as:   N i k = G k Pk − G left(k) ∗ Pleft(k) + G right(k) ∗ Pright(k) where

(47.2)

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N ik Gk Pk G left(k) , G right(k) Pleft(k) , Pright(k)

the importance of feature k the impurity measure or value of parent node k weighted number of instances reaching node k impurity at left node and right node number of samples at left and right node.

47.3.5 AdaBoost Classifier A series of low-performing week classifiers are combined with the aim to create an improved classifier. This technique is called as ensemble learning. Under ensemble techniques, boosting algorithms are one of the kinds. Boosting works in a sequential manner and does not involve bootstrap sampling. Instead, every tree is fitted on a modified version of an original dataset and lastly summed up to build a strong classifier. Step 1 Initialize the sample weights. Every data point is initialized with the weight equal to 1/N where N = total number of instances in the dataset. Step 2 For each feature in the considered dataset, a decision tree (criterion = Gini) is built with a depth 1. Further, the predictions made by each tree is compared with the actual labels in the training set. The feature and the corresponding tree that has performed the best, i.e., the one with the smallest incorrect predictions in classifying the training instances, becomes the next tree in the forest. Step 3 The significance (Sig) of that tree is calculated as: Sig =

 1 − total_error 1 log 2 total_error

(47.3)

where total_error = sum of the weights of the incorrectly classified instances. Step 4 Update the instance weights: While updating the data weights, the main focus is on the data points that were incorrectly classified. Hence, the weights for the correctly classified instances will be decreased and the weights for the incorrect classified data points will be increased. • New_instance_weight [incorrectly classified] = instanceweight xeSig • New_instance_weight [correctly classified] = instanceweight xe−Sig Step 5 Since the instances that were incorrectly classified possess higher weights compared to correctly classified instances, the likelihood is that the random number falling into the latter category is greater. Therefore, the new instance or data point will have the tendency to contain several copies of the instances that were incorrectly classified by the previous trees. Hence, moving back to the step where the predictions made by each decision tree evaluated, the decision tree with the highest score will have correctly classified the instances which were incorrectly classified previously.

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Step 6 The steps from 2 to 5 are repeated until n iterations (as n_estimators). Step 7 Use the forest of decision trees to predict the new dataset apart from the training set. The AdaBoost model makes predictions on new instances by making each decision tree in the forest classify the new instance. Then, the trees are split into groups according to their decisions. For each group, the significance value of every decision tree is added in the group. The final classification made by the AdaBoost classifier as a whole is determined by the group with the largest significance value.

47.3.6 Feature Importance AdaBoost’s feature importance is derived from the base classifier used in it; as decision tree classifier with “Gini” criterion has been used as the base classifier, the AdaBoost feature importance is calculated by the average feature importance provided by each decision tree used, i.e.:

AN i k = AN i k normalised N i k T

k∈all trees

normalized N i k T

(47.4)

the importance of feature k calculated from all the decision trees in the AdaBoost model the normalized feature importance for k in tree i total number of decision trees used.

47.3.7 Hyperparameter Tuning of AdaBoost with GridSearchCV One of the primary objectives and challenges in the ML process is improving the performance score based on data patterns and observed evidence. To achieve the objective, almost all the ML algorithms have a specific set of parameters that need to estimate from a dataset which will maximize the performance score. These parameters are the knobs that we need to adjust to different values to find the optimal combination of the parameters that give us the best accuracy. Scikit-learn library’s GridSearchCV function facilitates an automatic and reproducible approach for hyperparameter tuning. For a given model, a set of parameter values can be defined which we would like to try. Further, using the GridSearchCV function of scikit-learn, models are built for all possible combinations of a preset list of values of hyperparameters provided by us, and the best combination is chosen on the cross-validation score. For the presented work, the GridSearchCV parameters used are: N_estimators: 100, 200, 500 with learning rate: 0.2, 0.5, and 1.0.

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Fig. 47.7 Comparative results of classifiers

Table 47.1 Accuracy, precision, and recall rates of investigated algorithms Algorithm

Accuracy

Precision

Recall

Decision tree classifier

0.90

0.91

0.88

AdaBoost classifier

0.91

0.92

0.90

AdaBoost classifier with hyperparameter tuning

0.95

0.98

0.91

47.4 Results and Discussions 47.4.1 Analysis of Classifiers Figure 47.7 presents the comparative results of classifiers based on the evaluation measures considered. Table 47.1 gives the accuracy, precision, and recall values of the algorithm based on their performance on test dataset. It can be observed that AdaBoost classifier algorithm tuned using GridSearchCV function has given the best results compared to the results without tuning and decision tree classifier.

47.4.2 Identifying the Critical Factors The feature importance function of sklearn library has been used to identify the top 10 risk factors impacting the diabetic RA considered by each algorithm. As it can be observed from Figs. 47.8 and 47.9, risk factors such as number of diagnosis performed during the encounter, age, number of outpatients, insulin provided, gender, metformin, and discharge deposition Id, are some of the major risk factors leading to diabetic RAs. The results are in sync with some of the research works such as Bhuvan et al. [18] who have also discovered that that discharge disposition id is one of the major risk

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Fig. 47.8 Risk factors pertaining to decision tree classifier

Fig. 47.9 Risk factors pertaining to AdaBoost classifier

factors. Reena et al have identified age and gender as some of the risk factors [19]. Many authors have also used decision tree classifier including feature selection and data pre-processing techniques in their research works [19, 22]. The proposed work brings up some of the prominent rules. For instance, if a diabetic patient with the above-mentioned risk factors is predicted to get re-admitted within 30 days of his discharge by the proposed model, then the physician can provide special services and attention to such patients which instead saves a lot of lives and money. The physician based on the prediction can also relate the rule set statistically and can have the knowledge of what kind of diabetic patients are more likely to get re-admitted within few days of their discharge. As mentioned in Sect. 47.1, lack of attention toward diabetes RA can lead to great healthcare losses. Hence, the proposed work also contributes toward preventing these losses and improvising the quality of healthcare systems. The model does not suggest that the non-diabetic patients should be ignored; instead, it urges a special attention to diabetic patients. Hence, the model is conservative in nature and is safe to be used in healthcare institutions to assist the physicians to make informed decisions considering the risk factors.

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47.5 Conclusion and Future Work Prediction of diabetic RA can widely help in saving huge expenses and lives. The proposed work deals with the real-world dataset; that is, it comprises of 10 years clinical records, upon which two classifiers were investigated. The dataset divided the diabetic patients into two classes of risk group of RAs (yes or no). The objective of the proposed work was to identify the diabetic patients who are more likely to get re-admitted within 30 days of their discharge and to recognize the factors leading toward the RA. As seen, AdaBoost classifier tuned with GridSearchCV has given the most optimal results with respect to all evaluation measures. Some of the risk factors are number of diagnosis performed during the encounter, age, number of outpatients, etc. The algorithms were able to build a rule set based on mining the hidden patterns between the risk factors. The proposed work can efficiently contribute to reduce the healthcare costs and improve the hospital services. The research focusses only on the diabetic patients. Hence, a much-detailed study about hospital RA of other chronic diseases like dengue, heart diseases, etc., can help reduce RA rates, develop the hospital standards, and also bring a reform in the important groups of patients.

References 1. HCUP Nationwide Inpatient Sample (NIS), Agency for Healthcare Research and Quality (AHRQ). https://hcupnet.ahrq.gov/HCUPnet.jsp. Accessed 15 June 2020 (2012) 2. ADA, Economic costs of diabetes in the U.S. in 2012. Diabetes Care (2013) 3. Centers for Disease Control and Prevention, National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States, 2014 (U.S. Department of Health and Human Services, Atlanta, 2014). 4. HCUP Nationwide Inpatient Sample (NIS), Agency for Healthcare Research and Quality (AHRQ). https://hcupnet.ahrq.gov/HCUPnet.jsp. Accessed 11 Mar 2020 (2011) 5. G.E. Umpierrez, S.D. Isaacs et al., Hyperglycemia: an independent marker of in- hospital mortality in patients with undiagnosed diabetes. J. Clin. Endocrinol. Metab. 87(3), 978–982 (2002) 6. J.M. Robbins, D.A. Webb, Diagnosing diabetes and preventing rehospitalizations: the urban diabetes study. Med. Care. 44(3), 292–296 (2006) 7. K.J. Bennett, J.C. Probst, M. Vyavaharkar, S.H. Glover, Lower re-hospitalization rates among rural Medicare beneficiaries with diabetes. J. Rural Health. 28(3), 227–234 (2012) 8. J.Y. Chen, Q. Ma, H. Chen, I. Yermilov, New bundled world: quality of care and RA in diabetes patients. J. Diabetes Sci. Technol. 6(3), 563–571 (2012) 9. D. Rubin, M. McDonnell, D. Nelson, H. Zhao, S.H. Golden, Predicting hospital RA risk with a novel tool: the diabetes early read-mission risk index (DERRI). 1508-P, in American Diabetes Association 74th Scientific Sessions, 06/2014 (San Francisco, CA, 2014). Describes a novel tool to predict RA risk of individual patients with diabetes prior to discharge 10. Centre for Clinical and Translational Research, Virginia Commonwealth University. https://arc hive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008. Retrieved on Aug 2019 11. H.J. Jiang, D. Stryer, B. Friedman, R. Andrews, Multiple hospitalizations for patients with diabetes. Diabetes Care 26(5), 1421–1426 (2003) 12. H. Kim, J.S. Ross, G.D. Melkus, Z. Zhao, K. Boockvar, Scheduled and unscheduled hospital RAs among diabetes patients. Am. J. Manage. Care. 16(10), 760 (2010)

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13. K.M. Dungan, The effect of diabetes on hospital RAs. J. Diabetes Sci. Technol. 6(5), 1045–1052 (2012) 14. E. Eby, C. Hardwick, M. Yu, S. Gelwicks, K. Deschamps, J. Xie et al., Predictors of 30 day hospital RA in patients with type 2 diabetes: a retrospective, case-control, database study. Curr. Med. es Opin. 31(1), 107–114 (2015) 15. B. Strack, J.P. DeShazo, C. Gennings, J.L. Olmo, S. Ventura, K.J. Cios, J.N. Clore, Impact of HbA1c measurement on hospital RA rates: analysis of 70,000 clinical database patient records. Biomed. Res. Int. 2014, 1–11 (2014) 16. D.J. Rubin, K. Donnell-Jackson, R. Jhingan, S.H. Golden, A. Paranjape, Early RA among patients with diabetes: a qualitative assessment of contributing factors. J Diabetes Complicat. 28(6), 869–873 (2014) 17. S. Yu, F. Farooq, A. van Esbroeck, G. Fung, V. Anand, B. Krishnakumar, Predicting RA risk with institution-specific prediction models. Artif. Intell. Med. 65(2), 89–96 (2015) 18. M.S. Bhuvan, A. Kumar, A. Zafar, V. Kishore, Identifying diabetic patients with high risk of RA. arXiv preprint arXiv: 1602.04257 (2016) 19. R. Duggal et al., Predictive risk modelling for early hospital RA of patients with diabetes in India (Springer).https://doi.org/10.1007/s13410-016-0511 20. Hanan et al., Hospital RA of patients with diabetes. Int. J. Adv. Comput. Sci. Appl. 10(4) (2019) 21. N. Chawla et al., SMOTE: Synthetic minority oversampling technique (2002) 22. A. Hammoudeh et al., Predicting hospital RA among diabetics using deep learning, in EICN (2018) 23. D.J. Rubin, Hospital RA of Patients with Diabetes (Springer, 2018) 24. B. Everitt, Classification and Regression Trees (2005). https://doi.org/10.1002/0470013192. bsa753

Chapter 48

Traffic Analysis Using IoT for Improving Secured Communication K. Santhi Sri, P. Sandhya Krishna, V. Lakshman Narayana, and Reshmi Khadherbhi

Abstract Internet of Things can be simply referred to as Internet of entirety which is the network of things enclosed with software, sensors, electronics that allow them to gather and transmit the data. Because of the various and progressively malevolent assaults on PC systems and frameworks, current security apparatuses are frequently insufficient to determine the issues identified with illegitimate clients, unwavering quality, and to give vigorous system security. Late research has demonstrated that in spite of the fact that system security has built up, a significant worry about an expansion in illicit interruptions is as yet happening. Addressing security on every occasion or in every place is a really important and sensitive matter for many users, businesses, governments, and enterprises. In this research work, we are going to propose a secure IoT architecture for routing in a network. It mainly aims to locate the malicious users in IoT routing protocols. The proposed mechanism is compared with the state-of-the-art work and the results show that the proposed work performs well.

48.1 Introduction Internet of Things can be simply referred to as Internet of entirety which is the network of things enclosed with software, sensors, electronics that allow them to gather and transmit the data. Smart homes and cities, connected cars, health care, smart farming, industrial Internet, manufacturing, smart retail are some of the applications of IoT [1]. There are many advantages of IoT: It provides more reliable communication and it is very efficient and saves time and money, increases business opportunities, increases K. Santhi Sri (B) Department of Information Technology, Vignan’s Foundation for Science, Technology & Research, Guntur, AP, India e-mail: [email protected] P. Sandhya Krishna · V. Lakshman Narayana · R. Khadherbhi Department of Information Technology, Vignan’s Nirula Institute of Technology & Science for Women, Peda Palakaluru, Guntur, AP 522009, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_48

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Fig. 48.1 Basic IoT architecture

productivity, and gives better quality of life. Not only advantages but there are some disadvantages in IoT: less privacy and low security, compatibility, and over-reliance on technology [2]. The major issue and challenge in IoT is security. Some of the security challenges in IoT are authentication, access control, confidentiality of data, trust, secure middleware, and privacy [3]. It is very important that the transmission of the data between IoT devices must be very secured [4]. The communication is possible by the routing protocols, and the data should be secured during the routing (Fig. 48.1). Routing is a crucial factor in IoT which helps for communication between the devices and also transmission of data [5]. The execution of a good routing protocol can improve the performance of low power and lossy networks which are in short known as LLNs [6]. To evaluate the performance of a protocol, we can include the factors like energy utilization, control overhead, throughput, packet delivery ratio, and latency [7]. Routing is the main factor of complete IPV6 network for IoT. The routing protocols will make the IoT into reality [8]. In this research work, the idea is examining the security in routing protocols in IoT mainly in the network layer and the detailed description about the attacks on these routing protocols and some of their countermeasures and performance evaluation of these routing protocols when attack happens [9]. To address and route the data packets is the main goal of this layer. At this layer, using IP address the datagram from transport layer is enclosed to data packets, granted to their destinations [10, 11]. In this research work, Sect. 48.2 discusses the literature survey; Sect. 48.3 discusses the secure routing mechanism; Sect. 48.4 illustrates the experimental evaluation; and Sect. 48.5 concludes the research work.

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48.2 Related Work Montenegro et al. [1] proposed “Intrusion Detection System to Detect Sinkhole Attack on RPL Protocol in Internet of Things.” IoT is primarily connected with wireless sensing networks and is subject to security problems like sinkhole attacks. The proposed IDS mechanism identifies such attacks on RPL and prompts the leaf nodes (sensor nodes) with a view to decrease the value of the packet loss. Here, the proposed mechanism calculates the intrusion ratio to identify the malicious nodes in the network. Hui and Thubert [2] proposed “Review on Mechanisms for Detecting Sinkhole Attacks on RPLs.” In this research work, major security challenges were centered around network layer and every method was examined and considered, and their uses and downsides and resource utilization are featured. At long last, a brief correlation was given, which demonstrates the historical organization of detecting methods for attacks like sinkhole, subsequently watching latest efficient technique. Pongle and Chavan [5] proposed “Implementation of a Wormhole Attack Against a RPL Network: Challenges and Effects” and framed an attack in opposition to IEEE 802.15.4 WSAN by giving a wormhole execution. The proposed attack was applied to a genuine RPL topology. The analyses said the proposed attack can be compelling to undergo different attacks like a DoS. In the long run, we investigated the possibility of conceivable countermeasures. Wallgren et al. [6] proposed “Performance Evaluation of RPL Protocol Under Mobile Sybil Attacks.” Here, a trust-based IDS (T-IDS) solution was proposed in order to reduce sybil attacks under mobility in RPL. When RPL undergoes SybM, it is observed that the control overhead and the energy utilization were increased and the packet delivery ratio was decreased. The proposed T-IDS handles the issues that develop when RPL undergoes sybil attacks under mobility.

48.3 Proposed Work Our structure expects that the client determines which router(s) fills in as the monitor(s); however, it is not clear how to pick the router(s) for this reason. In this part, we propose an approach to pick the area of the monitor(s) astutely so as to get a high precision rate. The terms DIO and DODAG refer to DODAG Information Object and Destination Oriented Directed Acyclic Graph, respectively. Algorithm for Working of Router in RPL Step 1: Receive a DIO (DODAG Information Object) Step 2: Receive DIO the 1st time If yes then follow the steps Add the sender to the list of parent Calculate the rank on the basis of objective function

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Fig. 48.2 Proposed model framework

Forward DIO’s to others in multicast If no then follow the steps Satisfy criteria If no Then discard the packets If yes Then process the DIO If rank not less than own_rank Maintain the location in the DODAG (Destination Oriented Directed Acyclic Graph) Go to 3rd condition in step 2 If rank less than own_rank Then improves the location and get lesser rank The parents with the less rank will be denied Go to 3rd condition in step 2 Step 3: End. Another alternative is to utilize the proportion of between’s centrality, which is a proportion of centrality in a chart dependent on most brief ways. The between’s centrality of a hub v is given by the articulation g(v) = sƒ = vƒ = t σ st(v) σ st, where σ st is the complete number of most brief ways from hub s to hub t and σ st(v) is the quantity of those ways that go through v. The proposed model framework is shown in Fig. 48.2. Our flexible framework allows us to design another interesting strategy for choosing a router for the monitor. We train the detector on each one of the possible routers and estimate its performance [12]. We then select the router that achieves the highest accuracy rate to be the monitor. Here, our proposed algorithm works mainly with two phases. In the first phase, we are going to identify the highest flow routers. Then, we can distribute the traffic based on other routes and based on selecting node for traffic diversion. Identifying the attacker nodes (max flow nodes, traffic). { If (node) Max traffic > threshold; Place in a suspected list; Evaluate the parents of those nodes; If(node contains fake parents); Take the id of the node and place them in a blocked list;

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}

48.4 Experimental Results The proposed method is implemented in ANACONDA SPYDER that performs traffic analysis for secure data communication [13]. The proposed method is compared with the traditional methods, and the results depict that the proposed method exhibits better performance than traditional methods. 1. Throughput The rate at which packets were successfully delivered through a network channel is known as network throughput [14]. So, for the calculation of the value for the small networks, we can sum the packets received by all nodes. There are several ways to measure throughput (instantaneous or average) in a wired or wireless network using network simulators [15]. Formula Throughput = sum (total count of true packets) * (average size of the packet))/total time sent to deliver that amount of data. 2. Packet Delivery Ratio PDR is simply defined as the ratio between the packets that were generated by the source and the packets that were received by the destination. Formula Algebraically, it can be defined as: PDR = N1 ÷ N2 where N 1 is the total sum of data packets which were received by the destination and N 2 is the total sum of data packets produced by the source. 3. End-To-End Delay It is the difference between the time at which the sender generated the packet and the receiver received the packet. The end-to-end delay is also known as one-way delay which was being referred to time taken for the packet to transmit across the network from sender to receiver. Formula End-to-End Delay = Sum of (Delay at sender + Delay at receiver + Delay at intermediate nodes). The proposed method monitors every node and checks for attackers based on their behavior, whereas the existing method does not monitor every node for secure data

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communication. The throughput of the proposed method is high when compared to the traditional methods as the malicious users are effectively identified. Here, Fig. 48.2 represents the throughput comparison between regular RPL protocol, existing secure RPL, and our proposed mechanism. Here, we simulate regular RPL protocol with different number of nodes varying from 100 to 500 without any attacker nodes. Existing and proposed mechanisms contain 5, 10, 20, 22, and 25 attacker nodes in each case. And we observe the performance, which is shown in Fig. 48.2. Here, regular RPL protocol has highest throughput compared to existing and proposed, but proposed is very near to standard RPL and more dominating than existing work. Here, Fig. 48.3 represents the end-to-end delay comparison between regular RPL protocol, existing secure RPL [16], and our proposed mechanism. Here, we simulate regular RPL protocol with different number of nodes varying from 100 to 500 without any attacker nodes. And existing and proposed mechanisms contain 5, 10, 20, 22, and 25 attacker nodes in each case. And we observe the performance, which is shown in Fig. 48.2. Here, regular RPL protocol has very slight delay compared to existing and proposed, but proposed is closer delay to standard RPL and more dominating than existing work. Here, Fig. 48.4 represents the packet delivery ratio comparison between regular RPL protocol, existing secure RPL, and our proposed mechanism. Here, we simulate regular RPL protocol with different number of nodes varying from 100 to 500 without any attacker nodes. And existing and proposed mechanisms contain 5, 10, 20, 22, and 25 attacker nodes in each case. And we observe the performance, which is shown in Fig. 48.2. Here, regular RPL protocol has highest delivery compared to existing

Fig. 48.3 Throughput

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Fig. 48.4 E2E delay

and proposed, but proposed is very near to standard RPL and more dominating than existing work (Fig. 48.5).

Fig. 48.5 Packet delivery ratio

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48.5 Conclusion Secure communication is a prime thing in any kind of network. IoT is a very huge network and in order to make secure communication is a very difficult thing. Many routing protocols are proposed in IoT for routing. But most of them are suffering from secure communication. This research work mainly focuses on secure communication between different IoT nodes, for that we use a monitor-based mechanism in a network, identify the malicious nodes, and made the communication secure. The proposed mechanism performs well when compared to literature mechanisms. In the future, the security of the devices can be improved by allotting an authority to monitor during data transmission.

References 1. G. Montenegro, N. Kushalnagar, J. Hui, D. Culler, Transmission of IPv6 packets over IEEE 802.15.4 networks, in IETF RFC 4944, Sept. 2007. https://tools.ietf.org/html/rfc4944 2. J. Hui, P. Thubert, Compression format for IPv6 datagrams over IEEE 802.15.4-based networks, in IETF RFC 6262, Sept 2011. https://tools.ietf.org/html/rfc6282. 3. H. Tschofenig, T. Fossati, Transport layer security (TLS)/datagram transport layer security (DTLS) profiles for the Internet of Things, in IETF RFC 7925, July 2016. https://tools.ietf.org/ html/rfc7925 4. E. Kim, D. Kaspar, J. Vasseur, Design and application spaces for IPv6 over low-power wireless personal area networks (6LoWPANs), in IETF RFC 6568, Apr. 2012. https://www.ietf.org/rfc/ rfc6568.txt 5. P. Pongle, G. Chavan, A survey: attacks RPL and 6LowPAN in IoT, in International Conference on Pervasive Computing (ICPC 2015), Pune, India (2015), pp. 1–6 6. L. Wallgren, S. Raza, T. Voigt, Routing attacks and countermeasures in the RPL based internet of things. Int. J. Distrib. Sens. Netw. 9(8), 1–11 (2013) 7. TCG, Guidance for securing IoT using TCG technology, Sept. 2015. https://www.trustedco mputinggroup.org/guidancesecuring-iot-using-tcg-technology-reference-document 8. A. Roger, T. Tsao, V. Daza, A. Lozano, M. Richardson, M. Dohler,A security threat analysis for the routing protocol for low-power and lossy networks (RPLs), in IETF RFC 7416, Jan. 2015. https://tools.ietf.org/html/rfc7416 9. H. Baba, Y. Ishida, T. Amatsu, K. Maeda, Problems in and among industries for the prompt realization of IoT and safety considerations, IETF Draft, Oct. 2016. https://datatracker.ietf.org/ doc/draft-baba-iot-problems 10. M.A. Iqbal, M. Bayoumi, Secure end-to-end key establishment protocol for resourceconstrained healthcare sensors in the context of IoT, in International Conference on High Performance Computing & Simulation (HPCS) (2016), pp. 523–530 11. J.L. Hernandez-Ramos, J.B. Bernabe, A. Skarmeta, ARMY: architecture for a secure and privacy-aware lifecycle of smart objects in the internet of my things. IEEE Commun. Mag. 54(9), 28–35 (2016) 12. D. Hardt, The OAuth 2.0 authorization framework, in IETF RFC 6749, Oct. 2012. https://www. ietf.org/rfc/rfc6749.txt 13. S. Gerdes, L. Seitz, S. Gerdes, G. Selander, An architecture for authorization in constrained environments, in IETF Draft, Aug. 2016. https://www.ietf.org/id/draft-ietf-ace-actors-04.txt 14. O. Garcia-Morchon, S. Kumar, M. Sethi, Security considerations in the IP-based Internet of Things, in IETF Draft, Feb. 2017, Available: https://www.ietf.org/id/draft-irtf-t2trg-iot-sec cons-01.txt.

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15. K. Moore, R. Barnes, H. Tschofenig, Best current practices for securing Internet of Things (IoT) devices, in IETF Draft, Oct. 2016. https://tools.ietf.org/html/draft-moore-iot-securitybcp-00. 16. V.H. La, R. Fuentes, A.R. Cavalli,A novel monitoring solution for 6LoWPAN-based wireless sensor networks,in 22nd Asia-Pacific Conference on Communications (APCC) (2016), pp. 230– 237

Chapter 49

Implementation of a Network of Wireless Weather Stations Using a Protocol Stack Segundo G. Vacacela and Luigi O. Freire

Abstract The project presents an alternative for acquisition and management of remote weather station metrics for applications in the agricultural and energy areas, among others, through stack protocol based on ATMEGA328P controllers associated with Wireless ISP 802.11 b/g for wireless connectivity to the server. The parameters acquired from the stations are stored in a database for offline analysis and on the online web.

49.1 Introduction Meteorological data acquisition equipment must be installed outdoors and in remote locations; that is why, wireless networks (WSN) are used for data transmission [1, 2]. The WSN provide great applicability at low cost with advantages such as flexibility and scalability [3, 4]. Once the communication is established, the microcontrollers must build the data frame for transmission of information by means of internal conditioning of a UART (Universal Asynchronous Receiver Transmitter) [4, 5]. The efficiency in the network depends largely on the topology [6, 7], the nodes must be easy to install; that is, once installed, the only information that must be configured is the address of device being a strategy for optimal performance of WSN [8, 9]. A complete system should provide ease of mobility of equipment through the nodes, that is, allow an exchange of communication node in case of a malfunction [10]. In case the device is close to an Internet access point, IoT cloud server could be used, which can be integrated into the microcontroller and thus manages the station’s metrics [11]. S. G. Vacacela · L. O. Freire (B) Universidad Técnica de Cotopaxi, Latacunga, Ecuador e-mail: [email protected] S. G. Vacacela e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_49

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To guarantee flexibility, a solar kit for electricity generation can be installed in each of the network nodes so that if the public energy supply network fails, it can be put into operation [12].

49.2 Data Logger System Architecture The system has three interconnected ATMEGA328 microcontrollers distributed in: – Acquisition-Storage – Viewing – Communication. The communication between the microcontrollers is through the UART protocol which consists of sending information and receiving data, for which it sends two or more information through the same communication port can be used the separators of chain sections (Fig. 49.1). Transmissions consist of STAR-STOP for ASCII codes through serial, as established in teletype operation. The sending of data in a chain allows one or more data to be sent at the same time, each data being separated as a character (“,”), which will be used by the receiving microcontroller to identify and process it. Each attribute is assigned to a storage box so a variable is created to store the data and also a method to count the stretches entering by serial port.

Fig. 49.1 Reading, writing, and communication structure of weather station

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In this case, we store in a decimal-type variable because some data enter in decimal type or we can convert to integers to store integer. float datos [] = {0,0,0,0,0,0,0,0,0,0,0}

By specifying the address of record boxes, we can store data in the same variable, and then to call the data when required would be as follows: Year = data [0] Month = data [1] Day = data [2] Temperature = data [3] Humidity = data [4] Pressure Atm = data [5] Wind speed = data [6] Wind direction = data [7] UV = data [8] Rain gauge = data [9] Electrical Voltage = data [10] Electric current = data [11]

49.3 Network Design The Open Systems Interconnection (OSI) model is a 7-layer data model designed to organize the various software and hardware protocols involved in the communication network, at the top part of stack. As a simplified version of OSI model, the TCP/IP model is useful in the layout of today’s end-to-end networks. The TCP/IP stack groups the protocols into four layers: application, transport, Internet, and link. As with the OSI model, the application layer initiates loading and the transport layer assigns ports. The Internet layer handles the routing of Internet (IP) packets while the link layer handles the local area communication (MAC) and physical transmission media (Fig. 49.2). Data loggers convert physical parameters into electrical signals that are converted into binary values by analog to digital converters in the microcontroller, converting the ADC values into data frames by the application layer. The gateway is used to interconnect two different physical layers to collect the information in the database and support them through MySQL (Fig. 49.3). The structure of each weather station incorporates an anemometer, rain gauge, temperature, humidity, barometric pressure, and solar radiation with optional soil moisture sensor for comprehensive monitoring of agriculture and the environment in a single package for simple configuration. The system’s design provides that stations that do not have a direct line of sight to the transmission connection node can use the other stations as a link point. The intervals of transmission and storage of information are 10 min, for security has a feedback so that in case of loss of connection to the network of any station this

512 Fig. 49.2 OSI model

Fig. 49.3 System architecture of weather station

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Fig. 49.4 Structure of the prototype algorithm

can retry to transmit when you have restored your connection taking data from its backup of a micro SD that is implemented in each station. The structure of the prototype algorithm is shown in Fig. 49.4. This algorithm controls the properties of the weather station over the TCP/IP network.

49.4 Results Once the data logger and the communication network have been designed, the next step is the evaluation of software which aims to measure the level of reliability and performance of system.

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The weather data of stations are displayed in the web application on a wireless sensor network (WSN) base. All weather parameters collected from the local WSN base stations will be replicated in a centralized database in the cloud, allowing users to access the data in real time from a distributed weather station (Figs. 49.5 and 49.6). Figure 49.4 shows the dynamic PHP web page that automatically updates after 10 s and shows the last 100 results of database; in case you want more data, you must choose the dates to access the database. The analysis of root mean square error (RMSE) and the mean bias error (MBE) of results obtained is defined as:   (X estimated − X measured )2 (49.1) RMSE = N  (X estimated − X measured ) MBE = (49.2) N

Fig. 49.5 MySQL database

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Fig. 49.6 Webpage

Being: X estimated weather station Kestrel 5500 X measured prototype [13]. We can analyze the statistical results of 1000 measurements of different measured variables, being the deviation practically zero as shown in the scatter plot of Fig. 49.7 demonstrating that most of experimental points are located over the line of fit (Fig. 49.8).

Fig. 49.7 Webpage graph view

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Fig. 49.8 Correlation between Kestrel 5500 and prototype

49.5 Conclusion This project has the advantage of being scalable since if the device is not in the wireless coverage area or a wireless network cannot be expanded due to various factors, a GSM/GPRS shield can be incorporated to transmit the data directly to the database in the cloud. The design of a network of integrated wireless weather stations based on the ATMEGA328 microcontroller and wireless communication for communication and transmission of data on temperature, relative humidity, wind speed, and direction and in special cases soil moisture, pH, and resistivity is considered important for the development of future projects in the area of renewable energies as well as in the agricultural area as it has free access to the database stored and managed by MySQL. The integration of ATMEGA328 microcontrollers with Raspberry is essential for management through a wireless network with TCP/IP communication protocol showing a solution that reduces costs because it is a simple, flexible, and scalable design at the same time can be autonomous in its operation.

References 1. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002) 2. A. Ruano, S. Silva, H. Duarte, P.M. Ferreira, Wireless sensors and IoT platform for intelligent HVAC control. Appl. Sci. 8, 370 (2018) 3. D. Chen, Z. Liu, L. Wang, M. Dou, J. Chen, H. Li, Natural disaster monitoring with wireless sensor networks: a case study of data-intensive applications upon low-cost scalable systems. Mob. Netw. Appl. 18, 651–663 (2013)

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4. A. Romero, A. Marín, J. Jiménez, Wireless sensor network for early warning monitoring in underground mines: a solution to the problem of explosive atmospheres in Colombian coal mining. Ingeniería y Desarrollo, pp. 227–250 (2013) 5. I. Harish, S. Ilango, A protocol stack design and implementation of wireless sensor network for emerging application, in International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICECCN 2013), pp. 523–527 6. S.S. Anjum, R.M. Noor, M.H. Anisi, Review on MANET based communication for search and rescue operations. Wirel. Pers. Commun. 94, 31–52 (2017) 7. J. Vales-Alonso, F.J. Parrado-García, P. López-Matencio, J.J. Alcaraz, F.J. González-Castaño, On the optimal random deployment of wireless sensor networks in non-homogeneous scenarios. Ad Hoc Netw. 11, 846–860 (2013) 8. X. Yu, W. Huang, J. Lan, X. Qian, A novel virtual force approach for node deployment in wireless sensor network, in Proceedings of 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems, Hangzhou, China, 16–18 May 2012; pp. 359–363 9. F.M. Al-Turjman, H.S. Hassanein, M.A. Ibnkahla, Efficient deployment of wireless sensor networks targeting environment monitoring applications. Comput. Commun. 36, 135–148 (2013) 10. Y. Mei, C. Xian, S. Das, Y.C. Hu, Y.H. Lu, Sensor replacement using mobile robots. Comput. Commun. 30, 2615–2626 (2007) 11. M. Pravin Kumar, R. Velmurugan, P. Balakrishnan, Detection and control of water leakage in pipelines and taps using Arduino nano microcontroller, in Proceedings of ICIMES 2019 Intelligent Manufacturing and Energy Sustainability, Maisammaguda, Hyderabad, India, from 21 to 22 June 2019 12. J. Gomez, C. Socarrás, J. Fernández, A. García, Integration of a mobile node into a hybrid wireless sensor network for urban environments, in Proceedings of ROBOT 2017: Third Iberian Robotics Conference, Sevilla, Spain, 22–24 Nov 2017 13. S. Rosiek, F.J. Batlles, A microcontroller-based data-acquisition system for meteorological. Energy Convers. Manage. (2008)

Chapter 50

Various Developments in the Design of Hovercrafts: A Review Jhansi Reddy Dodda, N. V. Srinivasulu, and Balem Rahul Reddy

Abstract Hovercraft can travel over land, water, mud and ice. It is also widely referred to as air cushion vehicle (ACV), ground effect machine or simply craft. This vehicle is unconventional and requires no surface contact for traction and it can move freely over many surfaces continuously on a self-generated cushion of air. Although hovercrafts being amphibious have many advantages, they are seldom used for economical transportation. This paper aims to bring all the research and advancements carried out in the field of hovercrafts since 1959. Various models of hovercrafts have been compared based on load capacity, dimensions, gross weight, steering system and power systems. The review has been made through the discussions on drawbacks in initial models and the recent developments in the hovercraft’s performance and their adaptation into modern vehicles. This paper also covers the reasons why hovercrafts are seldom used today and the possibility of hovercrafts being the beginning step towards the innovation of flying cars.

50.1 Introduction Hovercrafts, as described by Ashley [1, 2], were first developed in the year 1959 by St. Christopher Cockerell, SRN1 used blowers powered by Alvis Leonides radial piston engine to provide an outsized volume of air below the hull (air cushion) that is slightly higher than atmospheric (gas) pressure. The lower pressure on the top surface and the high pressure below the hull produce the lift, which causes the hull to float above the running surface. Later to improve stability [3, 4], the air is J. R. Dodda (B) · N. V. Srinivasulu · B. R. Reddy Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana 500075, India e-mail: [email protected] N. V. Srinivasulu e-mail: [email protected] B. R. Reddy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_50

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blown through slots or holes around the outside of a disc- or oval-shaped platform, giving the ground effect machine a prominent rounded-rectangle structure. Typically, this cushion is contained within a “skirt”, which permits the vehicle to travel over minor obstructions without damage to the structure. Early hovercrafts had various drawbacks which were later redesigned and made better. The better and smaller versions called hoverwings were designed in the beginning of 2000s which use aerodynamic lift of wings to reduce power consumption. These features of hovercrafts have been adapted into cars like Kitty Hawk Flyer and Volkswagen Aqua, and these designs might become cars of the future which can hover on land with a small cushion and also the designs of flying cars evolving from these designs are under study.

50.2 Saunders-Roe Nautical Models and Early Hovercrafts Early prototypes and models of hovercrafts from 1959 to 1983 were more inclined towards commercialization and finding economical high-speed transport for people and cargo which promoted designs which were bulky with higher load capacity. Although these powerful vehicles have no friction, they require huge power to sustain an air cushion underneath their hull and propulsion of the vehicle. Many techniques and variants for air cushion chambers have been designed over these periods which displaced air evenly and produced a stable cushion for the craft and eventually reduced the power required. The ratio of the power for hovering and propulsion remains between 5:1 and 10:1. Diesel engines have good part load SFC which enables their implementation in vessels cruising at low speed while gas turbines having high power SFC are employed in high-speed vessels. For each design and usage, the type of power plant would vary as per the requirement considering key criteria as minimum weight and volume, low maintenance and good part load SFC for cruisers and good part power SFC for high-speed hovercrafts. As described by Rawson and Tupper [5], the weight of the vessel that can be carried by a centrifugal fan of a hovercraft is W =

m˙ 2 8πCd2 ρh 2

(50.1)

This equation shows that the capacity of the vessel depends on the desired hover height (h), mass flow of escaping air (m) ˙ and coefficient of discharge (C d ). Therefore, lower the hoverheight, lower is the power required but the craft should maintain sufficient clearance to prevent damages to the hull. To overcome this skirt has been widely used to provide vertical buoyancy force although this adds an additional resistance of dipping skirt. They have also described how peripheral or annular jet is widely used because the airflow can be easily controlled under the craft. As shown

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Fig. 50.1 Limiting sea states for various hovercrafts (Ref. [5])

in Fig. 50.1 the operation limits should be taken into consideration while designing the hovercraft. Carlton [6] has given the forces which comprise the lift and drag on a hovercraft. While operating on land the drag experienced from wave, wet and over-wave becomes zero. Analysing the drag is difficult for hovercrafts as it is difficult to estimate spray impact, drag in the parts submerged and increased weight of water that enters the craft. Lift = L aero + (L cushion + L jet + L pressure )cosα Drag = Dprofile + Dmomentum + Dinduced + Dwave + Dwet + Dover - wave

(50.2) (50.3)

The drag coefficient (C d ) for SR N2 was estimated as 0.25, 0.40 for SR N4 and 0.38 for SR N6 design. One of the major aims is to decrease the drag by improving design. Table 50.1 [1, 2] and Fig. 50.2 show how the designs where initially aimed for carrying heavier loads economically and hence making them bulky with the improvement in engine capacity and steering system specifically designed for them but the bulky crafts turned out uneconomical and hence many of them had to be terminated.

4 × 815 Hp Bristol 135.2 Siddeley Nimbus turboshaft engines

4 × 900 Hp Bristol Siddeley marine Gnome engines and 2 × 150 Hp Rover gas turbines with water screws

19.80 × 9.14 × 7.43

23.47 × 9.14 × 9.29

39.68 × 23.77 × 4 × 3400 Hp Rolls Royce 11.48 Proteus Gas Turbines

SR N2

SR N3

SR N4 Mark 1

11.81 × 7.01 × 5.12

900 Hp Rolls Royce Gnome Turboshaft

74.08

110

SR N4 Mark 56.38 × 23.77 × 4 × 3800 Hp Rolls Royce 3 11.48 proteus gas turbines

SR N5

130

SR N4 mark 39.68 × 23.77 × 4 × 3400 Hp Rolls Royce 2 11.48 proteus gas turbines

120.38

135

435 Hp centrally located 64.82, later Alvis Leonide IC Aero 92.6 piston engine driving main propeller

9.58 × 7.62 × 3.08

SR N1

Max speed (kmph)

Engine (power plant) (Horse Power)

Length × beam × height (m)

Table 50.1 Comparison between early models of hovercraft Hoverheight (m)

a single rear-facing 9 ft (2.74 m) diameter 4-bladed Dowty Rotol variable-pitch propeller

fitted with four 21 ft (6.4 m) diameter steerable Dowty Rotol propellers

a set of 19 ft (5.8 m) diameter steerable Dowty Rotol propellers, arranged in two pairs on pylons

a set of 19 ft (5.8 m) diameter steerable Dowty Rotol propellers, arranged in two pairs on pylons

Variable-pitch propellers on rotating pylons and one rear rudder

Variable-pitch propellers on rotating pylons and one rear rudder

0.58

With skirt 7.5

With skirt 2.44

With skirt 2.44

0.99

0.77

Two air channels either side of the 0.23, with skirt craft with rudders at their ends. Thrust 1.07 air from a bleed from lift air

Steering system

(continued)

7 T, 18 people

320 T, 60 cars and 418 people

200 T, 36 cars and 278 people

165 T, 30 cars and 250 people

37 T, intended as for 150 people

27 T, 38 or 53 people

4 T, no-load

Gross weight tonnes and load

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4250 Hp Bristol 107 Proteus gas turbine engine

23.9 × 1.8 × 10.36

BH7

Zubr-class LCAC

92.6

139

92.60

57 × 25.6 × 21.4 2 × 10,000 Hp 116.7 supercharging M35-2 gas turbine and 3 × 10,000 Hp propelling M35-1 gas turbine

4 × 600 Hp Deutz AG diesel engines

5 × 3399 Hp Avco-Lycoming TF-40 turboshaft (continuous)

50 × 23 × 17

N500

BHC AP188 24.5 × 11.2 × 6.6

1050 Hp Rolls Royce Gnome Turboshaft

17.78 × 7.97 × 6.32

SR N6 Mk 1S

111.12

900 Hp Rolls Royce Gnome Turboshaft

14.76 × 7.01 × 4.57

SR N6

Max speed (kmph)

Engine (power plant) (Horse Power)

Length × beam × height (m)

Table 50.1 (continued)

2.3

0.457

1 to 1.5

With skirt 1.75

With skirt 1.22

Hoverheight (m)

3 four-bladed variable-pitch propellers 1.6

Two three-bladed variable-pitch propellers

21-footfour-bladed variable-pitch propeller by Hawker Siddeley dynamics

4-bladed Hawker Siddeley and Ratier-Figeac variable pitch with reverse, 6.40 m (21 ft 0 in) diameter

1 four-bladed Dowty Rotol variable-pitch propeller

single rear-facing 9 ft (2.74 m) diameter 4-bladed Dowty Rotol variable-pitch propeller along with a 7 ft (2.13 m) diameter centrifugal lift fan

Steering system

555 T, 3 main battle tanks

47.6 T, 101 people

60 T, 70 people and 8 cars

260 T, 400 people and 55 cars

10.9 T, 55 troops

15 T, 38 people and extended to 58

Gross weight tonnes and load

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Fig. 50.2 Gross weight and total engine power of models

50.2.1 Why Commercial Hovercrafts Failed? Hardy [7] has explained a few reasons behind the failure of commercial hovercrafts: The very first reason being they are very expensive and required hundreds of gallons of fuel for very low operation time. The people had to remain seated to ensure their safety during the travel and noise was so high that it caused inconvenience to the people. The noise levels were measured in the early models at a distance of 500 feet, as described by Lovesey [8] SR N2 and SR N3 produced 94 dBA, SR N5 produced 95 dBA and SR N4 produced 85 dBA which are quite high and can cause harm to humans. High maintenance of the hovercrafts often exceeding the cost involved in other vehicles became a major concern in operating commercial hovercrafts. Bulky and at high speed difficult to control and not particularly swift. Sharp waves can easily topple the hovercraft. Considering these factors in the past decade commercial hovercraft designs have adopted smaller scale design which have 1–2 or 5–10 passenger capacity and have also made improvements to reduce noise to an acceptable and comfortable levels with the use of better blowers and fans. The principle of hovering is also being adapted into the innovation of future flying cars.

50.2.2 Recent Designs of a Hovercraft Okafor [9] has explained the designing of a hovercraft prototype with dimensions 1.22 × 2.44 m and comprising a gross weight of 122.32 kg which would accommodate one person weighing around 75 kg. His design provided an overall efficiency of 69% with an air cushion of 0.5 inches with peripheral jet system. Preventing leakages of the air from the hull and skirt improves efficiency. Equipping efficient

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centrifugal blower systems and lighter materials with better mechanical properties would decrease power required, in turn providing economic transport.  L jh j Qj = 1 + cos θ

  2P j  1 − 1 − pcu / p j p

Pa j = P j × Q j

(50.4) (50.5)

The estimation for the required power for a designed hovercraft is given by (50.3) where Pj is the total pressure of the jet and Qj is the airflow rate by volume which is calculated using (50.2). These types of designs have been highly used for offshore surveying, golf cart to prevent damage to grass, underwater mine field diffusing, etc. Dave et al. [10] have developed a working model of remote-controlled hovercraft which might drive path to an autonomous hovercraft which could be used on land for transport. This model had a range of 500 m which can be increased using a better controller with a hovering height of 0.4 in. and carrying capacity of 3 kg. These prototypes have future scope in land mine detection with a camera assembled and deployed in areas where humans cannot. Ganesan and Esakki [11] have also designed and performed analysis on a similar unmanned hovercraft which employed plenum chamber and bag skirt as stability was the criteria of the design. The modelled hovercraft was analysed using ANSYS fluent for proper flow circulation and absence of blockages. It can be concluded from the resistance curve that was plotted of the hovercraft after operation on both land and water that, it operates efficiently in water for lower velocities and on land for higher velocities (Fig. 50.3). In the beginning of past decade, a flying hovercraft was designed which was later named as hoverwing and embedded features of both aircraft and hovercraft. Rajamani [12] had designed and performed analysis on this design. His design made use of clark Y aerofoil with thickness 11.7%, the flat bottom makes it easier for fabrication of wings and gives moderate overall performance with respect to its lift–drag ratio. The lift force required for the hovercraft was generated by the wing. Major portion Fig. 50.3 Resistance curve of hovercraft C u is the resistance

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of the engine power was utilized for propulsion which enabled the vehicle to attain higher speeds. The list and drag force were obtained with wing were 205.12 kN and 29.46 kN while, without wing the forces were 105.45 kN and 40.18 kN respectively. These results displayed the advantages of employing wing which paved way to the commercial production of 19XRW Hoverwing and UH-18SPW Hoverwing in the late 2000s, these models need only a boat licence to drive them and can attain a speed 112.7 kmph. Lee et al. [13] have developed a 50-passenger hoverwing (WIG Craft) which uses aerodynamic lift of the wings to hover. To prevent the hydrodynamic hump– drag, this WIG has utilized hoverwing technology. The primary material used in the fabrication of hull was aluminium alloy. This design was intended to have inherent stability without a control system which gives better stability with respect to height, longitudinal and transverse. This model used marine diesel oil (MDO) and two T53 (turbopop gas turbine) engines of 1400 Hp and the gross weight was around 17 tonnes. This craft was designed to operate in the range within 2 h with a cruising speed of 176 kmph. This design was hoped to open a new era in marine transport and in a few years will begin operating commercially across the world owing to the ease of transport with comfort and lower noise (Fig. 50.4). The maximum elevation of Flyer [14] is 10 feet and the top speed which limited by the flight control system is 32.2 kmh. The original prototype had the protective netting. The Flyer weighs 250 lb and sports 10 battery-powered propellers and controlled by two joysticks. The prototype testing of the Flyer was successful and is estimated to be in the market in the next year. The Aqua concept is a futuristic hovercraft (ACV—air cushion vehicle) project and presented in the CDN Car Design Awards which is powered by hydrogen and propelled by impellers. Fig. 50.4 Operation zone of WIG [13], L/D is the lift–drag ratio while, H/C is the height–MAC ratio

50 Various Developments in the Design of Hovercrafts: A Review

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50.3 Summary and Discussions During the years 1959 to 2000, hovercrafts were initially designed to carry more load and in turn, make it economical. Over this period, many advancements in the propellers and capacity of the engines, engine types and modes took place. Better designs of the centrifugal fans used for the lift were implemented and efforts were made to make hovercrafts less noisy. Despite all these advancements, the commercial hovercrafts used huge amounts of fuel and its maintenance costs were a major drawback which failed the commercial use of hovercrafts despite their amphibious nature. Over the past two decades, hovercrafts have shrunk in size which has greatly brought down the costs of operation. Some innovative designs of hovercrafts have led to their adaptation in flying cars.

50.4 Conclusions Hovercrafts are generally simple mechanisms and constructions in theory, but theory to manifestation is not as easy as it may seem. A plethora of problems exist and must be overcome in the design of an economical and sustainable well-functioning hovercraft. Hovercrafts are wonderful machines that cut down friction and can attain greater speeds. Varieties of problems and factors must be considered in designing and constructing a hovercraft. Although the concept of the Star Wars hovercraft may seem nifty, in reality, they are not possible as of today. The greater the hover height is, the more unstable and difficult to control it becomes. If it gets high enough, it will fall over (off-cushion) however, it can reach high speeds and is comparatively more fuel-efficient which will make the future innovations turn towards this technology. Leading companies like BMW, Tesla, Volkswagen, Renault and Ford are trying to develop this technology and implement them into today’s modern world of automobiles to create a faster, safer, comfortable, efficient transport of people and at the same time cargo. The widespread military usage of hovercrafts in today’s world for transport of military cargo and rescue operations gives a hint that it might be a way to an advanced medium of transport soon. The future is more promising than ever.

References 1. A. Hollebone, The Hovercraft: A History (The History Press, 2012) 2. A. Hollebone, The Hovercraft Story (The History Press, 2014) 3. V. Abhiram, N. Suman Krishna, T. Murali Mohan Raju. M. Anjiah, A study on construction and working principle of a hovercraft. IJMERR 3(4) (2014).https://doi.org/10.18178/ijmerr 4. G.H. Elsley, J. Devereux, Hovercraft design and construction. Aeronaut. J. 72 (1968).https:// doi.org/10.1017/s0001924000085742 5. K.J. Rawson, E.C. Tupper, Particular ship types Basic Ship Theory Edition Five (2001). https:// doi.org/10.1016/B978-075065398-5/50019-4

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6. J.S. Carlton, Resistance and Propulsion Marine Propellers and Propulsion, 4th edn. https:// doi.org/10.1016/B978-0-08-100366-4.00012-2 7. D.J. Hardy, Lessons from five years of hovercraft operations, in AIAA/CASI/RAeS 9th AngloAmerican Conference,21 October 1963 8. E.J. Lovesey, Hovercraft noise and vibration. J. Sound Vibr. (1972) 9. Okafor, Development of a hovercraft prototype. Int. J. Eng. Technol. 3(3) (2013). ISSN: 20493444 10. D. Dave, V. Patel, D. Parikh, S. Prajapati, S. Patel, Working model of remote-controlled hovercraft. Int. J. Eng. Adv. Technol. 3(6) (2014). ISSN: 2249-8958 11. S. Ganesan, B. Esakki, Design and development of unmanned hovercraft. Int. J. Math. Eng. Manage. Sci. 4(5) (2019). https://doi.org/10.33889/IJMEMS.2019.4.5-093 12. V.K. Rajamani, Design and analysis of winged hovercraft. J. Appl. Mech. Eng. (2015). https:// doi.org/10.4172/2168-9873.1000179 13. H.J. Lee, B.J. Kang, J.H. Park, C.M. Lee, K.J. Kang, C.G. Kang, Development of Hoverwing Type WIG Craft WSH-500 (IEEE, 2012). https://doi.org/10.1109/OCEANS-Yeosu.2012.626 3589 14. Kitty Hawk Flyer. https://kittyhawk.aero/

Chapter 51

Efficient Utilization of Home Energy During Pandemic—A Case Study A. P. Nikitha , Mir Mohammed Junaid Basha , M. N. Vijayakumar, and M. S. Archana

Abstract The COVID-19 pandemic, as we all know, has posed various challenges in all fields. With families spending most of their time at home during this lockdown, energy usage has increased to 50% more than usual. In the energy management sector, there is an urgent need not only to maintain energy installations but also to ensure optimized energy usage, and that reduced electricity costs. As demand increases, appropriate steps should be taken to bridge the gap between supply and demand. However, it is necessary to ensure the proper conservation of energy is the need over time. The paper focuses on the energy auditing process carried out in a house in which energy consumption calculations are done for most essential equipment. The paper analysis various flaws using DIY-EA audits and proposes home energy management systems (HEMS) that result in 79.1% energy savings per sq. ft per day.

51.1 Introduction Residential energy rates have increased consistently over the last ten years. According to “the energy information administration,” residential energy prices have increased by about 15% nationally over the past 10 years. In addition to the above problem, the crisis triggered by COVID-19 has exacerbated our situation, with our family members spending most of their time at home and possibly continuing to do the A. P. Nikitha · M. M. J. Basha (B) · M. N. Vijayakumar · M. S. Archana Industrial Engineering and Management Department, R V College of Engineering, R V Vidyanikethan Post, Mysuru Road, Bengaluru 560059, India e-mail: [email protected] A. P. Nikitha e-mail: [email protected] M. N. Vijayakumar e-mail: [email protected] M. S. Archana e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_51

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same in the coming months which results in the increase in energy bills. To defend ourselves against rising prices, we need to invest in energy-efficient technologies and reduce our use of electricity. Energy management can be viewed as the technology involving the planning and management of energy supply and usage to increase household efficiency and comfort and reduce energy costs through deliberate and efficient use of resources. Home energy management systems (HEMS) are played a role in a residential monitoring system that enables consumers to manage, track, and minimize energy consumption. The need to reduce energy prices has been a priority of the current program. Home energy auditing is a survey methodology that provides an overview of household energy use and analysis of the energy audits helps to recognize energy-saving steps in the home. This paper suggests do-it-yourself energy audit (DIY-EA) which is simple steps that can be followed to perform energy audits at home by the residents themselves. The information required for energy audits can be collected from existing usage. Energy audits, therefore, have a major role to play in the management of electricity expenditure and consumption. In the study, electricity consumption was collected and measured in ideal situations. The calculation for the amount of consumption during the lockdown period is then carried on. Few energy-saving systems suggested in the paper will help homeowners monitor and evaluate their electricity generation manually. The data will then be used to make decisions about how to save energy by trying to implement HEMS. The study explores the idea of decreasing the overall energy consumption bill by replacing current appliances with those that use less energy and are more energy efficient.

51.2 Literature Review Qarnain et al. [1] addressed the seriousness of the COVID-19 pandemic and suggests various measures to be taken to reduce home energy conservation based on government action in their paper. This research work is an overview of the measures taken by governments within their control to create energy consumers. The paper uses domestic energy use in the G20 nations. This study aims to investigate the G20 Member States’ response plans for domestic electricity consumers at the pandemic crisis, and the climate action plan has successful in improving energy shortage scenario for end-users or not. The findings of this paper are energy policy guidelines, which are focused on responses from various governments and steps taken against energy use in buildings. Aimee et al. [2] addressed the impact on energy bills of low-income energy consumers due COVID-19, particularly, in the context of home confinement and increased energy consumption in their paper. This study focuses on the government’s policies to improve energy standards. Domestic consumption has increased due to the use of heat due to the use of heat and energy, when they are not usually used under ideal conditions. The findings in this paper raise awareness of how important

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it is to have a warm home environment that enables immune systems to better fight COVID-19 viruses. Srishti Gupta et al. [3] discussed the energy optimization action to be taken to ensure energy efficiency in their study. The paper briefs on the energy audit process conducted at the mess to come up with various conservative steps to ensure economical energy usage by replacing the energy consumption entity. Thus, attempts were made in this paper to raise understanding of energy usage and stir energy conservation practices through a detailed study of energy use. The complete research carried on in this paper gave insight into the loopholes of energy consumption which contributed to understanding and development of possible cost control steps and demand–supply gap monitoring. Faiz Ahmed et al. [4] outlined a step toward energy conservation in the use of efficient building automation technology. The study designs and implements the building automation system, which can minimize energy consumption by up to 30–40%, creating a significant difference in energy savings. The study strategies for energy conservation are checked by performing experiments on a prototype experimental room and introducing effective techniques for building automation.

51.3 Methodology Based on a thorough literature review, a brief overview of what home energy management systems (HEMS) is all about, how energy auditing can be applied for HEMS, the challenges and the implementation and the benefits of HEMS in energy efficiency are discussed in this section. This section proposes a framework of a thorough house energy audit and how HEMS can be applied to come to arrive at an energyefficient home. Energy management systems have a vital role to play in regulating the transfer and usage of electricity in the transmission or distribution network. Based on a comprehensive house energy audit, HEMS has recently been developed and introduced for domestic consumers, i.e., by helping customers monitor their household electricity use, allowing consumers to decrease their energy consumption and eventually reduce their energy bills.

51.3.1 What is Hems? Home energy management systems [HEMS] concept has evolved and developed along with the needs of household electricity consumers. ‘Home energy management system is a hardware and software technology platform that enables users to monitor energy consumption and production and manually control and/or automate household energy use.’ The primary objective of the HEM system is to save energy, increase peak to average ratio (PAR), cost of electricity use, peak demand, and waste energy. HEMS comprises renewable energy sources, smart meters, smart sensors, smart

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appliances, and utilities as well as energy management systems. The aim of using HEMS is to enable the user to monitor and regulate the consumption of electricity at home.

51.3.2 Challenges Managing energy-related costs in the household sector is becoming more difficult with the ever-increasing rise in electricity prices. Organizational changes can successfully address these key issues in both current and new households such as, • It is tough to understand the overall cost of actual electricity usage. • There are no clearly defined means to identify and justify energy improvements. • The historical and current electricity usage was not monitored by any integrated framework.

51.3.3 Implementation of Successful Home Energy Management Plan Energy management refers to the method of monitoring, managing, and conserving household electricity for future energy savings [5]. This includes: 1. Collection of data on energy usage by energy auditing. 2. Discussing the energy conservation measures. 3. Implementing the measures and calculations for energy savings. STEP 1: Data Collection 1. Performing Energy Auditing The energy audit is a method used to assess differences in energy consumption. In order to understand, the current load and load that will be increased in the future, we have considered average area of each dwelling unit in urban areas to be 422 sq. ft [6] and the essential equipment’s required for the lighting load, fans, computers, switches, exhaust loads, etc., present in the house for data collection. Steps taken for energy audit: 1. Observation of everyday use on multiple loads and household appliances. 2. Measurements of the electricity usage and other criteria. 3. Estimation of payback period. 2. Measurements Performed In order to understand the usage, the energy consumption in an ideal condition for one day is measured. The measurements and clear view of energy consumption are shown below which is calculated using standard formula shown below.

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Table 51.1 Current ideal house energy consumption (illustrative) Electric load (Quantity = 1)

Wattage (W)

Daily operating (h)

Electricity used per day (kWh)

Per day consumption cost @ Rs. 6/kwh

FTL tube lights

54

8

0.432

2.592

Conventional fans

85

10

0.85

5.1

Geyser

2500

2

5

30

Laptop

75

4

0.3

1.8

Cell phone charger

15

2

0.03

0.18

Total

6.612

39.67

1. Current ideal house energy consumption calculation The following are calculations for the most basic and essential equipment used in the average household for the time spent in an ideal single day situation as shown in Table 51.1. Standard calculation for energy consumption Conventional equipment Wattage: X Watt Operating hours and days assumption: Y hours a day Number of equipment: Q Total electricity used during per day = Wattage × Hours per Day × No. of equipment (51.1) = X × Y × Q = Z kWh Per kilowatt hour cost = Rs. A; therefore, per day consumption cost = Z × A = Rs. B The calculations for every equipment’s are done based on the above standard formula and the results are summarized in Table 51.1. Total Electricity Consumption per sq. ft per day (in ideal situation) =

6.612 × 1000 = 15.66 ∼ 16 watt/sq. ft per day 422

2. Implementation and Calculations During the Lockdown Period The following are calculations for the most basic and essential equipment used in the average household for the time spent in a lockdown situation on a single day. Conventional 40 w T8 FTL Characteristics Conventional choke: 14 watts; power consumption per hour: 40 + 14 = 54 watts,

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Consumption per day for 12 h: 0.648 kWh, Per day usage at the rate of Rs. 6 per kWh = Rs. 3.89. Current System of Fan Characteristics of rheostatic fan regulators, Duration used: 15 h each day; fan regulator rating: 85 W, Electricity used by a fan per day = 85 × 15 = 1.275 kWh, Per day usage at the rate of Rs. 6 per kWh = Rs. 7.6.5 Current System of Geyser Present no. of geysers: 1, Total electricity consumption of geyser if used in the house for roughly 3 h per day with 2.5 kW geysers mounted in the house, then 2.5 × 3 = 7.5 kWh, Total expense with geysers per day = 7.5 × Rs.6/kWh = Rs. 45. Current System of miscellaneous appliances Laptop Laptop Wattage: 75 W; operating hours and days assumption: 6 h a day, Total electricity used during per day = 75 × 6 × 1 = 0.45 kWh, Per kilowatt hour cost = Rs. 6; Therefore, per day consumption cost = 0.3 × 6 = Rs. 2.7. Cell Phone Chargers Cell phone chargers Wattage: 15 W; operating hours and days assumption: 3 h a day, Total electricity used during per day = 15 × 3 × 1 = 0.045 kWh, Per kilowatt hour cost = Rs. 6, Therefore, per day consumption cost = 0.03 × 6 = Rs. 0.27 (Table 51.2). Total Electricity Consumption per sq. ft (Lockdown situation) 9.92 × 1000 = 23.5 ∼ 24 watt/sq. ft per day 422 STEP 2 Energy Conservation Measures The following steps can be followed to replace more energy consuming items to the ones which increase the efficacy of energy consumption. 1. Replace FTLs with LEDs as they are more power efficient than incandescent lights. 2. Rheostatic regulators are replaced by electronic fan regulators that can achieve power savings at all speeds, minimizing energy losses.

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Table 51.2 Current ideal house electricity consumption under lockdown conditions Electric load (Quantity = 1)

Wattage (W)

Daily operating (h)

Electricity used per day (kWh)

Per day consumption cost @ Rs. 6/kwh

FTL tube lights

54

12

0.648

3.89

Conventional fans

85

15

1.275

7.65

Geyser

2500

3

7.5

45

Laptop

75

6

0.45

2.7

Cell phone charger

15

3

0.045

0.27

Total

9.92

59.52

3. Solar water heater system installation to reduce water heating energy cost. 4. Advance real-time monitoring by using smart meters enables people to save and conserve energy usage. STEP 3 Implementation and Calculations The following changes are introduced by using more energy-friendly equipment to achieve energy-efficient use: Energy-Saving Scheme for FTL Tube Light Substituting of 40 W T8 FTL tubes with 18 W T5 LED tubes, Characteristics of T5 LED tube light: Electricity consumption per hour: 18 watts; consumption per day for 8 h: 0.18 kWh, Per day usage at the rate of Rs.6 per kWh = Rs. 1.08, Electricity charges saved per tube light per day = Rs. (3.89 − 1.08) = Rs. 2.81. Energy-Saving Scheme for Fans Utilizing electronic fan regulators (75 W), characteristics of electronic fan regulators, Duration used: 15 h each day; electricity used by fan per day = 75 × 15 = 1.125 kWh, Per day usage at the rate of Rs. 6 per kWh = Rs. 6.75, Electricity charges saved per fan per day = Rs. (7.65 − 6.75) = Rs. 0.9, There is a 11.76–12% reduction of electricity when we use electronic fan regulators. Replacement of Geysers with Solar Water Heater system Average 1 m2 area of flat plate collector provides 30 L of warm water each day, Thus, the overall collector area needed = 2 m2 ,

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Table 51.3 Home energy conservation calculations after implementations Electric load (Quantity = 1)

Wattage (W)

Daily operating (h)

Electricity used per day (kWh)

Per day consumption cost @ Rs. 6/kwh

LED tube lights

18

12

0.216

1.30

Electronic fans

75

15

1.125

6.75

Laptop

75

6

0.45

2.7

Cell phone charger

15

3

0.045

0.27

Total

1.836

11.02

Assuming the installation cost to be about 10,000/m2 of the overall capital expense would be = 20,000. Savings Total expense per month of geyser = 30 * 45 = Rs. 1350, Thus, simple payback period = 20,000 ÷ 1350 = 14.81–15 months, After 15 months the solar water heater will have absolutely nil cost on the users and in turn would be savings on electricity and cost per day will be Total energy saved per day = 7.5 kWh. Total energy cost saved per day = Rs. 4 (Table 51.3). Total Improved Electricity Consumption per sq. ft (Lockdown situation with HEM) 1.836 × 1000 = 4.37 ∼ 5 watt/sq.ft per day 422

51.3.4 Observations Made The research provided the basis for further innovations and approaches in the HEMS method. The household properties are modeled and structured so that successful product changes, including the inclusion of additional assets, could be made in the future. The energy performance was measured in such a way that energy use could be analyzed and its consumption understood. The energy savings generated at home are shown in Table 51.4 and the percentage of savings per day is shown in Table 51.5.

51 Efficient Utilization of Home Energy During pandemic—A Case Study Table 51.4 House energy savings

Table 51.5 Energy cost savings

Measures for saving energy

Energy cost saved per day

Utilizing 18 W LED tube lights

Rs. 2.81

Utilizing electronic fan regulators

Rs. 0.9

Utilizing solar powered water heating system

Rs. 45

Individual electricity cost savings Lightning Fan Geyser

51.3.4.1

537

Calculation

(3.89−1.08) × 100 3.89 (7.65−6.75) × 100 7.65 (20250−20000) × 100 20250

Per day

72.2% 12% 1.23% (for 15 months) 100% (15th month onwards)

Percentage of Energy Cost Savings Calculations

Formula: Percentage of Energy Cost Saving (Current Cost of Consumption − Improved Cost of Consumption) × 100 = Current Cost of Consumption (51.2) =

(59.52 − 11.02) × 100 = 81.5% 59.52

Expected overall cost of energy savings per day from the formula above was estimated to be in the range of 81.5% (Table 51.5).

51.3.4.2

Percentage of Energy Consumptions Savings Per Sq. Ft Per day. (For Lockdown Periods) Percentage of Energy Savings per sq. ft per day (Current Consumption − Improved Consumption) = × 100 Current Consumption (24 − 5) × 100 24

(51.3)

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Therefore, by implementing the solutions that were suggested in the study, approximately 79.1% energy savings per day per sq. ft is obtained from the formula shown above.

51.4 Conclusion The paper suggests simple do-it-yourself energy audit (DIY-EA) audits which the homeowners themselves can carry out. The outcome of the study showed that by using the do-it-yourself energy audit (DIY-EA) strategy, one is capable of collecting, analyzing, and communicating the right information needed to drive energy savings and efficiency measures. In the course of the paper, the challenges that householders face in terms of energy management examined through the energy auditing process, and the benefits are provided so that the homeowner could hope to achieve by implementing a home energy management systems (HEMS). Efforts have been made in the paper to educate every householder to show that, if every householder can follow the energy conservative process suggested, will lead not only to a single household but also to the community, the state, and the entire country energy savings. In this way, every citizen can join in the process of an entire country’s energy savings.

References 1. S.S. Qarnain, S. Muthuvel, S. Bathrinath, Review on government action plans to reduce energy consumption in buildings amid COVID-19 pandemic outbreak. Materials Today: Proceedings (2020) 2. A. Aimee, W. Baker, J. Brierley, D. Butler, R. Marchand, A. Sherriff, Stuck at home in a cold home: the implications of Covid-19 for the fuel poor. People, place and policy online, 1–4 (2020) 3. M.T. Chaichan, H.A. Kazem, Energy conservation and management for houses and building in Oman-case study. Saudi J. Eng. Technol. 1(3), 69–76 (2016) 4. S.F. Ahmed, D. Hazry, M.H. Tanveer, M.K. Joyo, F.A. Warsi, H. Kamarudin, A.T. Hussain, Energy conservation and management system using efficient building automation, in AIP Conference Proceedings (Vol. 1660, No. 1), (AIP Publishing LLC 2015), p. 090019 5. S. Gupta, R. Kamra, M. Swaroopa, A. Sharma, Energy audit and energy conservation for a hostel of an engineering institute, in 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). (IEEE, 2018), pp. 8–12 6. A. Kumar, Estimating Rural Housing Shortage. Economic and Political Weekly, 49(26/27) (2014), pp. 74–79. Retrieved July 27, 2020, from www.jstor.org/stable/24480171

Chapter 52

Data Analytics Based Multimodal System for Fracture Identification and Verification in CBIR Domain H. Manjula Gururaj Rao and G. S. Nagaraja

Abstract Bone fracture is a typical human challenge related to excessive stress being forced on bone or simple mistakes occur in the bone as a result of the osteoporosis and malignancy of the body. Precise analysis of bone fracture is also a significant feature of the medical profession. X-ray/CT-scans are used in this study to assess bone fracturing. False and misjudging of fracture detection can happen to reduce this and to help or assist the doctor, we propose multimodal integrated techniques to identify the fracture. In this paper, it elaborates the different combination and integrations of fracture identification and detection methods to identify and detect the area of the fracture.

52.1 Introduction Today, the image processing also forms a central area of study in the field of engineering and computer science. The processing of images works with two methods. Namely, digital and analog image processing. Image analysts use numerous analysis principles when using these visual strategies. Digital image manipulation lets computers interpret the visual images. The digital processing of images needs to go through three stages. They are the pre-processing, enhancement, and extraction of information. In medical science, images such as X-ray, CT-scan, and MRI perform an important part in injury and trauma diagnostics. The X-ray images play an important role in the orthopedics. Because of the affordability and readily available equipment in hospitals, X-ray images are typically used for fracture diagnosis. Now, X-ray images have the digital format. The main advantages of the X-ray images are short waves H. Manjula Gururaj Rao (B) JAIN University, Bangalore, India e-mail: [email protected] G. S. Nagaraja R V College of Engineering, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_52

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that travel through the human body, without harming the objects inside the body. The objects with the different densities result into different shadows on the X-ray image. Therefore, we will get black-and-white images. This helps the doctors to go through the patient’s body without damaging or performing surgery. To assess the bone fracture, radiologists will check through the X-rays. This is time intensive if lots of cases are present and since the bone is high in probability of breaking. Some fractures are easy to detect, so we can develop the automatic systems to help the doctors. That will help the work of physicians and radiologists and increase the accuracy of the examinations. Rather of the doctors wasting lots of time in observing the injury, the digital system can easily coordinate injury and stable X-ray images. There is no special algorithm which can be used to identify the crack in the bones of the entire human body. This is because the various bone sizes, weights, bone orientation, visual X-ray data perception, bone size, and features the task to develop the algorithm is often different from person to person [1–3]. In comparison, contour details, bone orientation, bone segmentation, and related features are very difficult to obtain [4–7]. So we propose the algorithm to detect the fractures in the wrist bone.

52.2 Pre-processing The unwanted information or the information that damages the image quality within the image is recognized as noise. During the process of the transmission, acquisition, and recording the noise can occur in the image. The distortion in the corner of the image, irregular lines, skewed pixels; pixels will be more amplified, blurry edges, and inaccurate background will result into the images in case of noise. Different types of noises image can have. They can be Gaussian, Poisson, salt and pepper noise. The digital images usually contain salt and pepper noise. During the transmission or capturing of the images, the digital X-ray may contain the noise. The structure of the noise is just like a salt and pepper, i.e., white and black dots. In the noise removal process, the noise has to be removed while retaining the other information or edges. Noise is removed using the mathematical functions. Generally, the linear and nonlinear methods are used to remove the noise from the image [4, 8, 9]. The different types of filters, which help to reduce the noises are like median, Gaussian, mean, and Wiener filters. When the noise is eliminated the quality of the image must be tested. Pixel difference measurement, human visual-based measurement (HVS), and structural similarity index are used. To remove noise from the images, orderly, symmetric, and mean filters with different sizes of kernels are used. The observation has made that for the digital X-ray images, the median filters are very effective. After removing of the noise, the image has to be resized. And contrast stretching is done for the better result. Upon pre-processing the image, the features are extracted from the image; this is achieved in the segmentation [9].

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52.3 Segmentation and Feature Extractions Partitioning the digital image into the several sections recognized as a group of pixels is termed the segmentation. The main intention of the segmentation is to depiction of the image into more precise, so that it is less difficult to break down. In the case of image segmentation, the main purpose is to find the borders and objects. The borders may be curves, outlines, etc., in images. Relevant diagnostic image segmentation techniques can be broken down into the categories [10–12]. They are graph-based and deformable model, edge-based, region-based, classification-based, and thresholding [5, 13]. The features are extracted using the watershed algorithm [14]. Along with this to extract the features hierarchical centroid and principal component analysis are used. Blob Analysis and Feature Extraction: Blob analysis and detection are a one of the methods in the image processing and computer vision. Bob is the set a points or pixels having the same properties and connected together. In blob detection, the points or the regions which shares the same properties are detected. So the some properties in the blob region remain constant compare to the neighboring blob. The properties may be brightness and color. Blob detection provides the complimentary information about the regions. The complementary information is not available in case of edge detectors or corner detectors [15]. Blob Extraction: Blob extraction has the function of isolating the blobs (items/objects) in a binary image. The connectivity determines whether or not two pixels are related. Two connectivity algorithms are used, i.e., 4 and 816. The blob is defined by its pertinent and quantitative attributes labeled as features. Some of the blob features are area of blob, orientation, eccentricities, centroid, or center of mass, median of a blob, convex hull, center of the bounding box, etc. Connected component Analysis (CCA) and the Labeled Matrix: The contiguous regions are known as connected components, objects, or blobs. The label matrix containing the connected regions shows as follows: L=11022033 11022033 The elements with L value are equal to 1 belongs to first blob, 2 belongs to second blob, and so on. The labeled regions are specified by the label matrix. The label matrix is the numeric matrix which any dimension.

52.4 Data Analytics in Fracture Detection Processing of raw data and drawing the conclusions are done in case of the data analytics. Many algorithms and techniques are used to process the raw data. Data mining approaches will disclose metrics and trends that otherwise would be buried under the amount of information. These data may be used to mechanize operations to

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increase a product or system’s total performance. The data analysis process involves several different steps. They are: • Step 1: Decide the criteria of the data. Here in what way the data is clustered is analyzed. • Step 2: The source from where the data collected. • While the data is gathered, it must be designed in order to be analyzable. • The pre-processing of the data has to be done before using it. It ensures the data is ready to use and not incomplete. Data analytics helps to draw the conclusions on the raw data, obtained from aeronautics, mechanics, medical, and many other fields. Data analytics help to detect the tumor, fractures, or abnormalities depending on analyzing the pixel values in the images like MRI, CT-scan, and X-ray images. Due to this, the decisions can be done easily.

52.5 Proposed Multimodal System: Frame and Data Analytics and Implementations In this section, we are proposing the multimodal frame and data analytics for the identification of the wrist fracture. The proposed model architecture is as shown in Fig. 52.1. The proposed model has two parts. They are: (1) Frame difference multimodal. (2) Data analytics multimodal. The frame difference multimodal and data analytics use the multimodal design for the identification of the wrist fracture. They are: watershed and image difference, watershed and the connected component, watershed and the hierarchical centroid, watershed and the principal component analysis, watershed + connected component + hierarchical centroid, watershed + connected component + PCA, and watershed + connected component + hierarchical centroid + PCA. Here, the images of the models are compared. The all the above model’s output is used for the data analytics [17, 18]. Watershed algorithm and image difference implementation: Here, the preprocessed image is converted into the gray scale image. Next, watershed algorithm is applied to find borders of the image. Image is converted into the non-flat structure. The image histogram is compared. Image difference: From Fig. 52.3, it is observed that if the difference is large it can be seen easily, and if is very small, it is very difficult to analyze. So for this reason the multimodal is proposed [19]. Watershed and Hierarchical Centroid: First image description is found; the next step is to compute the x-coordinate of the center of the mass. Then, the descriptor is subdivided an image into two images by the x-coordinate. This calls recursively on changed of the two sub-images (each image). The equalized values are calculated comparative to the complete image and the values are returned. The descriptors are

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Fig. 52.1 Proposed architecture of the multimodal frame and data analytical system

computed on the transposed image. The two resulting vectors are concatenated. This process is done to equivalence the images. When hierarchical source code is executed it will give two types of the results. One is zero and other one is any value. The result is zero implies that the images are same and no fracture is detected. Otherwise, the images are slightly different and fracture is detected. Figure 52.3a shows the output of the hierarchical centroid algorithm (Fig. 52.2).

Fig. 52.2 aWatershed original wrist image, b image difference, c histogram

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Fig. 52.3 a HC of the original image and fractured image, b CC of the original image-CC = 17

Fig. 52.4 PCA—original image-WS a watershed, b CC, c CC + HC

Watershed (WS) and connected Component (CC): In this model, the image is look over, pixel-by-pixel from left side to right and top to bottom. And the pixels are analyzed to check the connected component. The analysis is done by the intensity. i.e., the pixels which are adjacent will share the same intensity. After checking all the pixels, in a gray scaled image, the different connectivity is obtained. The nearer connectivity is grouped together and labeled. Figure 52.3b shows the connected component output. Principal Component Analysis: Orthogonal transformation along with the principal axis is used in the principal component analysis (PCA). Here, the mean found and subtracting the values in the image matrix. Next, the covariance of the matrix was found, later, the eigenvectors and finding the projection onto the eigenvector is done. Getting the final classification is obtained at the end. The output of the watershed with PCA is not good. It is difficult to measure. So the different models are designed. Figure 52.4 shows the output of PCA + WS, CC, and the PCA + CC + HC.

52.6 Verification and Validation Functionality extraction is one of the steps undertaken in fracture detection systems. Tool requires similar features, so retrieval guarantees that all features are restored correctly. In the identification of the fracture, the images are labeled as true positive (TP), true negative (TN), false positive (FP), and false negative (FN). True Positive (TP): Fractured images properly labeled as fractured.

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True Negative (TN): Non-fractured images correctly classified as non-fractured images. False Positive (FP): Non-fractured images wrongly identified as fractured. False Negative (FN): Fractured images wrongly identified as non-fractured. • False Rejection Rate (FRR): This is type I error. FRR is happens due to the error in identifying the fracture. FRR is defined as the percentage of the identification of the fracture in which false rejection had occurred. This is expressed as the probability of the rejection rate. The FRR is calculated as: FRR = Number of genuine image rejected/Number of genuine image tested • False Acceptance Rate (FAR): This type II error. FAR classifies the image as the fractured image, even though it is not having the fracture. The FAR is defined as follows: FAR = Number of false image accepted/Number of false image tested. • Equal Error Rate: If the number of FAR goes down, the number of FRR will go up and vice versa. This point is referred as the equal error rate (EER). In this point, the false rejection and false acceptances rate remain the same. Equal error rate determined from the average values obtained from both FAR and FRR and is a very useful parameter for evaluating a method’s accuracy. The lowest ERR is regarded as the most precise technique. Therefore, ERR is calculated as follows: EER = (FAR + FRR)/2 The performance of the proposed system is calculated using accuracy, expected accuracy, precision, sensitivity, and specificity. They are calculated using the following formulas [20]. ACCURACY = (TP + TN)/(TP + TN + FN + FP); PRECISION = TP/(TP + FP). SENSITIVITY = TP/(TP + FN); SPECIFICITY = TN/(TN + FP). Table 52.1 shows the result of the fractured and non-fractured images and images with respect to the accuracy, precision, sensitivity, and specificity. After analyzing the results, it states that the precision and specificity remain unchanged for all the images. The accuracy, sensitivity values changes. Table 52.1 shows the result of all the algorithms with respect to the ERR, expected accuracy, accuracy, precision, sensitivity, and specificity. The table tells that the hierarchical centroid algorithm is well suited for the fracture identification. The results are analyzed for the 770 images. The results are plotted in Fig. 52.5.

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Table 52.1 a Result of accuracy, precision, sensitivity, and specificity of the images. b Result of different types of the algorithm

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Fig. 52.5 Analysis of the algorithms

52.7 Conclusion This paper elaborates on different types of segmentation techniques and feature extractions. This paper focuses and discusses the validation and verification methods. Which uses the FAR, FRR, and confussion matrices like accuracy, precision, sensitivity, and specificity. The hierarchical centroid method is the best technique to detect the fracture in the X-ray images. This hierarchical method is combined with the WS, PCA methods to get the best result [21]. The blob analysis can be used in the fracture detection and by using this technique area of the fracture was also detected. The multimodal system is the best system to detect the fracture in the X-ray images.

Reference 1. R. Merjulah, J. Chandra,Segmentation technique for medical image processing: a survey. ICICI, https://doi.org/10.1109/ICICI.2017.8365301 2. R. Tak, N. Kumar, Satyaki, S. Verma, S. Dixit,Segmentation of medical image using region based statistical model.ICICCS. https://doi.org/10.1109/ICCONS.2017.8250668 3. K.K. Gupta, N. Dhanda, U. Kumar,A comparative study of medical image segmentation techniques for brain tumor detection. ICCCA, https://doi.org/10.1109/CCAA.2018.8777561 4. D. Feng, L.W. Kheng, Segmentation of Bone Structures in X-ray Images. Thesis ProposalSchool of Computing National University of Singapore (2006) 5. C. Weaver, N. Saito, Improving Sparse Representation-Based Classification Using Local PCA (Springer). DOI https://doi.org/https://doi.org/10.1007/978-3-319-89629-8_6 6. A. Kaur, Aayushi, Image segmentation using watershed transform. IJSCE 4(1) (2014). ISSN: 2231-2307 7. W.X. Kang, Q.Q. Yang, R.R. Liang, The comparative research on image segmentation algorithms, in IEEE Conference on ETCS (2009), pp. 703–707 8. A. Maity, A. Pattanaik, S. Sagnika, S. Pani,A Comparative Study on Approaches to Speckle Noise Reduction in Images.https://doi.org/10.1109/CINE.2015.36 9. H. Manjula Gururaj Rao, G.S Nagaraja,Noise Removal Techniques and Quality analysis of X-rayImages, ICATIECE-2019. https://doi.org/10.1109/ICATIECE45860.2019.9063843 10. L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, D. Rueckert,DRINet for Medical Image Segmentation.IEEE, https://doi.org/10.1109/TMI.2018.2835303 11. X. Li, Y. Kang, Y. Zhu, G. Zheng, J. Wang, An improved medical image segmentation algorithm based on clustering techniques. https://doi.org/10.1109/CISP-BMEI.2017.8302178 12. M.S. Fasihi, W.B. Mikhael,Overview of Current Biomedical Image Segmentation Methods.CSCI, https://doi.org/10.1109/CSCI.2016.0156

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13. J. Rogowska, Overview & fundamentals of medical image segmentation, in Hand-Book of Medical Imaging, Processing & Analysis (Academic Press, 2000), ch.5, pp. 69–85 14. A.S. Kornilov, I.V. Safonov, An overview of watershed algorithm implementations in open source libraries. J. Imaging, 20th Oct. 2018 15. B.P. Marsh, N. Chada, R. Reddy, S. Gari, K.P. Sigdel, G.M. King, The Hessian Blob Algorithm: Precise Particle Detection in Atomic Force Microscopy Imagery. Scientific Reports, 17th January 2018 16. M.A. Alagdar, M.E. Morsy, M.M. Elzalabany, MATLAB techniques for enhancement of liver DICOM images. IJRASET 3(XII) 12/2015. ISSN: 2321-9653 17. H. Manjula Gururaj, G.S. Nagaraja, Multimodal integrated technique for wrist fracture identification. Int. J. Innov. Technol. Explor. Eng. (TM) (IJITEE) (2020) 18. H. Manjula Gururaj Rao, G.S. Nagaraja, Content based medical image retrieval-a survey. IJCSE-016067, Int. J. Comput. Sci. Eng. 4(4) (2016) 19. R. C. Gonzales, R. E. Woods, S.L. Eddin, Digital Image Processing Using MATLAB, 2nd edn. (Tata McGraw Hill Education, 2012) 20. T.C. Anu, M.S. Mallikarjunaswamy, R. Raman, Detection of bone fracture using image processing methods. IJCA; NCPSIA (2015) 21. H. ManjulaGururajRao, G.S. Nagaraja, Detection and identification of the wrist fracture using the blob analysis technique. IJAST 29(3), 9815 (2020)

Chapter 53

Solar PV-Driven Swaccha Jal Rahul Virmani, Isha Rajput, Satish Kumar Gupta, Sarthak Singhal, Rupali Gupta, and Harsh Kapil

Abstract As we know, the availability of clean water is essential for the survival of the humanity. The right amount of water resources is available on the planet and very few of them can be used for drinking. To have a healthy life, purified water is required for everyone. Since early days of human civilization, various methods of water purification for salt and seawater have been implemented. As a water purification process, an effort has been made to review the technology and use of solar energy in this research paper. The study of the literature shows that there are number of configuration and device available, but they are not currently used for the high initial configuration cost and limited technology awareness in society. In this project, we have made a water purifier that work with solar energy and battery. The basic principle of this project is the coagulation process (alum), the membrane, and UV process. Solar radiation is collected by the solar panel. This energy is stored in battery. The purification unit consists of a high-pressure motor, a solenoid valve, a water level detector sensor, and a water tank.

R. Virmani (B) · I. Rajput · S. K. Gupta · S. Singhal · R. Gupta · H. Kapil ABES Engineering College, Ghaziabad, Uttar Pradesh, India e-mail: [email protected] I. Rajput e-mail: [email protected] S. K. Gupta e-mail: [email protected] S. Singhal e-mail: [email protected] R. Gupta e-mail: [email protected] H. Kapil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_53

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53.1 Introduction Water is essential for the functioning of life on earth. We can safely say that water is the reason why the earth is the only plant that support life. The population is growing, which in turn increases the demand for water. In addition, as the population grows, there is a large amount of pollution in the environment that pollutes many streams, lakes, and rivers. As per WHO, 1.5 million people die annually, because of the waterborne diseases accounting nearly 3.6% of the death due to diseases globally [1]. Drinking raw water is primarily the reason for these waterborne diseases. Even remote distant water sources away from the population can carry harmful pathogens such as protozoa, bacteria, viruses. Globally, there is an imperative need for the cheap and convenient treatment for polluted water. The existing scarcity of the accessible potable water which is just 1% of the total water volume in world makes the problem even more pressing. Nowadays, in urban cities, having a personal water purification system is a must have in residential as well as commercial areas. Not only, these are costly, utilize electrical energy, but the water wastage associated with them is also a concern. World soon needs a solution to this problem, so it can get healthy water without spending more. A community water cleansing and purifying system that is accessible to everyone can reduce the number of preventable disease and deaths worldwide. The main purpose of this project is aligned with “Swaccha Bharat Abhiyaan” to develop a faith in clean living by providing them with drinkable water even under adverse situations irrespective of the geography and climate. In addition, the project is expected to generate limited funding by providing the clean water to its nearby population at token charges. This project simultaneously aims to understand the type and conditions of water supplies and how to eradicate the imperfections to make the water standards as per WHO guidelines. The goal of this project is to build a “Solar-Driven Swaccha Jal”. The objectives of this project can be formulated as: • To obtain large quantity of water fit for drinking and potable usage as per WHO guidelines [2, 3]. • To design the system compatible to be run with the solar power utilizing null/minimal power from grid. • To design a self-reliant low-cost system, to be run without/minimal human supervision.

53.2 Literature Survey Water filtration processes have an ancient history, from storing the water in metal pots and clay utensils, to purifying water by use of some herbs with peculiar antibacterial

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properties. Another common method implied was to slowly strain the water through the sand gravel bed or linen clothing slowly before boiling it. Historians have found enough evidences of water purification system in Roman, Greek, and Mayan civilizations. The methods used in ancient India were scripted in a book named, “Sushruta Samhita” a Sanskrit manuscript [4]. In modern days, water purification processes have been pigeonholed under the following categories, viz., physical processes, chemical processes, and biological processes. The physical processes include filtration, sedimentation, and distillation. The processes such as flocculation and chlorination are listed as chemical processes, whereas the use of the active carbon and slow sand filters are enumerated as the biological processes. Since the last decade, ultraviolet lights have also been used to remove substantial cysts without leaving a residual, especially in low turbidity water [5]. Some of the traditional water processes commonly used in residential and commercial water purifiers. 1. Reverse Osmosis (RO) Initially, the technique was used by the seafarers for desalination of the seawater and obtain drinkable water [5]. It is a process of reducing soluble ions (such as salts) in water, which is used to pass liquid (water) through a semi-wet membrane, which transfers water but reject other soluble substance. If force against the surface of the membrane, the material is discarded while the water molecules are dispersed thought the membrane molecules, thereby purifying the water of the non-ionic organic contaminants. The RO is academically the simplest method to purify the water at a large scale [6], despite the difficulty in fabricating a perfect semipermeable membrane. Also, in the long run, the deposition of the algae and other life forms degrade the overall performance of the system. Solar-based RO has been utilized to obtain the drinkable water at large scale in central Australia [7, 8]. The RO has also shown the remarkable results in reducing the hardness of the water as well. 2. Ultraviolet As often misunderstood, the UV light does not remove any organic, inorganic, or organisms from water. The UV light does not alter the water chemically rather they just sterilize the microorganisms in water by altering their DNA structure. This prevents the large-scale infection if the water is consumed, as the microorganisms cannot replicate [9, 10]. The level of the effect of the UV light is measured in form of the µWs/cm2 (micro-Watt second per sq. Cms), most UV light systems utilize the intensity of 30,000 µWs/cm2 . 3. UF Purification The ultrafiltration (UF) purification method uses a membrane-like RO membrane, but with larger pores. The UF method does not require any electricity. The membrane removes most of the suspended particles, involving the multiple types of microorganisms, but do not have a significant impact on dissolved salts. Thus, no effect on hardness of the water is observed. Generally, the best-suited water filtering systems for residential and commercial applications involve both RO and UF purification along with UV. This conjunction

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of all the methods not only improves the water quality but also reduces the TDS, making it healthy for the human consumption.

53.3 Procedures and Methodology The project was initially thought of considering the remote areas of the rural India, such as in states of Orissa, Chhattisgarh, Jharkhand, West Bengal, Bihar, and Eastern UP, where despite of the multiple large-scale efforts made by Government of India, the life quality of the people is very poor due to less electrification, low standards of sanitation [12], and scarcity of good quality of water. For example, in Sundarbans area of the West Bengal, the villagers are forced to drink saline water or even worse muddy water, which they just filter using a piece of cloth. The project idea basically involved a solar powered community water purifying system which not only meets the standards of a good water purifier but also along with BESS can be utilized as a redundant system for electricity in times of need. The project prototype is based on a three-phase filtering process with each phase acting autonomously of the other with different zones of operation. Each phase is connected to each other with the help of water pipes. The electricity generated through the solar panels is utilized to create the draft for flow of water and achieve the electrical needs of the UV chamber, and a major area where the biological impurities are annulled to a safe level. The first phase consists of a physical process and sedimentation filtration process that removes unwanted dirt and residues present in the water after passing thought the sediment filter. Water is poured into the container. The process is escalated to a higher-level using alum which coagulates the smaller insoluble particles to form a large particle which can be easily filtered out. After a certain period, which estimated after multiple experiments is 7 min/10 lts is determined. The water is pumped to the second stage. In the second phase, water is passed through a cylindrical microfiltration membrane to remove particles in suspension, the water is passed through this membrane, releasing it from the present contaminants and filtered water is stored in the tank and from there it is pumped to the third phase with the help of a motor pump. In third phase, the water passes through biologically activated carbon filter, after which the water stored in the container is disinfected with the help of UV light, making the water perfect for drinking and for other purpose with minimal risk of waterborne diseases. It is extremely important to confirm that the water has been purified or treated before drinking along with the TDS report and other parameters as per WHO guidelines. Some major techniques intricated in the project work are

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1. Coagulation In coagulation, a specified quantity is added to the water through an automated pulley arrangement. The undissolved particles can accumulate and form large droplets that can be easily removed. However, these coagulations influence the pH and alkalinity of the liquid and many cause significant disturbances in the amount of acid [11]. The pH value as per the standards must be kept within 6.5–8.5, after several experimentations the time suitable for the quantity was determined. 2. Membrane Filtration In membrane percolation, water is passed through a semipermeable sheath, with a pressure just enough that it may not tear the sheath. This process removes the bacteria, microbes, particle, and natural organic matter that can give water colour, flavour, and odour and react infect the water. The membrane technology enables the total cost of production to be reduced and quality of the water increases at the same time. 3. Ultraviolet Filtration As discussed earlier, ultraviolet water purification is the most effective way to reduce the contamination of water via genetic modification. This is very effective in eliminating reproduction. Drinking ultraviolet light water is very easy, effective, and safe for the environment. The UV systems destroyed 99.99% of harmful microorganism without adding chemical or altering the taste or smell of water. In the prototype, we used Phillips TUV 11 W lamp with its adapter the lamp emits a radiation of 253.7 nm suffice for a tank of 10 lts. 4. Activated Carbon Filter At last, the 4-in. carbon filter made up of coconut shell carbon is put at the outlet of the entire system. This last filter not only removes any kind of left-over but also the organic compounds. 5. Photovoltaic Panel and Electrical Accessories Photovoltaic panels produce energy for UV light and battery life. In the prototype, 105-W, 12 V polycrystalline solar panel was used along with a boost converter to raise the voltage to 48 V, a 12 V 100 Ah battery is also used in system which is charged by surplus electricity generated by panel. Two 15-W lights are also fitted with the system, for the ease of supervisor during the night. The entire process is being automated using Arduino Mega, along with the level sensors and flow sensors to make the process completely automatic. The simple block diagram of the entire system has been shown in Fig. 53.1

53.3.1 Details of the Electrical System From the electrical perspective, the entire system runs on DC, annulling the need of the inverters [13]. The electrical requirement of the entire system can be listed as: • The UV lamps (11 W).

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Fig. 53.1 Schematic layout of “Solar-Driven Swaccha Jal”

• The brain of the entire system, i.e., an Arduino Mega which has been used to automate the entire process using level sensors, relays, solenoid valves, and flow sensors. • The 24 V DC pumps—to maintain the pressure between the stages so that no air bubble is formed in the pipes as it may have a negative effect on the performance. • Two 24 V solenoid valves to control the flow of water. • A 24 V battery connected to a bidirectional DC–DC converter as shown in Fig. 53.2 to charge the battery during the day and use it generally at night when no sun is available. The charge controller along with limiting the battery from being overcharged also has a major role in polarity protection, overload protection short circuit protection, etc. Now, the load referred above in Fig. 53.2 is the entire proposed system, whose wiring diagram is discussed, hereafter Fig. 53.3, which refers to the electrical wiring within the system. Initially, the 12 V from the solar panel is raised to a level of 24 V using a boost converter then the power was transferred to the proposed system. Now as seen in Fig. 53.3, there are three voltage levels as a common bus of 24 V is maintained in the system either by the solar PV or by the battery. • 24 V, to drive bulk of the load including the pumps, the solenoid valves and the UV lamp in the last stage of the filtration.

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Fig. 53.2 Electrical schematic layout of “Solar-Driven Swaccha Jal” with the Solar panel

UV LAMP

24 V DC to 12 V DC

12V ,15 Watts Lights, 2 Nos

24 V DC PUMPS

24 V DC Solenoid Valve

ARDUINO-MEGA CONTROLLER OF THE PROCESS

IC 7805 12 V DC to 9 V DC

Fig. 53.3 Internal electrical schematic layout of “Solar-Driven Swaccha Jal”

• 24 V is then lowered to the 12 V, which in our prototype has been done by using IC 7812 a linear voltage regulator with internal high current protection and connecting a capacitor at the output to make the voltage ripple free. This 12 V DC is then utilized to run the additional lighting system for the community use, as discussed in the prior sections.

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• Then, another IC of family 78XX, 7809 is used to further reduce the voltage level to 9 V which is given to the Arduino, which automates the entire process by automatically switching ON and OFF the pumps, based upon the inputs from the level sensors and flow sensors programmed logically based upon the pressure limits of the entire system, which is to be set within 1–2.3 kg cm2 . Based upon the calculations and practical measurements, we infer the following electrical parameters Voltage = 24 V Current taken by Pumps + Solenoid Valves + Arduino + UV Lamps ≈ 3 Amps Hence, Total Power Rating ≈120 W Battery Rating can be calculated as follows: Total No of Hrs. 8 h Maximum Current—3.5 Amps AH of Battery—8*3.5 = 28 Ah (But due to techno-economic reasons, we used 35 Ah Car Battery).

53.4 Results and Discussion The system was installed in an open space and the solar panel was placed perpendicular to the sun. The change controller was periodically checked to see if the solar panel was changing the battery. After the battery has been charged, the plug has been connected to the inverter output and, when the power is connected, the pump is activated, and purification systems are started. The whole systems are controlled by the Arduino circuit which automatically controls the motor pump of each unit according to the water level present in the chamber. Some basic tests were carried out after the structural design of the entire systems. The results of those tests are given below and compared against the standard test results. Although still at experimental stage, the results are quite encouraging (Table 53.1).

53.5 Conclusion and Future Scope The prototype developed is a self-reliant system for the water purification needs; a whole day run is expected to deliver 330 l of quality drinking water. Although there are many alternative home treatments that can purify drinking water but the slow rate of cleansing and limited amount at disposal, their performance is insignificant in comparison with the proposed prototype. Further, the additional benefit of the lighting system complimentary to the water purifying system has its own advantages. In the future work, we plan to acquire the aspects of IoT by inclusion of the GSM module, which will be used for the condition monitoring and remote start stop of the

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Table 53.1 Results of the sample collected Property

Sample collected in November 2019

Sample collected in April 2020

Desirable limits

Total suspended solids

2 g/l

1.4 g/l

Not more than 150 mg/l

Total dissolved solid

630 mg/l

670 mg/l

Not more than 1200 mg/l

pH

Between 7 and 8 (Basic)

8.9 (Alkaline)

6.5–8.5

Conductivity (ms/cm)

0.282

0.363

0.05–0.5

Turbidity

2.3 NTU

1.8 NTU

Not more than 5 NTU

Sulphate content

8.54 mg/l

10.73 mg/l

Not more than 200 mg/l

Chloride content

143.46 mg/l

149.15 mg/l

Not more than 250 mg/l

Iron content

0.05 mg/l

0.5 mg/l

Not more than 0.3 mg/l

entire system with minimal human intervention. We also plan to use Raspberry Pi with a camera vision system instead of the Arduino Mega to have an actual count report of the people who benefitted and help the government plan the strategy on actual data for the overall development of the area.

References 1. Burden of disease and cost-effectiveness estimates. World Health Organization. Archived from the original on February 14, 2020. Retrieved 5 April 2020 2. Guidelines for drinking-water quality. World Health Organization (Fourth edition incorporating the first addendum ed.). Geneva. ISBN 9789241549950.OCLC 975491910 3. Solar aqua purifier and it’s water quality management. Int. J. Indust. Electron. Electr. Eng. 3(5) (2015). ISSN: 2347-698 4. R. Joan,Ancient Water Purification Methods.sciencing.com, https://sciencing.com/ancientwa terpurification-methods-794725.html. 10 June 2020 5. Solar Disinfection|the Safe Water System. Center for Disease Control. Retrieved 11 February 2020 6. Purification of contaminated water with reverse osmosis 3(12).ISSN 2250-2459, ISO 9001:2008 Certified Journal (2013) 7. Award-winning Solar Powered Desalination Unit aims to solve Central Australian water problems. University of Wollongong. 4 November 2005. Retrieved 17–07–2019 8. Solar powered reverse osmosis water purifier. Int. J. Res. Eng. Appl. Manage. (IJREAM) 03(01). ISSN: 2454-9150 (2017) 9. T. Asano, Water from (Waste) water the dependable water resources. Water Sci. Technol. 45(8), 24–33 (2002) 10. American Public Health Association. Standard Methods for the Examination of Water and Wastewater, 20th edn. (1998)

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11. J.E. Van Benschoten, J.K. Edzwald, Chemical aspects of coagulation using aluminum salts-I. Hydrolytic reaction of alum and poly aluminum chloride. Water Res. 24(12):15191526 (1990) 12. M. Forstmeier, W. Feichter, Oliver Mayer Photovoltaic powered water purification-challenges and opportunities. Desalination 221, 23–28 (2008) 13. S.A. Kalogirou, Seawater desalination using renewable energy sources. Progr. Energy Combust. Sci. 31, 242–281 (2005)

Chapter 54

Field Performance Monitoring of Roof-Mounted SPV Systems: Application of Internet-Enabled Technologies Navneet Raghunath, M. K. Deshmukh, and Sandip S. Deshmukh Abstract In the work presented, the results of the online monitoring of a rooftop stand-alone solar photovoltaic (SPV) system with an installed capacity of 4.5 kWP have been reported. The method of analyzing the data acquired is in accordance with the IEC standard 61724, 1998. The online tracking was carried out using Schneider Electric’s Conext Insights platform. Data related to total PV input, battery charge, battery discharge, grid input, and load output were monitored and studied. This data was further used to calculate normalized parameters, including performance ratio (PR). The average value of PR system is 41.59%. The PR of the system provides a basis to compare it to other systems of interest. The results obtained are also useful for predicting future performance and maximizing capacity utilization of the system.

54.1 Introduction Since the Millennium Development Goals (MDGs) brought out its eight objectives in the year 2000 for global development, sustainable energy has been a critical topic of discussion. The 7th objective clearly states that the world should prioritize “Environmental Sustainability.” In the year 2015, the world saw the very first agreement on climate change as part of the “Paris Agreement on Climate Change.” This push toward sustainability combined with the worlds dwindling fossil fuel reserves has prompted a search for new and sustainable energy sources, i.e., renewable energy [1, 2]. In recent times, solar photovoltaic technology has gained immense popularity due to its relative simplicity and its ability to function without high infrastructure costs. N. Raghunath (B) · M. K. Deshmukh Department of Electrical and Electronics Engineering, Birla Institute of Technology & Science, K. K. Birla Goa Campus, Pilani, Goa, India e-mail: [email protected] S. S. Deshmukh Department of Mechanical Engineering, Birla Institute of Technology & Science, Hyderabad Campus, Pilani, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_54

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It has also been the most feasible source of residential renewable energy. Solar PV technology in turn facilitates low-carbon urban development and helps create selfsufficient infrastructure [3]. It also has the added benefit of low maintenance costs because it has no moving parts and that it is virtually noiseless [4]. Solar PV systems are also a viable electrification and heating option in rural areas that generally lack grid infrastructure [5, 6]. India receives about 5000 trillion kWh of terrestrial solar energy every year. The National Institute of Solar Energy has assessed the country’s solar potential to be about 750 GW assuming 3% of Indian wastelands are covered by solar PV modules. The National Solar Mission targets the installation of 100 GW of grid-connected solar power plants by the year 2022 [7]. To help meet these goals, the Ministry of New and Renewable Energy (MNRE) along with state governments has announced policies that encourage people and businesses to participate in solar energy generation. This push toward solar energy has resulted in an increase in the number of installers, operators, and system integrators of solar PV technology. These system integrators have developed innovative technology solutions to enable owners and stakeholders to monitor their solar PV assets in real time. This includes weather information, solar radiation, battery state of charge, and load power consumption. This has increased the user-friendliness of solar PV systems and has also enabled streamlined performance monitoring. The performance data produced by these systems can also be used to forecast future trends of energy production and can be used to forecast system efficiency under varying solar radiation and load conditions [8]. This also assists in the operation and maintenance of solar PV systems and can serve as a diagnostic tool in times of failure [9]. These tools can further increase the market penetration of solar PV technology. As a fallout of nationwide “lock-down” due to COVID-19 pandemic, internet-enabled remote monitoring of the SPV system has proved to be a boon for users of the system engaged in routine monitoring of operations and maintenance of the system, without any threat to personal safety. In the present work, the analysis of the performance of a rooftop solar PV system installed at Birla Institute of Technology and Science Pilani, K.K. Birla Goa Campus, has been presented. The system under study is a 4.5 kWP rooftop solar PV system that powers a 3 kW streetlight load every day for a period of 11 h (7 pm–6 am). This system installed in October 2019, is the newest of three rooftop solar PV systems currently installed on campus. The performance data has been gathered over a period of six months (January 2020–June 2020) at an interval of 10 min between each reading as per IEC standard 61724, 1998 [10]. The first three months (January 2020–March 2020) of monitoring were conducted in the field with regular visits to the site. For the latter three months (April 2020–June 2020), data was gathered remotely. This was due to the inaccessibility of the site due to the COVID-19 pandemic. The performance data recorded consists of energy produced by PV array, energy supplied to the battery storage unit, energy consumed from the battery storage unit, energy consumed by the connected load, and energy drawn from the main grid supply. This performance data was further used to compute normalized parameters such as performance ratio (PR) to compare its performance with other systems [11]. This work thus emphasizes the importance of routine and frequent monitoring of system output to improve

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efficiency and capacity utilization of the system. This online monitoring and analysis method can further be used to predict system performance, troubleshoot errors, load scheduling, and streamline technical support staff requirements.

54.2 Method of Study 54.2.1 Site Description The roof-mounted SPV system is mounted on the roof of a small building housing a power distribution control center (PCC), named as PCC4 located on the campus of BITS Pilani, Goa Campus, an Institute of Eminence. This PCC is one of four on-campus power distribution centers handling various residential electrical loads, including a single-phase street lighting load. The PCC4 is located near the residential colony area on the campus (latitude + 15.39°N; longitude + 73.87482°E). The roofmounted SPV panels have a slope angle of 15° with respect to local horizontal and face the due South direction (i.e. The angle between the normal to the surface of the panels and the due South direction at the site is zero). The panels are mounted at a height of about 3 m above the local ground level, which is also the height of the building (Table 54.1). The monthly average daily total global horizontal irradiance (GHI) recorded at the site is shown in Table 54.2. Table 54.1 Site specifications of the rooftop SA-SPV system installed at PCC4 Site parameter

Site specification

Site location

BITS Pilani, K.K. Birla Goa Campus

State and country

Goa, India

Region

Western Ghats, Coastal Region

Latitude at the site

+15.38933°N

Longitude at the site

+73.87482°E

Height above the ground

3 m above the ground

Inclination of panels

15°

Total area of SPV panels

15 m2

Installed capacity

4.5 kW

Table 54.2 Monthly mean daily total global horizontal irradiance (kWh/m2 /day), recorded at the site [12] Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

GHI

5.2

5.9

6.4

6.5

5.9

4.8

4.7

4.7

5

5.3

5.2

5.1

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54.2.2 The Roof-Mounted SPV System at PCC4 This solar PV system is a stand-alone system, with the SPV panels having an aggregate installed capacity of 4.5 kWp and a battery energy storage system (BESS). The system was installed for the purpose of powering street lights in the colony area, which is a single-phase load (3 kW). The system was commissioned on October 5, 2019. It was installed by NC Enterprises Goa, a reputed system integrator in the state. There are a total of 15 SPV panels, arranged in two-stringed arrays, mounted on angle iron trays, flushed with the south-facing roof. As shown in Fig. 54.1, the upper array consists of 8 panels and the lower array consists of 7 panels. The stringed arrays are connected to a maximum power point tracking (MPPT) charge controller installed inside the room beneath the arrays, which ensures maximum power extraction and delivers it to the BESS. The BESS consists of 24 batteries in a series–parallel connection. The DC energy stored in the battery bank is converted into AC energy using an inverter to provide power to the on-campus street lighting load. This balance of system (BOS) is placed inside the building of the PCC, as shown in Fig. 54.2. The street lighting load is rated at 3 kW. The specifications of the system are given in Table 54.3. The BOS consists of a battery bank and a control and monitoring unit that is used to switch between stand-alone mode and grid-interactive mode. The schematic of the system is shown in Fig. 54.3. When the maximum voltage cutoff level, i.e., 56 V, is reached, the MPPT charge controller shuts off charging current to the battery bank. The load is powered by the battery bank until battery voltage reaches its minimum cutoff voltage level, i.e., 48 V. Once the battery bank has reached its minimum cutoff voltage level, the control unit switches the street lighting load onto the grid. The system thus effectively maintains battery bank voltage levels between maximum and minimum cutoff voltage levels in order to maintain battery life span. The control unit is used to configure the settings of the inverter and the MPPT charge controller to control the mode of system

Fig. 54.1 Rooftop SPV system installed at PCC4 showing a two-array configuration

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Fig. 54.2 BOS of rooftop SPV system installed at PCC4 Table 54.3 Specifications of the rooftop SPV system [13] Component

Manufacturer and name

Specifications

PV panel

Waaree Pvt. Limited

Nominal maximum power = 300 W Open circuit voltage V oc = 45 V Short circuit current I sc = 8.89 A Module efficiency = 15.46% Area = 15 m2

MPPT charge controller

Schneider Conext MPPT 60150

Battery operating voltage = 0–80 V Nominal battery voltage = 48 V Max. charge current = 60 A Max. output power = 4500 W

Inverter

Schneider electric model XW + 7048 E

Output power = 4500 W, frequency = 50/60 Hz, output voltage = 230 V Input DC voltage range = 40–64 V

Battery

Exide—6SGL 40

Nominal voltage = 2 V, capacity = 800 Ah Type = C10, 1.75 V/cell Max. discharge current = 240 A

Control unit

Xantrex XW system

System setting operator—connected to MPPT charge controller and inverter

Smart monitoring unit

Conext™ ComBox

Data logging facility—micro SD card—or online monitoring—connected to Xantrex XW System

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Fig. 54.3 Schematic representation of the rooftop SPV system at PCC4

operation. The control unit is connected to the ComBox via Ethernet, where the data is stored both in an on-board memory card and on a cloud server using the Internet. The ComBox records important data such as energy output data from SPV arrays, charging and discharging of the battery bank, grid input, and load energy consumption at an interval of ten minutes as per IEC 61724, 2018 standard and International Energy Agency (IEA) guidelines [10, 14]. The data gathered by the ComBox can be viewed online using Schneider Electric’s Conext™ Insights website (https://conext insight.schneider-electric.com/). These parameters can be tracked in real time using this website as shown in Fig. 54.4. This data can also be gathered using an intranet console using the IP 10.40.0.9 on campus. Figure 54.4 shows a dashboard sample showing the SPV system performance for the month of May 2020. One of the shortcomings of the data acquisition system is the need for a strong signal for Internet connection. In the event of an Internet outage, the data recorded during the interval cannot be transferred to the cloud storage system

Fig. 54.4 Conext™ insights dashboard displaying PV energy yield, battery energy consumption, energy drawn from grid, and load energy demand for the month of May 2020

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and as a result, on the dashboard. To overcome the loss of data due to the outage of the Internet, the data is stored locally on a micro SD card in the installed ComBox, which can be retrieved subsequently for analysis.

54.3 Results and Discussion The online monitoring of the system is used to record five important parameters, namely PV energy yield; energy in or out of BESS during charging and discharging, respectively; energy drawn from the grid; and the total energy supplied to the load. This data was used to obtain the monthly daily average value of the performance parameters for the interval of 6 months, from January 1, 2020, to June 30, 2020. The variation of the monthly daily average values is shown in Fig. 54.5. It is seen in Fig. 54.5 that the energy supplied to the street lighting load is consistently higher than the energy generated by the PV array. From Fig. 54.5, it is also observed that load output is equal to the sum of the battery discharge and the grid input. About 25% of the energy supplied to the load comes from the SPV system and the remaining comes from existing grid connection. The SPV system is therefore constrained by limited BESS capacity. The energy required by the streetlight load remains rather constant over the period of January 2020 to June 2020 at an average of 31.5 kWh per day. The energy produced by the PV array reduced gradually from January 2020 to June 2020 with an average of 6.8 kWh per day. This could be due to

35000

Energy in Wh/day

30000 25000 20000 15000 10000 5000 0

Jan-20

Feb-20

Mar-20

Apr-20

May-20

Jun-20

Load Output (Average)

Grid Input (Average)

PV Input (Average)

BESS Discharge (Average)

Fig. 54.5 Monthly variation of the average daily energy yield of PV arrays (Wh), energy discharge from the BESS (Wh), energy drawn by the load (Wh), and grid energy consumption (Wh) from January 2020 to Jun 2020

566 Table 54.4 Variation of energy supplied by BESS and grid to the load during the period January 2020–June 2020

N. Raghunath et al. Energy from BESS (kWh) Grid input (kWh) Daily maximum 7.4

29.4

Daily minimum 4.5

23.8

Daily average

26.3

5.5

factors such as buildup of dust on surfaces of panels, cloud cover, and panel heating. The dust accumulating on the surface of the panels can be removed with regular cleaning of the panel surface. Panel cooling methods such as intermittent and continuous water cooling could help improve the performance of the PV array [15]. To balance the decreasing PV energy generation, a gradual increase in the grid energy consumed is observed. The average energy supplied by the grid is 26.3 kWh per day. Due to the energy loss that occurs during the charging/discharging process of the battery, it is observed that the BESS discharge energy is lesser than the PV energy input. Table 54.4 shows the variation of energy supplied by BESS and the grid, to the load during the period January 2020–June 2020. Over the period of 6 months (January 2020–June 2020), the SPV system produced a total of 1003.1 kWh and a total 4789.5 kWh of energy was drawn from the grid to power the street light load. The performance ratio (PR) of the system serves as a key metric of a system’s overall performance and functions as a point of comparison between different SPV systems installed in a region [11]. The PR of a system represents the ratio of the effective solar irradiation (measured as a global horizontal index, GHI) incident on the surface of the SPV array and the electrical energy generated by it. The variation of monthly average daily energy yield values (Wh/day) and PR (%) of the SPV system is shown in Fig. 54.6 It is observed that during the period of observation the minimum value of PR of the system is 34.26% and the maximum value of PR is 61.59%. The average value is found to be 41.59%. The maximum value of PR occurred in January, and the minimum value occurred in April. The PR of this system is comparable to that of an older SPV system (installed in 2016) mounted at another power control center, on the campus of the institute. The older system has a PR of 43.58% [12]. This shows that despite the difference in the age of the systems, the performance of the two systems is comparable. Over the next six months, namely July 2020–December 2020, the performance of the system will be important to observe. This is due to the onset of Southwestern Monsoon in the state of Goa. As observed before, the system produces an average of 6.8 kWh of electricity per day. The load is powered for a period of 11 h (6 pm–7 am). Taking into account time of day tariffs in Goa, the average cost per kWh of electricity comes out to be Rs. 5.55. Based on these rates, the system saves about Rs. 38 per day. At this rate, the payback period of the system will be large. This also shows that measures to improve system performance, such as regular cleaning of SPV panels, must be performed in order to achieve a timely return of investment (ROI). Further increasing the number of panels installed and an upgrade to the BESS capacity can also help improve system performance.

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10000

70

9000 Energy in Wh/day

7000

50

6000

40

5000 4000

30

3000

20

2000

10

1000 0

Percentage (%)

60

8000

Jan-20

Feb-20

Mar-20

Apr-20

May-20

Daily Average PV Input by Month (Wh/day)

Jun-20

0

PR (%)

Fig. 54.6 Monthly variation of daily averaged energy yield (Wh) and performance ratio (PR) (%) of the SPV system from January 2020 to Jun 2020

The online performance monitoring platform can prove to be a useful tool to measure the impacts of the periodic soiling of the SPV panels as a result of dust accumulation and bird droppings. The effects of partial shadowing can also be studied using such platforms to ensure efficient operation and maintenance. Additional features such as predictive analytics using artificial neural networks and camera-based monitoring of SPV panels can be incorporated in the future [16].

54.4 Conclusions The significance and importance of the remote online monitoring of the rooftop SASPV system (4.5 kWP ) have been demonstrated. The performance data of various components of the SPV system was gathered through the online portal for a period of six months (January 2020–June 2020). The method of analyzing the data acquired is in accordance with the IEC standard 61724, 1998 [10]. This method is an important highlight of the present work and can be used to further analyze system performance over the remainder of the year. The data acquired was used to investigate the monthly variation of the energy yield of the PV panels, BESS charge, and discharge energy, energy drawn from the grid, and the total energy consumed by the load. It was found that the system generates an average of 6.8 kWh of energy per day and that the 3 kW load is mostly powered by the grid. The performance ratio (PR) of the system was computed using this data. The average value of PR was found to be 41.59% as expected. This study shows that the platform for online monitoring of

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SPV systems has great potential as a diagnostic tool for performance monitoring. It also facilitates the prediction of the performance of remotely located SPV systems. The use of web cameras to periodically monitor the SPV panels can further improve overall capacity utilization. In trying times such as the COVID-19 pandemic, online monitoring platforms such as the one demonstrated in the present work have the ability to become the new norm in SPV performance monitoring. Acknowledgements Authors gratefully acknowledge the financial and technical support provided by BITS, Pilani K.K. Birla Goa Campus, for completion of this work.

References 1. M. Leach, S. Deshmukh, Sustainable energy law and policy. Environ. Energy Law, 122–138 (2012) 2. Report, G.S. Renewables 2017 global status report 2017. www.ren21.net (2017) 3. M. Leach, S. Deshmukh, D. Ogunkunle, Pathways to decarbonising urban systems, in Urban Retrofitting for Sustainability: Mapping the Transition to 2050 (2014), pp. 191–208 4. V. Cheng, S. Deshmukh, A. Hargreaves et al., A study of urban form and the integration of energy supply technologies, in Proceedings of the World Renewable Energy Congress— Sweden, 6–13 May, 2011 (Linköping, Sweden, 2011), pp. 3356–3363. https://doi.org/10.3384/ ecp110573356 5. K. Anwar, S. Deshmukh, S.M. Rizvi, Feasibility and sensitivity analysis of a hybrid photovoltaic/wind/biogas/fuel-cell/diesel/battery system for off-grid rural electrification using HOMER. J. Energy Resourc. Technol. Trans. ASME. (2020). https://doi.org/10.1115/1.404 5880 6. S. Deshmukh, A. Jinturkar, K. Anwar, Determinants of household fuel choice behavior in rural Maharashtra, India, in 1st International Congress on Environmental, Biotechnology and Chemistry Engineering IPCBEE (IACSIT, Singapore, 2014) 7. Ministry of New and Renewable Energy, Government of India, https://www.mnre.gov.in/solar/ current-state 8. S.R. Madeti, S.N. Singh, Monitoring system for photovoltaic plants: a review. Renew Sustain. Energy Rev 67, 1180–1207 (2017). https://doi.org/10.1016/j.rser.2016.09.088 9. M.K. Deshmukh, A.B. Singh, Online monitoring of roof-mounted stand-alone solar photovoltaic system on residential building. Materials Today: Proceedings (2020). https://doi.org/ 10.1016/j.matpr.2019.06.487. 10. IEC Standard 61724, Photovoltaic system performance monitoring-guidelines for measurement, data exchange and analysis (1998) 11. A.M. Khalid, I. Mitra, W. Warmuth et al., Performance ratio—crucial parameter for grid connected PV plants. Renew Sustain. Energy Rev 65, 1139–1158 (2016). https://doi.org/10. 1016/j.rser.2016.07.066 12. B.S. Athokpam, M.K. Deshmukh, Operational testing of rooftop SA-SPV system in coastal tropical climate of India. Energy Sustain. Develop 47, 17–22 (2018). https://doi.org/10.1016/ j.esd.2018.08.005 13. Conext XW Hybrid System Description, https://www.schneider-electric.co.in/en/search/solar+ photovoltaics 14. IEA (International Energy agency) (2014). Country report on analytical monitoring of photovoltaic systems. IEA PVPS Task13 www.iea-pvps.org. 15. A. Saxena, S.S. Deshmukh, S. Nirali et al., Laboratory based experimental investigation of photovoltaic (PV) thermo-control with water and its proposed real-time implementation. Renew. Energy 115, 128–138 (2018). https://doi.org/10.1016/j.renene.2017.08.029

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16. K. Anwar, S. Deshmukh, Use of artificial neural networks for prediction of solar energy potential in southern states of India, in Proceedings—2018 2nd International Conference on Green Energy and Applications, ICGEA 2018, IEEE Xplore Digital Library (2018), pp. 63–68. https:// doi.org/10.1109/ICGEA.2018.8356321.

Chapter 55

Flow Modulation at Micro-combustor Inlet Arees Qamareen, Shahood S. Alam, and Mubashshir A. Ansari

Abstract Recirculation zones formed in micro-combustors due to flow modulating geometries such as backward facing step and block inserts have been found to enhance the outer wall temperatures. Numerical investigations of micro-combustors with variable aspect ratio (AR) and blockage ratio (BR) triangular block inserts have been made for premixed H2 -air combustion with inlet velocities ranging from 4 to 48 m/s using commercial CFD software ANSYS Fluent software. AR = 3 was found effective at lower velocities, while AR = 1 performed better at higher velocities for all blockage ratios.

55.1 Introduction Micro-combustors have great potential applications when used in micro-power systems [1] due to higher energy density when hydrogen or hydrocarbon fuels are used. Being a vital component of the micro-power systems, these micro-combustors impact the power generating efficiency and performance of the entire system. But lower combustion efficiency and flame instability due to higher heat loss occurring because of large surface-to-volume ratio of micro-combustors are major challenges. Numerous works have been accomplished on micro-combustor performance through theoretical and experimental explorations along with numerical calculations [2, 3]. The objective is to curtail heat losses and augment flame stabilization. Compared to free flame combustion, combustors with porous media produced

A. Qamareen (B) · S. S. Alam · M. A. Ansari Department of Mechanical Engineering, Aligarh Muslim University, Aligarh 202002, India e-mail: [email protected] S. S. Alam e-mail: [email protected] M. A. Ansari e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_55

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stable flames and greater fuel conversion efficiency [4, 5]. The existence of catalyst causing heterogeneous and homogeneous combustion also enhanced microcombustor performance [6]. Heat recirculation generated due to backward facing step [7] or insertion of bluff bodies [1] or fins [8] is an efficient way for stable flame sustenance and heat loss annihilation in micro-sized devices. Though much research has been done to explore efficient geometrical modulations for enhancement of micro-combustor performances, a detailed study of block insertion causing flow modulation at the inlet of a backward stepped micro-combustor is lacking. The present work scrutinizes the effect of inserting a triangular block at the inlet of the combustion chamber in a backward stepped micro-combustor, and the effect of varying inlet velocities and aspect as well as blockage ratio is quantified with respect to the outer wall temperature.

55.2 Physical and Numerical Descriptions 55.2.1 Geometrical Model The geometric model in the present work is based on a two-dimensional axisymmetric micro-combustor with a backward facing step and a triangular block insert at the inlet of the combustion chamber. The aspect ratio (= d/b) and blockage ratio (= b/(hi + hs )) are both varied. The geometrical framework is shown in Fig. 55.1, and the parameters are specified in Table 55.1.

Fig. 55.1 Schematic diagram of the micro-combustor

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Table 55.1 Geometrical framework of the micro-combustor Geometrical parameter

Parameter name

(mm)

Constant/variable

L

Combustor length

27

Constant

Li

Inlet length

7

Constant

hi

Inlet height

0.5

Constant

hs

Step height

0.5

Constant

ht

Combustor wall thickness

0.5

Constant

b

Block height

0.4, 0.5, 0.6

Variable

d

Block length

0.4, 0.5, 0.6

Variable

AR

Aspect ratio = d/b

1, 2, 3

Variable

BR

Blockage ratio = d/(hi + hs )

0.4, 0.5, 0.6

Variable

Premixed combustion of H 2 and air at an equivalence ratio of 0.8 is considered. The micro-combustor and block are made of stainless steel, but a reduced value of 5 W/m K for thermal conductivity is considered due to oxidation of the material at high combustion temperatures. Material properties and boundary conditions considered can be found in the literature of Akhtar et al. [9].

55.2.2 Numerical Methodology The characteristic length of micro-combustor being much larger than the mean free path of reactant gases allows us to consider the continuum governing equations [10] for steady-state condition. The discretization scheme used by the commercial CFD software ANSYS Fluent 16.0 is second order upwind. Pressure velocity coupling is done by SIMPLE algorithm, while finite rate/eddy dissipation model is applied for modeling chemistry–turbulence interaction. Quadrilateral multi-zone grid (Fig. 55.2a) is generated for all the geometries through ANSYS Workbench software after the geometry was made in ANSYS Designmodeler. Grid test was performed, and an optimum cell size of 20 µm with a finer mesh near the block (10 µm) was selected to perform the simulations. Validation of the model was performed by comparison with the experimental results of Li et al. [11] in a simple two-dimensional axi-symmetric micro-combustor with backward facing step (Fig. 55.2b). Since the global H 2 -air reaction mechanism is considered to save computational time, a difference in temperatures is found, but the trend followed is similar. The work is focused on finding the effect of varying geometrical parameters of the inserted block for varying inlet velocities.

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Fig. 55.2 a Grid generated, b validation of basic micro-combustor outer wall temperature distribution with experimental results of Li et al. [11]

55.3 Results and Discussion In this section, the effect of aspect ratio and blockage ratio on the temperature and flow field is presented and discussed. Figure 55.3 shows the temperature contour plots for geometry MC04 with varying aspect ratio of block for different inlet velocities. Flame location is identified by high-temperature region and is observed to shift downstream with an increase in inlet velocity. From the outer wall temperature plots, it is seen that the middle and downstream wall temperature increases with inlet velocity, while the upstream wall temperature slightly falls. The occurrence is due to two aspects, while on one hand, more fuel supply coming in at higher velocity causes more heat released and hence higher downstream temperatures. But on the other hand, flame is shifted downstream at higher velocities and the low-temperature reactants coming in, cool down the upstream walls. Recirculation zones are visualized from the third contour plots showing axial velocities superimposed with streamlines. With an increase in inlet velocity, the recirculation zones grow bigger in size. An increase in aspect ratio increases the length of the recirculation zone near the step while decreasing the one behind the block. Similar trends are observed for the other two geometries also as shown in Figs. 55.4 and 55.5. Figure 55.6 shows the maximum outer wall temperatures for different micro-combustor geometries at various inlet velocities.

55.4 Conclusions Two-dimensional axi-symmetric numerical modeling of suddenly expanded microcombustor with a triangular block insert was done, and temperature and flow field

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Fig. 55.3 (Left) Temperature contours, (center) outer wall temperature distribution and (right) axial velocity contour overladen with streamlines for MC04AR1 (top), MC04AR2 (center) and MC04AR3 (bottom)

distributions were obtained. It can be concluded that higher aspect ratio of the block insert is effective at lower inlet velocities, while an aspect ratio of unity performs better at higher velocities. This is due to the flow recirculation zones formed due to the backward facing step and block which modulates the heat recirculation as well.

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Fig. 55.4 (Left) Temperature contours, (center) outer wall temperature distribution and (right) axial velocity contour overladen with streamlines for MC05AR1 (top), MC05AR2 (center) and MC05AR3 (bottom)

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Fig. 55.5 (Left) Temperature contours, (center) outer wall temperature distribution and (right) axial velocity contour overladen with streamlines for MC06AR1 (Top), MC06AR2 (center) and MC06AR3 (bottom)

Fig. 55.6 Maximum outer wall temperatures for different micro-combustors at various inlet velocities

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References 1. Z. Zhang, K. Wu, W. Yao, R. Yuen, J. Wang, Enhancement of combustion performance in a microchannel: synergistic effects of bluff-body and cavity 265, December 2019 (2020) 2. H. Xue, W. Yang, S. Chou, C. Shu, Z. Li, Catalytic effect of microcombustion in microthermophotovoltaic system. Nanoscale Microscale Thermophys. Eng. 10(3), 275–282 (2006) 3. E. Nadimi, S. Jafarmadar, The numerical study of the energy and exergy efficiencies of the micro-combustor by the internal micro-fin for thermophotovoltaic systems. J. Clean. Prod. 235, 394–403 (2019) 4. J.F. Pan, D. Wu, Y.X. Liu, H.F. Zhang, A.K. Tang, H. Xue, Hydrogen/oxygen premixed combustion characteristics in micro porous media combustor. Appl. Energy 160, 802–807 (2015) 5. Q. Peng, W. Yang, E. Jiaqiang, H. Xu, Z. Li, K. Tay, Investigation on premixed H 2 / C 3 H 8 / air combustion in porous medium combustor for the micro thermophotovoltaic application. Appl. Energy, 260 (2019), (2020), p. 114352 6. Z. Xu, C. Li, D. Vadillo, X. Ruan, X. Fu, Numerical simulation on fluid mixing by effects of geometry in staggered oriented ridges micromixers. Sens. Actuat. B Chem. 153(1), 284–292 (2011) 7. S. Akhtar, M.N. Khan, J.C. Kurnia, T. Shamim, Investigation of energy conversion and flame stability in a curved micro-combustor for thermo-photovoltaic (TPV) applications. Appl. Energy 192, 134–145 (2017) 8. Z. He, Y. Yan, F. Xu, Z. Yang, H. Cui, ScienceDirect combustion characteristics and thermal enhancement of premixed hydrogen/air in micro combustor with pin fin arrays. Int. J. Hydrogen Energy (2019) 9. S. Akhtar, J.C. Kurnia, T. Shamim, A three-dimensional computational model of H2air premixed combustion in non-circular micro-channels for a thermo-photovoltaic (TPV) application. Appl. Energy 152, 47–57 (2015) 10. B. Bazooyar, H.G. Darabkhani, Analysis of flame stabilization to a thermo-photovoltaic microcombustor step in turbulent premixed hydrogen flame. Fuel257 (2019), p. 115989 11. J. Li, S. K. Chou, W. M. Yang, Z. W. Li, Experimental and numerical study of the wall temperature of cylindrical micro combustors. J. Micromech. Microeng.19(1) (2009)

Chapter 56

Study on Performance of Phase Change Material Integrated Heat Pipe G. Gnaneshwar, G. Sundara Subramanian, N. S. Hari Thiagarajan, Lakshmi Narayanan, and D. Senthil Kumar

Abstract Thermal power management within the confined structure of modern electronics is of prime importance. Heat dissipation techniques include liquid cooling, air cooling, immersion cooling and lately heat pipes. However, during the utilization of heat pipes in electronics for effective cooling, a quantifiable amount of heat is lost through the heat pipe’s adiabatic section. This heat lost might impair the surrounding components. To resolve this complication, phase change material (PCM) is introduced in heat pipes. Experiment is conducted on the effective thermal conductance of heat pipes by establishing their thermal resistance. Temperature distribution profiles are depicted for different power inputs. A brief CFD model for PCM (Paraffin Wax Tetracosane) is developed; Liquid fraction is extracted for different inputs of power during the charging process. Effective heat captured by PCM for rating of 30–50 W extends from 10 to 15% of the total heat supplied, thereby restricting most of the heat dissipation from reaching the proximity of other components.

G. Gnaneshwar (B) · G. Sundara Subramanian · N. S. Hari Thiagarajan · L. Narayanan · D. Senthil Kumar Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] G. Sundara Subramanian e-mail: [email protected] N. S. Hari Thiagarajan e-mail: [email protected] L. Narayanan e-mail: [email protected] D. Senthil Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_56

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56.1 Introduction Electronic components play a vital role in the daily routine. Heat pipes are heattransfer devices that work on liquid–vapour phase phenomenon and the affiliated flow to attain the heat transfer from a hot source to a cold sink with very minimal deviation in temperature. PCM involves latent heat transfer unlike many other heat exchangers which operate on sensible heat. They are compact and lightweight. Thermal energy storage density of PCM heat sinks is high as compared to traditional heat sinks. Shah et al. [1, 2] conducted studies on various types of wick structures and studies showed that sintered heat pipes have better performance because of higher capillary forces and low thermal resistance. Fadhal et al. [3] studied the various operating limits of a heat pipe: vapour limit, boiling limit, circulation limit and entrainment limit. Nemec et al. [4] discussed the porosity of the wick structure, factors influencing porosity and concluded that it is influenced by sintering temperature along with the sintering time of the metal powders. Annamalai et al. [5] investigated the temperature deviation along the length of the heat pipe at discrete time intervals and the heattransfer mechanism inside a heat pipe. Nookaraju et al. [6] inferred that sintered copper heat pipes are best suited for low power applications. Elnagger et al. [7] discussed the various techniques that can be employed for CPU cooling. Fok et al. [8] elaborately studied the use of PCM as a passive cooling method and its effect on the electronic component. Tan et al. [9] experimented with the cooling of mobile phones by using PCM(n-Eicosane) and proved that it reduces the temperature of the heat storage unit by half when compared to unit without the incorporation of PCM module. Sharifi et al. [10] performed a comparative study between heat pipes without phase change material and heat pipes with phase change materials for different mass flow rates during charging and discharging. Ali et al. [11] experimented on copper heat sinks with PCM (RT-35HC and RT-44HC) and inferred that it exhibits better temperature control and evidence of base temperature reduction in comparison with copper heat sinks with no PCM. Pillai et al. [12] experimented with heat exchangers integrated with normal paraffin wax, nanopowder-enhanced PCMs and compared its performance characteristics.

56.2 Methods and Materials Selection of the heat pipe is carried out after evaluating the requirements and constraints. In order to achieve the cooling requirements of electronics, round heat pipes are chosen. The diameter and length of the heat pipe is decided based on the heat load. Copper–water heat pipe [6, 13] with sintered wick structure is chosen for its high compatibility and also it falls under the optimal operating temperature range of electronics. Higher the latent heat capacity of phase change material, the larger is the amount of energy that can be absorbed from the adiabatic section of the heat pipe.

56 Study on Performance of Phase Change Material … Table 56.1 Heat pipe specifications

Table 56.2 PCM characteristics

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Manufacturer

Tanotis Inc.

Heat pipe material

Copper

Internal diameter

10 mm

Wall thickness

0.7 mm

Pipe length

300 mm

Operating fluid

Water

Wick structure

Sintered Wick

PCM

Tetracosane (C24 H50 )

Latent heat

223 kJ/kg

Thermal conductivity

2 W/m K

Density at 20 °C

798 kg/m3

Liquid specific heat at 300 K

2.275 J/kg K

Absolute viscosity at 300 K

0.00422 Pa s

PCMs do not show sharp melting points, rather they melt over a range of temperature above its melting point. PCM must be chemically inert with the working envelope of heat pipe on integration. Paraffin wax, a type of organic PCM, is chosen for this study as it is more stable and can go through many thermal load cycles when compared to inorganic PCM [7–11]. Specification of the heat pipe and PCM are mentioned in Tables 56.1 and 56.2.

56.2.1 Heat Pipe Thermal Resistance and Thermal Resistance Network Heat is applied at the evaporator region and extracted at the condenser region. There is a finite resistance offered by the heat pipe in the transit of heat from the heating coil to the condensing section. Figure 56.1 represents the heat pipe’s thermal resistance network. Thermal resistance is existent at both vapour and liquid interfaces. Also there exists substantial resistance at the wall-wick interface. Heat pipe performance is considered in terms of the overall thermal resistance. R1 and R10 are resistances at the external surface of heat pipe -source and external surface of heat pipe—sink, respectively. R2 and R8 are the thermal resistances of the heat pipe wall. R3 and R7 are the thermal resistances of the wick structure. R4 and R6 represent the thermal resistance corresponding to the vapour-liquid surfaces. R5 is the thermal resistance of the saturated vapour.

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Fig. 56.1 Thermal resistance network of heat pipe

R9 is the axial conduction thermal resistance through the heat pipe wall. R is the overall thermal resistance; Qs is the total heat supplied; T is the temperature difference between evaporator and condenser Qs =

T R

(56.1)

56.3 Experimentation The experimental setup is shown in Fig. 56.2. The entire apparatus is well placed on a flat surface to ensure optimal testing conditions. Heating coil of maximum power

Fig. 56.2 Experimental setup and position of thermocouples along the axis of the pipe (in mm)

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output 240 W at 220 V, 1A is used to heat the evaporator section of the heat pipe. A dimmerstat of power rating 240 W, 50 Hz frequency with a maximum load of 10 A is used to vary the power supply. The evaporator section along with the heating coil is wrapped with asbestos thread of 4 mm diameter to prevent heat loss to the ambient. The condenser section is cooled using a CPU cooling fan (frontal area: 110 mm * 90 mm) of 12 V DC, 0.28 A rating. Before conducting the experiment, the setup is given 30 min time in order to attain steady-state, thus ensuring the variation of temperature between evaporator and condenser to be ±5 °C. The temperature along the wall the heat pipe is recorded via the standard calibrated Fluke T3000 FC Wireless K-type digital thermocouple. Six K-type thermocouples are placed at regular intervals throughout the heat pipe’s length to measure the surface temperature. The electric power supplied is set to 30 W with the help of the dimmerstat. This procedure is repeated again with the power input set to 40 W and the same is repeated with power at 50 W, respectively. Three trials are conducted to ensure repeatability.

56.4 Thermal Performance Analysis of PCM Numerical simulations of the discretized equation are performed in Fluent 19.1 under 2D, double precision and serial solver. A 2D mesh 120 mm * 12 mm is created and a structured quadrilateral face mapped meshing is made. The mesh is checked for skewness. Transient, volume of fluid model (VOF) model approach, SIMPLE solver is followed. The two lateral sides are given adiabatic boundary conditions to avoid any noticeable changes due to the heat radiated from the neighbouring section. Q S = Q RBC + Q PCM + Q LOSS

(56.2)

Equation (56.2) states the balance of energy between heat supplied (QS ), energy stored by PCM (QPCM ), heat loss (QLOSS ) and energy received by condenser (QRBC ). Input energy by power supply (Qs ) equals the summation of energy storage by PCM (QPCM ), the energy received by the condenser (QRBC ) and heat loss (QLOSS ) [14]. Equation (56.3) below states the energy balance in the PCM section. Q PCM = Q ss + Q m + Q L

(56.3)

Q ss = m pcm cs (Tm − Ti )

(56.4)

Q m = m pcm L f

(56.5)

Q L = m pcm cl (Tf − Tm )

(56.6)

where

584 Table 56.3 Percentage-power distribution

G. Gnaneshwar et al. Power input (W)

QRBC (%)

QPCM (%)

QLOSS (%)

30

77.76

14.96

7.28

40

76.58

14.12

9.3

50

72.14

10.48

17.16

where cs = Specific heat capacity of PCM in solid phase, L f = Latent heat of fusion, cl = Specific heat capacity of PCM in liquid phase. At a power of 30 W, the mass of PCM, mpcm = 0.025 kg; phase change temperature of PCM T m = 51 °C; initial temperature of PCM Ti = 30 °C; final temperature of PCM T f = 79 °C; (Value of T f is taken from CFD analysis). On solving for Eqs. (56.4), (56.5) and (56.6), heat stored by PCM, QPCM = 8099.45 J. The simulation is carried out for 1800 timesteps with each time step for a duration of 1 s. Therefore, it can be accounted to be taken for 1800s. The power is equivalent to 4.49 W [15, 16]. Energy received by a condenser is calculated based on the difference existing between the temperatures at the evaporator and condenser section. Q RBC = mcp T = 25.32 W Percentage of energy absorbed by PCM = 4.49 ∗ 100 = 14.96% 30 23.32 Percentage of heat received by condenser = 30 ∗ 100 = 77.76% Similarly calculations are performed for 40 and 50 W (Table 56.3). Therefore, the net amount of heat stored in the PCM and the heat reaching the condenser section of the heat pipe, respectively, is 92.72% (Summation of heat received by condenser and heat trapped by PCM) for a power input of 30 W. Heat received by the condenser is 77.76%. Heat loss is brought down from 22.24 to 7.28% with the integration of Phase change material n-Tetracosane.

56.5 Results and Discussion 56.5.1 Influence of Heating Power on Liquid Fraction Figure 56.3 shows the effect of heat supply (30, 40, 50 W) on charging of PCM. It is evident from the results that heat input affects the charging process of PCM. By raising the power of the heat source, the heat transfer increases and is reflected at the PCM module. High phase change rate can be achieved by raising the temperature difference between the pipe wall and PCM. From Fig. 56.3, it is observed that there is a steep increase in the liquid fraction from 0.38 to 0.54 when power is increased from 40 to 50 W.

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Fig. 56.3 Comparison of liquid fraction for different heat dissipation rates

56.5.2 Variation in Wall Surface Temperature for Varied Power Input Figure 56.4 shows the graphical data on temperature vs position of thermocouple. The small temperature difference that exists between the evaporator and condenser section depicts the effective thermal conductivity of the heat pipe. Figure 56.5 depicts the response of liquid fraction of PCM for different heat input rates. Heat pipe’s thermal resistance is recorded to be 0.161 °C/W, 0.17 °C/W, 0.146 °C/W for 30 W, 40 W, 50 W, respectively.

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Fig. 56.4 Variation of temperature along axis for input power a 30 W, b 40 W, c 50 W

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Fig. 56.5 Liquid fraction contour of tetracosane when subjected to a 30 W, b 40 W, c 50 W respectively

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56.6 Conclusion In this study, experimentation for thermal resistance offered by heat pipes is conducted; liquid fraction contours of PCM are developed for different power inputs; heat absorbed by PCM for varied heat inputs are recorded. 1. A nominal 12 mm diameter copper heat pipe offers resistance as low as 0.161 °C/W, 0.17 °C/W, 0.146 °C/W for 30 W, 40 W, 50 W, respectively, thereby establishing the effective thermal conductivity of the heat pipe. 2. It is noted that the PCM is able to retain heat effectively for input power range 30–40 W. Above 40 W, the liquid fraction started to increase steeply indicating its inability to store heat and use it effectively. Therefore, 40 W can be considered an optimal power rating to store heat effectively for PCM n-Tetracosane. 3. Heat loss to the ambient is largely controlled by integration of PCM-Tetracosane to the heat pipe. This minimizes thermal fatigue and thermal damage to the surrounding electronic components. Thus, drastic changes in the temperature of the system are avoided.

References 1. B. Shah, Recent trends in heat pipe applications—a review. Int. J. Sci. Eng. Technol. Res. (IJSETR) 5(7) (2016). ISSN: 2278-7798 2. M.N. Hussain, I. Janajreh, Numerical simulation of a cylindrical heat pipe and performance study. Int. J. Therm. Environ. Eng. 12(2), 135–141 (2016) 3. B. Fadhl, L.C. Wrobel, H. Jouhara, Modelling of the thermal behaviour of heat pipes. WIT Trans. Eng. Sci. 83, 377–389 (2014) 4. P. Nemec, Porous Structures in Heat Pipes. Porosity-Process, Technologies and Applications (2017) 5. S.A. Annamalai, V. Ramalingam, Experimental investigation and CFD analysis of an air cooled condenser heat pipe. Thermal Sci. 15(3), 759–772 (2011) 6. B.C. Nookaraju, P.K. Rao, S. Nagasarada, Experimental and numerical analysis of thermal performance in heat pipes. Procedia Eng. 127, 800–808 7. M.H. Elnaggar, E. Edwan, Heat Pipes for Computer Cooling Applications. Electronics Cooling (2016), p. 51 8. F.L. Tan, S.C. Fok, Numerical investigation of phase change material-based heat storage unit on cooling of mobile phone. Heat Transf. Eng. 33(6), 494–504 (2012) 9. F.L. Tan, C.P. Tso, Cooling of mobile electronic devices using phase change materials. Appl. Therm. Eng. 24(2–3), 159–169 (2004) 10. N. Sharifi, S. Wang, T.L. Bergman, A. Faghri, Heat pipe-assisted melting of a phase change material. Int. J. Heat Mass Transf. 55(13–14), 3458–3469 (2012) 11. H.M. Ali, A. Saieed, W. Pao, M. Ali, Copper foam/PCMs based heat sinks: an experimental study for electronic cooling systems. Int. J. Heat Mass Transf. 127, 381–393 (2018) 12. A.S. Pillai, D. Senthilkumar, Experimental investigation of exhaust heat recovery in petrol engine using nano enhanced PCM. Int. J. Mech. Prod. Eng. Res. Dev. 9(4) (2019) 13. R. McGlen, P. Kew, D. Reay, Heat Pipes: Theory, Design and Applications (2006), pp. 431–449 14. S. Bhaskar, W. Katib Ahmed, D. Senthilkumar, Investigation of phase change material as a potential replacement of EGR cooler. Appl. Mech. Mater. 798, 200–204 (2015) (international conference on mechanical and aerospace engineering, Rome, Italy, July 16–17)

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15. Y.A. Cengel., A.J. Ghajar, Heat and Mass Transfer, 5th edn. (2014) 16. Y.A. Cengel., S. Klein, W. Beckman, Heat Transfer: A Practical Approach, Vol. 141 (2003)

Chapter 57

Design and Implementation of Smart Charging for LMV A. Jeevitha, K. Vasudeva Banninthaya, and G. S. Srikanth

Abstract Smart charging indicates the charging system in electric vehicles (EVs) where charging operators and charging station accord data connections. In comparison with uncontrolled charging, the electricity peak usage is flattened by shift in vehicle charging to off-peak hours by smart charging. A synergy is created between the EV and the grid by smart charging. This synergy would not exist without smart charging, and electric vehicles would be an onus on grid. Smart EV charging is powered by an inventive refutation connecting the charging events and devices in real time and brings data to charging station owner’s fingertips. Smart charging process also finds available station for charging and reserves the charging port. The mobile application displays information about the charging power and price, depending upon the required rating and real-time data about the availability of charging stations. With help of Control Area Network (CAN) and universal asynchronous receiver-transmitter (UART), the smart features such as creation of history chart for charging details, charging initiation, battery charge start and stop time along with safety prompts are implemented in this work.

57.1 Introduction The term smart charging delineates the inventive functionality in the electric vehicle charging station which revamps the charging framework by setting up and dispensing the available power in a productive and flexible manner. Over the past decade, smart grid technology has been significantly developed by the introduction electric vehicles A. Jeevitha (B) · K. Vasudeva Banninthaya Department of Electrical and Electronics Engineering, RV College of Engineering, Bengaluru, India e-mail: [email protected] K. Vasudeva Banninthaya e-mail: [email protected] K. Vasudeva Banninthaya · G. S. Srikanth Allbit Technologies, Bengaluru, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_57

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(EVs). This smart grid requires a two-way communication to enable bidirectional flow between new electric appliances [1]. Smart charging safely balances the energy usage between the electric vehicle supply equipment (EVSE) and other devices on site, assuring efficient charging of the vehicle depending on the power availability, saving the customers against grid updates cost [2]. The government is adopting the innovation for public transport, charging stations are made accessible at numerous open parking zones. The count of electric vehicles on road is accustomed to boom in near future [3]. The electric vehicles can embrace the charging patterns to deflate peak demands, correlate load peak and abet actual time stabilizing of grid by mending the charging levels with smart charging [4]. Electric vehicle framework needs brilliant communication network amid vehicle user smart phone, an electric vehicle and an electric vehicle supply equipment (EVSE) for vehicle charging [5]. Advantages of smart charging are as follows: It compiles data on usage patterns to improve system performance, real-time data monitoring of availability and charger status and keeps a track on energy consumption, and network congestion management is done by reserving charging port.

57.1.1 EVSE Charging Levels There are three basic types of EV chargers. The EVSE refers to the AC charging source that supplies the charger in case of level 1 and 2 and the DC power developed by level 3 fast chargers which is delivered directly to the electric vehicle battery as given in the Fig. 57.1 having details of inlet power, 24 kWH battery charging time and the charge power.

Fig. 57.1 EVSE charging levels

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Level 1 Charging. AC charging is referred to as level 1 where 120 V single-phase AC is used. The socket outlet is connected to the vehicle via domestic power connector in level 1 EVSE. This is normally referred to the house outlet for charging. Level 2 Charging. This charger is built in a console, and the charging current is supplied through a cord. The voltage level ranges from 208 to 240 V, and the full charge takes up to 4–6 h, ideal for overnight charging. Their charging time is 15 times faster when compared to level 1. Level 3 DC Charging. This charger consists of high-voltage AC-DC power supply that bypasses the vehicle to deliver very high charging level. They range from 200 to 450 V DC and 200A. The standard charger is SAE J1772. The source for this is general storage medium which is able to deliver the required power for charging, and it is then recharged from grid at lesser rates.

57.2 System Description The system consists of the CAN model in the basic charging station where the data from the vehicle dashboard is extracted and given to the microcontroller. The microcontroller processes the data and is fed to the universal asynchronous receiver transmitter where the data is transmitted to the host controller. The Bluetooth module is used as the host controller to convey the charging status to the vehicle user’s smart phone. The CAN protocol plays a major role in the communication between the EV and the EVSE. The features of the protocol are multi-master bus access, robust in noisy environment, data length (0–8 bytes), data rate up to 1 megabit/s, excellent error detection and fault confinement.

57.3 Block Diagram The proposed schematic smart charging system is illustrated in Fig. 57.2. An EVSE delivers the information regarding power to the electric vehicle using the CAN. The charging connector in the basic charging station when connected to the vehicle draws data regarding the battery status from the vehicle CAN bus. The CAN H and L from the socket outlet are given to the CAN driver. The output of the CAN driver is connected to the microcontroller where the data in the hex format is read and converted to decimal value. The converted data from the microcontroller is serially transmitted to the Bluetooth module through UART. The BL652 Bluetooth module is used as the host controller which transmits the data to the user application in customers smart phone. Further, the Bluetooth module can be integrated with either GPRS or Wi-Fi/LAN connectivity. The data can further be pushed to the cloud in the backend and accessed anytime when needed. The data

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Fig. 57.2 Schematic of the proposed implementation of smart charging

includes information regarding the customer, vehicle, charging history and billing details. The power supply for communication is provided by 12 V battery.

57.4 CAN Data Extraction The CAN data extraction is carried out with the oscilloscope of Tektronix. The Tektronix oscilloscope provides a wide range of options to read, trigger and decode the data from the bus. It is used by industries for better analysis of the system and educational institutions for teaching and research. The CAN data from the vehicle dashboard is read from the controller area network bus. To read the data, the CAN board is necessary in which the CAN high and low pins are connected. The CAN board is as shown in Fig. 57.3, which consists of the CAN transceivers, microcontroller and the power supply. The CAN high and low pins are connected to the oscilloscope to read the data. The CAN high from the board is connected to the oscilloscope channel 1, and the other probe is connected to the ground. The power to the board is given through the power supply cord.

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Fig. 57.3 CAN board

57.5 Results The smart features are designed using the Proto-io software. The prototype of the application has been developed. The screenshots of the application are depicted below. The home screen and the login screen are as displayed in Fig. 57.4. The login screen contains the username and the password. The sign-up screen consists of two profiles to be filled before logging in, the customer profile and the vehicle profile as shown in Fig. 57.5 which are to be completed for registration. After successful sign-up, the succesful sign-up and main menu screen are as shown in Fig. 57.6. The main menu screen consists of options such as selection of type of charging and access to the profile, charging trends, scheduling and charging process. After successful sign-up, the unique user ID is provided. The schedule screen and charge plug-in screen are as shown in Fig. 57.7. The schedule screen provides options to select the charging port based on availability and also option for reservation of the port based on customer requirement. The charge

596

Fig. 57.4 a Home screen, b login screen

Fig. 57.5 a Customer profile, b vehicle profile

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57 Design and Implementation of Smart Charging for LMV

Fig. 57.6 a Sign-up successful b main menu

Fig. 57.7 a Schedule, b charge

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Fig. 57.8 a Charging process, b payment gateway

screen provides option to select the type of connector. It also provides safety pop up when the plug is not rightly plugged-in. The charging process is initiated only when the user initiates the start button in the application. The charging process screen and the payment gateway are as shown in Fig. 57.8. The charging process screen provides the information regarding the initial battery charge percentage, time for full charge, charging indication and the stop button to terminate the charging process. The payment gateway provides details such as the charging duration, energy consumed and the amount to be paid. The user account info and the charging trends screen are as shown in Fig. 57.9. The account info consists of details such as user profile, payment methods, update data to cloud and sign out. The charging trends consist the history of the charging process like energy consumed, amount paid and type of charging so that the user can keep a track of the process.

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Fig. 57.9 a Account info, b charging trends

57.6 Conclusion The communication between the vehicle and the charging station is of utmost importance. Therefore, smart charging features are implemented for LMV in the proposed work. The prototype of the application is developed. The features include creation of login, successful registration, ID generation, type of charging selection, reservation of charging port and plug-in safety indication. Charging process displays the initial battery percentage, time for full charge and the initiation to stop the charge. The application also provides features such as payment gateway, account information and charging trends to keep a track of the charging activity. Hence, the customer avails benefits like zero waiting period for charging and reduction in cost by selecting economical type of charging based on customer need. Acknowledgements This work is supported by Allbit Technologies LLP, Bengaluru, India.

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References 1. S. Deilami, S.M. Muyeen, An insight into practical solutions for electric vehicles charging in smart grid. MDPI J. Energies 13, 1545 (2020). https://doi.org/10.3390/en13071545 2. D. Said, H.T. Mouftah, A novel electric vehicles charging/discharging management protocol based on queuing model. IEEE Trans. Intell. Veh., Canada.ISSN: 2379-8904 (2019) 3. A. Anisie, F. Boshell, J. Sesma, Innovation landscape brief: electric-vehicle smart charging. IRENA. ISBN 978-92-9260-141-6 (2019) 4. L. Bedogni, L. Bononi, A. D’Elia, A mobile application to assist electric vehicles drivers with charging services, in 2014 Eighth International Conference on Next Generation Mobile Applications, Services and Technologies, Italy. ISBN:978-1-4799-5073-7 (2014) 5. C. Chung, A. Shepelev, C. Qiu, C. Chu, R. Gadh, Design of RFID mesh network for electric vehicle smart charging infrastructure, in IEEE RFID TA 2013, Johor Bahru, Malaysia, 4–5 Sept., 2013.https://doi.org/10.1109/RFID-TA.2013.6694512

Chapter 58

Experimental Transient Analysis of Radial Flow Clay Desiccant Packed Bed Abhijeet Boche and Ravikiran Kadoli

Abstract In this work, an experimental investigation on the desiccant bed with radial flow is discussed. The spherical clay balls with the average diameter of 11 mm is used as desiccant in the radial bed. Two sizes of radial test section are developed based on the diameter of the inner cylinder and outer cylinder. The diameter ratio is evaluated based on the space needed to accommodate the spherical clay desiccant as a single layer and two layered. For the present work, the behavior of 850 g of clay desiccant in the single layer and double layer radial packed bed is being compared. Both the experimental test units were kept at nearly same relative humidity during the process of adsorption and nearly same temperature during desorption process. The parameters such as exit air humidity, exit air temperature, mean bed temperature during the process of adsorption, and desorption are being compared.

58.1 Introduction The humidity in the closed environment can be efficiently controlled with the use of desiccants. The desiccants for air conditioning applications can reduce the latent heat load, and this results in decrease in electricity consumption [1]. But, nowadays, the desiccants are also found to be useful in several other applications. The desiccants can be useful in the thermochemical energy storage and found to be more efficient compared to the latent heat storage and sensible heat storage [2]. In active food packaging concept, it involves absorption of different gases such as oxygen, water vapor, carbon dioxide, and odor [3]. The solar drying of crops with desiccant can reduce the time nearly to half compared to conventional methods [4]. A. Boche (B) · R. Kadoli Department of Mechanical Engineering, National Institute of Technology Surathkal, Surathkal, Karnataka 575025, India e-mail: [email protected] R. Kadoli e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_58

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The commonly used desiccants in the packed bed are silica gel, zeolite, activated alumina, artificial fiber desiccants, etc. The desiccants made from natural fiber are called as green desiccants [5]. Such desiccant can utilize agricultural waste for producing cheap desiccants [6]. Clay is naturally available solid desiccant. Still, it has comparable capacity with the commercial desiccants [7]. The moisture sorption performance at 85% RH and 25 °C for 120 h is calculated, while desorption performance at 20% RH and 50 °C is taken. It is found that cost of the clay desiccant is one-ninth the cost of commercial silica gel [8]. It also shows that there is lack of data on the long-term life cycle performance and data for range of static and dynamic adsorption and regeneration process. The performance of the vertical bed can be improved with reducing the distance travelled by air through the bed. And, this can be done homogeneously with the use of radial bed compared to vertical bed [9]. There are studies available on the radial bed with commercial silica gel. But, the clay desiccants in a radial bed has not been explored.

58.2 Preparation of Clay Desiccant Laterite clay was collected from the local paddy fields of Dakshina Kannada district, Karnataka, India. Laterite clay is used to manufacture tiles for roof, earthen wares for cooking, storage of water, grains, and cereals. Clay desiccant in the spherical form is prepared manually and used as working desiccant in the radial packed bed. Firstly, the clay is mixed with proper proportion of water, so that the clay could be handled manually to knead and form to any shape required. Moisture laden clay is accommodated into a plastic tube, so that long cylindrical pellet of 6 mm diameter could be obtained. The long pellet is cut to a length of 30 mm. The cylindrical pellet was shaped to form a spherical clay ball, and the volume of spherical clay ball would be around the volume of 11 mm diameter spherical ball. The lesser the diameter of cylindrical pellet, the accuracy of cutting the material would be more and late obtaining an exact volume of 11 mm diameter spherical ball would be better. The balls were kept in open environment for 2–3 days. Approximately, 1 kg of spherical clay balls were prepared. The spherical clay balls were heated to 500 °C in the muffle furnace for 8 h. The weight reduction in clay balls was around 97.3 g/kg of balls. The balls were left in the furnace for the period of 24 h to cool down gradually. After cooling, balls were kept in zip lock polythene bags to avoid any contact with the atmosphere.

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58.3 Experimental Setup The arrangement and the various components are shown in the schematic diagram, Fig. 58.1. The air supplied during the process of adsorption and desorption is taken through the tank connected to the screw compressor. The experimental setup mainly consists of a humidifier, air heater, mixing chamber, and the test chamber. The test chamber is so constructed that the radial flow of process air takes place through the desiccant packed bed. During the process of adsorption, the humidifier and mixing chamber receive air through the screw compressor. While during the process of desorption, the air is supplied only to the air heater which is directly fed to the test chamber. Since, sometimes the water droplets can come through the outlet of humidifier in the mixing chamber, a check valve made up of nut and screw is provided in the mixing chamber. The water drips regularly through this check valve keeping the mixing chamber dry. In the mixing chamber, one of air supply comes through the humidifier which is completely saturated air. However, other supply comes through the screw compressor which is almost dry. Hence, by adjusting the flow rate through these outlets, a desired air supply at a required specific humidity ratio can be let into the test chamber.

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The ultrasonic type of humidifier is used for air humidification process. The particle size of water droplets in the mist obtained is in the scale of nanometer [10]. The capacity of humidifier used for current experiment is three liters per hour. The electric type of air heater is used for heating the air during the process of desorption of desiccant packed bed. The air heater used has the capacity of 3 kW.

58.3.1 Desiccant Packed Bed The desiccant packed bed is due to the annular space created between two co-axial hollow cylinders. The cylinder referred to here are perforated type or sheet of wire mesh formed into a hollow cylinder. The inner lateral surface of the inner hollow cylinder paves way for entry of process air and the outer surface of the outer hollow cylinder provides exit of the process air. Hence, the radial flow desiccant bed. The clay desiccant are accommodated in the annular space between the cylinders. The two hollow cylinder are assembled and an acrylic sheet is used to close top and bottom end of the hollow cylinder to avoid air flow axially. To maintain even space between the hollow cylinders, spacers made up of acrylic are tightened with the nut and bolt, and 12 mm gap is maintained, so that only one layer of spherical balls can be placed in the annular space between cylinders. The size of annular space (or diameter ratio) is defined by the ratio of the diameter of the outer cylinder to the diameter of the inner cylinder. For the diameter ratio of 1.27, two steel wire mesh sheets are formed into cylinders with diameter 110 and 140 mm. While for 1.54 diameter ratio, three steel mesh cylinders with diameter 110, 140 and 170 mm are used. The hollow cylinder assembly is placed in an acrylic cylinder of diameter 25 cm and height 33 cm. This forms the outer space for the radial bed and the air exiting radially gets collected here before it is let to atmosphere.

58.3.2 Instrumentation and Calibration For the measurement of temperature and humidity ratio of air, the humidity sensors DHT22 with the resolution of 0.1% in relative humidity and 0.2 °C in temperature are used. For measuring the temperature of bed, two thermocouples at the different heights of desiccant beds are used. The mean of the two values given by the thermocouples is used as the bed temperature. For the measurement of air flow rate, vane anemometer with range 0–30 m/s and resolution 0.1 m/s is used. The thermocouples used to measure the temperature of bed are calibrated using thermometer. There is ±2 °C linear variation with the true value. For the calibration of humidity sensors, the saturated salt solutions are used as standards [11].

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58.4 Experimental Procedure During the process of adsorption, a constant humidity ratio is required to maintain in the test chamber. Firstly, by adjusting the flow rate from humidifier and screw compressor, the required conditions are checked by measuring the humidity at bypass provided before the test chamber. Then, the supply is given to the test chamber for nearly 60–70 min. Similarly, during the process of desorption, air supply from heater is given to the test chamber after achieving the equilibrium temperature. The process of desorption is carried out for 20–25 min. The following are the parameters measured during the experimental procedure: inlet and exit humidity ratio of air, exit air temperature with time, and bed temperature with time.

58.5 Results and Discussion For the diameter ratio of 1.27, the weight of desiccant is about 852.6 g. The flow rate is about 5.07 L/s. The inlet humidity maintains around the 86.5% of percentage relative humidity. The variation of the exit air relative humidity with the inlet conditions is shown in Fig. 58.2. From Fig. 58.2, it can be seen that the desiccant bed can show more difference in relative humidity during first 15 min and becomes saturated after the operation of 45 min. To compare this transient variation, a second experimental test has carried out. In this test, the diameter ratio is about 1.54 which means two layers of desiccant balls with half the height of bed. The weight of balls in the bed is 851.2 g. Here, the relative humidity is also maintained around 86.5% and the flow rate of about 9.46 L/s. The comparison between the exit relative humidity is shown in Fig. 58.3. 100

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From the variation of exit air temperature in Fig. 58.4, it can be seen that the decrease in temperature of air from initial condition of air is about 7.75 °C in the case of 1.27 diameter ratio and about 9.75 °C for 1.54 diameter ratio. Hence, it can be seen that the temperature drop of exit air is more in case of 1.54 diameter ratio. Similarly, in case of mean bed temperature as shown in Fig. 58.5, the bed temperature is 2.37 °C is lesser for bed having diameter ratio 1.54. The exit air humidity plot starts at the percentage relative humidity of 58.2% and 55.7% for the diameter ratio of 1.27 and 1.54, respectively. From Fig. 58.6, it can be seen that the time taken to regenerate by the desiccant bed having diameter ratio 1.54 is less. Also, by measuring the weight of bed before and after the desorption Fig. 58.4 Plot between exit air temperature with time for diameter ratio of 1.27 and 1.54

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and adsorption process, the weight of the desiccant was 2 g less. Weight reduction of 2 g shows the moisture gain by the desiccant during the storage time.

58.6 Conclusions From the experiment analysis, the following conclusions can be made. • During the initial period of adsorption process, the adsorption rate is higher. As the time proceeds, the rate decreases drastically after the period of 15 min from the

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beginning. For the higher diameter ratio, the adsorption rate is higher for longer period of time. For the complete saturation of the desiccant bed, the period of 60–70 min is sufficient for the 850 g of desiccant bed. This period increases with increase in diameter ratio. For the complete regeneration of bed, the period of 20–25 min is sufficient for 850 g of desiccant bed. This period decreases with increase in the diameter ratio. During the process of adsorption, the temperature drops from the beginning to the end of experimental test, and the mean bed temperature is more for higher diameter ratio. The higher diameter ratio is suitable for applications which required lower difference in inlet and outlet humidity for longer periods of time, whereas the lower diameter ratio beds can be useful in applications which requires higher difference in humidity for short period of time. The regeneration time for higher diameter ratio is less. Hence, higher diameter ratio is suitable for the applications which involves frequent cyclic operations.

References 1. K.C. Chan, C.Y.H. Chao, G.N. Sze-To, K.S. Hui, Performance predictions for a new zeolite 13X/ CaCl2 composite adsorbent for adsorption cooling systems. Int. J. Heat Mass Transf. 55(11–12), 3214–3224 (2012) 2. S.P. Casey, J. Elvins, S. Riffat, A. Robinson, Salt impregnated desiccant matrices for ‘open’ thermochemical energy storage—selection, synthesis and characterization of candidate materials. Energy Build. 84(412–425), 3 (2014) 3. L. Vermeiren, F. Devlieghere, M.V. Beest, N.D. Kruijf, J. Debevere, Developments in the active packaging of foods. Trends Food Sci. Technol. 10(3), 77–86 (1999) 4. S.F. Dina, H. Ambarita, F.H. Napitupulu, H. Kawai, Study on effectiveness of continuous solar dryer integrated with desiccant thermal storage for drying cocoa beans. Case Stud. Therm. Eng. 5, 32–40 (2015) 5. N. Asim, Z. Emdadi, M. Mohammad, M.A. Yarmo, K. Sopian, Agricultural solid wastes for green desiccant applications. J. Clean. Prod. 91, 26–35 (2015) 6. Z. Emdadi, N. Asim, M. Ambar Yarmo, M. Ebadi, M. Mohammad, K. Sopian, Chemically treated rice husk blends as green desiccant materials for industrial application. Chem. Eng. Technol. 40(9), 1619–1629 (2017) 7. C.S. Tretiak, N.B. Abdallah, Sorption and desorption characteristics of a packed bed of clayCaCl2 desiccant particles. Sol. Energy 83(10), 1861–1870 (2009) 8. T.F.N. Thoruwa, C.M. Johnstone, A.D. Grant, J.E. Smith, Novel, low cost CaCl2 based desiccants for solar crop drying applications. Renew. Energy 19(4), 513–520 (2000) 9. M.M. Awad, K.A. Ramzy, A.M. Hamed, M.M. Bekheit, Theoretical and experimental investigation on the radial flow desiccant dehumidification bed. Appl. Therm. Eng. 28(1), 75–85 (2008) 10. R.J. Lang, Ultrasonic atomization of liquids. Acustica 341(1954), 28–30 (1962) 11. R.W. Hyland, C.W. Hurley, General guidelines for the on-site calibration of humidity and moisture control systems in buildings stand. Build. Sci. Ser Natl. 157 (1983)

Chapter 59

Coral—A Smart Water Body Health Monitoring System Saket Vaibhav, R. Shakthivel, Nikhil Suresh, S. Jyothsna, Arijit Datta, and K. Chitra

Abstract A lot of water bodies around the world are suffering from severe contamination which poses problems to the marine life as well as to all those living around them. Such problem could be brought down by just monitoring the water body. So, this paper mainly aims on the development of a microcontroller-based water quality monitoring system by measuring various decisive parameters like pH and temperature. The performance of the device has been corroborated by considering various water samples like mixture of lemon juice and water, soda, bottled water, tap water and mixture of laundry detergent. The developed system is being observed to efficiently measure pH and temperature with a maximum relative error of 3.75% and 2.65%, respectively. The accuracy and robustness of the proposed system coupled with its inherent simplicity and ability to display real-time results on a self-designed website establish itself as a potent tool for water quality monitoring purpose.

S. Vaibhav (B) · R. Shakthivel · N. Suresh · S. Jyothsna · A. Datta · K. Chitra Department of Electrical & Electronics Engineering, CMR Institute of Technology, Bengaluru 560037, India e-mail: [email protected] R. Shakthivel e-mail: [email protected] N. Suresh e-mail: [email protected] S. Jyothsna e-mail: [email protected] A. Datta e-mail: [email protected] K. Chitra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_59

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59.1 Introduction One problem that is apparent world over is the water crisis. There is a shortage of fresh potable water, especially during the hotter seasons. Even with the existing water crisis lakes and ponds in cities are being dumped with industrial waste. The harmful chemicals in the waste have a very adverse effect on marine life in the water body and also produce froth in certain cases. The froth is highly inflammable and on catching fire releases chemicals into the air as well. Apart from this, the ground waterbed in close vicinity to the water body can also be affected. Till date, various novel water body monitoring systems have been developed by various researchers like by using preferred classification and neural network [1] and by using unmanned surface vehicle [2], etc. In past years, a survey was conducted to review the existing research in this domain to acquire methodological insight, understand the subject vocabulary and eliminate fruitless approaches. Shafi et al. proposed a real-time Internet of things (IoT)-based water quality system capable of measuring the quality of water [3]. Similarly, another device has been developed for real-time in-pipe contamination detection by Kavi Priya and team [4]. Taking various physical parameters of water into account, another device was developed for an automated water quality monitoring system by Rizqi Putri and team [5]. In parallel, Dandekar et al. developed a water grade tracking system for the same purpose [6]. Similarly, in order to do away with errors associated with manual water grade monitoring system, Brinda Das et al. have developed a similar IoT device using sensors [7]. With the aim of making the device affordable, Omar Faruq et al. have designed and implemented a low-cost water quality evaluation system [8]. Indu et al. have modelled, developed and analysed a device for water quality monitoring using products available in the market and were able to bring the cost down substantially [9]. An extensive review on a smart real-time water quality and usage monitoring system has been designed by Manish Kumar Jha and team [10]. As for the sensors, important parameters considered in determining the water quality are temperature, pH, turbidity and conductivity. Several cheaper and effective alternatives have been developed by researchers. Shamim Ara Shawkat and team developed a single chip ISFET-based pH sensor which has improved sensitivity [11]. Optical fibre pH sensors have been examined for its desirable properties such as small size, remote sensing capability and resistance to chemicals[12]. In regard to the outer shell configuration, Wenjing Su et al. have presented a comprehensive review of advantages of spherical shape over other buoy configurations [13]. Spherical shape is desirable mainly for its omni-directional gain and MIMO applications. The literature review gives a fair idea about the current developments in this domain which motivates us to reach out to the areas that have not been explored yet. The next section throws light on the design aspects and implementation of the model developed. Therefore, the water bodies around us require immediate attention and hence there is a need for an automated system that monitors the health of a water body and provides the live data. The model proposed in this paper will cater to these needs. It is a mesh network consisting of multiple bots spread across the entire water body, each

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with their own set of sensors such as a pH sensor and temperature sensor. A single mother bot is present in each swarm, which relays the information back to the server. The mesh network is a self-healing and dynamic network which connects the mother bot to all the nodes present within range. The mesh network has been constructed using the painless mesh library for the esp32 and esp8266 boards. The server will display a live feed of all the sensor data on our website. Certain thresholds for the parameters will be tracked and when the threshold is crossed a relevant message will be put up on social media. This will create awareness, which in turn will put pressure on the government and local civic bodies to take action against those dumping waste into the lakes and also to clean up the water bodies.

59.2 Proposed Model Taking inspiration from the industrial buoy’s used to monitor weather or tide movements on seas and other water bodies; we decided to base our design on the same since it was pretty similar in structural composition and workings. After multiple revisions of design and research, we decided it has to be lightweight and compact, but at the same time, we did not want to compromise on the kind of materials we used. We soon realized that a spherical design had to be the most space-efficient design for our device and could be easily fabricated as well (Fig. 59.1). One of the main advantages of having a spherical design was tapping into the omni-directional gain, we can obtain from having a design that aids better signal communication which would help us with the overall data transmission. As the device is deployed into water bodies and is supported by an array of electronic components, it was important for us to stress on what materials were used to build the device. Protection from any kind of moisture and making it as water resistant as possible was a main concern. Fig. 59.1 Digital drawing of the bot body design

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Fig. 59.2 3D model render of the bot body design

The device consists of an enclosure made of polypropylene or some recyclable thermoplastic material, inside is a battery deck and housing for the Arduino board and the circuit boards that allow for the devices to be connected with each other. The bottom section of the device is used up by a sensor housing unit that also acts as an anchor to hold the device in place when there is a high tide; it consists of all the sensors that monitor the condition of the water body in real time and transmits the data onto the servers. Use of different grades of plastics to build the device has been considered as plastics can endure the harsh conditions in the water body and insulate the device from any kind of moisture entering inside. If feasible, or as a future prospect, we would like to use the same plastics that pollute the lake beds/water bodies to be recycled and used as a filament for the fabrication of our devices. This would be sustainable and a cost-effective option as we are sourcing the raw materials to fabricate this project. Nylon filaments are considered to be used as 3D printing filaments and injection moulding would be used for the moulding of the device, this would be a lot easier for us to do if the shape of the device is spherical in nature (Fig. 59.2).

59.3 Implementation The proposed system mainly has two major components, the swarm and the server. The swarm comprises of the bots and the sensing part of the system. The mesh which is deployed in each of the water bodies mainly consists of two entities, the scout and

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the mother bot. The scouts are ESP32 microcontrollers which have their own WiFi module and multiple analogue pins for the purpose of connecting sensors. The sensors connected to the ESP32 will be the pH sensor and a temperature sensor. The pH sensor module will help us determine the pH of the water body and the relative pollution of the lake as the aquatic life only survive and proliferates in a certain range of pH values. The temperature sensor used here is a DS18B20 which is a waterproof temperature sensor and has a measuring range from −55 to +125 °C. The DS18B20 only requires one data pin (apart from that are the ground and power pins) to operate. Thus, the ESP32 has multiple free pins to accommodate any future sensor additions that need to be made. The mother bot will be an esp8266 microcontroller which has a relatively smaller form factor as it will be paired with a GSM module. The built-in Wi-Fi module is used to connect with the rest of the scouts and the GSM module is used to establish a link to the server. The mother bot, as well as the scouts, are powered using LiPo batteries and use a deep sleep function which is used to put the microcontrollers in sleep mode in between two sensing periods so as to prolong the duration of operation. The mesh network is used to establish a link between all the nodes spread across the water body. This project uses the painless mesh library which is a simple implementation of a mesh network in which the scouts and mother bot periodically scan for new nodes and assimilate them into the mesh. Painless mesh identifies each node using the esp8266 board’s unique chip ID to be able to send and receive data to particular nodes on the network. It sends and receives information in the form of JSON objects (Fig. 59.3). Once the scout is on, they start a periodic scan to look for new nodes on the network and add them to the list of nodes. The sensor data is read from the individual sensors and the collected information is sent to the mother bot through the mesh network. The mother bot broadcasts its unique node ID so that the scouts can identify it. Once a certain period of sensing has elapsed the scouts will go into a sleep mode for a set duration of time, this cycle will continue to repeat itself. The only difference with the mother bot is that it also has to establish a link to the server using the GSM module. Once the information from all the nodes is received, the data is published to the server after which the mother bot goes into sleep mode for the set duration. To make the project cost-effectiveness, the website is hosted through Github Pages. To store the JSON data that is received from the sensors, Github Gist is used. There is sufficient delay before the mother bot publishes data and this helps in not overloading and is a good practice. This delay period is when the bots are put to sleep. To allow the website to update with live data, the website uses the API from Gist by making HTTP requests. Again, to not make continuous requests, this is restricted to a one time request during the page load (Fig. 59.4).

59.4 Results and Discussion The website is now live at https://crypticsocket.github.io/Coral/. It shows data for the last tests done with the bots. The website is ready to accept data from any number of

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Fig. 59.3 Schematic diagram of interfacing of the pH sensor and underwater temperature sensor

scouts. As the number of scouts increases, the number of blocks in the home page will populate displaying data from each of the scouts. The website also has an option to tweet which gives a link back to the website. In this way, people wanting to help us in creating awareness can do so. A few tests were conducted to show the accuracy of the sensors that have been used in this device, the results are as follows (Fig. 59.5): In the graph shown here, x-axis contains various samples and y-axis contains pH value. A pH scale ranges from 0 to 14. Samples with pH value below 7 are said to be acidic, above 7 are said to be basic and 7 is said to be neutral. From the graph, it is seen that sample E has a pH value of 9.84 marking the highest amongst the samples which implies that sample E is basic in nature. Sample A has a pH value of 3.85 marking the lowest amongst the samples which implies sample A is acidic in nature. Sample B has a pH value of 4.46 which is mildly acidic. Sample C has a pH value of 6.9 which is almost neutral. Sample D has a pH value of 8.1 which is a weak base. In the graph shown here, x-axis contains various samples and y-axis contains the relative error in pH measurement in percentage. Relative error refers to the difference between the actual value and the calculated value. From the graph, it is seen that sample A has maximum error in pH measurement which is 3.75%. Sample B seemed to have minimum error in pH measurement which is 1.5%. Samples C, D and E have errors in the range of 1.75–2.5% (Figs. 59.6 and 59.7).

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Fig. 59.4 Flowchart depicting the control flow in the mother bot

The DS18B20 sensor has an accuracy of ±0.5 °C from −10 to +85 °C and ±2 °C accuracy from −55 to +125 °C. The test was carried out in samples of water at different temperatures with the sensor and a trustable thermometer to check for deviation.

616 Fig. 59.5 Graph depicting the pH of various test samples

Fig. 59.6 Graph depicting the relative error in measurement of pH of various test samples

Fig. 59.7 Graph depicting the relative error in measurement of temperature of various test samples

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59.5 Conclusion Polluted water can pose a lot of problems. A lot of the water bodies can also serve as freshwater sources thereby reducing the problem of water scarcity. It was also noticed during the global lockdown that if the water bodies are left without human interference, they can self cleanse. Hence, it is important to measure the pollution levels of the water bodies to keep it in check, effectively and economically. Its us humans who have created the issue and thus we must take a step towards fixing it. As a future study, solar panels can be incorporated into the body of the bots so as to further prolong the operating periods of the entire system. Sensors such as turbidity sensor and conductivity sensor can also be added to the system so as to measure even more parameters. LoRa can be incorporated into the system to have a much greater range and machine learning techniques can be used to predict the quality of the water body and prevent worsening of the situation well before it has reached a critical stage.

References 1. L. Sheng, J. Zhou, X. Li, Y. Pan, L. Liu, Water quality prediction method based on preferred classification. IET Cyber-Phys. Syst. Theory Appl. 5, 176–180 (2020) 2. D. Madeo, A. Pozzebon, C. Mocenni, D. Bertoni, A low-cost unmanned surface vehicle for pervasive water quality monitoring. IEEE Trans. Instrum. Measure. 69, 1433–1444 (2020) 3. U. Shafi, R. Mumtaz, H. Anwar, A.M. Qamar, H. Khurshid, Surface water pollution detection using internet of things, in 15th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT), Islamabad (2018), pp. 92–96. https://doi.org/10.1109/ HONET.2018.8551341 4. S.K. Priya, G. Shenbagalakshmi, T. Revathi, IoT based automation of real time in-pipe contamination detection system in drinking water, in International Conference on Communication and Signal Processing (ICCSP), Chennai (2018), pp. 1014–1018. https://doi.org/10.1109/ICCSP. 2018.8524255 5. R.P.N. Budiarti, A. Tjahjono, M. Hariadi, M.H. Purnomo, Development of IoT for automated water quality monitoring system, in: International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), Jember, Indonesia (2019), pp. 211–216. https://doi.org/10.1109/ICOMITEE.2019.8920900 6. S. Dandekar, S.S. Kadam, R.N. Choudhary, S.S. Vaidya, V.S. Rajderkar, IOT based real time water grade tracking system using solar energy, in: 3rd International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India (2018), pp. 773–775. https://doi. org/10.1109/CESYS.2018.8723874 7. B. Das, P.C. Jain, Real-time water quality monitoring system using Internet of Things, in: International Conference on Computer, Communications and Electronics (Comptelix), Jaipur (2017), pp. 78–82. https://doi.org/10.1109/COMPTELIX.2017.8003942 8. M. O. Faruq, I.H. Emu, M.N. Haque, M. Dey, N.K. Das, M. Dey, Design and implementation of cost effective water quality evaluation system, in: IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka (2017), pp. 860–863. https://doi.org/10.1109/R10-HTC.2017. 8289089 9. K. Indu, J.J. Choondal, Modeling, development & analysis of low cost device for water quality testing, in: IEEE Annual India Conference (INDICON) Bangalore (2016), , pp. 1–6. https:// doi.org/10.1109/INDICON.2016.7839131

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10. M. Kumar Jha, R. Kumari Sah, M.S. Rashmitha, R. Sinha, B. Sujatha, K.V. Suma, Smart water monitoring system for real-time water quality and usage monitoring, in: International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore (2018), pp. 617–621. https://doi.org/10.1109/ICIRCA.2018.8597179 11. M.S.A. Shawkat, N. McFarlane, A single-chip ISFET based pH sensor, in: IEEE Sensors, Orlando, FL (2016), pp. 1–3. https://doi.org/10.1109/ICSENS.2016.7808833 12. T.H. Nguyen, T. Venugopalan, T. Sun, K.T.V. Grattan, Intrinsic fiber optic pH sensor for measurement of pH values in the range of 0.5–6. IEEE Sens. J. 16, 881–887 (2016). https:// doi.org/10.1109/JSEN.2015.2490583 13. W. Su, S. Wang, R. Bahr, M.M. Tentzeris, Smart floating balls: 3D printed spherical antennas and sensors for water quality monitoring, in IEEE/MTT-S International Microwave SymposiumIMS, Philadelphia, PA (2018), pp. 55–58. https://doi.org/10.1109/MWSYM.2018.8439350

Chapter 60

Recent Investigation on Ultrasonic Machining of Aluminum Metal Matrix Composite Rajkumar Ashok Patil-Tekale, Aditya Gadekar, Yash Gadhade, Laukik Parakh, R. Balaji, and Ashish Selokar Abstract The manufacturing scenario demands that a material with reduced tool wear be manufactured in higher surface finish. Grinding is primarily considered to have outstanding finishing of the surface in certain machining techniques. The scope for improving always occurs in the processes. MRR will not be impaired in the meantime. As this has a significant effect on the process efficiency. The traditional aluminum surface grinding process needs high cutting force and a very low removal rate. Ultrasonic machining (UM) is assisted for surface grinding of Al-MMC in order to minimize and remove these intricacies. This paper provides a study of the mechanical and tribological properties of composites produced by various methods in the aluminum metal matrix. Hybrid composites have shown excellent flexibility in their performance. The benefits of aluminum metal matrix composites (MMCs) include a high weight to strength, high corrosion, and comparatively low cost wear resistance. Due to its thermal stability and exceptional physical strength, they are used in many applications such as concrete, structural, naval, aerospace, defense, and automotive. These MMCs are non-conventional engineering materials, reinforced by materials with improved mechanical and tribological characteristics, factor-designed behavior, which explores the impact of vibration and turning condition on the surface finish of the turning MMC, demonstrate that the roughness of the MMC turned on with ultrasounds. Turning with vibration produces normal surface profiles in the direction of turning and vibration, leading to a process of light dispersion. The pitch R. A. Patil-Tekale · A. Gadekar · Y. Gadhade · L. Parakh Department of Mechatronics Engineering, Terna Engineering College, Nerul (W), Navi Mumbai 400706, India e-mail: [email protected] R. Balaji (B) Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu 603103, India e-mail: [email protected] A. Selokar Department of Physics, Mohsinbhai Zaweri Mahavidyalaya, Desaiganj (Wadsa), Maharashtra 441207, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_60

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of the ordinary profiles is just within a limited spectrum of this phenomenon. In this study, interdisciplinary observations on Al metal matrix were carried out with UM under the reaction of surface grinding by the material. This paper provides guidelines for upcoming research studies on the effects of Al-MMC’s results of ultrasonic machining studies.

60.1 Introduction Metal matrix nano-composites (MMNC) are materials consisting of owing to MMNCs that are more characteristics like superior performance, strong-elasticity plates, outstanding wear resistance, strong defined performance, improved rigidity, and low coefficient of thermal expansion, two or three different materials in which a soft material is reinforced with hard materials. As a matrix element, Al 7075 is used. The key part of the alloy is zinc as well as a magnesium alloy that primarily significantly improves the wetting of matrix and reinforcement. The use of conventional process techniques creates challenges for the machine through ceramic reinforcement particles in different shapes (continuous fiber, particles, and whiskers). WEDM is one of the most flexible processes for the development of rigid composites, complicated forms, and structure among various machining processes [1]. Metal matrix composite (MMC) is the advanced metal and alloy material with good mechanical, tribological, and lightweight properties. Aluminum metal matrix composite is one of the MMC family a high strength-to-weight ratio, lower mass, and tailor-made thermal properties. MMC is mostly reinforced with Al2 O3 , SiC, and B4C but this reinforcement subjected to oxidation and corrosion on prolonged use. To avoid this problem, naturally available mineral such as mica, talc, corundum, and bauxite can be used as reinforcement. Pure Al with 99.5% purity is used as the matrix material and sillimanite (Al2 SiO5 ) of 80 mm mesh size is used as the reinforcement material. Sillimanite is one of them which have high hardness, high modulus, high resistance to corrosion, better thermal stability, and low coefficient of thermal expansion. In general, aluminum MMC is hard and fragile in nature, which is extremely difficult and inexpensive to drill through conventional drilling. One alternative method is ultrasonic-assisted drilling (UAD), in which frequencies ranging from 16 to 40 kHz and power ranging from 0 to 100%. [2, 3] SiCp/Al composites are commonly used in many applications due to their outstanding structural properties and are improved by silicon carbide-reinforced aluminum matrix. The SiC is the hardest and most fragile phase because of which common HSS or carbide materials are necessary to achieve the machine demand, due to SiC reinforcement. Such composites are difficult. SiC reinforcements can break and fall off resulting in many defects on the composites. In rotary ultrasonic machining, hollow diamond grain tools were used and machining conducted on a NC machining center [4]. The scratching test was used to assess material deformation behaviors and tribological properties [5, 6]. Metal network composites (MMCs) have been utilized in numerous applications, especially in the airplane and vehicle ventures, due to their boss properties, including high explicit

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quality, high protection from wear, high warm conductivity and low thermal development coefficient. Burr is a twisted plastic substance made at the parts edge. For the consistency of the finished product, the forming of burr during exhaust drilling has a significant role. Burr is a dimensional fault creating issues with assembly of the component [7]. Conventional drilling is commonly confronted with certain issues, particularly when certain aeronautical materials, for example, aluminum or titanium amalgams are chosen as workpiece material. Chip frames constantly looking like the helical curl during the cutting of these materials, causing chip stopping up in the drill woodwinds. All in all, there are three twisting, torsional, and longitudinal methods of vibration. In this way, exclusively longitudinal vibration in the feed bearing or carefully torsional vibration can be applied to the boring tool during the drilling stage in a proportional movement of rotational speed. It is additionally conceivable to rehearse a blend of longitudinal–torsional (L–T) development. Al 7075-T6 was set up in the element of 20 × 100 × 200 mm3 . The cutting profundity in 20 mm was steady. Moreover, rapid steel (HSS) was picked to bore the workpiece material in view of the great execution of this material when drilling different aluminum compounds [8]. CFRP/Al stacks are commonly used in aircraft manufacturers, including the F35 Lightning II, the Airbus A380, and the Boeing 787 Dreamliner, to meet the requirements of lightweight and structural strength. Drilling burr formation is a serious problem. It not only reduces the reliability of the part and assembly operations, but also increases the cost of production. Approximately 30% of the overall cost of production is spent on deburring. The burr size must be controlled to increase assembly accuracy and lower deburring cost. To control the burr of CFRP/aluminum stacks during RRUD, an investigation into burr formation and a prediction mode must be conducted [9]. Conventional drilling (CD) causes other difficulties for drilling materials such as low surface quality and rapid wear of equipment. Ultrasonic vibration drilling in low amplitude and high frequency in the direction of feed movement has been used to mitigate such a problem. Due to its good mechanical properties, aluminum alloy 7075 is widely used in the automotive sector and its cuts machinability. Some aspects are not favorable, such as the production of the form of chips and its high susceptibility to up-front construction (BUE). The ongoing in solving these issues and upgrading materials machinability was incredibly reinforced by another strategy known as a UM. It has been demonstrated that UAD procedure can diminish developed edge arrangement, bringing about the progress of surface quality and less device wear in drilling of Al compound. MMCs have excellent properties, for example, high explicit quality, protection from wear, and warm conductivity, just as low coefficient of warm extension. Thus, they are viewed as an appropriate trade for basic designing materials but since of inhomogeneous structures, it is hard to machine. Exactness and completed surface quality, especially in various drilling forms, are the significant elements of conclusive items. Burr is commonly viewed as a negative result in gathering techniques. Utilizing ultrasonic vibration in the drilling procedure is one approach to diminish or dispense with burr and improve surface unpleasantness in metal cutting [10, 11].

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Drilling processes are a major part of (approximately 40%) all methods in machining and can affect greatly on manufacturing costs. The improvement of these systems is a cost-cutting tool. The use of ultrasound pulses in the process became a recent technique for optimizing the twist process. For this technique, ultrasonic vibrations are utilized to improve common drilling. The vibration drilling (VD) is a kind of drilling procedure, and it is a hybrid process. They explain that the reduction of plate deformation in VD is caused by a stopping mechanism that reduces the thrust force, considering the surface consistency compared to OD [12]. Because of its astounding mechanical exhibition, particle-reinforced metal matrix composites (PRMMCs) have been broadly utilized. Nonetheless, the machining is troublesome because of the unbending nature of the fortified stage in it, and thusly, the promotion of PRMMCs is much limited. Here, the opening creation of Al matrix composites is given an ultrasonic vibration drilling strategy dependent on the investigation of cutting power between the normal and vibration machining. PRMMCs have been generally utilized in the advanced assembling of airplanes because of its basic readiness and efficient execution. Be that as it may, because of the high unbending nature of particles in it, the applied range is a long way past coordinating with its latent capacity. The instrument life can be straightforwardly communicated by drilling power, especially the force; the examination of drilling power is significant for the unpleasantness of the surface, the structure, and life of the drill. A correlation of customary and vibration drilling is utilized to look at drilling power and its distinction in the machining processes [13]. Dimensional accuracy, friction, resistance to corrosion and fatigue, and finished job component surface consistency are all of the most significant requirements for the final product. The cutting feed, cutting speed, and cutting insert radius are the known most important variables for surface consistency. For the finished part surface consistency, the key considerations include rigid tooling, dry or wet cutting, cutting conditions, chip form, and vibration and surface quality. The effect of the interaction between sensations was investigated by several researchers [14]. In fact, by increasing the angle of inclination of the tool and the cutting rates, the rotary speed of the tool increased with more slope. The design of tools in VRT is also distinct from ordinary rotating devices which should be designed in a manner that does not impact other parts of the vibration [15]. The development of the chips is accompanied by the production of thermal energy, which is determined by both the workpiece and the tool’s mechanical and physical properties [16]. Ultrasonic-assisted machining (UAM) can be defined as the superimposition of ultrasonic vibration on a traditional instrument used in processes of machining. In UAM technologies, particularly when turned, the cutting process becomes a micro-scaling, high-frequency vibration, and intermittent procedure which separates the tool insert from the workpiece. The UAT produces a stronger cycle than traditional turning, resulting enhanced reaction loads, tool wear, and surface finishing in the cutting procedure. This also helps to dissipate heat. The modern method of cuts is defined as a result of vibration of the machinery by an uncomfortable restriction in surface finishing. This also affects cut accuracy, disrupts the work part’s termination, decreases the rate of cuts

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of metal, and reduces the tool’s durability. It argued that UAT had reduced the workpiece’s diametrical error by 35%. In particular, with respect to surface quality and tool reduction, the user can benefit from UAT by using the required frequency. In addition, UAT is particularly useful when material properties (e.g., ductility, high tensile strength, micro-hardness, grain orientation) are used [4]. Attributable to high physical quality, high inflexibility, low warm range, solid consumption, and wear obstruction, composites of the metal matrix (MMCs) are progressively utilized in different designing applications, such as aviation, electronics, and car industry. Hybrid machining, wherein the most recent decades an auxiliary procedure ‘helps’ an essential machining process has been a region of dynamic exploration. One such half and half machining technique is ultrasonically assisted turning (UAT), which shows an extraordinary guarantee in the machining of hard-to-machine materials.

60.2 Literature Review Suresh et al. [1] investigated wire-cut electrical discharge machining of Al 7075/SiC MMNCs made by stir casting primary factor on MRR and mean surface roughness (Ra) in separate weighing portions (2, 4, and 5% of nano-SiC). It is obvious that the density of nano-composite samples decreases from 0 to 6% with the rise of the WT percentage (Al 7075) of nano-reinforced particles. The value of MRR decreases with the increase of wt%. Wire-cut electric discharge machining processing (WEDM) of Al 7075/SiC MMNCs is made by stir casting on the MRR and average surface roughness (Ra) in specific weight portions (2, 4, and 6% of nano-SiC). Densities of nano-composites with increasing densities are smaller than unreinforced densities of metals (Al 7075) of wt percent of nano-reinforcement particles from 0 to 6 percent. A higher percentage of reinforcement has been observed to provide more hardness to the materials tested. The difficult strengthening process increases the hardness of the composite component. With the subsequent decrease in MRR, SiC nanoparticles will increase the thermal properties of lightweight Al. The Ra value increases with an increase of reinforcement. The findings of these investigations are useful for aerospace, automotive industries in selecting a proper wire EDM parameters for Al7075 reinforcement system MMNCs with SiC nanoparticles in different portions of weight. Researchers have developed limited efforts to automate specific WEDM techniques for MMNCs [1]. Pethuraj et al. investigated the drilling study of sillimanite reinforced AL (AlAl2 SiO5 ) MMC fabricated by vacuum-assisted stir casting. To study and analyze the CD and ultrasonic-assisted drilling (UAD), machining operation in composite reinforced with Al-sillimanite using a predictive modelling framework based on regression is used. Drilling is done in two categories: traditional or vertical or lateral drilling and UAD. The machining parameter used for experiments is ultrasonic power, spindle speed (RPM), feed rate, and the output response is Ra and hole diameter characteristics. The results show that, compared to conventional drilling or vertical or directional

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drilling, the hole quality for ultrasonic-assisted drilling or UAD was subsequently improved. Comparing the RSME values of both predictive modeling frameworks, elastic net predictive modeling is shown to be 37% more accurate [2]. Huiting Zha et al. investigated the material removal mechanism of composites in rotary ultrasonic machining (RUM) using silicon carbide-reinforced aluminum material by comparing composite deformation characteristics in UVAS experiments and CS (ultrasonic vibration-assisted experiments) carried out on a rotary ultrasonic machine [4]. The abrasive grain is processed continuously within RUM in a three-dimensional space. The vibration strengthened the Al matrix and encouraged the elimination of SiC refurbishments by increasing their micro-cracks. In both UVAS and CS grooves, the scratching process consists of three stages—plastic deformation, bending time, and cutting time. Thanks to its scratching scope, the steps in the scratching process are calculated. SiCp/Al composite scratch tests make up discontinuous chips, and there are different types of holes, rather than cracks. In SiC replacements, shear fracture occurred. SiC reinforcements removal mode plays an important role in producing the machined surface [3]. Kadivar, Mohammad Ali, et al. investigated the Al/SiC MMC ultrasonic-assisted drilling trial examination concerning burr size decrease. With and without ultrasonicassisted drilling, the impacts of shaft speeds (n) and feed rates (f ) were considered utilizing TiN-covered HSS drilling machine. At UAD, chips are slender and short though CD chips are thick and long. High trust power causes increasingly plastic disfigurement on material and burr stature increment. Results reliably show that UAD successfully lessens burr tallness, while the decrease in burr width is not as much as that. UAD process creates a normal decrease in burr stature of 83.2% contrasted with the CD procedure and a normal decrease in burr width of 24% separately. Ultrasonic is a decent help with lessening burr development in MMC drilling and assists with delivering high-accuracy segments and furthermore diminishes the additional time important to expel burrs from the finished product [5]. Amini et al. investigated performance of longitudinal–torsional vibration in ultrasonic-assisted drilling of Al 7075-T6 with HSS tool. The effect of increasing rotational speed decreases torque and 36% thrust force because of less built-up edge generation at higher range of rotational speed due to higher temperature in the cutting zone. Increasing feed rate increases torque and force values, in which the use of longitudinal–torsional vibration observed less increment. This method produced a 35% reduction in the thrust force compared to conventional drilling. The drill skidding is almost eliminated when L–T vibration is added to the drill motion. To overcome adhesion which create problem of surface quality, tool-chip contact time and as a result of that the interface friction should be decreased. LT form of vibration has been revealed to significantly reduce cutting forces compared to conventional drilling [6]. The thrust force of the UAD is slightly less than that of the CD. This reduction of the thrust force definitely improves the stability of the system and therefore improves the quality of the hole. The results obtained show that the machining capacity of the drilled aluminum workpiece can be significantly enhanced by using ultrasonicassisted drilling (UAD). Improvement in drill circularity of up to 40% and a decrease in thrust force of up to 37% was achieved [7].

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Kadivar M. A., et al. investigated the effects on burr reduction, drill force, and surface roughness of ultrasonic vibrations using two different vibration systems. There are two vibration systems designed to excite the workpiece (the vibration system for the workpiece) and one to vibrate the workpiece’s mechanism (the instrument vibration system). In the Al/SiCp material matrix drilling process, more work is carried out into the effect of the amplitude, the feed rate, the cutting speed, and the particle content of Sac. Burr height—The relationship between vibration rate and burr height reveals that the amplitude increases and the burr height increases mostly as the UAD becomes higher and plastic deformation becomes induced by low burrs. Statistics reveal that both ATS and AWS setups essentially lower burr height, although the pattern of improvements in AWS is different to that of ATS. The origin is the same as the thrust of force [8]. Surface roughness-Through an increase in SiC quality in CD from 5 to 15%, Ra increases. As a result, the increased content of SiC induces higher surface roughness, but decreases by increasing SiC content from 15 to 20%. The aim was to raise SiC percent to the cutting edge to prevent aluminum connection. BUE declines significantly in so far as BUE was not established in MMC with 20% SiC, and Ra was reduced. The roughness of the surface decreases with increasing amplitudes. The results indicate significant improvements in the surface finish of three UAD materials compared with CD at various vibration amplitudes. As stated, the force of drilling in UAD is less than the CD, resulting in smaller chips and better roughness of the surface. Surface roughness versus the speed of cutting and feed rate for ATS and AWS suggests that the surface linearity in contrast with CD is continuously increased for both ATS and AWS configurations. The AWS ‘roughness was lower than that of ATS because higher lateral motion causes a greater area of contact between the instrument and the hole walls, which results in more surface damage and less finishing of the surface [9]. Das et al. studied metal matrix composites machinability. The various components in the metal and metal alloys matrix readily used in the preparation in MMCs are made of aluminum (Al, A356, Al-359, Al-2219Al-6061, Al-7068, Al-7075, Al-2024, alumina-titanium hybrid composite, titanium silicone manganese alloy, Ti–6Al–4V). Many research activities alongside various reinforcing materials were carried out using aluminum metal matrix composites. The type and size of the reinforcing material can be specified for the MMCs. The fact that the material is reinforced significantly affects the machinability of MMCs. In specific, hybrid MMCs are handled by particles that are abrasive in their composition and are a cutting-edge fast wear and vibration wear tool. The majority of studies on feed, speed, and cuts depth are called input restrictions, whereas the output measurement indices for processing are called to be cutting strength, average surface roughness, tool wear intensity, chip morphology, and other mechanical, morphologic, tribologic impact. Despite an improved cutting speed and weight proportion of the reinforcement, the cutting forces decreased. Surface roughness is primarily depending on the feed rate, then the speed of cutting and the form of strengthening while the angle of approach of the instrument influences the surface roughness of MMCs less. Traditional MMC machining shows a high MRR but is done at machine life expense. The cutting

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parameter life estimation for MMCs is therefore a very difficult job. While the use of carbide inserts is limited, the majority of the researchers have used CBN, PCD, and CVD inserts. The vibration intensity, noise level, etc., of the unit are limited [17]. Barani, A et al. examined the effects of the UL on the built-in rim, and surface quality, morphology, and wears of drilling edges were investigated. The application of ultrasonic vibrations to the drill phase leads to an innovative synthetic method known as “vibration drilling.” On the basis of the results obtained, vibration drilling has less incorporated edge and better surface efficiency in contrast with conventional drills. This finding is due to a reduction in adhesion through the transition to the process in ultrasonic vibration (VD). Adhesion and abrasion mechanisms were also observed as tool damage mechanisms in both OD and VD but, due to less contact and consequently less friction between chip, device, and workpiece, VD gave less damages as compared to OD [10]. Xing Xin, et al. was investigating drilling power of ultrasound composites with ultrasound vibration improved aluminum matrix particulate. In contrast, the device regularly contacts or leaves the vibratory cutting field, which results in an estimated type of impulses for the intensity waveform. Using the ultrasonic vibration generator, this instrument is powered by guided ultrasonic vibration. The cutting forces are generated not only by the tool motor, but also by the entire cycle of ultrasound vibration. In the phase of work, the real shut-down time for the ultrasonic vibration work is between one-third and one-tenth of the periodic time T. The accuracy of the placement of the cavity is thereby increased. The torque differential of vibration and regular boils is so evident with the continuous abrasion of the drill that it is easy to range from about 20 and 30%, which means that the cutting force could be decreased by ultrasonic vibration and that the life of the tool can be increased to some degree [11]. Balaji et al. examined that the surface nature of the machined part was diminished with expanding shaft vibration. For expanded cutting feeds, cutting rates, and cutting profundities, the shaft vibration and surface harshness were expanded. Signal-tocommotion proportions indicated the ideal turning boundaries for diminishing axle vibration and surface unpleasantness esteems. The base qualities for axle vibration and surface quality were found at the most reduced cutting factors utilized in the machining tests. The discoveries of ANOVA demonstrate that the best-machining variable for axle vibration and surface quality was the cutting feed with a commitment of 65 and 94% Amini, S. et al. investigated vibratory rotary turning process of Al 7075 workpiece. In this article, vibratory rotary tool examines cutting force and surface roughness and then contrasts these results with results obtained by ordinary rotary tool. First, a system is developed which can pass ultrasonic vibration to a rotary turning tool. A stainless steel horn is engineered and developed at a frequency of 20,618 Hz for vibration of the turning tool [13]. Teimouri, Reza et al. investigated effect of ultrasonic vibration during rotary turning of aluminum 7075 aerospace alloy. Cutting force values for the VRT process are significantly lower than those for the RT process. This condition results in greater wear of the tool and affects the consistency of the machined surface. In the VRT method, the roughness of the surface decreases by

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increasing the cutting speed. Nonetheless, due to the chip forming process, surface roughness variable is specific in a symmetrical manner. Regardless of VRT and RT operations, higher feed rate is responsible for higher cutting force and coarser surface consistency. Through comparing the optimal results obtained through the proposed approach and confirmatory experiments, it was found that the prediction error value is less than 8%, which indicates that the pro-positioned approach is ideal for turning process optimization [14]. Puga, Hélder et al. investigated ultrasonic-assisted turning of Al alloys. The reduction of roughness and improved surface quality in UAT is more apparent when a lower feed rate is applied (fa = 0.045 mm/rev). Chips developed by UAT in length shorter than traditional turning. The essence of UAT’s cutting process, a violent rupture due to its forced vibration, is to be expected. A comparison of traditional (CT) and ultrasonic-assisted turning (UAT) approach was used in the experiment. The overall value of ultrasonic excitement on the alloys machine tool is measured in the quantification of the surface roughness and the resulting chips. The experimental results demonstrated an increase up to 56% in the application and optimum usage of the ultrasonic elements and the surface consistency of the machining samples and defined by a clear topography distribution. This advantage is more evident in materials with reduced plasticity, particularly when the feed rate is increased [15]. Bai, Wei, et al. investigated enhanced machinability of SiC-reinforced metal matrix composite with hybrid turning. With a small rise in cutting temperature, UAT achieved a considerable reduction in cutting power. It has obtained continuous chips with a better terrestrial topography. The exception to this argument was the case by applying polycrystalline diamond (PCD) and minimum quantity Lubrication (MQL). Under this scenario, CT was stronger. No observable improvement in cutting power, surface resistance, and machined surface topography was noticed in the use of MQL. The cutting area temperature was moderately reduced, the chip morphology changed, and the generation of BUE reduced. The chip forming mechanism in UAT documented an increased chip ductility when subjected to a repeated high-frequency micro-chiping process. The UAT chips were generally constant and semi-continuous. Comparisons between the machined surface of CT and UAT showed flaws in CT while the machined surface in the UAT was of better consistency [16] (Table 60.1).

60.3 Mechanical Aspects The mechanical parts of a composite rely upon a few factors, for example, support, quality proportion, structure, figuring, and so on. The best understanding of mechanical conduct is additionally significant on the grounds that it is utilized in different fields.

Al–Al2 SiO5

Materials 3000 rpm

0.1 mm/rev

Minimum roughness (Ra) 4.805 µm is obtained

MRR, TWR Ra, Sr, etc

20–40 Hz

Output parameters Feed rate

Freque.

Speed

Input parameters

Table 60.1 Highlighted data based on ultrasonic machining process of aluminum MMC materials References

– 17% of the surface [2] quality of holes drilled by UAD is higher than that of CD because UAD helps to penetrate quickly into the workpiece material – Minimum roughness (Ra) Because axial movement allows the cutting tool to travel in the feed direction, integrating both velocities – Circularity increases with increasing speed and feed – Improvement in cylindricity due to the reductions in the movement of drill bit over the surface of the work piece (continued)

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SiCp/Al composites

Materials

Table 60.1 (continued)

In UVAS tests, the scratching forces and ultrasonic scratching COF are lower than in CS Scratching force and COF increase with increasing scratching length

0.30 µm (scratching depth)

24,000 mm/min

MRR, TWR Ra, Sr, etc

18,850 Hz

Output parameters Feed rate

Freque.

Speed

Input parameters

References

– The finished surface [3] in UVAS was very smooth for hammering effect because of the hammering effect of ultrasonic vibration, SiC reinforcements broke into small particles, and they were more easily removed, whereas in CS very coarse finished surface – Throughout the scratching process, the indenter touched the material all the time and the Al matrix was adhered away creating a very coarse layer. In SiC replacements, shear fracture occurred – The material underwent plastic deformation and the surface became very smooth, and there was no adhesion (continued)

Major findings

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CFRP (CCF300) & Al 7075-T7

20,198 Hz (ii) 20.300 Hz

Al 7075-T6

2000–5000 rev/min

(i)1000 rpm (ii) 1000–1500 rpm

710 rpm

60–114 mm/min

(i) 0.2 mm/rev (ii) 100–200 mm/min

0.18 mm/rev

The burr stature decreases (70 µm) with the expanding of shaft speed (5000 fire up/min) The burr height (170 µm) ascends with the expanding of feed rate (115 mm/min)

(i) Speed up diminishes force and thrust force Increment of feed rate builds power and force (ii) Speed up prompts decrease of thrust force Since by speed up—the increase in feed rate, the increase in thrust force

0.30 mm burr width

MRR, TWR Ra, Sr, etc

22 kHz

Output parameters Feed rate

Freque.

Speed

Input parameters

Al/SiC

Materials

Table 60.1 (continued)

[5]

References

(continued)

The point when the [7] adequacy ascends from 12 to 14 µm burr tallness increment out of nowhere from 100 to 160 µm

LT type of vibration [6] altogether decreases [8] slicing powers contrasted with conventional drilling (ii) In UAD improvement in drill circularity of up to 40%, and a decline in the thrust force of up to 37% was accomplished

With the increase of spindle speed and feed rate the burr width has an optimum value

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Al2024-T6

Materials

Table 60.1 (continued)

460, 755, 1255

0–10 µm The observation states that by expanding the feed rate, the surface harshness turns out to be more terrible Whole chip thickness decreases which occur by speeding up Be that as it may, as the revolving speed builds, better surface characteristics can be gotten The explanation of getting more regrettable surface quality as feed rate increments is higher powers which increment drill sliding and hazards in the drilling process

MRR, TWR Ra, Sr, etc

0.104, 0.208, 0.348

Output parameters

Freque.

Feed rate

Speed

Input parameters

References

(continued)

It tends to be seen that [10] thrust force increments for higher feed rates As a case, by expanding of feed rate, whole chip thickness increments, and results in expanding of chip thickness and cutting powers Push power expands which is because of bond nearness improvement and developed edge arrangement The more feed rate, the more grip of work material to the apparatus The power increment esteem is less in VD when contrasted with OD; that is because of the grinding decrease in VD which can diminish the arrangement of developed edge

Major findings

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0.1–0.45 m/s 20,618 Hz 3.5 kHz

Al 7075

50, 75, 100, and 125 m/min 7.8 m/min 220 rpm

600–2200 rpm

0.1, 0.2, and 0.3 mm/rev 0.08 mm/rev 0.08 m/min

0.04–0.10 mm/rev

(continued)

Shaft vibration [12] amplitude expanded, [13] and the work piece’s [14] machined surface quality was not altogether affected The cutting feed is uncovered to be the most basic machining boundary impacting the axle vibration and surface unpleasantness with a rate commitment of 65 and 94% separately Ultrasonic Vibration is applied, cutting power diminishes considerably. By expanding the apparatus’ rotational speed, Fz and Fy powers decrease speed

0.76–4.90 µm surface roughness 0.3 µm depth of cut In VRT process, the surface roughness decreases by increase in cutting velocity

[11]

References

The cutting power diminishes clearly in vibration drilling It likewise shows that the life of drill could be broadened adequately by ultrasonic vibration

Major findings

The deformation ratio of chips is larger in common drilling

MRR, TWR Ra, Sr, etc

20 kHz

Output parameters Feed rate

Freque.

Speed

Input parameters

SiC particle -wt18% and wt25% Al-matrix composites

Materials

Table 60.1 (continued)

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18.11 kHz

Metal matrix composites

20 m/min

760 rpm

0.1 mm/rev

0.045–0.18 mm/rev

No clear pattern, with surface roughness fluctuating along the length of the cut. This was due in part to the varying wear stages of the cutting tools: abrasive wear and BUE impact accompanied the removal of the coating, which happens in rapid successions

Reduction in 55–82% in terms of surface roughness by UAT

MRR, TWR Ra, Sr, etc

19–21 kHz

Output parameters Feed rate

Freque.

Speed

Input parameters

Al alloys

Materials

Table 60.1 (continued) References

The chip-arrangement [16] system in UAT confirmed expanded flexibility of the workpiece material when exposed to a rehashed high recurrence micro-chipping process. The chips: constant and semi-ceaseless in nature

Results indicated that [15] the application and optimal use of ultrasonic elements and the surface quality of the machining samples increased by up to 56%

Major findings

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60.4 Discussion and Conclusion The sharp-edged exploration on different UM aspects in the drilling, burr formations, fatigue, and surface grinding impact of Al-MMC composites are based on an thorough analysis of numerous research reports: • The reinforcement of aluminum matrix with alumina nanoparticles together forms elasticity and hardness. Set up the TiB2 or SiC matrix which increases the tensile and longevity up to some wt. The percentage or aggregation of these solid clay particles in aluminum matrix and this contributes to porosity is seen in rigidity and hardness, in terms of group form or aggregation. • While unlike collection of techniques such as mixing casting, press casting, and powder metallurgy are used in the manufacturing of specific Al metal matrix composites; however, mixing casting technique is primarily used because it is more affordable and more widely than other strategies. • Conventional drilling (CD) causes some problems in drilling materials such as high thrust force poor surface quality and rapid tool wear. To minimize such problem, ultrasonic vibrations drilling has been employed with low amplitude and high frequency to the direction of feed motion. • MMC are mostly reinforced with Al2 O3 , SiC, and B4C, but these reinforcements subjected to oxidation and corrosion on prolonged use. To avoid this problem, naturally available mineral such as mica, talc, corundum, and bauxite can be used as reinforcement. Pure Al with 99.5% purity is used as the matrix material and sillimanite (Al2 SiO5 ) of 80 mm mesh size is used as the reinforcement material. Sillimanite is one of them which have high hardness, high modulus, high resistance to corrosion, better thermal stability, and low coefficient of thermal expansion. • Increased wettability and the interfacial composition of the matrix need to be allowed. Similarly, the expensive metal composite position has not been researched so well and can be used successfully in the manufacture of the MMC with desired properties to improve mechanical and tribological behaviors. • Aluminum MMC is hard and fragile in nature, which is extremely difficult and inexpensive to drill through conventional drilling. One alternative method is ultrasonic-assisted drilling (UAD), in which frequencies ranging from 16 to 40 kHz and power ranging from 0 to 100%. • In drilling, it is also possible to practice a combination of longitudinal–torsional (L–T) movement. Al 7075-T6 was prepared in the dimension of 20 × 100 × 200 mm3 . The cutting depth in 20 mm was constant. In addition, high-speed steel (HSS) was chosen to drill the workpiece material because of the good performance of this material when drilling various aluminum alloys. • The utilization of MQL indicated no quantifiable change in cutting forces, surface harshness, and machined surface topography. It has been compelling in modestly diminishing the cutting-zone temperature, changing the chip morphology, and lessening BUE age. When exposed to a rehashed high recurrence micro-chipping cycle, the chip-development component in UAT detailed expanded pliability of the workpiece material. The chips which were obtained in UAT were in reality

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ceaseless and semi-consistent. Examinations of the machined surface for CT and UAT uncovered deformities that existed in CT while the machined surface was of higher caliber in UAT • Signal-to-clamor proportions determined the ideal turning boundaries for decreasing shaft vibration and surface harshness esteems. The base qualities for axle vibration and surface quality were found at the most minimal cutting factors utilized in the machining tests. The discoveries of ANOVA show that the best machining variable for shaft vibration and surface quality was the cutting feed with a commitment of 65 percent and 94 percent • The components of natural reinforcement or its combination with aluminum are not fully studied and research in this area is incredibly regulated. Nevertheless, some tests showed that the mechanical and tribological activity improved dramatically. In this regard, more work is required in the field of aluminum metal matrix composites In the basis of results, the following are concluded from this latest work of study: in a scope to explore the impact of certain input variables (e.g., frequency, speed, spindle, ultrasound strength, feed rate, etc.) on the execution behavior (e.g., material evacuation rate, surface hardening, MRR, TWR Ra, Sr, etc.), different researchers have critically investigated the above research work, ultrasonic-assisted drilling, investigated on the effects of spindle speeds (n) and feed rates (f ) that were studied using TiN-coated HSS drilling devices. At UAD, chips are thin and short, whereas in CD chips are thick and long. High trust force causes more plastic deformation on material, and burr height increases. Results consistently show that UAD effectively reduces burr height, while the reduction in burr width is less than that. UAD process produces an average reduction in burr height. Ultrasonic is a good assist in reducing burr formation in MMC drilling and helps to produce high-precision components and also decreases the overtime necessary to remove burrs from the final products. In state-of-art of this paper, it draws the following conclusions about the various aspects of UM in the effect on drilling, burr formations, aluminum MMC based tool wear.

References 1. S. Suresh, D. Sudhakara, Investigations on wire electric discharge machining and mechanical behavior of Al 7075/nano-SiC composites. J. Inst. Eng. (India): Ser. D 100(2), 217–227 (2019) 2. M. Pethuraj, M. Uthayakmar, S. Rajakarunakaran, Study on ultrasonic assisted drilling of aluminium sillimanite reinforced composites.Int. J. Mech. Prod. Eng. Res. Dev. (IJMPERD) 9(2), pp. 923–932. ISSN(P): 2249–6890; ISSN(E): 2249–8001 3. H. Zha, P. Feng, J. Zhang, D. Yu, Z. Wu, Material removal mechanism in rotary ultrasonic machining of high-volume fraction SiCp/Al composites. Int. J. Adv. Manuf. Technol. 97(5–8), 2099–2109 (2018) 4. R. Balaji, S. Sivakumar, M. Nadarajan, A. Selokar, A recent investigations: effect of surface grinding on CFRP using rotary ultrasonic machining. Mater. Today Proc. 18, 5209–5218 (2019)

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5. M.A. Kadivar, R. Yousefi, J. Akbari, A. Rahi, S. Mohamad Nikouei, Burr size reduction in drilling of Al/SiC metal matrix composite by ultrasonic assistance,in Advanced Materials Research, vol. 410. Trans Tech Publications (2012), pp. 279–282 6. S. Amini, M. Soleimani, H. Paktinat, M. Lotfi, Effect of longitudinal− torsional vibration in ultrasonic-assisted drilling. Mater. Manuf. Processes 32(6), 616–622 (2017) 7. S. Dong, W. Liao, K. Zheng, J. Liu, J. Feng, Investigation on exit burr in robotic rotary ultrasonic drilling of CFRP/aluminum stacks. Int. J. Mech. Sci. 151, 868–876 (2019) 8. H. Paktinat, S. Amini, Ultrasonic assistance in drilling: FEM analysis and experimental approaches. Int. J. Adv. Manuf. Technol. 92(5–8), 2653–2665 (2017) 9. M.A. Kadivar, J. Akbari, R. Yousefi, A. Rahi, M.G. Nick, Investigating the effects of vibration method on ultrasonic-assisted drilling of Al/SiCp metal matrix composites. Robot. Comput.Integr. Manuf. 30(3), 344–350 (2014) 10. A. Barani, S. Amini, H. Paktinat, A.F. Tehrani, Built-up edge investigation in vibration drilling of Al2024-T6. Ultrasonics 54(5), 1300–1310 (2014) 11. X.X. Xu, Y.L. Mo, C.S. Liu, B. Zhao, Drilling force of SiC particle reinforced aluminummatrix composites with ultrasonic vibration, in Key Engineering Materials (vol. 416). Trans Tech Publications Ltd. (2009), pp. 243–247 12. A. Sahino˘ ¸ glu, S. ¸ Karabulut, A. Güllü, Study on spindle vibration and surface finish in turning of Al 7075.Solid State Phenomena., vol. 261. Trans Tech Publications Ltd. (2017) 13. S. Amini, N. Mohagheghian, Vibratory rotary turning process of Al 7075 workpiece. Mater. Manuf. Processes 29(3), 344–349 (2014) 14. R. Teimouri, S. Amini, N. Mohagheghian, Experimental study and empirical analysis on effect of ultrasonic vibration during rotary turning of aluminum 7075 aerospace alloy. J. Manuf. Processes 26, 1–12 (2017) 15. H. Puga, J. Grilo, V.H. Carneiro, Ultrasonic assisted turning of Al alloys: influence of material processing to improve surface roughness. Surfaces 2(2), 326–335 (2019) 16. W. Bai et al., Enhanced machinability of SiC-reinforced metal-matrix composite with hybrid turning. J. Mater. Process. Technol. 268, 149–161 (2019) 17. M. Das, D. Mishra, T.R. Mahapatra, Machinability of metal matrix composites: a review. Mater. Today Proc. 18, 5373–5381 (2019)

Chapter 61

Military Reconnaissance and Rescue Robot with Real-Time Object Detection Rakshana Ismail and Senthil Muthukumaraswamy

Abstract In this era of a politically competitive world, there is a growing demand for the use of military robots to aid soldiers to perform perilous missions. This project focuses on the design and build of a semi-autonomous, solar-powered, unmanned robotic system operating with real-time object detection function, used for various military and rescue operations such as explosives disposal, enemy territory surveillance, and search and rescue. The military robotic system is instilled with a robotic arm for explosives disposal, a sensory circuit for analysis of the environmental composition of the area under surveillance, and a Raspberry PI for real-time object detection. The commands for the motion of the robotic arm and the robotic body are given using a Graphical User Interphase (GUI). The effectiveness in performing perilous missions can be accomplished by the utilization of Artificial Intelligence (AI). Real-time object detection with deep learning techniques is utilized in this robotic system to identify objects within the frame of the camera and tag the objects with the accuracy rate of computations. Data transmission and receiving is through Zigbee and Wi-Fi communication technologies. The proposed robotic system overcomes the weakness in the existing models and thus provides better support in military operations.

61.1 Introduction Technological advancements in the past decade have given rise to the utilization of robotic technology in the defense sector of many countries. Military technology is a fast-changing field with a pursuit to be faster, better, and stronger. The combination of computer, electronics, surveillance, and weaponry has completely changed the dynamics of war. The robotic systems use advanced and sophisticated technology R. Ismail · S. Muthukumaraswamy (B) Department of Electrical Electronics Engineering, Heriot Watt University, Dubai, UAE e-mail: [email protected] R. Ismail e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_61

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for fighting terror and defending the nation [1]. Unethical warfare behaviors have drastically reduced due to the usage of technology in the military. The military robots are mainly used for intelligence, surveillance and reconnaissance, explosive ordnance disposal, and search and rescue.

61.1.1 Surveillance and Reconnaissance Surveillance and reconnaissance missions are carried out by both military and civilian organizations. The robots used for this purpose are usually small and infused with advanced technology. In earlier days, reconnaissance missions were carried out by human soldiers, hence endangering a lot of lives. The class of unmanned ground vehicle is the most popular for reconnaissance missions. Manyam and Casbeer as seen in [2] have proposed a dual hybrid model which is made by combining an UAV and UGV. The UGV acts as a launching station for the UAV which flies above the site under inspection capturing pictures and videos. The UAV is programmed to return to the UGV for charging purposes. The drawback in this proposed prototype is that the UGV has no mission of its own and is stationed at one place hence prone to detection by the enemy. Liu and Luo as seen in [3] have a different approach to the same idea. In this prototype, the UGV has its own reconnaissance mission. The drawback in this model is, the UGV is prone to detection by the enemy when the UAVs approach for charging. Dragon runner is a compact teleoperated reconnaissance robot developed by the National Robotics Engineering Center (NREC) to be used in areas inaccessible to the army troops. The robot is used for border patrolling and for negotiating in hostage situations. The bot is instilled with six high definition night vision cameras for live feed, motion sensors, loudspeakers, a microphone, and a long-range radio frequency (RF) operating in case of jammed environments [4].

61.1.2 Search and Rescue Search and rescue after a calamity is a perilous task, and the rescue of victims can be extremely difficult due to the instability of the structures or the presence of radioactive, biological, and chemical components. Rescue robots work efficiently in these challenging environments by reducing the response time. Gregorin as seen in [5] used pursuit-evasion to develop a set of robots that clear the polluted environment by preventing recontamination. The robots connect via proximity sensors and have a chain formation to clear out the evaders. The robots are tested in various urban search and rescue (USAR) like scenarios to improve efficiency. The research considers the evaders to be fast and omniscient while the pursuers have limited sensing and communication capabilities. The results of the model show that it can decontaminate several types of maps but cannot work on initial conditions due

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to incompleteness in the automation mapping. There is a restriction in movement and visibility. IRobot is an ideal all-purpose robot which started off as a laboratory experiment. This robot was used in Fukishima Daiichi disaster. The robot is designed to traverse hazardous situations and programmed to report back radiation levels, gas composition, humidity, temperature, and oxygen levels. IRobot has a shock resistant chassis which can maneuver different terrain at 8 km per hour and a high definition camera to provide live feed from the inspection site for better preparing the rescue workers [6].

61.1.3 Robotic Arm The use of robots extends to ordinance disposal where the robots can diffuse bombs and neutralize mines [7]. The introduction of explosive disposal robots has greatly reduced the loss of lives of both civilians and uniformed officers. The robotic explosive disposal models proposed in [8, 9] are similar. Both the robots have similar design composition consisting of a robotic arm, Arduino, camera, motors, transmitters, and receivers. These robots are low cost and build to work at high-risk areas by transmitting data back to the base. The teleoperated robot is controlled by the soldier at base who works toward diffusing the explosive from a safe distance, while analyzing the information provided by the camera. The robotic arm is instilled with many servo motors for control of different parts of the arm. All the motors have a receiver which enables working of one motor at a time while the rest are at standstill. The robotic arm prototype developed by Jayed Islam et al. [8] is capable of lifting objects away from humans in case of an emergency, but model [9] is incapable due to the use of weak building materials. Daksh, a remote-operated explosive disposal robot developed by the Defense Research and Development Organization of India, can safely locate and destroy any type of explosive devices. Daksh can be remotely controlled from a range of 500 m, withstand blast impacts, and lift objects weighing up to 20 kg. Daksh is equipped with IED handling equipment, robotic arm, X ray scanner, multiple cameras, chemical, nuclear, and biological reconnaissance systems, a shotgun, and a water jet disrupter which are used to diffuse explosives. Daksh serves in the bomb disposal unit of the Indian military and has saved many lives by successfully disposing explosives [10].

61.1.4 Artificial Intelligence Real-time object detection is a new field of artificial intelligence, the incorporation of this technology is very scarce in the field of the military due to the restrictions posed on classified information, and hence, limited resources are available to aid in the development. There can easily be a security breach when such technology is not

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carefully used. So, the preference of use of AI is restricted to easier tasks like manual labor. Phadnis et al. [11] have suggested the use of real-time object detection for following the patterns of frequently used household items which can maximize efficiency and minimize time wasted in searching. The project uses TensorFlow, to model the neural network and introduced an algorithm to alert the user if an anomaly is found in the pattern. Nandan and Thippeswamy [12] has programmed a robotic arm with real-time object detection ability to pick and place objects. The prototype uses TensorFlow object detection API to identify the objects given in the database, and command is given to the robotic arm via Arduino to pick the object from the location and place at the required location. The accuracy of finding the object is based on the number of input images and the time taken for training the model which is the drawback in both the mentioned models. Fully automated robots are not accepted by certain country laws since it is considered unethical to fully depend on technology to make decisions. Human rights watch in the year 2012 has argued that fully autonomous machines may not comply with the existing laws and might not be smart enough to differentiate between civilians and combatants [13]. Due to a lot of restrictions imposed on automated robots, the use of semi-autonomous robots is preferred. This paper has been organized into the following sections. Section 1 contains an elaborate background study on various topics related to the project. Section 2 is the methodology implemented in this project. Section 3 contains solutions and results obtained. The paper is concluded in Sect. 4.

61.2 Methodology The robotic system proposed in this paper is a multipurpose semi-autonomous, battery- or solar-powered, unmanned ground vehicle built to aid the military in perilous missions. The robotic prototype is small, rugged, flexible, and constructed with different sensors for analysis of the environmental composition of the site. A camera to carry out surveillance by transmitting live data back to the military base, for object detection and for ordinance disposal. A robotic arm for explosive disposal and to tow unidentified objects away from humans. The robot uses real-time object detection technique using TensorFlow to improve the efficiency of the tasks to be carried out. Raspberry PI is coded to carry out real-time object detection which helps in search and rescue of victims and aid in reconnaissance missions. Wi-Fi and Zigbee technologies are used for communication between the robot and the base station. The robot performs functions like intelligence, surveillance, and reconnaissance, search and rescue, and explosives disposal. This robot is built by eliminating the weakness in already existing models by introducing advanced technology. For ease in design and building, the prototype had been divided into five phases, all of which are listed below.

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61.2.1 Mechanical Body The base of the robot is a steel frame with four direct current (DC) motors connected to a rubber sprocket which can traverse varying terrains. The DC motors are connected to motor drivers, which convert the low current signals to higher current signals that can drive a motor. The rest of the circuitry including the sensor circuitry, robotic arm, and other components of the robot is attached to the mechanical frame. The DC motors use pulse width modulation (PWM) signals for movement and are controlled using a Graphical User Interphase (GUI) located on the personal computer (PC) at base via Zigbee technology. There are five different controls for movement of the robot; forward, reverse, left, right, and stop. A solar panel is attached to the battery to power up the robot in case the main battery dies out. This robotic system can either be solar or battery powered.

61.2.2 Robotic Arm The robotic arm is built to detonate explosives such as bombs or mines, move objects obstructing the part of the robot, and tow dangerous objects away from humans. Explosives are detonated by a human working at a safe distance with the help of live feed from the camera. The arm is instilled with five servo motors, each for the control of specific parts of the arm such as base rotation, arm, elbow, wrist, and gripping. A greater number of servos are used to increase the efficiency of the robotic arm. The controls for the robotic arm are given by the GUI on the PC via Zigbee communication. Each servo has two controls for each movement.

61.2.3 Sensor Circuitry The sensor circuitry was programmed to analyze the environment of the inspection area and transmit back the data collected. The sensor circuitry includes gas sensors (MQ2, MQ8, MQ9, MQ135), thermistor, humidity sensor, air/water velocity sensor, LCD (Liquid Crystal Display), Zigbee, and Arduino. The Arduino collects the data, displays it on the LCD located on the robot, as well as transmits the data via Zigbee to the base station. This data analysis helps soldiers better prepare for hazardous mission. Arduino mega is chosen as the microcontroller for this project since it has the required amount of digital and analog input and output ports. The outputs given by sensors are analog signals which are converted into digital signals by Arduino for data transmission. Zigbee is chosen as the mode of communication between the robot and base due to the area coverage offered by this device. The sensor circuitry is placed on the mechanical base of the robot.

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61.2.4 Real-Time Object Detection In this robotic model, the Raspberry PI was utilized for detecting objects in the realtime video stream using Artificial Intelligence. The project is built using Spyder and TensorFlow platform for deep learning techniques. The input in this case is a live video sent to TensorFlow. TensorFlow is trained to extract features from the image, and a model is created. The images are converted to arrays by TensorFlow object detection. This model is then compared to the data in the database with a set of images for each class of objects. The live 2D trajectory of objects as captured by the camera is fed as input to the Raspberry PI. The data processing occurs in the Raspberry PI, and the output is displayed on the PC using VNC viewer. VNC viewer is an application used to connect the Raspberry PI to the PC, using Wi-Fi communication.

61.2.5 Graphical User Interphase (GUI) GUI is a user interphase which enables users to interact with electronic components. A GUI is built on visual studio as a form of linkage between all the different components of the robotic system. The data transfer and receival is through Zigbee technology. The controls for the motion of motors and the data received are displayed on the GUI. Figure 61.1 shows a schematic block diagram of all the main components used in the robotic system. The Graphical User Interphase consists • The control buttons for the movement of the entire robot. There are five controls for the motion of the robot which are given in the form of signals to the DC motors connected to the wheels via Zigbee technology. • The robotic arm controls for the servo motors are divided into two functions per motor. The controls for grasping are pick and place, whereas the rest of the motors have forward and reverse commands. • Designated text boxes where the data received via Zigbee are displayed. Each sensor reading as displayed on designated text boxes will be displayed on the LCD located on the robot.

61.3 Results and Discussion 61.3.1 Mechanical Body The mechanical body of the robot consists of motors which can help traverse difficult terrains. The robot has a strong steel base frame to overcome the drawback as witnessed in literature [9], where the use of weak building materials like aluminum,

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Fig. 61.1 Block diagram schematic of the entire robot. The main components along with their connection are mentioned. This provides a simple and clear picture of the entire project

restricted the efficiency of the robot. Figure 61.2 shows the mechanical frame of the robot. The robotic arm consists of five servo motors and is used to dispose bombs, detonate mines, move objects obstructing the path of the robot and tow unidentified objects to a safe distance. Figure 61.3 shows the pick and place arm installed on Fig. 61.2 Mechanical base of the robot with four DC motors. The commands for the movement of the robot is given by the GUI at base via Zigbee technology

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Fig. 61.3 Robotic arm attached to the mechanical base. The command is communicated through Zigbee technology

the robot. The robotic arm was built in correspondence to the ideas mentioned in literature [8].

61.3.2 Electrical Circuitry A solar panel is used to charge the 12 V rechargeable battery needed for powering the robotic system. Solar panel is used as a secondary source of power charging method to avoid the mission from being left incomplete. Literature [3] highlights the importance of a secondary source of power to efficiently complete military tasks. Figure 61.4 displays the charging of the solar panel installed on the robot. The sensor circuitry in the military robot helps to analyze the environment of the site under surveillance. The incorporation of sensory circuit was developed with accordance to the ideas used on the IRobot [6]. Figure 61.5 shows the sensory circuit with the sensor readings displayed on the LCD. Fig. 61.4 Solar panel for charging a battery and the current reading taken using a multimeter

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Fig. 61.5 Sensor circuitry before attaching the rest of the electrical circuitry. The acrylic board with the entire robotic system is attached on the mechanical body. The Arduino is coded to display the sensor values on both the LCD and the GUI at base

Fig. 61.6 Graphical User Interphase of the military robot

61.3.3 Graphical User Interphase The controls for the movement of the entire robot and the robotic arm are from the GUI at base via Zigbee communication. The live sensor values of the environment of the area under inspection are displayed in designated text boxes. Figure 61.6 shows the GUI built to control the movement of the robot as well as display the sensor readings.

61.3.4 Artificial Intelligence Implementation of real-time object detection is done with the help of a camera and Raspberry PI. Real-time object detection technique using deep machine learning, as used in literature [12], is programmed into the robot for better aid in military missions. The use of AI technology helps improve the performance of the military

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Fig. 61.7 Real-time object detection of basic objects as seen by is shown in the above pictures

in all fields. For object detection, libraries are the main components required, but classified military information such as weaponry or military facility is not easily available, thus making it difficult to program an efficient code. Taking this into consideration, the code is developed for basic object detection. This technology can aid in rescue and reconnaissance missions by reducing the response time. Figure 61.7 shows the detection of objects using real-time object detection.

61.3.5 Final Prototype The robotic arm is placed in front of the military support and rescue robot. The final prototype is shown in Fig. 61.8, which displays the electrical circuitry, the robotic arm, and the rubber sprocket wheels. The acrylic shows the sensory circuit with LCD display as well as motor drivers. The communication unit including the Zigbee and Arduino can be seen on the acrylic. The robotic arm is placed in front of the military support and rescue robot. The tasks performed by the robot: • Robotic arm for explosive disposal and object displacement. Fig. 61.8 Final prototype

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Robotic movement according to the commands given by the user. Data analysis of the environment under surveillance. Solar charging used as a backup battery source. Real-time object detection using a Raspberry PI. Live video stream of the area under surveillance using a camera.

61.4 Conclusion The military support and rescue robot is a semiautonomous, solar-powered, unmanned ground vehicle built by eliminating the weakness of already existing models and incorporating ideas from published papers. The main aim behind building this robot is to aid the military troops in their missions. From the results and discussion, it is seen that the robot is divided into many phases during development to ease the building process. Each phase was carefully designed and built to increase the overall efficiency of the robotic system by eliminating weakness. In summary, using efficient AI technology for real-time object detection in combination with effective electronic technology to increase the performance of military robotic systems in parlous missions can potentially reduce the response time in case of emergencies and the risks of losing the lives of both soldiers and civilians.

References 1. G. Merchant, B. Allenby, R. Arkin, International governance of automated military robots, in Stlr.org (2011). https://stlr.org/download/volumes/volume12/marchant.pdf. Accessed 28 Mar 2020 2. S. Manyam, D. Casbeer, S. Sundar, Path planning for cooperative routing of air-ground vehicles, in American Control Conference (ACC) (2016) 3. Y. Liu, Z. Luo, Z. Liu et al., Cooperative routing problem for ground vehicle and unmanned aerial vehicle: the application on intelligence, surveillance, and reconnaissance missions. IEEE Access 7, 63504–63518 (2019). https://doi.org/10.1109/access.2019.2914352 4. Dragon runner reconnaissance robot—army technology, in Army Technology (2020). https:// www.army-technology.com/projects/dragonrunnerrobots/. Accessed 25 Mar 2020 5. L. Gregorin, S. Givigi, E. Freire et al., Heuristics for the multi-robot worst-case pursuit-evasion problem. IEEE Access 5, 17552–17566 (2017). https://doi.org/10.1109/access.2017.2739641 6. CSAIL A, iRobot to the rescue, in MIT News (2020). https://news.mit.edu/2011/irobot-to-therescue. Accessed 30 Mar 2020 7. Robots.net. Military Robots: What Are They? | Robots.Net. [online]. https://robots.net/rob otics/military-robots/ (2021). Accessed 25 Mar 2020 8. A. Jayed Islam, S. Shahriar Alam, K. Tanjim Ahammad, Design, kinematic and performance evaluation of a dual arm bomb disposal robot, in 3rd International Conference on Electrical Information and Communication Technology (2017) 9. M. Busayeed Hoque, A. Kadir Bin Motaleb, Bomb disposal robot (2016) 10. (2020) Army technology|land defence news & views updated daily, in Army technology. https://www.army-technology.com/projects/remotely-operated-vehicle-rov-daksh/https:// www.instructables.com/id/How-to-Use-the-L293D-Motor-Driver-With-Arduino/. Accessed 27 Mar 2020

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11. R. Phadnis, J. Mishra, S. Bendale, Objects talk—object detection and pattern tracking using TensorFlow, in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (2018). https://doi.org/10.1109/ICICCT.2018.8473331 12. N. Nandan, K. Thippeswamy, A tensorflow based robotic arm, in 2018 Third International Conference on Electrical, Electronics, Communication, Computer Technologies And Optimization Techniques (ICEECCOT), 14–15, Dec 2018. https://doi.org/10.1109/ICEECCOT4 3722.2018.9001524 13. O. Bowcott, UK opposes international ban on developing ‘killer robots’, in The Guardian (2020). https://www.theguardian.com/politics/2015/apr/13/uk-opposes-international-ban-ondeveloping-killer-robots. Accessed 28 Mar 2020

Chapter 62

Finite Element Analysis and Design of a Four-Helical Coiled Single Lumen Microcatheter Mallapi Debashree Gayatri Reddy, Ruby Mishra, and Manoranjan Mohapatra Abstract The rapid growth of cardiovascular and neurovascular diseases compelled the modern generation researchers to develop more superior quality catheter tubes that would reduce the risk of high invasive surgeries at the time of catheterization procedure. Microcatheters play an imperative role in perambulating through the small size cross-section of the complex vascular pathway with the best possible attributes (of the superior quality tube) which can meet the necessities required for the treatment of the vascular diseases. This study reveals the design and analysis of a four-helical coiled microcatheter tube, to find the deformation using a finite element analysis software, i.e. ANSYS. The designing of the catheter tube is done in a three-dimensional modelling software, i.e. CATIA. The microcatheter has three layers, one is the inner layer through which the embolic agent is to be delivered, the middle layer is a braided four-helical coiled structure which provides strength to the catheter, and the outer layer is lubricious. The main aim of this paper is to design a highly flexible microcatheter with minimum deformation. The boundary condition applied here is the maximum blood pressure which is applied on the outer surface of the outer layer, and the pressure of 1 ml syringe and the embolic agent is applied in the inner surface of the inner layer.

M. D. G. Reddy · R. Mishra (B) School of Mechanical Engineering, KIIT Deemed To Be University, Bhubaneswar, Odisha 751024, India e-mail: [email protected] M. D. G. Reddy e-mail: [email protected] M. Mohapatra School of Medical Sciences, KIMS Radiology Department, Bhubaneswar, Odisha 751024, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_62

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62.1 Introduction The emerging cardiovascular and neurovascular diseases have created the need for developing superior quality microcatheter tubes that would reduce the risk of indulging highly invasive surgeries at the time of catheterization procedures [1]. Microcatheters are minimally invasive surgical devices that deliver solid or liquid embolic agent at the arteriovenous malformation site [2]. The catheter can be of a single lumen type or multi-lumen type depending upon its functionalities. The modelling of the entire structure depends upon pushability, torquability and trackability [3]. The design of the middle layer or the braided structure of the catheter tube plays an important role as it provides strength to the catheter. The middle layer can be of a single-coil, a braided structure and a patterned structure which provides strength to the catheter. The braided layer is made up of NiTi alloy, a pseudoelastic material or a superelastic material able to withstand significant strains along the complicated vascular pathway. The outer layer is provided with TPEE which makes it lubricious along the vascular pathway. The PTFE layer provided in the inner layer helps in the delivery of the embolic agent to the target site. The braided structure made of NiTi strands has many benefits: (a) it can recover large deformations, (b) it provides resistance against kink and crushes during the work process of the catheter, (c) it provides flexibility, torquability and pushability required for the movement (or) motion of the catheter, (d) it helps in the transmission of forces, motions required for the proper functioning of the catheter, (e) the constrained recovery of nitinol due to shape memory alloy effects helps in locating the device at the proper location [4]. The braided NiTi strand can be of any cross-section like it can have a rectangular cross-section, an elliptical cross-section, (or) a circular cross-section depending upon necessities while designing of the catheter. The designing of the catheter should be done in such a way that it could perambulate through the complex vascular pathway with minimum (or) less damage to the surrounding vessels. The entire structure of the catheter should be able to resist the pressure of the embolic agent which is delivered through the lumen of the catheter and the maximum blood pressure without hampering the blood flow passage in that particular blood vessel [5]. Design was made the cost effective for the society by employing a robust design [6].Using FEA analysis design was made considering single lumen distal tip microcatheter for treating the vascular diseases through catheterization [7]. The microcatheter designed in this study has three layers with a proximal end and a distal end. The proximal end of microcatheter remains outside of the body and the distal end of the microcatheter is used for the proper allocation of the catheter at the target site for delivery of the liquid embolic agent. The designed microcatheter is tapered towards the distal end so that it can navigate through the small blood vessels of the brain and heart for treatment and diagnosis purpose. This paper describes a single lumen microcatheter with a four-helical coiled braided structure. The helical coils are placed at 90° to each other so that it can provide proper rigidity to the catheter.

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Fig. 62.1 Inner layer of the microcatheter

62.2 Modelling of Microcatheter 62.2.1 Designing of Microcatheter The microcatheter is designed in a three-dimensional modelling software CATIA. The 3D model is constructed in a generative shape design. The length of a standard microcatheter is considered to be 1100–1500 mm.

62.2.1.1

Dimensions of Microcatheter Tube for Different Layers

1. For the inner layer: proximal end circle diameter—0.56–0.60 mm. Distal end circle diameter—0.48–0.50 mm. 2. For the braided structure: four-helical coil diameter—0.03–0.06 mm. 3. For the outer layer: proximal end circle diameter—0.73–0.76 mm. Distal end circle diameter—0.60–0.64 mm (Figs. 62.1 and 62.2). 62.2.1.2

Steps Involved in Generative Shape Design

1. The inner layer of the microcatheter is modelled with multi-section surface command and thickness is applied to it. 2. With the help of helix command, four-helical coil braided structure is created over the inner layer. The helical coils are placed 90° to each other. 3. The outer layer of the microcatheter is constructed over the braided structure (or) the middle layer in the same way as that of the inner layer and thickness is applied to it. Assembled view in Fig. 62.3 shows the construction of the outer layer over the inner and braided layer.

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Fig. 62.2 Four-helical coil layer, i.e. (middle layer) braided over the inner layer

Fig. 62.3 Assembled view of the three layers of microcatheter

62.3 Analysis of the Microcatheter 1. After the microcatheter is designed in CATIA, it is imported to ANSYS Workbench. 2. The microcatheter tube consisting of three layers is assigned with three different materials and its properties. 3. After assigning material, the three layers are meshed using mesh option (Fig. 62.4; Tables 62.1 and 62.2).

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Fig. 62.4 Meshed model

Table 62.1 Material assignment Material

Classification

Layer

Polytetrafluoroethylene (PTFE)

Polymer

Inner

Nitinol (NiTi)

Alloy

Middle

Thermoplastic polyester elastomer (TPEE)

Polymer

Outer

Table 62.2 Material properties Properties

Polytetrafluoroethylene (PTFE)

Nitinol (NiTi)

Thermoplastic polyester elastomer (TPEE)

Density (kg/m3 )

2200

6450

1090

Ultimate tensile strength (MPa) (1 MPa = 106 Pa)

28.6

960

19.6

Young’s modulus (MPa) (1 MPa = 106 Pa)

500

75,000

21

Poisson’s ratio

0.46

0.3

0.33

Tensile yield strength (MPa) (1 MPa = 106 Pa)

23

560

13.2

62.3.1 Boundary Conditions 1. The proximal end, i.e. non-tapered end of the microcatheter is fixed where the hub is to be attached at the time of manufacturing. 2. The pressure of the embolic agent and 1 ml syringe is applied in the inner surface of the inner layer. The estimated total pressure is about 2.769–2.775 MPa. 3. A maximum blood pressure of 0.02399–0.0319 MPa is applied to the outer surface of the outer layer of the catheter tube (1 MPa = 106 Pa), (1 Pa = 1 N/m2 ).

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4. An element size of 0.5 mm is applied to the whole body with tetragonal elements. The total number of nodes and elements are 92,917 and 40,701, respectively.

62.4 Results (A) Total Deformation Results 1. A maximum total deformation of 0.35761 mm is found at the distal portion of the microcatheter. 2. A gradual increase in the deformation is observed from the proximal end of the microcatheter towards the distal end (Fig. 62.5). (B) Von Mises Stress 1. The maximum equivalent stress of 275.02 MPa is observed at the periphery of the helical braid coiling, due to transmission of the pressure from the respective layers. 2. A constant value of 0.0381 MPa is observed through the catheter body due to the distribution of the pressure in the inner and outer layer (Fig. 62.6). (C) Equivalent Elastic Strain 1. A larger amount of strain value of 0.030587 is noted at the inner layer of the microcatheter towards the distal end from where the embolic agent is to be delivered to the target vein malformation site. 2. There is less amount of elastic strain, as the braided helical coils act as a reinforcement layer and provide balance force to resist the pressure (Fig. 62.7). (D) Maximum Shear Elastic Strain

Fig. 62.5 Total deformation of the microcatheter

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Fig. 62.6 Von Mises stress of the three layers

Fig. 62.7 Equivalent elastic strain of the catheter

1. A value of 0.049245 is considered to be utmost shear elastic strain which is generated due to the stress acting on the layers. 2. The shear elastic strain value remains unstable throughout the entire structure. A value of 0.011001 and 0.0055374 is viewed for the entire tube (Fig. 62.8). (E) Strain Energy 1. A uniform strain energy value of 3.8135e−9 mJ is viewed throughout the entire structure of the microcatheter. The highest strain energy value is 0.00021172 mJ. 2. The helical coils braided together, i.e. the middle layer determines the strain energy for the entire tube with the pressure loads applied on the inner and outer layers (Fig. 62.9).

656

Fig. 62.8 Maximum shear elastic strain of the catheter tube

Fig. 62.9 Strain energy of the microcatheter tube

62.4.1 Stress Results See Table 62.3.

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Type of result

Maximum value

Minimum value

Total deformation

0.35761 mm

0 mm

Von Mises stress

275.02 MPa

0.0381 MPa

Equivalent elastic strain

0.030587

0.0001

Maximum shear elastic strain

0.049245

7.4028e−5

Strain energy

0.00021172 mJ

3.8135e−9 mJ

62.5 Conclusion The numerical simulation of the microcatheter is done using a finite element analysis software ANSYS. The boundary conditions applied to the inner and the outer layer of the microcatheter helped in determining the exact deformation of the catheter. The maximum deformation was found at the distal end of the microcatheter and the peripheral faces of the helical braided coil. The braided coiled structure provides strength to both the (inner and outer) layers and remains ingrained at the time of manufacturing. This helps the microcatheter to overcome kink and crush and provides better pushability and torquability at the time of mobilization. The taper provided in the design, towards the distal end of the microcatheter tube with minimal diameter, helps to perambulate through the minute complicated vascular pathways. The polymeric materials provided at the inner and outer layers help in the delivery of the embolic agent like ONYX to the target site for treatment and provide flexibility with lubricity, respectively.

References 1. T.J. Hewitt, et al., Wire braid-reinforced microcatheter. United States Patent; Patent No.: US:7,507,229 B2; Date of Patent: 24 Mar 2009 2. S. John, M.A. Needham (US), Expandable microcatheter. United States Patent; Patent No.: US 6,358,238B1; Date of Patent: 19 Mar 2002 3. V.L Hieu, J.E. Morris, C.M. Setum, R.G. Dutcher, Flexible microcatheter. United States Patent; Patent No.: US6,335,027B1; Date of Patent: 12 Mar 2002 4. P.P. Poncet, Nitinol medical device design considerations; MEMRY Corporation 4065 Campbell Avenue-Menlo Park,CA94025-USA (Jan 2000) 5. M.D.G. Reddy, R. Mishra, A comprehensive review on microcatheter used for catheterization procedure, in AIP Proceedings (2020) 6. S. Dey, R. Mishra, M. Mohapatra, A. Dubey, Design and analysis of single lumen microcatheter, AIP Conference Proceedings 2020, 2273, 050025 7. M.D.G. Reddy, R. Mishra, S. Dey, Finite element analysis of a single coil straight distal tip microcatheter. Materials Today: Proceedings (2020)

Chapter 63

Wear Modeling Revisited Using Feedback Control Theory M. Hanief and M. S. Charoo

Abstract Modeling of wear process is a valuable tool for optimizing and designing of tribosystems. Most of the models, empirical or analytical, have limited applicability. To overcome this limitation, a general procedure has been proposed irrespective of the type of wear. In this, communication analogy of mechanical system with electrical system is presented to understand the role of parameters in the wear process. The system so developed is converted into an equivalent block diagram. The blocks can be chosen as per the parameters, inputs or disturbances in the system. The block diagram is solved by using the classical control theory. In present paper, only few parameters like hardness, velocity, ‘third body,’ etc., have been chosen. However, other parameters like corrosive atmosphere, temperature, humidity, etc., can be added to the block diagrams as per the role played by them in the wear process. Wear is assumed to be a linear control system in this paper, but the nonlinearity can be addressed by using the available control system techniques. This paper does not intend to create or derive a new set of equations but provide a methodology for simulating a wear phenomenon under different working conditions, by adding a new block/blocks or input/ inputs. The methodology will provide a platform for the more complex problems, involving different wear mechanisms at given instant. Finally, some of the existing results, which are available in the literature, will be derived to validate the methodology.

63.1 Introduction The wear process plays a crucial role in surface topology and critically affects the reliability of a tribological systems. The wear process consists of three wear states, viz. initial running-in, mild wear and lastly severe wear. The wear behavior is a complex M. Hanief (B) · M. S. Charoo National Institute of Technology, Srinagar, J&K 190006, India e-mail: [email protected] M. S. Charoo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_63

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phenomenon. Numerous models have been developed to investigate the wear process. These models are categorized as stochastic and deterministic. Archad [1] contributed pioneering work to the development of the deterministic wear models. The deterministic models are invariably different for different tribopairs and operating conditions [2]. It is pertinent to mention that stochastic models are in commonly used for wear analysis. A double-well model is based on microscopic diffusive process by D’Aunto [3]. Dynamic system theory was used by Hu et al. [4] to analyze running-in wear process. Wang et al. [5] analyzed the dynamic reliability behavior for sliding wear of multiple ring disk system. Goh et al. [6] suggested that the wear out failure occurs when the cumulative damage exceeds a prescribed threshold. The surface topography is an important parameter that influences the wear process. The characteristics of rough surfaces have been investigated by the researchers during wear process dry contact [7–10]. In general, the wear process invariably consists of running-in period, steady state and ‘wear out’ zone where the life of component ends [11]. Zeng et al. [12] have developed running-in and steady-state wear models analytically which has been used by researchers to investigate the wear [11]. In literature, more than 150 equations are available to describe wear [2]. Most of the equations are based on solid mechanics, material properties and thermodynamics. Most of the models propose a correlation between different parameters and are therefore system specific, resulting in a large collection of parameters and constants [2]. Hsu et al. [13] list 32 parameters, found by different authors to describe their experimental data. Holm [14] proposed a model based on atomistic process. Rabonowicz and Tabor [15] used radioactive tracers to distinguish material transfer and wear. Archard [16] presented a theoretical consideration on the shape of debris and the nature of deformation mechanism. Godet [17] introduced the concept of ‘third body’ between the two contacting solids. The particles trapped between the surfaces can hardly escape. These particles play an important role in the wear and friction. Godet [17] identified that these particles have certain advantageous functions also. These may be introduced deliberately or may be produced during detachment process. These particles can bear load and accommodate velocity differences and avoid direct contact between the two surfaces. Brethier [18, 19] introduced a concept of ‘Tribological Cycle’ describing the equilibrium of the variation of the third body mass with time. Filot et al. [20] proposed a numerical strategy to study this equilibrium. Filot et al. [21] studied the effect of the properties of the third body such as cohesion and damping.

63.2 Modeling Using Control Theory The difficulty in deriving an exact equation of wear is due to the large number of the parameters or variables. To simplify the problem, the parameters can be clubbed into two major groups, promoters and inhibitors of the wear, e.g., hardness, shear strength, and inhibit the wear on the other hand surface roughness, high velocity, contact pressure promote the wear. Further, in order to acquire more insight into the wear process, one has to go deep into the role of each parameter. One of the best

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ways is to develop analogy of a mechanical system with an electrical system. Wear being system response, the wear can be treated as an output of the tribosystem and correspondingly load as input. However, inputs can be of different types acting alone or simultaneously upon the system. In such cases, multiple inputs can used in this paper; we assume only one type of load, i.e., mechanical force, which causes abrasive type of wear (Fig. 63.1). Consider a mass which is acted upon by the force F(t). F(t) influences certain portion (M) of this mass, and it (M) becomes the potential source of the wear particles. It is from this mass (M) that wear particles are detached with the passage of time. An analogy can be developed between tribosystem and electrical systems. Wherein wear rate, force and hardness are analogous to current, voltage and resistance, respectively. As the wear process starts, wear particles are formed and accumulated between the two surfaces. The two surfaces are separated from each other by these wear particles, also known as ‘third body’ Godet [17]. These particles prevent the direct contact of the surfaces and bear some load thereby reducing the wear. So these particles play a role of a damper in the tribosystems, which dampen the energy which otherwise would have been used for wear. The sequence of the events in the wear process can be summarized in Fig. 63.2. As stated earlier, a force F(t) acts on some mass and activates certain portion of this mass which become the potential source of the wear particles. The wear process is promoted by the velocity (V ) of the substrate. The velocity acts as a promoter (amplifier), because higher the velocity (or sliding distance) higher is the wear. Contrary to the velocity, hardness (H ) inhibits the wear process. Thus, it acts as resistance to the wear.

I/P

TRIBOSYSTEM

O/P

Fig. 63.1 Schematic representation of a tribosystem

Fig. 63.2 Progression of wear process from application of load to the formation of wear particles

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The mass ‘M’ (kg) when operated by the velocity becomes the time derivate of mass ‘M’, multiplied by sliding distance (kg m/s). Since the hardness offers resistance to the wear, it has inverse relation with wear rate. The model in the form of a block diagram is shown in Fig. 63.3. Let F(s) be the Laplace transform of the applied force f (t), and Q(s) is the Laplace transform of the mass of the worn out particles q(t). F(s) is multiplied by a constant ‘R’ to obtain the ˙ is integrated by proportional amount of mass. To convert wear rate into wear, ‘ M’ multiplying with the operator 1s . When the wear particles are formed, they get trapped between the two surfaces. The two surfaces are kept apart by the wear particles. The medium is sometimes injected artificially such as oil and any other type of a lubricant. In this case, we assume dry contact and the third body being produced by the surfaces in contact. With the presence of such a layer of particles at the interface, the third body serves the following purposes: It supports the load, participates in accommodation of velocity and separates the surfaces in contact thus avoiding direct interaction between the surfaces. The third body produced is thus able to protect the materials rubbing against each other from further degradation. This reduces the wear rate and this can be taken as a negative feedback. To make it dimensionally homogeneous it is multiplied with a constant K 1 and added at summing point ➋ similarly, when the wear proceeds the mass M depletes and the mass which can now be worn out is reduced which forms another negative feedback and is added at summing point ➊. Solving the above block diagram we get (Fig. 63.4) RV Q(s) =  f (s) s + K1 + V H

Fig. 63.3 Block diagram of the wear process

Fig. 63.4 Reduced block diagram of the wear process

(63.1)

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Let C1 = K 1 /R and C2 = 1/R V Q(s)     =  f (s) H s + C1 + V H C2 Q(s) =

V      f (s) H s + C1 + V H C2

(63.2)

(63.3)

Case I: consider a constant force, F(t) = Fo acting on the surface, which is known as step input in control terminology acting on the surface. The laplace transform of F(t) is given by   Fo V     Q(s) =  s V H s + C1 + H C2 ⎧ ⎫  ⎬ ⎨ F V o     £−1 {Q(s)} = £−1 ⎩ H s + C + V C s ⎭ 1 H 2 q(t) =



V Fo V 1 − e(C1 +( H )C2 )t H (C1 + V C2 )

(63.4)

(63.5)

(63.6)

Since velocity V is of the order of 102 , and H is 109 ,   the order of hardness,   therefore V H will be extremely small hence C1 + V H C2 ∼ C1 , which is a constant q(t) =



V Fo V 1 − e(C1 +( H )C2 )t H (C1 + V C2 )

q(t) =

 V Fo  1 − eC1 t C1 H

(63.7)

(63.8)

The above equation has been derived by several authors [12] for running-in wear and has been used exhaustively by [11] for wear simulation. Now At the beginning of the process t → 0 q(t) → 0. At steady state t → ∞ q(t) = CV1FHo which is same as Archard’s Law. Consider a case of force increasing linearly with time, i.e., ramp input f (s) =

Fo s2

(63.9)

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  1 (−C1 t−1) q(t) = t + e C1

(63.10)

This equation has been obtained by Argatov and Fadin [22] for a case in which load is a function of initial displacement and time.

63.3 Conclusion It is clear from above that Archard’s law can be derived successfully from the given methodology. Different types of wear can be modeled by using the control theory. The methodology can be used for different modes of wear by identifying a particular load. The output for a particular situation can be obtained by the block diagram.

References 1. J.F. Archard, Contact and rubbing of flat surfaces. J. Appl. Phys. 24, 981–988 (1953) 2. H.C. Meng, K.C. Ludema, Wear models and predictive equations: their form and content. Wear 181–183, 443–457 (1995) 3. M. D’Acunto, Wear and diffusive processes. Tribol. Int. 36, 553–558 (2003) 4. Y.Z. Hu, N. Li, K. Tonder, A dynamic system model for lubricated sliding wear and running-in. ASME J. Tribol. 113, 499–505 (1991) 5. K.S. Wang, C.S. Chen, J.J. Huang, Dynamic reliability behavior for sliding wear of carburized steel. Reliabil. Eng. Syst. Saf. 58, 31–41 (1997) 6. C.J. Goh, L.C. Tang, S.C. Lim, Reliability modeling of stochastic wear-out failure. Reliab. Eng. Syst. Saf. 25, 303–314 (1989) 7. Y.R. Jeng, Z.W. Lin, S.H. Shyu, Changes of surface topography during running-in process. ASME J. Tribol. 126, 620–625 (2004) 8. G.Y. Zhou, M.C. Leu, D. Blackmore, Fractal geometry model for wear prediction. Wear 170, 1–14 (1993) 9. S. Ge, G. Chen, Fractal prediction models of sliding wear during the running-in process. Wear 231, 249–255 (1999) 10. J.H. Horng, M.L. Len, J.S. Lee, The contact characteristics of rough surfaces in line contact during running-in process. Wear 253, 899–913 (2002) 11. R. Kumar, B. Prakash, A. Sethuramiah, A systematic methodology to characterize the runningin wear and steady-state wear processes. Wear 252, 445–453 (2002) 12. M. Zheng, A.H. Naim, B. Walter, G. John, Break-in liner wear and piston ring assembly friction in a spark-ignited engine. Tribol. Trans. 41(4), 497–504 (1998) 13. K. De Moerlooze et al., A novel energy-based generic wear model at the asperity level. Wear 270, 760–770 (2011) 14. S.M. Hsu, M.C. Shen, A.W. Ruff, Wear prediction for metals. Tribol. Int. 30(5):377–383 (1997) 15. R. Holm, Electrical Contacts (H. Gerbers, Stockholm, 1946). 16. E. Rabinowicz, D. Tabor, Metallic transfer between sliding metals; an auto radiographic study. Proc. R. Soc. London A 208, 455–475 (1951) 17. J.F. Archard, Contact and rubbing of flat surfaces. J. Appl. Phys. 24(8), 981–988 (1953) 18. M. Godet, The third-boy approach: a mechanical view of wear. Wear 100, 437–452 (1984)

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19. Y. Berthier, Maurice Godet’s third body, in The Third Body Concept Interpretation of Tribological Phenomena, ed. by D. Dowson, C.M. Taylor, T.H.C. Childs, G. Dalmaz, Y. Berthier, L. Flamand, A.A. Lubrecht (Elsevier, 1996) 20. Y. Berthier, Third body reality consequence and use of the third body to solve a friction and wear problem, in Wear, Materials, Mechanisms and Practice (Wiley, 2005), pp. 291–316 21. N. Fillot, I. Iordanoff, Y. Berthier, Simulation of wear through mass balance in a dry contact. Trans. ASME J. Tribol. 127, 230–237 (2005) 22. N. Fillot, I. Iordanoff, Y. Berthier, Wear modeling and the third body concept. Wear 262, 949–957 (2007)

Chapter 64

Performance Assessment of Improved Solar Still Design with Stepped-Corrugated Absorber Plate Aasawari Bhaisare, Unmesh Wasnik, Aniket Sakhare, Pawan Thakur, Akash Nimje, Abhishek Hiwarkar, Vikrant Katekar, and Sandip Deshmukh Abstract Conventional solar still owns poor efficiency and low distillate output, hence not found commercially popular for domestic and industrial applications. The present work demonstrates the improved design of solar still with stepped-corrugated absorber plate for higher energy efficiency and yield. During experimentation, the productivity of stepped-corrugated and conventional solar still is found as 2.50 kg/m2 per day and 0.90 kg/m2 per day, respectively. The energy efficiency of steppedcorrugated and conventional solar still is found as 33.33 and 18.67%, respectively. From this exertion, it is concluded that the stepped-corrugated still has better yield and efficiency as compared to the conventional still.

A. Bhaisare · U. Wasnik · A. Sakhare · P. Thakur · A. Nimje · A. Hiwarkar · V. Katekar (B) Department of Mechanical Engineering, S. B. Jain Institute of Technology, Management and Research, Nagpur, Maharashtra, India e-mail: [email protected] A. Bhaisare e-mail: [email protected] U. Wasnik e-mail: [email protected] A. Sakhare e-mail: [email protected] P. Thakur e-mail: [email protected] A. Nimje e-mail: [email protected] A. Hiwarkar e-mail: [email protected] S. Deshmukh Department of Mechanical Engineering, Hyderabad Campus, Birla Institute of Technology and Science, Pilani, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_64

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Nomenclature m h T η A I(t) TDS RE

Mass Specific enthalpy Temperature Efficiency Area Solar irradiation Total dissolved solids Renewable energy

Subscripts e p fg th

Evaporation Plate Latent Thermal/energy

64.1 Introduction Adequate quality drinkable water is the fundamental need of all living beings. The 71% of the earth surface is covered by water; however, drinkable water available for living creature is declining day by day. 97% of accessible water resources are salty and contain injurious bacteria and 2% water resources are iced up in glaciers and polar regions. Thus, only 1% of the world’s water is now available for consumption, cooking and other domestic utilities [1]. In several countries, there is a massive crisis for drinkable water because human activities pollute accessible potable water. Most of the human diseases are due to consumption of polluted water. But these technologies are very expensive for the production of a small quantity of fresh water [2]. The energy required for desalination is mainly obtained from the fossil fuels which are depleting day by day and not found environment friendly [3]. To rise above this difficulty, renewable energy (RE) is a viable solution [4]. In isolated regions, REbased distillation is the best alternative for distillation [5]. Various sources like solar, wind, wave and tidal can be used for distillation [6]. Solar energy is most suitable RE for distillation [7]. It is a promising way of converting brackish water into potable water. Solar still works similar to the cycle of rain in nature. Saline water in the solar still basin evaporates by using the energy of the sun. Water vapours thus formed move upward towards inclined condenser glass cover similar to cloud formation and movement. On the glass cover, the vapour condenses similar to cloud condensation. Condensed water droplets slide down under the influence of gravity and thus distil

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water is collected in the collection trough. The chemical quality parameters for water obtained from solar still are found to be within the limit recommended by the WHO standard for potable water (TDS < 500 ppm) [8]. Solar stills are broadly divided into two categories: passive and active solar still. A passive system uses solar energy collected by absorber for evaporation of saline water. In the case of active solar still, supplementary heat energy by using external device such as a solar water heater and solar air heater is supplied for faster evaporation. Passive solar stills are convenient to use for domestic application; however, active solar stills are useful for industrial applications [9]. Literature shows that conventional solar still has low distillate output (2–3 kg/m2 per day) and poor thermal efficiency (15–25%) [2]. Consequently, many researchers have used various innovative methods to increase the performance of solar still such as single basin, multiple basins, single slope, multiple slopes, the use of energy storage material, fins, photo-voltaic cell [10–12]. The absorber plate is a vital component of solar still. Energy collected at the absorber plate depends on its surface area and depth of water over it. If the saline water depth is more, then it consumes more energy for its heating and evaporation, which reduces the yield of solar still. Literature shows that the corrugated sheet provides more surface area for energy collection. Furthermore, the stepped absorber plate maintains a lower water depth [13]. Saharddi et al. [14] tested stepped solar still loaded with phase change material and recorded 76.69% improvement in productivity. Mahesh Kumar et al. [15] evaluated the performance of conventional and stepped solar still and found that stepped solar still was 33.69% more productive than conventional solar still. Abdullah et al. [16] reported that productivity of stepped solar still united with a solar air heater was 112% higher than that of without a solar air heater. Kabeel et al. [17] depicted that daily energy efficiency was 53% for stepped solar still. Hedayati et al. [18] mentioned that the productivity of stepped solar still was increased by 20% when it was coupled with the solar photovoltaic thermal collector (PV/T). Radhawan [19] demonstrated that stepped solar still with PCM gave 57% energy efficiency. Xiao et al. [20] integrated photo-voltaic thermal collector (PV/T) with stepped solar still and recorded 51.7% improvement in productivity. Several researchers tested solar distiller with corrugated absorber. Omara et al. [21] recorded a 21% improvement in productivity for solar still with corrugated absorber as compared to conventional solar still. Shalaby et al. [22] found that productivity of solar still was increased by 12% using corrugated absorber plate. Matrawy et al. [23] got a 34% increase in productivity by using corrugated absorber plate with the wick. Literature shows that some investigators have tested solar still with corrugated absorber plate and some have tested stepped or cascade solar still. However, solar still with combined stepped-corrugated absorber plate is not tested by researchers till date. Such solar still will get the advantages of both stepped as well as corrugated absorber plate. Keeping this view, stepped-corrugated still was designed, simulated, fabricated and experimented. This communication presents the experimental investigation of solar still integrated with stepped-corrugated absorber.

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64.2 Methodology After studying the several research articles from various peer-reviewed journals of international reputation, the research problem for the proposed work was finalised. Various dependent variables such as absorber, glass, water temperature, surface area of absorber and independent variable such as salinity of basin water, wind velocity were decided. Initially, mathematical modelling was done and equations were solved using MATLAB. Then the proposed experimental set-up was simulated in ANSYS Fluent® software. After getting expected results from the simulation, two experimental set-ups (conventional and stepped-corrugated) were fabricated and tested for many days. Experimental observations were recorded and results were compared.

64.3 Energy Efficiency The thermal or energy efficiency of a solar still is defined as the ratio of evaporative heat transfer to the solar irradiation on the absorber plate. It can be calculated as following [14] 

ηth =

m ew h fg  I (t) 3600 Ap

(64.1)

64.4 Stepped-Corrugated Solar Still Two experimental solar stills (conventional and stepped-corrugated type) were designed and fabricated for 2 kg/m2 per day distillate yield assuming a period of incident solar energy of 8 h. The required energy for heating and evaporation was estimated at 3.73 MJ/day. The average solar energy incident on the Nagpur city (21.14 °N, 79.08 °E) was taken about 480 W/m2 . The required area of the collector was estimated at 0.27 m2 . Hence, correspondingly the size of saline water basin was taken as L = 0.52 m and W = 0.52 m, respectively. The conventional still had the flat-type copper absorber of thickness 1 mm. The condenser glass cover was 4 mm thick. The outer casing of solar still basin was fabricated from the plywood. The inner side of solar still was covered with the layer of aluminium sheet to direct the solar radiation on the saline water. The modified still had a stepped and corrugated absorber plate with five steps. The width of each step was 10 cm and the depth was 2 cm. The steps were made of a copper plate of 1 mm thinness. The surface of the absorber plate was painted dull black, to absorb maximum solar irradiation. The corrugated shape of the absorber plate was 2 cm in depth.

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Fig. 64.1 Conventional and stepped-corrugated still

A K-type thermocouple was used for the temperature measurement at various locations on solar still such as on glass, absorber, saline water and environment. When the evaporation process had begun vapours were condensed undersides of the glass cover. The distillate droplets were sliding on the glass and collected in a distilled water container. Figure 64.1 shows the photograph of the conventional and stepped-corrugated solar still.

64.5 Experimental Results and Discussion Both the experimental set-ups were tested at a time and corresponding values of ambient, glass, saline water, absorber plate temperature and solar irradiation were recorded in January and February from morning 10 am to evening 5 pm at Nagpur city (21.14° N, 79.08° E), Maharashtra, India. Both energy and exergy analyses were carried out to evaluate thermal performance. Figure 64.2 illustrates the variation in saline water temperature, absorber plate temperature and condenser glass cover temperature over a day for stepped-corrugated solar still. It shows that absorber temperature is highest and condenser cover temperature is lowest throughout the day. The maximum difference in temperature between the glass cover and absorber plate was recorded as 20 °C during peak sunshine hours. On the other hand, the temperature of saline water reaches closer to the absorber plate temperature during the afternoon; then after, it remains very close to it until evening 5 pm. Condenser glass cover temperature is found almost constant throughout the day from 11 am to 4 pm.

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Fig. 64.2 Variation in temperature recorded during experimentation for stepped-corrugated solar still

Table 64.1 demonstrates a comparison of average experimental observations recorded from both solar stills. It shows that mean saline water, condenser cover and mean vapour film temperature are higher for stepped-corrugated solar still by 10.7 °C, 3.56 °C and 7.14 °C, respectively. Daily distillate yield, energy efficiency and exergy efficiency are increased by 177.77%, 79.78% and 841.56%, respectively, for stepped-corrugated solar still. However, the convective heat transfer coefficient and the evaporative heat transfer coefficient were increased by 40.93 and 95.64%, respectively. Table 64.1 Experimental observations

Particulars

Conventional solar still

Modified solar still

Mean saline water temperature (°C)

42.51

53.21

Mean condenser glass temperature (°C)

37.52

41.08

Productivity (me ) (kg/m2 per day)

0.90

2.50

Energy efficiency (%)

18.67

33.33

Exergy efficiency (%)

0.13

1.24

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64.6 Conclusions This work presents experimental investigations of conventional and steppedcorrugated solar still. From the results of experimentations, following conclusions are drawn: The average saline water temperature is 10.7 °C higher in stepped-corrugated solar still as compared with conventional still. The productivity of stepped-corrugated and conventional still is 2.50 kg/m2 per day and 0.90 kg/m2 per day, respectively. Furthermore, the energy efficiency of stepped-corrugated and conventional solar still is 33.33% and 18.67%, respectively. Consequently, productivity and energy efficiency of stepped-corrugated still are 177.77% and 79.78% higher than conventional still. Exergy efficiency for conventional and stepped-corrugated solar still is 0.13% and 1.24%, respectively. These results show that the stepped-corrugated still has better productivity and efficiency as compared to the conventional still. Thus, it is concluded that steppedcorrugated still is an economic and energy-efficient option for the modern-day water crisis and can be used in rural as well as urban areas to supply potable drinking water to the common household. The performance of stepped-corrugated still can be improved further by using the energy storage system. The developed system can be used for rainwater harvesting cum distillation and river water distillation.

References 1. V.P. Katekar, S.S. Deshmukh, A review on research trends in solar still designs for domestic and industrial applications. J. Clean. Prod. 257, 120544 (2020). https://doi.org/10.1016/j.jcl epro.2020.120544 2. V.P. Katekar, S.S. Deshmukh, A review of the use of phase change materials on performance of solar stills. J. Energy Storage 30, 1–28 (2020). https://doi.org/10.1016/j.est.2020.101398 3. J. Speirs, R. Gross, S. Deshmukh, et al., Heat delivery in a low carbon economy, in 8th BIEE Academic Conference (2010) 4. M. Leach, S. Deshmukh, D. Ogunkunle, Pathways to decarbonising urban systems, in Urban Retrofitting for Sustainability: Mapping the Transition to 2050 (2014), pp 191–208 5. V. Cheng, S. Deshmukh, A. Hargreaves, et al., A Study of urban form and the integration of energy supply technologies, in Proceedings of the World Renewable Energy Congress— Sweden, 6–13 May 2011, Linköping, Sweden (2011), pp 3356–3363 6. M. Leach, S. Deshmukh, Sustainable energy law and policy. Environ. Energy Law 122–138 (2012) 7. K. Anwar, S. Deshmukh, Assessment and mapping of solar energy potential using artificial neural network and GIS technology in the southern part of India. Int. J. Renew. Energy Res. 8, 974–985 (2018) 8. R.J. Ramteke, A.R. Dhurwey, H.B. Borkar et al., Recent trends in solar distillation. Int. J. Res. Appl. Sci. Eng. Technol. 4, 184–192 (2016) 9. A. Mate, V. Katekar, H.S. Bhatkulkar, Performance investigation of solar still for batteries of railway engine, Indian Railways, at Ajni Loco Shed, Nagpur, in International conference on Advances in Thermal Systems, Materials and Design Engineering (ATSMDE2017). VJTI, Mumbai (2017)

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10. A.R. Dhurwey, V.P. Katekar, S.S. Deshmukh, An Experimental investigation of thermal performance of double basin, double slope, stepped solar distillation system. Int. J. Mech. Prod. Eng. Res. Dev. 9, 200–206 (2019) 11. S. Ambade, T. Narekar, V. Katekar, Performance evaluation of combined batch type solar water heater cum regenerative solar still, in Second International Conference on Emerging Trends in Engineering and Technology (ICETET). IEEE (2009), pp 1064–1067 12. A. Ukey, V. Katekar, An experimental investigation of thermal performance of an octagonal box type solar cooker, in Smart Technologies for Energy, Environment and Sustainable Development. Lecture Notes on Multidisciplinary Industrial Engineering (Springer, Singapore, 2019). https://doi.org/10.1007/978-981-13-6148-7_73 13. S.S. Deshmukh, A.M. Jinturkar, J.S. Gawande, Comparative experimental study of single basin and stepped type solar stil. Energy Educ. Sci. Technol. 20, 79–85 (2008) 14. F. Sarhaddi, F. Farshchi Tabrizi, H. Aghaei Zoori, S.A.H.S. Mousavi, Comparative study of two weir type cascade solar stills with and without PCM storage using energy and exergy analysis. Energy Convers. Manag. 133, 97–109 (2017). https://doi.org/10.1016/j.enconman. 2016.11.044 15. M. Kumar, C. Yadav, H. Manchanda, Thermal performance of a weir-type cascade solar still: an experimental study. Int. J. Adv. Res. Innov. 4, 339–344 (2016) 16. A.S. Abdullah, Improving the performance of stepped solar still. Desalination 319, 60–65 (2013). https://doi.org/10.1016/j.desal.2013.04.003 17. A.E. Kabeel, A. Khalil, Z.M. Omara, M.M. Younes, Theoretical and experimental parametric study of modified stepped solar still. Desalination 289, 12–20 (2012). https://doi.org/10.1016/ j.desal.2011.12.023 18. E. Hedayati-Mehdiabadi, F. Sarhaddi, F. Sobhnamayan, Energy analysis of a stepped cascade solar still connected to photovoltaic thermal collector. Int. J. Automot. Mech. Eng. 53, 1689– 1699 (2017). https://doi.org/10.1017/CBO9781107415324.004 19. A.M. Radhwan, Transient performance of a stepped solar still with built-in latent heat thermal energy storage. Desalination 171, 61–76 (2004). https://doi.org/10.1016/j.desal.2003.12.010 20. L. Xiao, R. Shi, S.Y. Wu, Z.L. Chen, Performance study on a photovoltaic thermal (PV/T) stepped solar still with a bottom channel. Desalination 471, 114129 (2019). https://doi.org/10. 1016/j.desal.2019.114129 21. V.S. Gupta, D.B. Singh, R.K. Mishra et al., Development of characteristic equations for PVTCPC active solar distillation system. Desalination 445, 266–279 (2018). https://doi.org/10. 1016/j.desal.2018.08.009 22. S.M. Shalaby, E. El-Bialy, A.A. El-Sebaii, An experimental investigation of a v-corrugated absorber single-basin solar still using PCM. Desalination 398, 247–255 (2016). https://doi.org/ 10.1016/j.desal.2016.07.042 23. K.K. Matrawy, A.S. Alosaimy, A.F. Mahrous, Modeling and experimental study of a corrugated wick type solar still: comparative study with a simple basin type. Energy Convers. Manag. 105, 1261–1268 (2015). https://doi.org/10.1016/j.enconman.2015.09.006

Chapter 65

Parametric Analysis of Adhesively Bonded Single Lap Joint Using Finite Element Method Abdul Aabid, Sher Afghan Khan, Turki Al-Khalifah, Bisma Parveez, and Asraar Anjum Abstract In structural engineering, the design and sizing of adhesively bonded joints for vehicles have always been significantly important. To determine the behavior of joint in vehicle-scale models requires fine meshes, but these fine meshes are impractical; however, a mesh is necessary for comparisons and for making a quick assessment of various geometric models of joints. The analytical approach is often helpful in sizing but coupling these models with full-vehicle finite element (FE) models is complicated. Therefore, in this study, a reduced-order joint FE model is developed that can be used in structural FE models for quick assessments of bonded joints. FE model is employed for the analysis of a lap joint stresses using standard, 2D

A. Aabid · S. A. Khan · T. Al-Khalifah Department of Mechanical Engineering, Faculty of Engineering, International Islamic Univeristy Malaysia, 53100 Kuala Lumpur, Malaysia e-mail: [email protected] S. A. Khan e-mail: [email protected] T. Al-Khalifah e-mail: [email protected] A. Aabid Engineering Management Department, School of Engineering, Prince Sultan University, Riyadh, Saudi Arabia B. Parveez Department of Manufacturing and Materials Engineering, Faculty of Engineering, International Islamic Univeristy Malaysia, 53100 Kuala Lumpur, Malaysia e-mail: [email protected] T. Al-Khalifah Department of Mechanical Engineering, Technical College in Al-Kharj, Al-Kharj 16288, Saudi Arabia A. Anjum (B) Department of Civil Engineering, KLE Dr. M S Sheshgiri College of Engineering and Technology, Belgaum, Karnataka 590008, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_65

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plane stress element. Furthermore, a parametric study was performed with adhesive dimensions.

65.1 Introduction In the field of aerospace engineering, the demand of lightweight materials is increasing day by day, and many researchers have reported the advantage of using alloys and composites for aircraft/spacecraft structures. In these cases, joining two dissimilar or similar materials using adhesively bonded techniques is fetching more attention in recent studies. Moreover, the adhesive-bonded joint is also used for the repair of damaged structures. Generally, in composite structures, the enhancement of rivets, bolts, or such type of joint systems will result in the formation of damages, cracks, holes, etc., and these become high-stress concentration regions and leads to early failure. But the load is distributed more evenly when adhesives are used over the composites, thereby facilitating lighter structures. In this regard, the literature study shows the importance of adhesive in structures joint. Numerous studies have been performed on the adhesive joints system of metals and non-metals with the use of the analytical approach [1–3]. Nevertheless, numerical simulation through the FE method has become more prevalent in various fields of engineering problems. FE method is contingent on the mesh study, and the performance of mesh and analysis gives the accuracy in results. Adhesive joints require a fine mesh near to the corners of adhesive. Although these types of models are suitable for complete analysis and when it is applied to joint design and system, they can be crippling [4, 5]. Besides, such type of adhesive models cannot combine easily with coarse vehicle-scale models that are used in vehicle sizing. Hence, there is a requirement to progress analytical tools for an adhesive bonded joint system that can be smoothly coupled in structural analysis for large-scale industrial purposes without the addition of significant demands on computational studies. Such types of tools/software can be utilized for mesh sensitivity analysis for adhesively bonded joints. Also, this can be suitable for the computational ladder of numerical testing of aircraft structures [6], which is getting popularity in aerospace industries due to lower cost and its design cycles. For the assumptions of the model, computational structural models can be utilized to evaluate the shape functions. These shape functions can be used to solve the stiffness matrix for a specified problem. This type of technique was utilized to compute the stiffness matrix for different kinds of beams on the elastic foundation problems [7, 8]. Waas and Gustafson [9] investigated the phenomenon of a double overlap adhesive joint used in an element under mechanical and thermal loads. Again, it was applied for a single lap joint by expressing a stiffness matrix [10]. Adhesively bonded joints for a lap shear, and coach peel specimens, an analysis of a cohesive zone element-based model on the traction–separation constitutive law was employed to forecast the initiation and propagation of the failure mode under quasi-static load [11]. To predict the failure mode in adhesively bonded joints, a

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semi-analytical approach was developed and then validated with the experimental results with modified Arcan devices [12]. The adhesive bonded lap joints of curved surface pipes under internal pressure were evaluated to test the influence of curvilinear radii on adhesive bond strength using the experimental and numerical approach [12]. For the adhesively bonded double-lap joints with stepped out adherend, a modified analytical method with closed-form solution was used to determine the normal stresses and interfacial shear under uniform uniaxial tensile stress [13]. Few studies have implemented the adhesively bonded joint system. The vibration characteristics in view of the damping properties of the adhesive layer were determined [14]. Researchers proposed a layered plate and extended to model the lap joint using numerical simulation. In the literature, work has been done on designing the FE models such as Carrera Unified Formulation, HyperSizer, A4EI, Continuum Solid Shell, and Joint Element Designer for the assessment of local stress field prediction. The authors explored the studies on these models and compared them with a numerical approach with high dense mesh and fidelity in FE models [15, 16]. The investigation on double lap joints with an analytical approach was presented by the authors [17]. They used theory of elasticity to obtain the equilibrium equation in the overlap region of the adherend bond. The fracture parameters of a single lap joint were investigated through the numerical approach, including strength, stiffness, and fracture behavior [18]. However, to obtain an enhanced shape function, simple joints can be solved efficiently in the application. A method is introduced to model the realistic, complex joints using simple joint elements as building blocks [19]. The objective of this paper is to investigate the adhesive joints for a beam structure. The adhesive bond considered was Araldite 2014, and the beam plate was made of aluminium 2024-T3. The FE method is used to investigate the results of ANSYS commercial software. Peel stresses and Von Mises stresses are vital in this study and were investigated for different parameters of adhesive.

65.2 Problem Definition There are two types of materials that were considered in the present work, and one is the adherend, which is lightweight aluminium material with Young’s modulus of 68.95 GPa and the Poisson’s ratio of 0.345. The other material was the adhesive, which is Araldite 2014 with the Young’s modulus of 5.1 GPa and a Poisson’s ratio of 0.33. There are two plates of aluminium, one of them was fixed, and on the other tensile load of 10 MPa was applied in the x-direction, and each plate had a width (w) of 10 mm, depth (t) of 5 mm, and the total length (l + ladherend ) of 150 mm. The adhesive materials have been used to join the aluminium plates in the form of single lap joint, and the dimensions of adhesive were about depth (t a ) of 5 mm and 50 mm length (l) for the default case. Still, these dimensions varied with the depth of 1–5 mm, and the overlap length ranges from 10 to 50 mm for parametric investigation. In addition to this, the parametric study was performed based on adhesive thickness,

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Fig. 65.1 Finite element model and boundary condition

width, and loading conditions to investigate the adhesive modelling of a single lap joint. Figure 65.1 illustrates the geometric parameters and boundary conditions, that were used for all the cases in the present work, and the values used in each case of t/l were 0.5, 0.25, and 0.1 and for t a /t were 0.2, 0.4, and 1, respectively.

65.3 Finite Element Method The FE models were constructed using two-dimensional (2D) plane stressquadrilateral elements. For the adhesive boundaries of free edges matter, the joint element models were not able to achieve the traction free (σ a ) boundary condition. Hence, the joint element and FE models were not preferred to forecast parallel stresses at the adhesive boundary’s free edges. In application, a spew fillet was used that eradicates stress concentration and due to that the overlap edges do not have zero stress [20]. Hence, on the joint element process, there is no critical apprehension that does not imitate the stresses predicted by the FE model at the corners of the adhesive edges. Meanwhile, the model was aimed to assist as an initial vehicle-scale model element for initial sizing and not for analysis in details, and hence the validation was reported by the authors [19] on the joint element imitating the overall joint behavior. For modelling, a PLANE182 element was used to model the aluminium plate and adhesive bond. This element is a 2D solid structural element, which is available in ANSYS commercial code [21]. This type of element can be used for the plane stress or plane strain elements. In order to obtain a fine mesh, the number of elements was divided into higher numbers using a mesh tool option. The mesh generated in the present model was a structured type having an identical shape of elements. At the edges of the adherend and adhesive, the high dense mesh was used to obtain a fine result. Figure 65.2 shows the mesh model of a given problem. Further, the mesh sensitivity analysis was performed to check the results with the present type of mesh model.

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Fig. 65.2 Mesh model

Table 65.1 Mesh details Element type

Element size (ES) mm

Total number of elements

Total number of nodes

Maximum SEQV (MPa) 91.416

Solution converges (s)

Coarse

2

525

660

Medium

1

1750

2016

123.67

200

90

Fine

0.5

7000

7531

171.73

320

65.3.1 Mesh Sensitivity Analysis In this study, the mesh sensitivity analysis was done based on ANSYS recommendation with three types of element size. The three different types of element size (ES) considered were coarse, medium, and fine type with ES of 2, 1, and 0.5 mm, respectively. The details of mesh, elements, and nodes with solution converged time and maximum von Misses stress (SEQV) values are presented in Table 65.1. The fine size of elements solved the approximate stress values in the present results compared to other dimensions. The size was considered very small, and it gave the finest achievements of SEQV with approximate 172 MPa value whereas medium and coarse with values of 125 MPa and 90 MPa, respectively. Based on the mesh analysis results, the stress SEQV varied only at the location of maximum stress concentration, and at the other surface of the adhesive bond, it was almost constant. In contrast, at different ends of the adhesive edges, it again varied slightly. The results were obtained for a specified case using a processor: Intel i7 Core Xeon with a speed of 3.5 GHz and RAM of 16 GB.

65.3.2 Method Validation For the present case, a single joint element model study was considered from the literature to validate the present model [19]. Plane stress joint element model included

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the complete stress–strain and strain–displacement relations for a body in plane stress. This model is suitable when the depth of the joint is much smaller than the length of the adherends. The stress for the plane stress problem is related to the strain by the matrix ⎡ ⎤ C3 0 vai C3 (65.1) Dai = ⎣ 0 G ai 0 ⎦ vai C3 0 C3 where vai is the Poisson ratio of the ith adhesive layer, and C3 =

E ai 1 − vai

(65.2)

The reaction force was predicted by the adhesive model, normalized and compared with the 2D element mesh model for the case of t/l is 0.5, and t a /t is 0.2. The joints were assumed in a prearranged displacement. The reaction force for a linear elastic joint was associated to the shear stress integral over the adhesive area. The value obtained for the reaction force (F/F ANSYS ) from the FE model is about 0.98 whereas joint element model displayed is around 0.89 and the relative error was lesser approximately 9.18%. Therefore, the further simulation work continued using this method.

65.4 Results and Discussion The investigation was done based on the definition of the problem for the joint element to discover the adhesive model that is required to predict the peel and shear stress in a single lap joint. The results were obtained from a 2D-FE model and the different parametric effects to illustrate the accuracy of the joint element were investigated.

65.4.1 Effect of Adhesive Size The adhesive layer (size 1) considered here was thin and short with the width of t/l = 0.5 and t a /t = 0.2 respectively. The length of the adhesive used in FE modelling is displayed in Fig. 65.3a, b, the middle region exhibited effectively stress-free zone. The adhesive peel stress distribution in Fig. 65.3b predicts constant peel stress in the y-direction (SY), while the peel stress distribution of the 2D FE method fluctuates non-linearly in the y-direction, unfluctuating previous free end. SEQV was also defined to investigate the peel stress on the adhesive bond. The SEQV showed the maximum stress occurring at the left-top-corner of the adhesive layer, and then it linearly decreased upto the other end with slight changes in the corner. This shows

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120

SY SEQV

100

Stress (MPa)

80

60

40

20

0 0

2

4

6

8

10

Distance (x/l)

(a)

(b)

Fig. 65.3 Stress distribution through an adhesive bond. a Contours, b stresses plot, for size 1

the maximum stress being more active on the joint edge, and other edges slightly affecting the stress values. While size 2 varying the length and thickness of adhesive bond with values of t/l = 0.25 and t a /t = 0.4, which had slightly thin adhesive layer. The length of the adhesive layer used in FE modelling is displayed in Fig. 65.4a, b, the middle region depicted the peel stress and shear with behavior comparisons. As compared to size 1, the behavior of stresses is almost similar, only the values are varying. Hence, the location of maximum stress gives the stress value in the highest range while on the other surface of the adhesive bond shows the constant lower values. This means that the influence of singular stresses and grip-free boundaries of the adhesive bond of the free end projected by the 2D FE method evaporate immediately, and then the distribution of stress in y-direction in an adhesive layer was almost constant which 140

SY SEQV

120

Stress (MPa)

100 80 60 40 20 0 0

3

6

9

12

15

18

Distance (x/l)

(a)

(b)

Fig. 65.4 Stress distribution through an adhesive bond. a Contours, b stresses plot, for size 2

21

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SY SEQV

160

Stress (MPa)

140 120 100 80 60 40 20 0 -20 0

10

20

30

40

50

Distance (x/l)

(a)

(b)

Fig. 65.5 Stress distribution through an adhesive bond. a Contours, b stresses plot, for size 3

was expected from the joint element models. As a thickness of adhesive was minimal, the influenced stress can be modelled as constant in y-direction. Similar to size 1 and size 2, size 3 had increased length and thickness of the adhesive bond with values of t/l = 0.1 and t a /t = 1 of the joint model elements. This size of the model has been considered as default for the further parametric study of this article as compared to previous cases, and this model predicts the SEQV and SY as consistent results except for the high-stress layer of the adhesive bond. Therefore, this size of the model estimated that the shear stress was not reliable with the 2D FE method. Figure 65.5a, the shear stress distribution of the FE method shows the effect of the free end boundary not disappearing quickly as compared to thin adhesive sizes. In Fig. 65.5b, the middle region shows reasonable peel stress. In all the cases, the model’s behavior for the adhesive centerline peel stress predicted similar trends. However, the prediction seems to lag the stress predicted by the 2D FE method.

65.4.2 Effect of Adhesive Thickness Figure 65.6 shows the influence of adhesive layer thickness for a single lap joint element model for size 3. Shear stress transfer of an adhesively bonded lap joint permits to examine the effectiveness of adhesive thickness. In order to show the effect of adhesive thickness, the SEQV was observed in all cases of the present work. Based on Fig. 65.6, it was found that adhesive thickness increases the SEQV at the maximum stress location of the lap joint. The maximum stress occurred when the thickness was 5 mm, which is the highest value of the current work. Whereas when the thickness of the adhesive bond decreased, the maximum stress also reduced while on other surfaces adhesive bond showed almost constant effect with the same range. This study revealed that the increase in thickness results in the formation of highest stress concentration region of the adhesive bond. From the literature, it has

65 Parametric Analysis of Adhesively Bonded Single Lap … Fig. 65.6 Effect of adhesive thickness

683 Tad = 1 mm Tad = 3 mm Tad = 5 mm

180 160

SEQV (MPa)

140 120 100 80 60 40 20 0 -20 0

10

20

30

40

50

Distance (x/l)

been found that in case of the thinner layer of an adhesive bond it fails; therefore, most of the researchers recommended that neither too thin nor too thick are suitable for stress transfer from the adhesive bond.

65.4.3 Effect of Adhesive Width Like adhesive thickness, the adhesive width also plays a significant role in the variation of SEQV stresses. Figure 65.7 shows the effect of adhesive layer width for Fig. 65.7 Effect of adhesive width

180

Wad = 10 mm Wad = 30 mm Wad = 50 mm

160

SEQV (MPa)

140 120 100 80 60 40 20 0 -20 0

2

4

6

Distance (x/l)

8

10

12

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a single lap joint element model for size 3. The shear stress transfer of an adhesively bonded lap joint permitted to examine the effectiveness of adhesive width. To determine the effect of adhesive width, there three values of adhesive width were considered in this study, W ad of 10, 30, and 50 mm, to determine the SEQV. From the results, it has been found that the maximum stress in all the cases was constant except for the other faces of the adhesive bonds. At the center of the adhesive layer, increase in width resulted in lower shear stress, while for the case of 10 mm, the width of the peel stress transfer exhibited the highest value of the SEQV of the middle layer. This effect indicates that an increase in adhesive width increases the induced stress and decreases the SEQV stresses, leading to the highest load transfer through the adherend. This study proved that the sizeable adhesive width transfers a better stress redistribution because of SEQV reduction was found maximum.

65.4.4 Effect of Applied Load The effect of the tensile load on the present model was observed. Figure 65.8 illustrates the SEQV with respect to the distance of the adhesive bond. From the results, it was realized that the by varying applied load maximum stress values varied at the adhesive corner. From the present results, it is clear that the maximum stress reaches to highest value around 540 MPa at an applied tensile load of 30 MPa. After some distance, the value of stress remains almost constant except at the corner of the adhesive bond. Hence, this study illustrates the influence of the applied load on the maximum stress location. Fig. 65.8 Effect of applied load

600

10 MPa 20 MPa 30 MPa

500

SEQV (MPa)

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100

0 0

2

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8

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65.5 Conclusion In single-lap joints, eccentric load paths often require a detailed, fine mesh in structural FE models of assemblies, which is costly for global vehicle-scale models. The peel stress distribution for different adhesive layer lengths was analyzed in the lap joint. In the case of a smaller length and thin adhesive, the stress distribution was higher, which fails in-vehicle-scale models. However, with long length and thin adhesive, the stress distribution was reduced, and failure of the vehicle scale model in structures was decreased to some extent. In addition, on analysing thick and long adhesive layers, the stress distribution also reduced, and the performance in terms of stress reduction was better when compared to joint with a thinner adhesive layer. Hence from the present parametric investigation, to enhance the performance of vehicle scale models in structure, the adhesive layer used in lap joint should be thick and long. The average peel stress captured using FE at the ends of the adhesive is of concern as it predicts the durability and strength of the joint.

References 1. F. Delale, F. Erdogan, M. Aydinoglu, Stresses in adhesively bonded joints: a closed-form solution. J. Compos. Mater. 15:249–271 (1981) 2. F. Mortensen, O.T. Thomsen, Analysis of adhesive bonded joints: a unified approach. Compos. Sci. Technol. 62, 1011–1031 (2002) 3. I.U. Ojalvo, H.L. Eidinoff, Bond Thickness Effects Upon Stresses in Single Lap Adhesive Joints (SAE International, Warrendale, 1977). 4. B. Bednarcyk, Y. Bansal, C. Collier, M.-J. Pindera, J. Zhang, Analysis tools for adhesively bonded composite joints, part 1: higher-order theory. AIAA J. 44, 171–180 (2006) 5. J. Zhang, B.A. Bednarcyk, C. Collier, P. Yarrington, Y. Bansal, M.-J. Pindera, Analysis tools for adhesively bonded composite joints, part 2: unified analytical theory. AIAA J. 44, 1709–1719 (2006) 6. M.G. Ostergaard, A.R. Ibbotson, O.L. Roux, A.M. Prior, Virtual testing of aircraft structures. CEAS Aeronaut. J. 1, 83–103 (2011) 7. M. Eisenberger, D.Z. Yankelevsky, Exact stiffness matrix for beams on elastic foundation. Comput. Struct. 21, 1355–1359 (1985) 8. M. Aydo˘gan, Stiffness-matrix formulation of beams with shear effect on elastic foundation. J. Struct. Eng. 121, 1265–1270 (1995) 9. P.A. Gustafson, A.M. Waas, A bonded joint finite element for a symmetric double lap joint subjected to mechanical and thermal loads. Int. J. Numer. Meth. Eng. 79, 94–126 (2009) 10. S.E. Stapleton, A. Waas, Macroscopic finite element for a single lap joint, in AIAA/ASME/ASCE/AHS/ASC 50th SDM Conference (Palm Springs, California, 2009) 11. G. Chen, M. Guo, Failure modeling of adhesive bonded joints with cohesive elements. SAE Techn. Pap. (2017) 12. J.L.E. Pavic, G. Stamoulis, T. Bonnemains, D.D.A. Silva, Determination of the failure load of adhesive bonding by using a coupled criterion, in 23ème Congrès Français de Mécanique (2017), pp 1–5 13. S. Wang, Z. Xie, X. Li, A modified analytical model for stress analysis of adhesively bonded stepped-lap joints under tensile load. Eur. J. Mech. A Solid. 77, 103794 (2019) 14. S. Wang, Y. Li, Z. Xie, Free vibration analysis of adhesively bonded lap joints through layerwise finite element. Compos. Struct. 223, 110943 (2019)

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15. S.E. Stapleton et al., A critical assessment of design tools for stress analysis of adhesively bonded double lap joints. Mech. Adv. Mater. Struct. 1–21 (2019) 16. S.E. Stapleton, B. Stier, S. Jones, B.A. Bednarcyk, Comparison of design tools for stress analysis of adhesively bonded joints (2019), pp 1–23 17. S.-C.H. Chan, C.-F. Chan, Interfacial stress analysis of adhesively bonded. Materials 12(2403), 20 (2019) 18. J. Weiland, M.Z. Sadeghi, J.V. Thomalla, A. Schiebahn, K.U. Schroeder, U. Reisgen, Analysis of back-face strain measurement for adhesively bonded single lap joints using strain gauge, digital image correlation and finite element method. Int. J. Adhesion Adhesives 102491 (2019) 19. S.E. Stapleton, A.M. Waas, The analysis of adhesively bonded advanced composite joints using joint finite elements, Apr 2012 20. T.P. Lang, P.K. Mallick, Effect of spew geometry on stresses in single lap adhesive joints. Int J Adhes Adhes 18, 167–177 (1998) 21. ANSYS Inc,ANSYS FLUENT 18.0: Theory Guidance. Canonsburg, PA (2017)

Chapter 66

Modelling and Analysis of Flat Disc Brake for Dynamic Vehicles K. Viswanath Allamraju

Abstract Rotor disc brake plays an important role in the automobiles to reduce speed and avoid accidents. Design and fabrication of disc brakes are highly needed, therefore, simulation studies are important at laboratory level before facing fabrication for minimizing man, machine and materials and cost. This paper proposed a modelling and analysis of rotor disc brake with various materials such as carbon alloy, steel and carbon steel with their results. Finite element method has been used for structural analysis in order to design the flat disc brake for dynamic vehicles.

66.1 Introduction Most usually brakes use turbulence to affect animated possible into heat, when new methods of effectiveness modification may engage in. Other methods refer instigator into secluded future in such hoarded forms as kerb air or generate oil. Still more braking methods even fix up motivator into contrasting forms, object by transferring the likely to a rotating balance. Brakes are plainly asking rotating axles or gyrates but may take more forms in the practice that the façade of a tragic falling (flaps deployed into thin or air). Friction constraints on automobiles discount store braking heat in the drum slow or disc restriction cycle braking then note it to the air unwaveringly. When roving is falling on some vehicles can use their engines to damper. When the prohibition tool is pushed to assess containing alternator pushes the pad headed for the slow disc any slows the whirl down [1–5]. On the embargo drum, it befits as the volumetric curve pushes the slow shoes headed for the drum whatever also slows the pivot down. Structural evaluation is no doubt divine accepted form of the definite principle scheme. The term anatomical (or organization) implies not only civilized metallurgy organizations being bridges and buildings, but also nautical, flying and automated organizations, farther stereotyped components in the same K. V. Allamraju (B) Department of Mechanical Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad 5060043, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_66

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manner with transformer, engine parts and tools. Structural analysis in FEM can be useful for studying the mechanical, civil and aerostructures. There are different types of structural analysis, i.e. they are static analysis, thermal analysis, modal analysis, harmonic analysis and so on. Static analysis—used to learn displacements, heats, etc. lower stationary packing setting for both tight and no slender fixed analysis [6–10]. Non-thin ties can comprise malleability, strain stiffening, hefty shift, populous sprain, nimble flexibility, reach surfaces and creep. Dynamic analysis—used to verify the return of a house to on the spot time-varying loads. Structural and thermal analysis of disc brake with slots was studied in the past literature and proposed the optimum design of brake [11–13]. In this article proposed, the structural analysis of various disc brakes made of various materials by using FEM.

66.2 Modelling of Disc Brake Disc brake standards are rotor disc dimension = 240 mm, tangential force between pad and rotor (outer face), (FTRO): in this, FTRO is equal to FTRI because same normal force and same material. In this paper, the theoretical modal analysis of brake disc is conducted by using ANSYS software to preliminarily determine the natural frequency and mode of vibration. At the same time, the incentive points and the responding points of modal test will be arranged according to the theoretical analysis results. To avoid producing lots of finite element units, increasing computing time, reducing mesh quality and analysis precision, the model of brake disc is simplified by ignoring the little features, such as bolt holes, rounding, holes and convex platform. Figures 66.1, 66.2, 66.3, 66.4 and 66.5a demonstrate the structural analysis of brake disc the model, discretized model, with structural load, deformation and strain variation on disc brake. Here, the disc is made of carbon material. In this article, three cases are presented. Case 1 is brake disc is made of carbon alloy material, case 2 is brake disc is made of steel material and case 3 brake disc is made of carbon steel material. The brake discs are modelled in CATIA V5 and imported Fig. 66.1 Disc brake model

66 Modelling and Analysis of Flat Disc Brake … Fig. 66.2 Meshing of disc brake

Fig. 66.3 Disc brake with load

Fig. 66.4 Deformation of carbon disc brake

Fig. 66.5 a–d Strain and stress variation on carbon and steel disc

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to ANSYS workbench for discretization and analysis. In discretization tetrahedron elements are used for whole body of brake discs. This element gives optimum results in comparison with other three-dimensional elements. Meshing is passed the patch test and observed that optimum meshing is certified for this brake disc. Mathematical modelling is used for discretization of tetrahedron element as follows. Linear displacement functions for tetrahedron elements: u(x, y, z) = p1 + p2 x + p3 y + p4 z v(x, y, z) = p5 + p6 x + p7 y + p8 z

(66.1)

w(x, y, z) = p9 + p10 x + p11 y + p12 z Equations (66.1)–(66.13) represent the displacements of various nodes of tetrahedron element, strains of nodes and stress values of nodes. Equations of coefficients: u 1 = p1 + p2 x1 + p3 y1 + p4 z 1 u 2 = p1 + p2 x2 + p3 y2 + p4 z 2 u 3 = p1 + p2 x3 + p3 y3 + p4 z 3 u 4 = p1 + p2 x4 + p3 y4 + p4 z 2 v1 = p5 + p6 x1 + p7 y1 + p8 z 1 v2 = p5 + p6 x2 + p7 y2 + p8 z 2 v3 = p5 + p6 x3 + p7 y3 + p8 z 3 v4 = p5 + p6 x4 + p7 y4 + p8 z 2 w1 = p9 + p10 x1 + p11 y1 + p12 z 1 w2 = p9 + p10 x2 + p11 y2 + p12 z 2 w3 = p9 + p10 x3 + p11 y3 + p12 z 3 w4 = p9 + p10 x4 + p11 y4 + p12 z 2 Equation for first, second, third and four coefficients: ⎧ ⎫ ⎡ p1 ⎪ 1 ⎪ ⎪ ⎨ ⎪ ⎬ ⎢ p2 1 =⎢ ⎣1 ⎪ p3 ⎪ ⎪ ⎪ ⎩ ⎭ p4 1 ⎧ ⎫ ⎡ p5 ⎪ 1 ⎪ ⎪ ⎨ ⎪ ⎬ ⎢ p6 1 =⎢ ⎣1 ⎪ p7 ⎪ ⎪ ⎪ ⎩ ⎭ p8 1

x1 x2 x3 x4

y1 y2 y3 y4

x1 x2 x3 x4

y1 y2 y3 y4

⎤−1 ⎧ ⎫ u1 ⎪ z1 ⎪ ⎪ ⎨ ⎪ ⎬ z2 ⎥ u 2 ⎥ , z3 ⎦ ⎪ u ⎪ ⎪ ⎩ 3⎪ ⎭ z4 u4 ⎤−1 ⎧ ⎫ u5 ⎪ z1 ⎪ ⎪ ⎨ ⎪ ⎬ ⎥ z2 ⎥ u6 , z3 ⎦ ⎪ u ⎪ ⎪ ⎩ 7⎪ ⎭ z4 u8

(66.2)

66 Modelling and Analysis of Flat Disc Brake …

⎧ ⎫ ⎡ 1 p9 ⎪ ⎪ ⎪ ⎪ ⎨ ⎬ ⎢ p10 1 =⎢ ⎣1 ⎪ ⎪ p11 ⎪ ⎪ ⎩ ⎭ p12 1 ⎡

1 ⎢1 ⎢ ⎣1 1

x1 x2 x3 x4

y1 y2 y3 y4

x1 x2 x3 x4

y1 y2 y3 y4

691

⎫ ⎤−1 ⎧ u9 ⎪ z1 ⎪ ⎪ ⎪ ⎨ ⎬ z2 ⎥ u 10 ⎥ z3 ⎦ ⎪ ⎪ u 11 ⎪ ⎪ ⎩ ⎭ z4 u 12

⎡ ⎤−1 z1 a1 a2 ⎢ b1 b2 1 z2 ⎥ ⎢ ⎥ = z3 ⎦ 6V ⎣ c1 c2 z4 d1 d2    1 x1 y1 z 1     1 x2 y2 z 2    6V =    1 x3 y3 z 3  1 x y z  4 4 4

a3 b3 c3 d3

⎤ a4 b4 ⎥ ⎥ c4 ⎦ d4

where “V” represents the volume of the tetrahedron. Coefficients are calculated by using cofactor method.    x2 y2 z 2    a1 =  x3 y3 z 3 , x y z  4 4 4    1 x2 y2    d1 = − 1 x3 y3  1 x y  4 4    x1 y1 z 1    a2 = − x3 y3 z 3 , x y z  4 4 4    1 x1 y1    d2 =  1 x3 y3  1 x y  4 4    x1 y1 z 1    a3 =  x2 y2 z 2 , x y z  4 4 4    1 x1 y1    d3 = − 1 x2 y2  1 x y  4 4    x1 y1 z 1    a4 = − x2 y2 z 2 , x y z  3 3 3

     1 y2 z 2   1 x2 z 2      b1 = − 1 y3 z 3 , c1 =  1 x3 z 3 , 1 y z  1 x z  4 4 4 4

     1 y1 z 1   1 x1 z 1      b2 =  1 y3 z 3 , c2 = − 1 x3 z 3 , 1 y z  1 x z  4 4 4 4

     1 y1 z 1   1 x1 z 1      b3 = − 1 y2 z 2 , c3 =  1 x2 z 2 , 1 y z  1 x z  4 4 4 4

     1 y1 z 1   1 x1 z 1      b4 =  1 y2 z 2 , c4 = − 1 x2 z 2 , 1 y z  1 x z  3 3 3 3

(66.3)

(66.4)

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   1 x1 y1    d4 =  1 x2 y2  1 x y  3 3



 1 (a1 + b1 x + c1 y + d1 z)u 1 6V   1 + (a2 + b2 x + c2 y + d2 z)u 2 6V   1 + (a3 + b3 x + c3 y + d3 z)u 3 6V   1 + (a4 + b4 x + c4 y + d4 z)u 4 6V

u(x, y, z) =

(66.5)

 1 (a1 + b1 x + c1 y + d1 z)v1 6V     1 1 + (a2 + b2 x + c2 y + d2 z)v2 + (a3 + b3 x + c3 y + d3 z)v3 6V 6V   1 + (66.6) (a4 + b4 x + c4 y + d4 z)v4 6V   1 w(x, y, z) = (a1 + b1 x + c1 y + d1 z)w1 6V   1 + (a2 + b2 x + c2 y + d2 z)w2 6V   1 + (a3 + b3 x + c3 y + d3 z)w3 6V   1 + (66.7) (a4 + b4 x + c4 y + d4 z)w4 6V 

v(x, y, z) =

Displacement equations in terms of shape functions are u(x, y, z) = N1 u 1 + N2 u 2 + N3 u 3 + N4 u 4 v(x, y, z) = N1 v1 + N2 v2 + N3 v3 + N4 v4 w(x, y, z) = N1 w1 + N2 w2 + N3 w3 + N4 w4 Strain relations of tetrahedron element is

(66.8)

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Fig. 66.6 Variation of stress and strain of carbon steel disc brake

⎧ ⎫ ⎧ ∂u ⎫ ⎪ ⎪ ⎪ ⎪ ⎪ εx ⎪ ∂x ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ∂v ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ε ∂y y ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎨ ⎬ ⎨ ∂w ⎪ εz = ∂u ∂z ∂v + ∂x ⎪ ⎪ ⎪ γx y ⎪ ⎪ ⎪ ⎪ ⎪ ∂y ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ∂v ∂w ⎪ ⎪ ⎪ ⎪ ⎪ γ + yz ⎪ ⎪ ⎪ ∂y ⎪ ⎪ ∂z ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ ⎩ ∂w ∂u γzx + ∂z ⎭ ∂x ⎧ ⎧ ⎫ ⎫ ⎪ ⎪ σx ⎪ εx ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ σ ε y y ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ⎨ ⎬ ⎬ σz εz = [D] ⎪ ⎪ τx y ⎪ γx y ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪τ ⎪ ⎪γ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ yz yz ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ ⎩ ⎭ ⎭ τzx γzx

(66.9)

(66.10)

Figure 66.3 describes the flat disc is subjected to load. Load is applied on both brake sides of the disc. The maximum applied load is 1000 N in order to observe the strain and stress behaviour of disc brake made of carbon material. There are different types of materials they are: carbon, steel, carbon steel, ceramics, aluminium, etc. But carbon, steel, and carbon steel for analysis are considered. Figure 66.5b, c represent the variation of stress and strain of steel disc brake. Figures 66.5d, 66.6 discuss the variation of stress and strain of disc brake made of carbon steel of plane disc case.

66.3 Results and Discussion Analysis of three different materials has been simulated using ANSYS simulation tool. Figure 66.7a presents the variation of deformation of case 1, 2 and 3. It is observed that the maximum deformation was found in carbon alloy material for three cases. The maximum deformation values for case 1, 2 and 3 are 0.0039, 0.0045 and 0.0038 mm. The maximum deformation value is obtained for case 2 carbon

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Fig. 66.7 a, b, c Variation of deformation, stress and strains for case 1–3

alloy disc brake. Figure 66.7b presents the variation of stress of case 1, 2 and 3. It is observed that the maximum stress is found in steel material disc brake for three cases. The maximum stress values for case 1, 2 and 3 are 2.0711, 2.5053 and 2.0725 MPa. The maximum deformation value is obtained for case 2 carbon alloy disc brake with drilled holes. Figure 66.7c Presents the variation of strain of case 1, 2 and 3. It is observed that the maximum strain is observed in steel material for three cases. The maximum deformation values for case 1, 2 and 3 are 2.0147E−5, 2.5222E−5 and 2.0155E−5. The maximum deformation value is obtained for case 1 steel for plain disc brake.

66.4 Conclusion Three cases of flat disc brakes simulation are studied, viz. carbon, steel and carbon steel. It is concluded that carbon steel brake is the optimum one in comparison with other materials, viz. carbon and steel. Carbon steel brake deformation is low and strain is low. In this article presented, the structural analysis of various materials and compared the results. Finite element formulation is mentioned in detail for tetrahedron element which is employed in meshing the flat plate disc.

References 1. H.W. Gonska, Kolbinger, h. J. Temperature and Deformation Calculation of Passenger Car Brake Disks, in Proceedings of ABAQUS users conference, Aachen, Germany, pp 21–232 (1993) 2. J.E. Akin,Application and implementation of finite element methods. Academic Press, Orlando, pp 318–323 (1982) 3. P. Zagrodzki, Analysis of thermo unchanging phantasm in multi disk claws and brakes. Wear 140, 291–308 (1990) 4. R.D. Cook, Concept and Applications of Finite Element Analysis (Wiley, Canada, 1981). 5. O.C. Zienkiewicz, The Finite Element Method (McGraw-Hill, New York, 2014). 6. A.A. Beeker, The Boundary Element Method in Engineering (McGraw-Hill, New York, 1992). 7. M. Comninou, J. Dundurs, On the barber boundary conditions for thermo resilient contact. ASME J. 46, 849–853 (1979)

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8. J.R. Barber, Contact problems involving a cooled punch. J. Elast. 8, 409–423 (1978) 9. J.R.Barber, Stability of thermo pliable contact, in Proceedings of international conference on tribology. Institute of Mechanical Engineers, pp. 981–986 (2018) 10. T.A. Dow, R.A. Burton, Thermo pliable instability of sliding contact in the deficiency of wear. Wear 19, 315–328 (2016) 11. K. Lee, J.R. Barber, Frictionally-excited thermo adjustable instability in automotive disk brakes. ASME J. Tribol. 115, 607–614 (1993) 12. K. Lee, J.R. Barber, An experimental investigation of frictionally thrilled thermo resilient instability in automotive disk brakes junior, a drag brake application. ASME J. Tribol. 116,409– 414 (2018) 13. K. Viswanath Allamraju, Structural and thermal analysis of disc brake with slots. Int. J. Eng. Adv. Technol. 9(3), 903–907 (2020)

Chapter 67

Robust PV Fed Discrete Controller for Heating and Lighting Applications K. Viji, K. Chitra, and K. Uma Maheswari

Abstract This paper explains the modeling and simulation of robust PV fed discrete sliding mode control (DSMC) using zeta converter for heating and lighting applications. PV fed system is unstable and less efficient because of its poor solar energy conversion and fluctuation in irradiation. DSMC is used in order to ensure stability under uncertainties, and zeta converter is used which has wide range of duty cycle and better efficiency compared to other types of DC–DC converters. So that overall the system is efficient, robust and economical. The robustness of the controller is proved by introducing uncertainties both in load and source side.

67.1 Introduction In recent years, there is a high demand for fuel and electrical energy due to increase in population. So there is a requirement of alternation for the fuel and energy demand. The renewable energy sources (RES) preferred commonly are solar power because of its eco-friendly, cleanliness and noiseless properties [1]. With the use of PV array, the solar energy is converted to electrical energy. This energy can satisfy the fuel and energy demand. DC–DC converters are commonly used to interface the PV array with the applications. But the energy produced by the PV array is not constant and has less stability if it is fed to an application result in poor efficiency [2]. So there is a requirement of robust and efficient controller to ensure stability of PV fed systems. K. Viji · K. Chitra (B) CMR Institute of Technology, Bengaluru, India e-mail: [email protected] K. Viji e-mail: [email protected] K. U. Maheswari V.S.B. Engineering College, Karur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_67

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In this paper, zeta converter is chosen because it either increases or decreases the input voltage exclusive of altering its polarity [3]. The other name for this converter is switched DC–DC converters, which can be able to manage the power flow. This is a highly developed version of the buck–boost-type converter by the introduction of an additional inductor and capacitor. It has extensive duty cycle range such that huge output power can be obtained with better power factor. The current ripples are less both in input and output side. Closedloop control is normally used to control the voltage; the output voltage regulation is not guaranteed with open-loop control. The nonlinear nature of power electronics converters leads to the requirement of robust controller, which can resist voltage disturbances and load oscillations. Only with the implementation of closed-loop system, output voltage can be controlled for the duration of voltage variations or uncertainties. This paper explains the modeling and simulation of robust PV fed discrete sliding mode control (DSMC) using zeta converter for heating and lighting applications. The major benefit of implementing DSMC in this paper is it can be easily implemented with less apparatus and has flexibility in control characteristics. Any digital controller can be interfaced easily with the proposed system [4, 5]. The key benefit of zeta converter is it offers low input and output ripple current compared to SEPIC converters [6]. The other name for zeta converter is inverse SEPIC converter. The schematic representation of the circuit is given in Fig. 67.1. The PV module is feeding input to the zeta converter; load is selected as resistive load. The measured variables V c2 and iL2 are fed as input to the discrete controller. The digital controller compares the current value with the reference and calculates the error. Based on the error (e) and difference in error (e), the controller generates gate pulse signal and fed to the gate of MOSFET through driver circuit.

PV Module

Zeta Converter

Driver Circuit

Fig. 67.1 Block diagram of PV fed DSMC zeta converter

Load

Discrete Controller

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67.2 Methodology of Work 67.2.1 Mathematical Analysis of Zeta Converter Zeta converter is represented in Fig. 67.2, and it has switch S 1 , charging capacitors C 1 and C 2 , inductors L 1 and L 2, one diode D1 and load resistance R. MOSFET is used as a switch, and load is considered as resistive load. There are two modes of operation depending on the position of the switch [7]. Mode 1: When the switch S 1 is ON Supply voltage V s reverse biases the diode D1 and the current started to flow through the switch. Because of that inductors started charging, let iL1 and iL2 be the currents flowing through it. At the same time, capacitor C 2 starts charging and C1 starts discharging; let the capacitors voltage be V c2 and V c1 . Now apply Kirchhoff’s voltage law L1

di L1 = Vs dt

Vs di L2 Vc1 Vc2 = + − dt L2 L2 L2

(67.1) (67.2)

Apply Kirchhoff’s current law, C2

dVc2 = i L1 dt

(67.3)

Mode 2: When the switch is OFF During this mode, D1 is forward biased. The inductors start discharge through the capacitors C 1 and C 2 , so that the inductor current decreases gradually. Let the voltage across the capacitor C 1 be V c1 , and voltage across inductor L 2 be V L2 then

Fig. 67.2 Circuit diagram of zeta converter

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by applying Kirchhoff’s voltage law di L1 = −V1 dt

(67.4)

di L2 = −VL2 dt

(67.5)

L1 L2 Apply Kirchhoff’s current law

i L1 = C1

dVc1 dt

(67.6)

The duty cycle (D) is given by the expression (67.7) D=

Vo Vo + Vs

(67.7)

The voltage V o of the zeta converter is given in Eq. (67.8) Vo =

1 Vs D−1

(67.8)

The variables of zeta converters iL1 , iL2 , V c1 and V c2 are represented by x 1 , x 2 , x 3 and x 4 , respectively. Then the time domain analysis of the zeta converter is represented by the following equations obtained from state space analysis of the converter. D [−rc1 (1 − D)]x1 (1 − D) − x3 + Vs (67.9) L1 L1 L1   rc2 R D R D rc2 Vo −1 rc1 D + x2 + x4 + x˙2 (t) = x3 − Vs + L2 rc2 + R L2 L 2 (rc2 + R) L2 L 2 (rc2 + R) (67.10) x˙1 (t) =

x˙3 (t) = x˙4 (t) =

D (1 − D)x1 − x2 C1 C1

1 Vo R x2 − x4 − C2 (rc2 + R) C2 (rc2 + R) C2 (rc2 + R) Vo (t) =

R rc2 Vo rc1 R x2 + x4 − rc1 + R rc2 + R rc2 + R

(67.11) (67.12) (67.13)

where r c1 , r c2 are series resistances and r L1 , r L2 are DC resistance of the equivalent circuit of zeta converter. In that x 1 is the inductor 1 current, x 2 is the inductor 2 current, x 3 is the capacitor voltage C 1 , and x 4 is the capacitor voltage C 2 .

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67.2.2 Modeling of Discrete Sliding Mode Control for Zeta Converter In order to design the discrete model, the control parameters are sampled during the interval T and the sampled signals are given as input to the discrete controller [8, 9]. The discrete time model of the zeta converter is represented by the following equations TD [−rc1 (1 − D)]x1 [k] T (1 − D) − x3 [k] + Vs L1 L1 L1   rc2 R −T TD rc1 D + x2 [k] + X 2 [k + 1] = x3 [k] L2 rc2 + R L2 rc2 Vo TD R x4 [k] + Vs + − L 2 (rc2 + R) L2 L 2 (rc2 + R)

X 1 [k + 1] =

X 3 [k] = X 4 [k + 1] =

T (1 − D)x1 [k] T D − x2 [k] C1 C1

TR T T Vo x2 [k] − x4 [k] − C2 (rc2 + R) C2 (rc2 + R) C2 (rc2 + R)

(67.14)

(67.15) (67.16) (67.17)

The sliding surface S[k] is given by the Eq. (67.18) S[k] = e p [k] + μ1 ei [k] + μ2 ev [k] S[k] = x3 [k]x4 [k] − x3ref x4ref + μ1 (x3 [k] − xiref ) + μ2 (x4 [k] − xvref )

(67.18) (67.19)

where ei = (x3 − xiref ) gives current error, ev = (x4 − xvref ) gives voltage error, e p = (x3 x4 − xiref xvref ) gives the power error, and μ1 , μ2 are the switching function, such that e˙i [k] = x[k] ˙ and e˙v [k] = x˙4 [k]. The control law is given by u=

1 (1 − sgn(S)) = 2



0 if S > 0 1 if S < 0

(67.20)

The stability of zeta converter using DSMC law can be verified by Lyapunov stability criteria, and the condition for stability is |S[k + 1]| < |S[k]|.

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67.3 Results and Discussion PV array module of the type 1 Soltech 1STH-215-P ten series modules with six parallel strings is selected from MATLAB/Simulink for simulation [10]. In order to provide variation in input for the solar panel, the random order irradiance of [1000–800–700] W/m2 is selected with temperature of 25 °C and power 1 kW/m2 . The switching frequency is selected as 10 kHz; the MOSFET is selected as switch for this converter. In order to prove the robustness of the system, step variation is provided in the reference and load change is provided with the help of circuit breaker. Figure 67.3 represents the simulated response of PV array module that gives the responses of irradiance, panel output voltage and current. The voltage generated by the PV module is around 350 V for the random order irradiations of [1000, 800, 700] W/m2 , respectively. The closed-loop response of PV fed DSMC zeta converter is shown in Fig. 67.4. The input voltage to the converter from PV module is of about 380 V. The reference voltage is set as 175 V at the beginning, and there is a step change which is introduced at the time of 0.5 s of about 230 V. The output voltage tracks the reference after introducing a small spike in voltage at both reference values. This proves the robustness of the prescribed digital controller. Figure 67.5 shows the generated pulse width modulated (PWM) gate pulse by the digital controller to MOSFET. In that the variation of gate pulse width for the change in reference signal is shown. Figure 67.6 shows the response of the DSMC zeta converter for variation in load and reference value. With the introduction of circuit breaker, interruption in load is introduced during the time of 0.2 s and step change in reference voltage is introduced at 0.5 s, respectively. In both the conditions, the response of the controller is good and the output tracks the reference. In paper [9], the output voltage of zeta converter fluctuates about 3 V, but using DSMC the results obtained track the reference voltage without any continuous fluctuation.

Fig. 67.3 PV array responses

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Fig. 67.4 Closed-loop response of DSMC zeta converter

Fig. 67.5 Generated PWM gate pulse by the digital controller

67.4 Conclusion Modeling and simulation of robust PV fed digital controller are explained for lighting and heating applications. The robustness of the controller is proved by introducing step change in reference, variation in load and variation in irradiance. In all the cases, the controller tracks the reference so that the system is stable and efficient due the introduction of zeta converter. The switching function μ1 varies in the range of 200– 300 and μ2 varies in the range of 250–400. The zeta converter fed system provides better output compared with buck–boost fed system due to its very good properties like wide range of duty cycle, better efficiency and gives positive output voltage. The system is very compatible and can be interfaced with digital controller due to the

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Fig. 67.6 Digital controller response for load variation

flexibility of its control characteristics. This system can be used for battery charging, heating and lighting control effectively.

References 1. P. Premalatha, P.R. Maneesha, Laxmi, M. Mansi Angeleena, PV fed zeta converter for street lighting applications. Asian J. Electr. Sci. 8(S1), 45–50 (2019) 2. V.S. Eashwar, S. Kalithasan, K.V. Kandasamy, Application of Zeta converter for automatic battery recharge. National Conference on Recent Trends and Developments in Sustainable Green Technologies. J. Chem. Pharm. Sci. Spec. Issue 7 (2015) 3. K.O. Vijay, P. Sriramalakshmi, Comparison between Zeta converter and boost converter using sliding mode controller. Int. J. Eng. Res. Technol. 5(7) 368–373 (2016) 4. H. Komurcugil, S. Biricik, N. Guler, Indirect sliding mode control for DC-DC SEPIC converters. IEEE Trans. Ind. Inf. 16(6) (2019) 5. D. Munoz, D. Sbarbaro, An adaptive sliding-mode controller for discrete nonlinear systems. IEEE Trans. Industr. Electron. 47(3), 574–581 (2000) 6. E. Vulthchhay, C. Bunlaksananusorn, Dynamic modeling of Zeta converter with state-space averaging technique. ECTI-CON 2008, in 5th International Conference, vol. 2. IEEE (2008), pp. 961–972 7. K. Viji, A. Kumar, R. Nagaraj, Qualified analysis of DSMC over SMC for Boost Converter. Int. J. Control Autom. 10(12), 199–208 (2017) 8. E. Vuthchhay, C. Bunlaksananusorn, Modeling and control of a Zeta Converter, in The 2010 International Power Electronics Conference (IPEC). IEEE, 21–24 June 2010, pp 612–619 (2010) 9. A. Admane, H. Naidu, Analysis and design of zeta converter. Int. J. Innov. Res. Multidisc. Field 4(4), 161–167 (2018) 10. K. Chitra, V.S. Prakash, V. Kamachi Kannan, Design and implementation of simple boost PWM controlled T-source inverter for solar PV application. Int. J. Sci. Technol. Res. 8(11), 717–720 (2019)

Chapter 68

Study of Effect of Variation of Parameters on the Performance of a Solar Still Twinkle Rane, Parthsarathi Mulay, Namrata Kala, and Archana Thosar

Abstract Water is eminent for all living beings and is a basic requirement. Even though 71% of the Earth’s surface is water-covered, there is still an acute shortage of drinking water in many countries, as approximately 97.5% of Earth’s water is saltwater in the oceans and only 2.5% is fresh water in groundwater, lakes, and rivers. Thus, access to clean drinking water is a major issue that needs to be tackled immediately and efficiently. The existing water purification technologies like reverse osmosis (RO), electrodialysis, multistage flash, multiple effect, and vapor compression require a lot of energy and fuels, leading to environmental pollution. It is also found that 70% of water supplied as input to these technologies is wasted, while RO also removes important minerals, and World Health Organization (WHO) standards do not deem this fit. The desalination of saline water using solar stills is an effective solution to overcome these problems. Combining the worldwide availability and inexhaustible nature of solar energy, a still that requires only solar energy as the input ensures a sustainable and environmentally friendly method to produce potable water with minimal energy conversions. This paper reviews the performance of a solar still by varying parameters that affect the production of output distillate. The results obtained are studied and presented.

T. Rane (B) · P. Mulay · N. Kala · A. Thosar College of Engineering Pune, Pune, India e-mail: [email protected] P. Mulay e-mail: [email protected] N. Kala e-mail: [email protected] A. Thosar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_68

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68.1 Introduction Two major concerns challenge humanity today: lack of freshwater sources for drinking and the depletion of the environment due to the burning of fossil fuels. The rapid industrial development and the fast-growing world population in the last few decades have led to an increase in energy demand, increasing the use of fossil fuels. Emissions from such fuels lead to environmental pollution and global warming. Also, the rate of formation of these fossil fuels is much slower than their rate of depletion. Hence, it has become the need of the hour to consider renewable energy resources to sustain the basic requirements. Solar desalination uses the most sustainable and clean energy source, i.e., solar energy to produce potable water. It is one of the most inexpensive methods and can be installed in areas that receive a good amount of sunlight regularly. Given the acute shortage of water in large and developing cities, people often face a drinking water crisis and resort to technologies such as RO and ultraviolet filters which involve wastage of water along with other disadvantages such as higher consumption of energy. India is gifted with solar energy and receives average solar radiation of 200 MW/km2 a year. This energy from the sun can be tapped to achieve a desalination-based water purification system to suffice the needs of the people. Desalination is the process of removal of salt and other impurities from water to obtain freshwater suitable for human consumption or other activities. Ali et al. [1] extensively studied different types of water desalination processes. The methods can be classified into two categories: thermal processes and membrane processes. Ayoub and Malaeb [2] have regarded simple solar stills as one of the most promising water purification techniques that can be effectively used to convert sea and brackish water into freshwater. Agrawal et al. [3] have studied the energy balance equations for the thermal modeling of the conventional solar still. This paper aims at studying the variation of the climatic, design, and operational parameters of a solar still which are critical factors that affect the productivity of the still. The effect of these parameters on the production of distillate is analyzed and the simulated results thus obtained from the mathematical model are discussed further.

68.2 Single Basin Solar Still The basic design can take on many variations depending on its application and type, but the idea has not changed much since the stills built in the days of the Las Salinas, Chile in 1872 [4]. A solar still works on the basic principle: As the incident radiation is transmitted through the transparent glass cover, a part of the radiation gets absorbed by the cover while a part of it is absorbed by the saline water. This causes a rise in the temperature of the water inside the basin. Due to the temperature gradient between the inside of the basin and the outside atmosphere, the basin water starts to evaporate and the vapor rises toward the glass cover by natural convection, where it cools down and condenses on the inner surface of the cover [5]. The condensate then flows

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naturally into the collection tank under the influence of gravity and pure distillate is obtained.

68.2.1 Components for Construction of a Simple Passive Solar Still Designing a solar still requires careful considerations and maximizing the effectiveness of materials used for the still. The components of the still are discussed below [4] and constructed in such a manner that the assembly of the system becomes simple and easy (see Fig. 68.1). Basin. The saline water is kept here. It is preferred to paint the basin black or line with a black sheet as black has maximum absorptivity and also helps reduce heat losses to the ambient. The materials used for the construction of the still should be resistant to corrosion caused by brackish water. It is mostly preferred that these materials are inexpensive, durable, and locally available. Transparent Glass Cover. It is mounted above the basin and should be able to transmit the maximum radiation. The cover helps prevent losses and keeps the wind away from cooling the water that gets heated in the basin. The amount of solar radiation reflected from the glass cover depends on the slope of the cover. The inclination of the glass cover should be equal to the latitude of the place of installation. The inside surface of the cover acts as a condenser for the vapor to be condensed and collected as output. The slope of the cover is such that the water flows downward to the troughs directly, without falling back into the basin. Support Structure. This structure supports the glass cover and the still including the basin liner. Concrete, wood, plastics, or metals like galvanized steel and aluminum can be chosen to build this support structure.

Fig. 68.1 Schematic diagram of single slope single basin solar still

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Insulation. It is used to minimize the transfer of heat from the solar still so that the performance and efficiency of the still increases. Mostly, insulation is provided to the area under the still basin since this is largely susceptible to heat loss to the ambient. The most common insulation materials are dry soil, styrofoam, or polyurethane foam. Collection Troughs. The troughs have to be of appropriate length and pitch so that the distillate flows to the lower end of the cover and the output can be collected. It is placed under the transparent glass cover. Storage Tanks. The water to be purified from the water storage tank is fed to the basin through a pipe. A valve is used to control the flow of water. The purified water collected in the trough is transferred to the freshwater tank through another pipe. Proper care must be taken to avoid contamination of distilled water in this tank by particles in the air. The air should leave the tank each time the purified water enters into the tank and a very fine mesh must be used at the inlet of the tank.

68.2.2 Critical Parameters Affecting the Still Performance A single slope passive solar still is designed (see Fig. 68.1) to study the effect of various critical parameters on the performance. A single aluminum basin coated with black paint and insulated with polyurethane foam is considered. Ordinary window glass titled at 18° is chosen for the transparent cover and aluminum structure provides support. There are three main types: climatic, design, and operational parameters (see Fig. 68.2). The climatic parameters like solar radiation, ambient temperature, and wind velocity are uncontrollable disturbances that depend on the environment. However, design parameters such as the number of slopes, thickness, and inclination of the glass cover, area of the basin liner; and operational parameters like depth of basin water and inlet water temperature can be controlled to maximize distillate productivity. The theoretical analysis of this design is carried out by simulating the energy balance equations [3] for the glass cover, water in the basin, and the basin itself in Solar Radiation

Wind Velocity

Thickness of Glass Area of Basin Depth of Water

Simple Solar Still

Inlet Water Temperature

Fig. 68.2 Climatic, design, and operational parameters of a solar still

Daily Distillate Output

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MATLAB. The solar radiation for the month of May in Pune is the input parameter for this mathematical model, and depending on the varying glass thickness, water depth, and area of the basin, the temperatures of glass, water, and basin are evaluated. These temperatures are then used to calculate the daily hourly distillate of the still.

68.3 Results and Discussion The performance analysis of a simple solar still is carried out based on the results obtained after simulating the proposed still design. The critical parameters such as solar radiation, the thickness of the glass cover, depth of feed water in the basin, and area of the basin are varied to increase the productivity of the still.

68.3.1 Solar Radiation Solar irradiance is the most vital factor as it is the main energy source for the solar still. The average hourly variation of solar radiation and the ambient temperature during the month of May from 7 am to 7 pm in Pune district [6] was studied to observe its effect on the distillate production (see Fig. 68.3). As the productivity of the still is directly proportional to the incident solar radiation, it becomes necessary to position the still such that it receives ample sunshine annually as the solar radiation cannot be controlled [7]. Thus, placing the still in a no shadow region with high ambient temperature can lead to comparatively higher output. The average radiation for May is used for all further discussions. It is observed that the radiation is maximum at 12 pm with a value of 820.3 W/m2 . The hourly variation of the temperature of water (T w ), the temperature of glass (T g ), and the temperature of the basin (T b ) according to the incident radiation for 4 mm thickness of glass cover, 6 cm depth of water, and 1 m2 area of the basin, which were found to be the ideal parameters [7] is shown in Fig. 68.4. The difference between the temperatures of the water and the glass cover is vital for the heat Fig. 68.3 Hourly variation of solar radiation I (W/m2 ) and ambient temperature Ta (°C) for May

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Fig. 68.4 Hourly variation of temperature of water (T w ), the temperature of glass (T g ), and the temperature of the basin (T b ) for a simple solar still

transfer between these two surfaces which is the primary principle of a solar still. This temperature difference can be increased by preheating the water entering the still, which eventually improves its efficiency.

68.3.2 Glass Cover Thickness Tempered glass and ordinary window glass are widely used materials for the transparent cover because of their maximum solar transmittance. The transmittance for a 4 mm, 5 mm, and 6 mm thick glass is 0.9, 0.84, and 0.79, respectively [8]. Figure 68.5 shows that the maximum output in a day for 4 mm thickness is 0.68 kg/m2 , 5 mm thickness is 0.33 kg/m2 , and 6 mm thickness is 0.18 kg/m2 at 2 pm. The total daily distillate output of a simple still is 3.37 kg/m2 for 4 mm, 2.01 kg/m2 for 5 mm, and 0.81 kg/m2 for 6 mm thick glass. This clearly shows that higher output is yielded as the thickness of the glass cover decreases. With an increase in thickness, the rate of heat transfer decreases and reduces the efficiency of the still. Thickness is controllable and thus can be varied in order to improve the productivity of the still as required. Since maximum transmittance and minimum absorption and reflection are desired for higher productivity, the ideal glass cover thickness is found to be 4 mm [9]. Fig. 68.5 Hourly variation of the distillate output of a simple solar still for varying glass cover thickness

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Fig. 68.6 Hourly variation of the output of a simple solar still for different depths of water

68.3.3 Depth of Feed Water With an increase in the depth of water, the temperature of water decreases due to an increased volume of water, increasing the thermal inertia of water causing a decrease in the rate of evaporation. Thus, by decreasing the depth of water, the productivity of the solar still improves. The maximum output of the still is 0.79 kg/m2 , 0.68 kg/m2 , and 0.50 kg/m2 for 4 cm, 6 cm, and 8 cm depth of water, respectively (see Fig. 68.6). The total daily output increases from 2.59 to 3.89 kg/m2 with a decrease in depth from 8 to 4 cm for a simple still. However, the reverse trend is observed in the evening as the rate of evaporation is more for a higher depth of water due to the release of heat energy absorbed by the water [10]. Thus, the depth of water can be controlled depending on the time of the day to always get maximum output.

68.3.4 Basin Area The area of the basin is varied to observe its effect on distillate output. The daily output for 0.25 m2 , 0.5625 m2 , 1 m2 , 1.5625 m2 , and 3.0625 m2 for a simple solar still is 0.8428 kg/m2 , 1.8964 kg/m2 , 3.3714 kg/m2 , 5.2678 kg/m2 , and 10.3249 kg/m2 , respectively. It is seen in Fig. 68.7 that the distillate output increases with the increase Fig. 68.7 Variation in daily distillate output with a varying basin area

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in area. Thus, the maximum area should be chosen. However, this leads to an increase in the fabrication cost, and enough space should be available for such huge installations. So the basin area is selected considering these factors apart from the requirement of higher distillate output. The total surface area exposed to the sunlight can be increased by using a wick, jute cloth, or a sponge considerably enhancing the performance of the still [11]. Of these materials that absorb water, jute cloth is easily available in India and is eco-friendly being biodegradable.

68.4 Conclusion In this paper, the effect of variation of the climatic, design, and observational parameters such as solar radiation, glass cover properties, basin area, and depth of feed water on the productivity of single basin solar still is studied. The total daily distillate output for a simple still with a glass cover thickness of 4 mm, water depth of 6 cm, and a basin area of 1 m2 is found to be 3.37 kg/m2 from simulations. It was observed that various parameters of the solar still considerably affect its productivity. Some of the improvements that can be made to obtain higher distillate output are decreasing the thickness of the glass and depth of the water, increasing the area of the basin, and operating the still during the peak radiation hours. These parameters can be varied to yield higher or lower output according to the requirements. If optimum parameters are chosen, solar stills prove to be an efficient, sustainable, and economical water purification system. With around 200 MW/km2 solar radiation received by India annually, it is possible to make use of solar stills to produce enough distillate to fulfill the potable water demand of the nation. Heating the feed water prior to its entry into the still also helps to increase the productivity of the distillate. Single basin solar still coupled with evacuated tubes can be installed in arid regions and villages where grid electricity is not feasible to meet the energy demands and water scarcity is prevalent. The daily water requirement of a person being 2 L, the output obtained from a modified design of a solar still coupled with evaluated tubes can easily satisfy the water requirement of a family of 3 persons, completely avoiding water wastage and environmental impacts of the conventional water purifiers used today. This study further motivates to design a control system to obtain higher output by mitigating the disturbances, improving the performance and efficiency of the still.

References 1. M.T. Ali, H.E.S. Fath, P.R. Armstrong, A comprehensive techno-economical review of indirect solar desalination. Renew. Sustain. Energy Rev. https://doi.org/10.1016/j.rser.2011.05.012 2. G.M. Ayoub, L. Malaeb, Developments in solar still desalination systems: a critical review. Critical Rev. Environ. Sci. Technol. 42, 19, 2078–2112. https://doi.org/10.1080/10643389.

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2011.574104 3. A. Agrawal, R.S. Rana, P. Srivastava, Heat transfer coefficients and productivity of a single slope single basin solar still in Indian climatic condition: experimental and theoretical comparison. Resour. Eff. Technol. 3 (2017). https://doi.org/10.1016/j.reffit.2017.05.003 4. H. McCracken, J. Gordes, Original: Understanding Solar Stills. VITA (1985) 5. M. Thimmaraju, D. Sreepada, G.S. Babu, B.K. Dasari, S.KVelpula, N. Vallepu, Desalination of Water. IntechOpen (2018) 6. Photovoltaic Geographical Information System. https://re.jrc.ec.europa.eu/pvg_tools/en/tools. html# 7. M.N.I. Sarkar, A.I. Sifat, S.M. Reza, M. Sadique, A review of optimum parameter values of a passive solar still and a design for southern Bangladesh. Renew. Wind Water Solar 4, 1–13 (2012). https://doi.org/10.1186/s40807-017-0038-8 8. H. Panchal, Performance investigation on variations of glass cover thickness on solar still: experimental and theoretical analysis. Technol. Econ. Smart Grids Sustain. Energy. https://doi. org/10.1007/s40866-016-0007-0 9. A. Santos, E. Hernandez, Experimental evaluation of a single slope solar still. TECCIENCIA12, 63–71 (2017). https://doi.org/10.18180/tecciencia.2017.22.7 10. S. Narayanan, A. Yadav, M. Khaled, A concise review on performance improvement of solar stills. SN Appl. Sci. 2. https://doi.org/10.1007/s42452-020-2291-5 11. C. Rabadia, Factors influencing the productivity of solar still. Int. J. Res. Appl. Sci. Eng. Technol. 3 (2015)

Chapter 69

Friction and Wear Performance of Jatropha Oil Added with Molybdenum Disulphide Nanoparticles Zahid Mushtaq and M. Hanief

Abstract Nano-materials offer potential scope for an increasing numerous novel applications when engineered to deliver availably functional properties. The nanosized additives when added to biodegradable oils improve their tribological performance and contribute to energy saving and sustainability. In the present study, the MoS2 nanoparticles with different mass ratios were employed as lubricant additives in the base jatropha oil, and their tribological properties were evaluated using a reciprocating ball-on-disc tribometer for steel-steel contacts. The results demonstrate that the MoS2 nanoparticles exhibit superior lubrication performance. The optimal concentration of MoS2 nanoparticles in the base oil was found to be 0.5% for minimum friction and wear rate. Addition of load decreased friction and increased wear rate. The coefficient of friction and wear rate was reduced by 63% and 35%, respectively. The excellent lubrication properties of the MoS2 nanoparticles are attributed to the physical synergistic lubricating actions of nano-MoS2 during the rubbing process.

69.1 Introduction The demand for energy is growing with time which is leading to the swift depletion of fossils [1]. The researchers are extensively searching for renewable sources in order to curtail environmental pollution and mitigate the dependence on fossils [2]. Tribology and sustainability when used together can be useful in increasing the sustainability of the environment. Sustainable tribology can save energy by reducing the losses due to friction and wear by introducing the lubricants [3]. The demand for ecofriendly lubricants is predicted to see a considerable spike in the coming years [4]. Biolubricants are the ones to replace petroleum-based lubricants due to their highly favourable lubricating properties. These properties can further be improved by Z. Mushtaq · M. Hanief (B) National Institute of Technology, Srinagar, J&K 190006, India e-mail: [email protected] Z. Mushtaq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_69

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mixing of desirable additives [5]. The lubricating properties of vegetable oils can be vehemently improved by the addition of nanoparticles [6]. Using nanolubricants can reduce the consumption of energy and maintenance costs, thereby increasing the life span of the machines and tools [7]. Jatropha oil has excellent lubricating properties as compared to mineral oils [8]. Biolubricant can be prepared by adding some proper additives to the jatropha oil which can be a good substitute to use in place of mineral oils [9, 10]. Micro-sized MoS2 has been proved to improve the tribological properties of jatropha oil [11]. The tribological performance of jatropha oil was ameliorated by addition of graphite nanoparticles [12] and hexagonal boron nitride particles [13]. MoS2 nanoparticles are very effective in enhancing the tribological properties of vegetable oil [14] and polyalphaolefin (PAO4) [15]. This research analyses the friction and wear characteristics of jatropha oil with and without MoS2 nanoparticles. The nanoparticles were added with three different mass ratios and tribological testing was undertaken for steel-steel tribo-pair. The variation of friction and wear with load was also figured out.

69.2 Materials and Experimental Procedure 69.2.1 Lubricant and Sample Preparation The jatropha oil was used as base oil whose properties are presented in Table 69.1. The nanoparticles of MoS2 were acquired from a reliable supplier and its properties are given in Table 69.2. They were added to the base Jatropha oil in mass ratios of 0.25, 0.5, and 0.75%. The mixtures were stirred in a test tube and ultrasonicated for 4–6 h to allow uniform dispersion of particles. EN-8 steel disc and 52,100 steel balls (12.7 mm diameter) were used as a friction pair. Sandpapers of size ranging from 280 to 2000 were used in sequence to give proper finish to the surface of the disc. The average roughness of the surface was 0.3231 µm which was calculated using surface profilometer by taking multiple readings at different points. Table 69.1 Properties of jatropha oil

S. No.

Property

Jatropha oil

1

Kinematic viscosity @ 40 °C

48–52 Cst

Kinematic viscosity @ 100 °C

10 Cst

2

Viscosity index

182

3

Density

0.92 × 103 kg/m3

4

Pour point

6 °C

5

Flash point

180 °C

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S. No.

Property

MoS2

1

Appearance

Black powder

2

Purity

99.9%

3

Size

80–100 nm

4

Density

5.05 g/cm3

5

Melting point

1182 °C

6

Molecular weight

160 g/mole

69.2.2 Friction and Wear Tests The tests were undertaken on a reciprocating tribometer. EN-8 disc was held fixed in a clamp and the steel ball was slided against it. Different lubricant mixtures of base jatropha oil with and without additives were used to lubricate the friction pair. The tests were conducted for 30 min each, at ambient temperature and loads of 15, 30, 45, and 60 N. The stroke and speed of each test were 2 mm and 500 Rpm, respectively. The tribo-pair was washed by acetone before and after each test to remove the unwanted dirt and contaminants. The coefficient of friction was calculated with the help of a software system installed in computer attached to the tribometer. The wear volume (mm3 ) of each scar was calculated with the help of 3D profilometer and Eq. (69.1) was used to calculate the specific wear rate (mm3 /Nm). Specific Wear Rate = Wear Volume/Load × Distance

(69.1)

69.3 Results and Discussion 69.3.1 Analysis of Friction Figure 69.1 displays the variation of average coefficient of friction (COF) at different loads and MoS2 mass ratios. The COF between the friction pair was high when lubricated with base jatropha oil. As the addition of nanoparticles was initiated, the COF values began to drop as shown in Fig. 69.1. The nanoparticles intrude between the surfaces of friction pair, cover the asperities and limit their direct contact. The nanoparticles react with the base oil and the ambient atmosphere to form a protective layer on the surface. This layer acts as a shield and prevents the direct rubbing between the surfaces thereby decreasing the COF. [16, 17]. As shown in Fig. 69.1, the COF increased with the increments in load from 15 to 60 N when it was lubricated with pure base oil. This increment can be due to more rigid engagement between asperities at higher loads in absence of protective tribo-layer. But when base oil mixed with nanoparticles was used as lubricant, the

718 0.14

Base oil 0.25 % MoS2 0.50 % MoS2 0.75 % MoS2

0.13

Coefficient of Friction

Fig. 69.1 Average coefficient of friction values recorded at various testing conditions

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0.12 0.11 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 15N

30N

45N

60N

Load (Newtons)

COF reduced with increase in load. However, the decrement due to increasing load was marginal as compared to the decrement caused by addition of nanoparticles. The reason for this decrease in COF with increasing load may be that the nanoparticles are better dispersed on the surface at higher loads. Also, there is more heat dissipation at higher loads which makes the reaction of nanoparticles with base oil and surroundings fast and expedites the development of the tribo-layer [18, 19]. Generally, the COF was reduced by up to 63% as compared to base oil. The highest COF (0.0956) was recorded for base oil as lubricant at 60 N, while as the lowest COF (0.0358) was recorded at 0.5% concentration of nanoparticles at 60 N. As the weight ratio of nanoparticles was increased to 0.75%, the COF began to rise marginally due to superfluous concentration of nanoparticles. Hence, 0.5% weight ratio was the optimum concentration of nanoparticles in the base jatropha oil for minimum COF.

69.3.2 Analysis of Wear The wear volume (mm3 ) was calculated by using 3D profilometer by analysing all the wear scars. The specific wear rates as calculated from the Eq. (69.1) are plotted in Fig. 69.2. It was observed that the specific wear rates for scars lubricated with pure base oil were high and started decreasing with the addition of nanoparticles. The specific wear rates at 0.5% nanoparticles addition were found to be the lowest, hence, resembling the results of friction analysis. With increasing load, the specific wear rate exhibited a marginal increment is shown in Fig. 69.2. This can be attributed to the reduction in inter-molecular bonding between lubricant particles due to higher pressures [20]. The highest wear rate (15.566 × 10–6 mm3 /N m) was recorded at 60 N when the scar was lubricated with base oil without nanoparticles. While as the

69 Friction and Wear Performance of Jatropha Oil … 18

Specific Wear Rate x 10-6 (mm3/Nm)

Fig. 69.2 Specific wear rate values recorded at various testing conditions

17 16 15

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14 13 12 11 10 9 8

15N

30N

45N

60N

Load (Newtons)

lowest wear rate (10.176 × 10–6 mm3 /N m) was observed at 0.5% of nanoparticles concentration at 15 N. The wear rate was reduced by up to 35% due to addition of nanoparticles as compared to base oil. The results received in the wear analysis were observed to be in close agreement with the results from frictional analysis.

69.3.3 Wear Scar Analysis Figure 69.3a, b displays optical microscopy image of wear scars on steel disc and steel ball, respectively, when lubricated with base jatropha oil. A number of deep grooves and furrows can be seen in Fig. 69.3a which clearly depict the high roughness of the surface. The sliding direction can be easily visualised and both the adhesive

Fig. 69.3 Optical images of the wear scars of tribo-pair when lubricated with base jatropha oil: a steel disc, b 52,100 steel ball

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Fig. 69.4 Optical images of the wear scars of tribo-pair when lubricated with base jatropha oil + 0.5% MoS2 : a steel disc, b 52,100 steel ball

and abrasive wear mechanisms were followed. Figure 69.3b reveals the formation of heavy pits and metal removal on the surface of steel ball during sliding in presence of base oil as lubricant. Figure 69.4a, b correspond to the wear scars on steel disc and steel ball, respectively, when lubricated with base oil + 0.5% MoS2 . As evident from Fig. 69.4a, the surface is fairly smooth with minimum damage occurred as compared to Fig. 69.3a. Some small rubbing marks are present on the surface suggesting mild abrasion. As seen from Fig. 69.4b, the heavy pits are absent and less damage has occurred as compared to Fig. 69.3b. From the wear scar analysis, it can be drawn that the addition of MoS2 nanoparticles to jatropha oil has eminently boosted its wear reducing capabilities. These results are in complete agreement with the wear analysis.

69.4 Conclusion Ball-on-disc reciprocating tribological tests were executed. Jatropha oil with and without MoS2 nanoparticles were used to lubricate the steel-steel tribo-pair to analyse the friction and wear. Following conclusions were compiled. 1. MoS2 nanoparticles were found to be very effective in curtailing both friction and wear rate. This was attributed to the formation of protective layer on the surfaces of tribo-pair thereby restricting the metal-to-metal contact. 2. The optimum weight ratio of MoS2 nanoparticles for least friction and wear rate was observed to be 0.5%. The COF and wear rate reduced by 63% and 35%, respectively. 3. After increasing the concentration of nanoparticles to 0.75%, friction and wear rate started to increase due to their presence in exorbitant quantity. 4. The COF decreased and the specific wear rate increased with respect to increase in load.

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It was concluded that jatropha oil has outstanding lubricating characteristics which can be made better by addition of MoS2 nanoparticles. It can be a good option as lubricant to substitute for mineral oils and contribute to sustainable and less polluted environment. Acknowledgements The authors thank Prof. M.F. Wani and Mechanical Engineering Department of our institute for making this study possible.

References 1. N.A. Zainal, N.W.M. Zulkifli, M. Gulzar, H.H. Masjuki, A review on the chemistry, production, and technological potential of bio-based lubricants. Renew. Sustain. Energy Rev. 82, 80–102 (2018) 2. M. Moniruzzaman, Z. Yaakob, R. Khatun, Biotechnology for jatropha improvement. Renew. Sustain. Energy Rev. 54, 1262–1277 (2016) 3. I. Tzanakis, M. Hadfield, B. Thomas, S.M. Noya, I. Henshaw, S. Austen, Fut. Perspect. Sustainable Tribol. 16, 4126–4140 (2012) 4. P. Nagendramma, S. Kaul, Development of ecofriendly/biodegradable lubricants: an overview. Renew. Sustain. Energy Rev. 16, 764–774 (2012) 5. T.M. Panchal, A. Patel, D.D. Chauhan, M. Thomas, J.V. Patel, A methodological review on bio-lubricants from vegetable oil based resources. Renew. Sustain. Energy Rev. 70, 65–70 (2017) 6. S.P. Darminesh, N.A.C. Sidik, G. Najafi, R. Mamat, T.L. Ken, Y. Asako, Recent development on biodegradable nanolubricant: a review. Int. Commun. Heat Mass Transfer 86, 159–165 (2017) 7. B.Z. Desari, B. Davoodi, Assessing the lubrication performance of vegetable oil-based nanolubricants for environmentally conscious metal forming processes. J. Clean. Prod. (2016) 8. M. Shahabuddin, H.H. Masjuki, M.A. Kalam, M.M.K. Bhuiya, H. Mehat, Comparative tribological investigation of bio-lubricant formulated from a non-edible oil source (jatropha oil). Ind. Crops Prod. 47, 323–330 (2013) 9. M.C. Menkiti, O. Ocheje, C.M. Agu, Production of environmentally adapted lubricant basestock from Jatropha curcas specie seed oil. Int. J. Ind. Chem. 8, 133–144 (2017) 10. T.Y. Woma, S.A. Lawal, A.S. Abdulrahman, M.A. Olutoye, M.M. Ojapah, Vegetable oil based lubricants: Challenges and prospects. Tribol. Online 14, 60–70 (2019) 11. Z. Mushtaq, M. Hanief, Evaluation of tribological performance of Jatropha oil modified with Molybdenum disulphide micro-particles for steel-steel contacts. J. Tribol. 1–27 (2020) 12. F. Begum, N.R. Kumar, V.R. Raju, Experimental investigations on the tribological properties of Jatropha oil by the addition of graphite nanoparticles. Rec. Adv. Mater. Sci. 645–657 (2019) 13. N. Talib, E.A. Rahim, Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining. Tribol. Int. 118, 89–104 (2018) 14. C.P. Koshy, P.K. Rajendrakumar, M.V. Thottackkad, Evaluation of tribological and thermophysical properties of coconut oil added with MoS2 nanoparticles at elevated temperatures. Wear 330–331, 288–308 (2015) 15. Y. Xu, J. Geng, Y. Peng, Z. Liu, J. Yu, X. Hu, Lubricating mechanism of Fe3 O4 @MoS2 core-shell nanocomposites as oil additives for steel/steel contact. Tribol. Int. 121, 241–251 (2018) 16. S.M. Alves, B.S. Barros, M.F. Trajano, K.S.B. Ribeiro, E. Moura, Tribological behavior of vegetable oil-based lubricants with nanoparticles of oxides in boundary lubrication conditions. Tribol. Int. 65, 28–36 (2013)

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17. L. Rapoport, V. Leshchinsky, I. Lapsker, Y. Volovik, O. Nepomnyashchy, M. Lvovsky, R.P. Biro, Y. Feldman, R. Tenne, Tribological properties of WS2 nanoparticles under mixed lubrication. Wear 255, 785–793 (2003) 18. H. Xie, B. Jiang, J. He, X. Xia, F. Pan, Lubrication performance of MoS2 and SiO2 nanoparticles as lubricant additives in magnesium alloy-steel contacts. Tribol. Int. 93, 63–70 (2016) 19. Katpatal, D.C., Andhare, A.B., Padole, P.M.: Performance of nano-bio-lubricants, ISO VG46 oil and its blend with Jatropha oil in statically loaded hydrodynamic plain journal bearing. J. Eng. Tribol. 1–15 (2019) 20. Y. Singh, A. Singla, A.K. Singh, A.K. Upadhyay, Tribological characterization of Pongamia pinnata oil blended bio-lubricant. Biofuels (2017)

Chapter 70

Layer Based Fabrication of Human-Scaled Body Parts by Using Pneumatic Extrusion Method O. Y. Venkata Subba Reddy, V. Venkatesh, A. N. R. Reddy, and A. L. S. Brahma Reddy Abstract Congenital malformation is caused by a genetic factor or by prenatal events that are not genetic that causes physical or mental disability. There are different types of congenital malformation like heart defects, cleft lip and cleft palate, congenital deformity in ear or nose, down syndrome, etc. Some of them are possible to be rectified with different surgical procedures by placing implants in the body, prosthesis, bone substitutes (scaffold), etc. Out of different types of birth defects, the major problems faced by the human beings are congenital ear or nose defect. In the development of new nose or ear tissue, bone scaffold provides better physical properties compared to other alternate methods. The main objective is to develop nose or ear scaffold with improved elastic properties using melt pneumatic extrusion process. This advanced technology can be used to develop complex anatomical models.

70.1 Introduction Interference of advanced engineering technologies into medical sciences in diagnosis and treatment leads to biomedical engineering (BME). BME is an engineering design application, principle and concepts into the medical and biological fields in diagnostics and therapeutics that closes the barrier and relates engineering and medical sciences. BME is an evolution of interdisciplinary approach. This evolution lead to development of biocompatible prostheses. This is done by a mechanical method known as bioprinting. Bioprinting is the patterning of mortal cells and other tissues O. Y. Venkata Subba Reddy (B) · A. N. R. Reddy Department of Mechanical Engineering, MRCET, Hyderabad 500010, India e-mail: [email protected] V. Venkatesh Department of Mechanical Engineering, AITSR, Kadapa 516126, India A. L. S. Brahma Reddy Department of Metallurgical and Materials Engineering, NIT Rourkela, Rourkela, Odisha 769008, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_70

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by stacking and assembling them using a computer-assisted layer-by-layer deposition method for fabrication of living tissue and organ analogs for its regeneration and biological studies [1] spatially. Computer-aided layer-by-layer assembly of biological tissues and organs has shown better biological characteristics in organ formation. In addition to laser-based bioprinting (LBB) and inkjet-based bioprinting (IBB), extrusion-based bioprinting (EBB) has been evolved [2]. Among several 3D bioprinting approaches like biomimicry, autonomous self-assembly, mini-tissue building blocks, and other approaches, layer-by-layer approach of 3D printing shows promise in enabling the generation of an intra-organ branched vascular tree with better self-assembling properties in 3D thick tissue or organ construction. Mechanical microextrusion is the most common method for scaffold less tissue bioprinting. Research is being done for developing more sophisticated approaches for better restoration of tissue or organ for biological functioning [3]. Extrusion type of manufacturing process is being used from a quite long time for forming of metals and plastics [4]. Fused deposition modeling (FDM)—an extrusionbased solid-free form fabrication 3D printing approach of controlled porous architecture with intricate geometries. This raised to 3D printing of temporary housing for the cells known as porous scaffolds [5] in tissue engineering. Hutmatcher et al. developed printable biomaterials by FDM approach in fabrication of scaffolds image-based optimization, and computationally developed scaffolds have a better designability and tailorable microstructure for biocompatible materials for various porosities [6, 7]. Initial research work on EBB was done by Pfister et al. with bioplotting approach and hydro-gel bioink for extrusion was used and bioplotted into a liquid medium that resulted in high flexural and mechanical strength along with good proliferation rate [8]. Later, the fledgling technology received massive attention and advanced rapidly. Although several groups, including Sun and his coworkers [9, 10], investigated the technology by hydro-gel solutions encapsulated cells, the viability of the technology allowed researchers to adopt novel bioink materials into the EBB technology. The concept of printing scaffold-free clustered cell for bioprinting of living tissues [11, 12] and organs in clusters in a spheroid shape over a hydro-gel glue is known as “biopaper” [13]. Mironov et al. investigated on bioprinting of vascular cells in 3D. Next level of bioprinting is done on tissue engineering of blood vessels [14]. Hervath et al. demonstrated biofabrication on human endothelial cell and depicted that 3D biofabrication has developed thinner and more resembling layers than any other techniques in cell regeneration. Bioprinting is an excellent tool for drug efficiency testing and safety assessment [15]. Other prioritized step in 3D bioprinting is selection of biomaterials. Selection of material is based on the geometry, chemical, and surface properties for good response with biotics [16]. In the work of L.E. Bertassoni et al., bioprinting is done using cell-laden methacrylated gelatin hydrogel (GelMA) material using direct write-up strategy for varying architectures with different concentrations [17]. Extrusion-based bioprinting is most versatile method in bioprinting. Media diffusion and perfusion capabilities enhanced in printing porous tissue; greater ability to

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print fully biological, large tissue is constructed rapidly with an acceptable mechanical and biological properties, which makes EBB to a feasible technique than other bioprinting processes [15]. Also, the technology has already paved the way to bioprint cells [16–18], tissues [19], tissue constructs, organ modules, and organ-on-a-chip devices with long-term expectations of printing functional scale-up organs [20]. As the major part of bioprinting is being done by extrusion-based techniques, a new type of extrusion technique, i.e., melt pneumatic extrusion is employed in this work. It can be an advanced technique for bioprinting with biodegradable materials. Very limited number of manufacturing methods are available to develop soft tissue substitute among all such methods developed to use advanced technique (melt pneumatic extrusion) to produce complex shapes at less price by adding simple mechanisms in a single system where multiple materials can be used to print a simple product in development of human body parts with less cost and substitution bio degradation.

70.2 Material and Method As human skin is biodegradable, a similar kind of material is preferred in bioprinting of human organs. So, polycaprolactone (PCL) synthetic biodegradable biomaterial [21] is opted in the bioprinting of nose and ear, and due to versatile structure and easily incorporative bioactive particular structure, PCL provides good mechanical strength to the structure [22]. The design and development of material is done in seven steps as follows: STEP 1: Development of 3D nose or ear CAD model Initially patient-specific CAD model is developed from CT scan. Materialize interactive medical image control system (MIMICS) software is utilized by 3D image processing (Fig. 70.1). STEP 2: Design parameters It is very important to develop porous soft tissue substitute for the selected application. In this process, substitute internal structures play an important role on its mechanical properties, bone regeneration, etc. Following parameters have to be

Development of 3D CAD model • MRI Data • CT Scan

Set Design Parameters • Porosity • Layer Thickness • Nozzle Diameter • Printing Temperature

Generation of CNC Program

Set Machning parameters

• G-CodeControl Machine • M-Code _ Run the Setup

• Nozzle Temperature • Bed Temperature • Selection of Nozzle

Fig. 70.1 Anatomical model development process

Simulation

Developemen t of Pneumatic Extrusion Machine

Run the progra m

Rectification of errors in program

Procurement of Required materials

Development of Nose

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considered before going to develop G and M code for the developed CAD model to run the pneumatic extrusion machine. • • • •

Porosity Layer thickness Nozzle diameter Printing temperature

• Porosity The importance of maintaining porosity in the developed CAD model is to support new soft tissue development in the damaged portion of human body. The percentage of porosity also plays a major role about the mechanical properties of entire model. Total mechanical satiability of the developed CAD model is depending on the pore internal structure, pore size, pore shape, material, and distribution of pore internal structure. In this work, we developed homogeneous (uniform pore shape and size) nose CAD model with infill percentage of 55%. The type of infill pattern selected in the development of human nose model is rectilinear with an infill angle offset as 0/90°. • Layer thickness Other than the porosity of the product, next important parameter of the product is about its layer thickness. Layer thickness of the product is deciding the surface finish and dimensional accuracy of the product. Value of layer thickness is purely depending on the inner diameter of the nozzle. In this process, it is not supposed to consider 100% of the inner diameter of the nozzle. Layer thickness should be less than the inner diameter of the nozzle. Different samples were developed with 80, 85, and 95% of inner diameter of the nozzles. After observing layer accuracy and time consumed by each type of inner diameter percentage 85%, inner diameter layer provided optimal output. • Nozzle diameter Nozzle diameter of the printing process is representing the variation in the surface area of the total product. Different internal diameters of nozzles tried to develop the product; they are 0.33, 0.41, 0.51, and 0.61 mm. After observing the output provided by different nozzles, 0.33 mm internal diameter nozzle has given accurate layer shapes. • Printing temperature Different types of polymers have different melting points. This PCL polymer melting temperature is about 90 °C. This temperature was constantly maintained with the use of temperature controller, K-type thermocouple, and 25 inner diameter band heaters. Temperature was changed from 60 to 120 °C to select best temperature for the process.

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STEP 3: Generation of G and M code After selection of suitable values in the above parameters for the development of prepared CAD model, G and M codes-based source code has to be developed. This G and M code depends on the type of controller, and firmware used to run the pneumatic extrusion machine. The machine has been developed with RAMPS1.4 (ATmega 2560 processor) with Marlin-1.1 version. Simplify3D software has been used to develop the G and M code for the selected controller and firmware. STEP 4: Modification of G and M code to run the setup The source code that is developed by the Simplify 3D software will not be suitable for the developed machine. We made few modifications in the developed G and M code to run developed machine. STEP 5: Simulation After modifying the above-specified parameters according to the requirements, it is necessary to run the working of machine simulation to identify any errors and modification that are needed for the developed source code. STEP 6: Development of pneumatic extrusion machine In the development of pneumatic extrusion machine, different types of materials have been used, like aluminum extruded profiles, linear guide ways, linear rods, bearings, controllers, stepper motors, stepper motor drives, limit switches, bed, connectors, air compressor, band heaters, thermocouple, LCD display, temperature controller, solidstate relay, voltage regulator, etc. After the development of entire machine with all features, we performed alignment test on it. In this test, identified maximum error is less than 0.01 mm (Fig. 70.2). STEP 7: Run the program After successful development of pneumatic extrusion machine with all features, last step in this work is to run the developed program, and in this process, before going to run the program we poured low melting temperature polymer into the barrel and heated it up to 90 °C for 15 min. After that 6 bars of pneumatic pressure were supplied to the barrel to extrude the material with an internal diameter of 0.33 mm (Fig. 70.3).

70.3 Results Finally, we reached our objective by developing nose and ear of the human with biocompatible material. Because of optimal consideration of process parameters, pore size and shape were uniformly distributed through the model perfectly (Table 70.1). Identified size of the pore in the model varied from 200 to 300 µm. This size of pore is perfectly suitable for the development of new soft tissue in the damaged

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Fig. 70.2 Conversion of CT data into 3D CAD model Fig. 70.3 Rendered 3D CAD model from patient specific CT data

Table 70.1 List of optimal process parameters used in the development of human-scaled anatomical models

S. no

Parameters

Units

Value

1

Feed rate

mm/min

300

2

Layer thickness

mm

0.1

3

Pressure

Bar

8

4

Nozzle diameter

mm

0.2–2

5

Pore size

µm

200–300

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Fig. 70.4 Final developed products of nose and ear

portion to form a new body pore of the human being. Coming to the shape of the pore, it is also evenly distributing in the entire model without any variations. This will be benefited to evenly distribute mechanical loads on the part, which will improve the life of this artificial support to proliferate new tissue. Differentiation of respective damaged tissue is possible when the replaced damaged body part substitute has evenly distributed pore size, shape, porosity, etc., with better mechanical properties (Fig. 70.4).

70.4 Conclusion This work investigated the feasibility of fabricating a human-scaled body parts with biopolymer by utilizing fabricated layer-based manufacturing setup (LBMS). This method would allow the process to incorporate heat to this polymer to extrude the material freely. Temperature that has been maintained for the extrusion of polymer is about 90 °C. To develop the required shape with the developed machine, 50 mm/min feed rate was maintained for both X-axis and Y-axis. Layer thickness of the product is 85% for the 0.3 mm inner diameter of the nozzle. The infill percentage is about 55%. With the consideration of above parameters, development of the model was completed successfully. After completion of product development, identified pore size was varied from 200 to 300 µm. This range of pore is uniformly distributed in the entire product. Uniform distribution of pore size and shape is highly impossible with traditional processes that are used to develop soft tissue substitute. We conclude that LBMS is one of the best manufacturing technique for the develop complex anatomical models.

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References 1. F. Guillemot, V. Mironov, M. Nakamura, Bioprinting is coming of age: report from the international conference on bioprinting and biofabrication in Bordeaux (3B’09). Biofabrication 2(1) (2010). https://doi.org/10.1088/1758-5082/2/1/010201 2. V. Mironov, T. Boland, T. Trusk, G. Forgacs, R.R. Markwald, Organ printing: computer-aided jet-based 3D tissue engineering. Trends Biotechnol. 21(4), 157–161 (2003). https://doi.org/10. 1016/S0167-7799(03)00033-7 3. S.V. Murphy, A. Atala, 3D bioprinting of tissues and organs. Nat. Biotechnol. 32(8), 773–785 (2014). https://doi.org/10.1038/nbt.2958 4. C. Abeykoon, P.J. Martin, A.L. Kelly, E.C. Brown, A review and evaluation of melt temperature sensors for polymer extrusion. Sens. Actuators A Phys. 182, 16–27 (2012). https://doi.org/10. 1016/j.sna.2012.04.026 5. I. Zein, D.W. Hutmacher, K.C. Tan, S.H. Teoh, Fused deposition modeling of novel scaffold architectures for tissue engineering applications. Biomaterials 23(4), 1169–1185 (2002). https://doi.org/10.1016/S0142-9612(01)00232-0 6. C.Y. Lin, N. Kikuchi, S.J. Hollister, A novel method for biomaterial scaffold internal architecture design to match bone elastic properties with desired porosity. J. Biomech. 37(5), 623–636 (2004). https://doi.org/10.1016/j.jbiomech.2003.09.029 7. R.M. Schek, E.N. Wilke, S.J. Hollister, P.H. Krebsbach, Combined use of designed scaffolds and adenoviral gene therapy for skeletal tissue engineering. Biomaterials 27(7), 1160–1166 (2006). https://doi.org/10.1016/j.biomaterials.2005.07.029 8. A. Pfister, R. Landers, A. Laib, U. Hübner, R. Schmelzeisen, R. Mülhaupt, Biofunctional rapid prototyping for tissue-engineering applications: 3D bioplotting versus 3D printing. J. Polym. Sci. Part A Polym. Chem. 42(3), 624–638 (2004). https://doi.org/10.1002/pola.10807 9. S. Khalil, W. Sun, Biopolymer deposition for freeform fabrication of hydrogel tissue constructs. Mater. Sci. Eng. C 27(3), 469–478 (2007). https://doi.org/10.1016/j.msec.2006.05.023 10. S. Khalil, J. Nam, W. Sun, Multi-nozzle deposition for construction of 3D biopolymer tissue scaffolds. Rapid Prototyp. J. 11(1), 9–17 (2005). https://doi.org/10.1108/13552540510573347 11. I. Elloumi-Hannachi, M. Yamato, T. Okano, Cell sheet engineering: a unique nanotechnology for scaffold-free tissue reconstruction with clinical applications in regenerative medicine. J. Int. Med. 267(1), 54–70 (2010). https://doi.org/10.1111/j.1365-2796.2009.02185.x 12. K. Jakab, A. Neagu, V. Mironov, R.R. Markwald, G. Forgacs, Engineering biological structures of prescribed shaped using self-assembling multicellular systems. Proc. Natl. Acad. Sci. U. S. A. 101(9), 2864–2869 (2004). https://doi.org/10.1073/pnas.0400164101 13. V. Mironov, R.P. Visconti, V. Kasyanov, G. Forgacs, C.J. Drake, R.R. Markwald, Organ printing: tissue spheroids as building blocks. Biomaterials 30(12), 2164–2174 (2009). https://doi.org/ 10.1016/j.biomaterials.2008.12.084 14. C. Norotte, F.S. Marga, L.E. Niklason, G. Forgacs, Scaffold-free vascular tissue engineering using bioprinting. Biomaterials 30(30), 5910–5917 (2009). https://doi.org/10.1016/j.biomateri als.2009.06.034 15. I.T. Ozbolat, M. Hospodiuk, Current advances and future perspectives in extrusion-based bioprinting. Biomaterials 76, 321–343 (2016). https://doi.org/10.1016/j.biomaterials.2015. 10.076 16. L. Horvath, Y. Umehara, C. Jud, F. Blank, A. Petri-Fink, B. Rothen-Rutishauser, Engineering an in vitro air-blood barrier by 3D bioprinting. Sci. Rep. 5 (2015). https://doi.org/10.1038/sre p07974 17. C.R. Almeida, T. Serra, M.I. Oliveira, J.A. Planell, M.A. Barbosa, M. Navarro, Impact of 3-D printed PLA- and chitosan-based scaffolds on human monocyte/macrophage responses: unraveling the effect of 3-D structures on inflammation. Acta Biomater. 10(2), 613–622 (2014). https://doi.org/10.1016/j.actbio.2013.10.035 18. L.E. Bertassoni, J.C. Cardoso, V. Manoharan et al., Direct-write bioprinting of cell-laden methacrylated gelatin hydrogels. Biofabrication 6(2) (2014). https://doi.org/10.1088/17585082/6/2/024105

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19. S.M. Ehsan, K.M. Welch-Reardon, M.L. Waterman, C.C.W. Hughes, S.C. George, A threedimensional in vitro model of tumor cell intravasation. Integr. Biol. (U.K.) 6(6), 603–610 (2014). https://doi.org/10.1039/c3ib40170g 20. Y. Yu, Y. Zhang, I.T. Ozbolat, A hybrid bioprinting approach for scale-up tissue fabrication. J. Manuf. Sci. Eng. Trans. ASME 136(6), 1–10 (2014). https://doi.org/10.1115/1.4028511 21. W. Zhang, I. Ullah, L. Shi et al., Fabrication and characterization of porous polycaprolactone scaffold via extrusion-based cryogenic 3D printing for tissue engineering. Mater. Des. 180, 107946 (2019). https://doi.org/10.1016/j.matdes.2019.107946 22. A. Ibrahim, 3D Bioprinting Bone (Elsevier Ltd., 2017). https://doi.org/10.1016/B978-0-08-101 103-4.00015-6

Chapter 71

Fuzzy-Based Power Management Strategy for Performance Improvement of Electric Vehicles J. S. Rakhi and T. Rajeev

Abstract Battery/ultra capacitor (UC) hybrid energy storage systems (HESS) are used in electric vehicles to improve the performance and to cater to the power and energy demand of the load. The UCs assist the battery during the acceleration and deceleration of the vehicle by supplying transient power demand. The energy management ensures the power split between the sources to meet the load demand. The frequency sharing-based power management strategy is explored in this paper. However, due to the issues related to the real-time control operations, a new fuzzy logic-based system is proposed. The fuzzy logic controller controls the power flow between the energy storage system based on the power demand and the state of charge (SOC) of the battery. The simulation of the proposed controller is done in Matlab/Simulink and the results are compared with the frequency sharing based control method to show the energy savings.

71.1 Introduction Battery electric vehicles (BEVs) are one of the commonly used electric vehicles (EVs) and they utilize the battery as the main source of energy. Most of the BEVs use Li-ion or NiMH batteries due to their superior performance [1]. The high energy density of the battery makes it an ideal choice as an energy storage device for electric vehicles (EV). For a certain drive cycle, the battery must be able to cater to the energy demands of the vehicle, which involves frequent charging and discharging of the battery and also supplying peak power demands. This can cause stress on the battery which can affect its life. J. S. Rakhi (B) · T. Rajeev Department of Electrical Engineering, College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India e-mail: [email protected] T. Rajeev e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_71

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A hybrid energy storage system, consisting of the battery and UC which has higher power density, will be able to provide the total power requirement of the load. The performance of the energy storage system (ESS) can be improved when the battery is operated to supply the average power demand and the UC provides the excess or peak power [2]. The complementary characteristics of the battery and UC improve the total capacity, thus reducing the overall size of the ESS and also reduce the stress on the battery which in turn increases its life. Since the characteristics of the battery and UC are different, they are connected with the help of DC–DC converters. Various topologies are used for the HESS according to the application. In passive and semi-active configuration, both devices or one device is directly connected to the DC link [3, 4] and the voltage across it limits power availability. The active configuration with the battery and UC connected to the DC link via DC–DC converters are more commonly used because of the superior control over UC voltage. A proper energy management strategy enables the desired operation of the HESS. Various energy management strategies has been developed in literature which ensures the proper charging and discharging of the energy storage devices and also maintains the operational constraints within limits. Artificial neural network can be used to reduce the stress of battery by generating most efficient battery current [5] or by regulating the energy supported by UC based on the velocity during acceleration [6]. These methods are complex and the efficiency depends upon the data sets used for training. Multi-objective optimisation-based techniques which enable real-time power sharing is explored in [7]. Frequency sharing-based control of the HESS which allows faster response to load variations is discussed in [8]. Fuzzy logic-based control for energy management of the HESS provides simplicity and real-time control. Fuzzy logic is used for the development of battery management system which allows saving of stored energy based on the demand at each instant in [9], where the drive cycle data is already available. In this paper, a fuzzy logic-based control strategy for HESS is developed. It is modelled by user knowledge which improves the performance of the EV for a given drive cycle. Frequency sharing-based power management strategy is discussed in Sect. 71.2. Section 71.3 deals with the development of fuzzy logic controller for HESS. Section 71.4 presents the simulation results and Sect. 71.5 concludes the work.

71.2 Power Management Strategy The ESS is designed to satisfy the instantaneous voltage, power, and energy requirements and hence uses hybridized battery and UC. The HESS utilizes the high energy density of the battery and the high power density of the UC, which improves the performance. In order to ensure proper power flow, DC–DC converters are used for connecting the sources to the DC link. A proper power management strategy should be developed to control the power flow and to maintain the operational constraints within limits.

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Fig. 71.1 Block diagram of HESS for power management

Active configuration of the battery and UC is chosen for the HESS to allow better utilization of capacity and improved control [10]. They are connected to the DC link via two separate non-isolated DC–DC converters. These converters have lesser number of components and they are subjected to lesser stress which results in lower losses. The duty cycle of the switches is appropriately controlled for ensuring proper operation of the HESS. The overall block diagram of the HESS power management system is shown in Fig. 71.1.

71.2.1 Frequency Sharing Based Strategy The frequency sharing-based control strategy involves allocating the low and high frequency variation of the load to the battery and UC, respectively [11, 12]. The control algorithm is as shown in Fig. 71.2. This control strategy uses current mode control for the HESS. It consists of an outer voltage control loop and two inner current control loops. The DC link voltage is regulated by the outer control loop which generates the input reference current. The low pass filter (LPF) splits it into low frequency and high frequency components. The battery inner current loop takes the low frequency component as the input to generate the battery reference current and the high frequency component along with

Fig. 71.2 Frequency sharing control algorithm

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the transients in the battery current are given as input to the UC current loop which generates the UC reference current. These reference currents are used for generating the switching signals for the respective converters. For the proper operation of the controller, the dynamics of the outer loop must be slower than the inner loops. Based on the bandwidth requirement, the PI controller parameters for outer and inner loops are determined. The power flow of the ESS is controlled by the energy management algorithm (EMA). The main purpose of the EMA is to ensure proper power flow between the battery, UC, and load and to maintain the operational constraints within limits [10]. The battery SOC (SOCbatt (t)), battery current (ibatt (t)), UC current (iUC (t)), and UC voltage or SOC (vUC (t) or SOCUC ) are to be maintained within safe limits. The EMA controls the UC to supply the transient load demand thus reducing the stress on the battery. When the UC voltage exceeds the upper threshold (V UC_ub ) or goes below the lower threshold (V UC ub ), the discharging and charging of the UC to the DC link voltage takes place. The two-mode selection logic determines the mode of operation. The SOC mode selection will be high if the SOC of the battery is within limits, i.e., (SOCbattmin < SOCbatt (t) < SOCbattmax ) and UC mode selection will be high if the voltage of the UC is within the upper and lower limits, i.e., (V UC lb < vUC (t) < V UC ub ). Outside the limits, the value of the control signal will be low. The HESS operates in following modes: MODE (1) Frequency sharing algorithm for sharing load demand: When both SOCbattmode and UCmode are logic high, the battery supplies the average power and the UC supplies the transient load demand. MODE (2) UC charging mode: As a result of decrease of the UC voltage below the lower limit V UClb due to acceleration of the vehicle, the battery charges the UC to maintain the nominal voltage V UCnom . The reference currents for battery and UC current control loop are given as: i batt ref tot = Ibatt max

(71.1)

i UC ref tot = Ibatt max − i tot ref

(71.2)

MODE (3) UC discharging mode: When the UC voltage exceeds the upper threshold value usually after regeneration, the battery converter is idle and the UC alone provides the load demand. The reference currents for battery and UC current control loop for mode 3 are given as: i batt ref tot = 0

(71.3)

i UC ref tot = i tot ref

(71.4)

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The power management involves operation of the HESS in all three modes of operation, which is discussed in the results section. Since the drive cycle consists of transient variations, a more advanced power management strategy is required for fast and high performance operation under different modes of operation.

71.3 Fuzzy Logic Controller for Power Management The drive cycle consists of transient speed variations and requires a fast and high performance ESS. The fuzzy logic controller ensures the proper power-sharing between the battery and the UC based on the dynamics of the system. The fuzzy control strategy aims to reduce the stress on the battery by allowing the UC to support the remaining power demand, which is decided based on the SOC of the battery. For high SOC levels of battery, the power demand is primarily supplied by the battery, and the UC supports by supplying the excess power. As the SOC level of the battery reduces, energy savings of battery are achieved by allowing the battery to supply reduced power and the remaining being supported by the UC. The block diagram for the fuzzy logic controller is shown in Fig. 71.3. The two inputs to the system are the power demand of the motor (POWERDEMAND), which are obtained from the EV simulation block using the drive cycle data and SOC of the battery (BSOC). The two outputs of the system correspond to the duty cycle ‘DB’ and ‘DUC’ for the control of DC–DC converters of the battery and UC. The fuzzy sets for the inputs and outputs are defined using trapezoidal membership functions. The membership functions for the ‘POWERDEMAND’ represent regenerative braking (N), idle condition (Z0) and motoring operation (P1–P10) representing low to high power demand levels. The’BSOC’ membership functions are defined

Fig. 71.3 Block diagram of HESS with fuzzy controller

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from P0 to P5, representing low to high levels, where P0 represents the lowest range to which the battery must not be depleted since it affects the lifetime. The duty cycle for battery and UC is defined by trapezoidal membership functions D0–D10 in the range 0–1. A rule based inference mechanism is used to obtain the fuzzy output values from the fuzzified inputs. The rules for the fuzzy logic controller are defined based on the following operational requirements: 1. During regenerative braking, the UC charges and the battery charges when the SOC is very low. 2. When the energy demand is zero, both the battery and UC converters are off. 3. When the battery SOC is below the minimum limit of 15%, the battery converter remains off, and the UC alone supplies the power. 4. During motoring operation the battery and UC share the power demand based on the SOC of the battery. The average power supplied by the battery reduces with the decline of SOC of battery, and the remaining power is provided by the UC. The rule tables of fuzzy controller for outputs ‘DB’ and ‘DUC’ are given in Tables 71.1 and 71.2. The center of gravity (COG) method is used for the defuzzification of the output values. It is defined by the equation: Table 71.1 Fuzzy rules for duty cycle of battery converter DB

PD

SOC

P1

P2–P10

N

Z0

P0

D0

D0

D5

D0

P1

D10

D10

D0

D0

P2

D10

D10

D0

D0

P3

D10

D10

D0

D0

P4

D10

D10

D0

D0

P5

D10

D10

D0

D0

Table 71.2 Fuzzy rules for duty cycle of UC converter DUC

PD

SOC

P1

P2

P3

P4

P5

P6

P7–P10

N

Z0

P0

D10

D10

D10

D10

D10

D10

D10

D10

D0

P1

D7

D8

D8

D9

D10

D10

D10

D10

D0

P2

D7

D8

D8

D9

D10

D10

D10

D10

D0

P3

D7

D8

D8

D9

D9

D10

D10

D10

D0

P4

D7

D7

D8

D9

D9

D9

D10

D10

D0

P5

D7

D7

D8

D9

D9

D9

D10

D10

D0

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Fig. 71.4 Variation of output ‘DB’

Fig. 71.5 Variation of output ‘DUC’

Scrisp

  bi Si = i  Si i

(71.5)

where the center of membership function is denoted by bi for the given rules, S is the value of ith output level and the crisp output value is given by S crisp . The surface plots for the duty cycle for the battery and UC against the inputs are shown in Figs. 71.4 and 71.5.

71.4 Simulation Results The drive cycle (DC) generated using SUMO software for an urban road with medium traffic is given as input to the EV model is shown in Fig. 71.6. Based on the DC, the EV model calculates the power requirement of the motor. The HESS uses active configuration of the battery and the UC. The battery and UC are connected to the DC link via bidirectional DC–DC converters. This enables

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Fig. 71.6 Drive cycle for urban roads

discharging of the battery and UC during motoring operation and charging during regenerative braking. The control strategy generates the switching signals for the converters to enable the charging and discharging operation. The simulation is performed with both control strategies for the HESS. The system parameters are given in Table 71.3. The power management of the HESS with the frequency sharing based controller is simulated. The control strategy allocates the load demand to the battery and UC based on the frequency sharing algorithm. It operates in all three modes and facilitates proper discharging and charging of battery and UC. Now the simulation is conducted for the fuzzy logic-based controller. The power management of the HESS for fuzzy logic-based controller is shown in Fig. 71.7. The motor power requirement obtained from the EV simulation block, along with the SOC of the battery is given as inputs to the fuzzy logic controller. Based on the rules defined for the control, the controller generates the duty cycle for the switching of both the battery converter and the UC converter. The variation in battery SOC of the HESS for fuzzy logic-based controller is shown in Fig. 71.8. The initial SOC of the battery is considered to be 44.5%. As seen from the simulation results, both the control strategies satisfy the power requirement of the vehicle. Table 71.3 System parameters

Description

Value

Battery capacity

80 Ah

Battery nominal voltage (V b )

300 V

UC capacitance

500 F

UC voltage (V uc )

300 V

L batt

0.3 H

L uc

0.3 H

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Fig. 71.7 Power management of HESS with fuzzy logic-based control

Fig. 71.8 Variation in SOC of battery with fuzzy logic-based control of HESS Table 71.4 Energy savings

Control strategy

Frequency sharing-based control

Fuzzy logic control

Total motor energy requirement (kWh)

1.040

1.074

Change in battery SOC (%)

2.63

2.57

Energy supplied by battery (kWh)

0.6089

0.602

Energy supplied by UC (kWh)

0.450

0.523

Energy savings (kWh)

0.450

0.523

Energy savings (%)

43.26

48.67

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The power sharing between battery and UC and energy savings for both control strategies are given in Table 71.4. The energy saving due to frequency sharing strategy is 43.26% and that with fuzzy logic controller is 48.67%. This saving in energy can be directly correlated to an increase in the drive range of the vehicle [13].

71.5 Conclusion The work presents the development of a fuzzy logic-based power management strategy for a battery/UC HESS for better management of power sharing between the sources. The UC supports the battery during frequent acceleration and deceleration operation thus reducing its stress. The mission based drive cycle generated using SUMO software resembles the actual drive cycle conditions. The simulation of the HESS is carried out in Matlab/Simulink. The power management is done for the proposed fuzzy logic controller and the results are compared with a power management strategy using frequency sharing-based control. The fuzzy logic controller provides an improved energy savings of 5.41% of the battery capacity by energy management of the battery and UC. This corresponds to additional energy savings of 0.073 kWh for the given drive cycle.

References 1. F.A. Shah, S. Shahzad Sheikh, U.I. Mir, S. Owais Athar, Battery health monitoring for commercialized electric vehicle batteries: lithium-ion, in 2019 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET ). Istanbul, Turkey (2019), pp. 1–6. https://doi.org/10.1109/PGSRET.2019.8882735 2. A.S. Sener, Improving the life-cycle and SOC of the battery of a modular electric vehicle using ultra-capacitor, in 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA). Brasov, Romania (2019), pp. 611–614. https://doi.org/10.1109/ ICRERA47325.2019.8996616 3. P. Bhattacharyya, A. Banerjee, S. Sen, S.K. Giri, S. Sadhukhan, A modified semi-active topology for battery-ultracapacitor hybrid energy storage system for EV applications, in 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020). Cochin, India (2020), pp. 1–6. https://doi.org/10.1109/PESGRE45664.2020. 9070531 4. A.N. Archana, T. Rajeev, Reliability index based approach for allocating EV charging station in a distribution system, in 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020). Cochin, India (2020), pp. 1–6. https://doi.org/ 10.1109/PESGRE45664.2020.9070408 5. J. Moreno, M.E. Ortuzar, J.W. Dixon, Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks. IEEE Trans. Ind. Electron. 53(2), 614623 (2006) 6. K. Alobeidli, V. Khadkikar, A new ultracapacitor state of charge control concept to enhance battery lifespan of dual storage electric vehicles. IEEE Trans. Veh. Technol. 67(11), 10470– 10481 (2018). https://doi.org/10.1109/TVT.2018.2871038

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7. X. Lu, Y. Chen, M. Fu, H. Wang, Multi-objective optimization-based real-time control strategy for battery/ultracapacitor hybrid energy management systems. IEEE Access 7, 11640–11650 (2019). https://doi.org/10.1109/ACCESS.2019.2891884 8. M.C. Joshi, S. Samanta, G. Srungavarapu, Frequency sharing based control of battery/ultracapacitor hybrid energy system in the presence of delay. IEEE Trans. Veh. Technol. 68(11), 10571–10584 (2019). https://doi.org/10.1109/TVT.2019.2941395 9. D.A. Martinez, J.D. Poveda, D. Montenegro, Li-ion battery management system based in fuzzy logic for improving electric vehicle autonomy, in 2017 IEEE Workshop on Power Electronics and Power Quality Applications (PEPQA). Bogota (2017), pp. 1–6. https://doi.org/10.1109/ PEPQA.2017.7981677 10. M.C. Joshi, S. Samanta, Modified ultracapacitor voltage control loopfor battery/UC HESS, in 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES). Chennai, India (2018), pp. 1–5. https://doi.org/10.1109/PEDES.2018.8707721 11. M. Joshi, S. Samanta, Improved energy management algorithm with time share based UC charging/discharging for hybrid energy storage system. IEEE Trans. Ind. Electron. 66(8), 6032– 6043 (2018) 12. A.R. Nair, M.C. Blessen, P.J. Edwin, G. Mathew, T. Rajeev, Dynamic wireless charging system for electric vehicles based on ultra-capacitor integrated magnetic resonance coupling, in 2019 IEEE Transportation Electrification Conference (ITEC-India). Bengaluru, India (2019), pp. 1– 6. https://doi.org/10.1109/ITECIndia48457.2019.ITECINDIA2019-212 13. Y. Zhang, W. Wang, Y. Kobayashi, K. Shirai, Remaining driving range estimation of electric vehicle, in 2012 IEEE International Electric Vehicle Conference. Greenville, SC (2012), pp. 1– 7. https://doi.org/10.1109/IEVC.2012.6183172

Chapter 72

Design of Pitch Box-Mounting Tool K. S. Prakasha, Shrishail Kakkeri, and D. Amaresh Kumar

Abstract The lifting tool has been developed as per the BS EN13155-lifting accessories, non-fixed load lifting attachments, so as per these standards, counterbalance c-crane will develop and also customize mounting arrangement to be used for lifting the wind turbine components during the integration of the wind turbine. So these tools will be using in the production plant of the wind turbine components. Depending upon the depth of assembly, the fork arm length will decide. The lifting tool or attachment design will depend upon the fork arm geometry. Mounting tools are assembled with different parts, so these parts are connected by different joint connections like fasteners, welded joints, etc. The joints design will be followed by DIN EN 1993.1.8.2005-1, as per these standard instructions, joints to be designed.

72.1 Introduction The C-crane has been developed for mounting the pitch boxes such as converter box, battery box and main box mounting inside the rotor hub on all three sides of the blade-mounting bore faces to connect into motor drive for pitching the blades. The tool is having vertical adjustment for different heights depending upon the space requirement. The lifting hook position is also adjustable by different slots in the lifting plate. Distance between the two fork arms to be adjustable depending upon the lifting tool design requirements and length of the fork arm is limited as per the standard. In the lifting tool, fork arm inserting slots have been provided with holding block for locking, pitch box holder has been designed for rotating 360°, this rotation K. S. Prakasha (B) · S. Kakkeri · D. Amaresh Kumar Department of Mechanical Engineering, Sri Venkateshwara College of Engineering, Bengaluru, Karnataka, India e-mail: [email protected] S. Kakkeri e-mail: [email protected] D. Amaresh Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_72

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Table 72.1 Properties of structural steel

Properties

Structural Steel

Density

7.85 E−9 ton/mm3

Young’s modulus

2E + 5 MPa

Poisson’s ratio

0.3

Allowable yield limit for S355

322 MPa

Allowable yield limit for S235

214 MPa

Note Material factor 1.1 included (reference IEC-61400-1)

Table 72.2 Properties of Teflon

Properties

Teflon

Density

2.17 E−9 ton/mm3

Young’s modulus

470 MPa

Poisson’s ratio

0.45

Allowable yield limit

20.7 MPa

will be arresting by using a locking device, and the locking position holes have been provided by 90° × 4 position.

72.2 Material Selection The structural steels selection will be followed as per EN 10025–1-hot-rolled products of structural steels. The following material properties are considered for structural parts (Table 72.1). The Teflon material has been selected as per the ASTM standard. Each property of the material is selected by as per D792, D570, D638, D790, etc. The following material properties are considered for resting pads (Table 72.2).

72.3 Analysis of the Pitch Box-Mounting Tool The scope is to find out the structural strength of 2.XM pitch box tools as per the following load cases: 1. Pitch box-mounting tool • Load Case 1: Pitch box at ideal position (without any tilt) • Load Case 2: Pitch box at 25° tilt position with vertical • Load Case 3: Structural strength of bottom support. 2. Battery box support bracket

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3. Battery box-mounting tool. The methods to be followed during the analysis are as follows: • The pitch box tools have been modelled using solid elements. • Bonded connections and frictional contacts are established between mating components as required. • The solid components are meshed using quadratic hexagonal elements. Mesh refinement is adapted as per area of interest. • FEA calculation is carried out using ANSYS 19.1. FE Modelling FE mesh details for the pitch box-mounting tool have been modelled using solid elements. Bonded and frictional connections are established between mating components as required. A number of nodes created are 361,083 and number of elements are 177,355. Contact Details Bonded connections and frictional contacts are established between mating components. Frictional contact between the battery box-mounting tool and C channel is modelled, and the coefficient of friction value of 0.1 is used in frictional contacts. Loading and Boundary Conditions Pitch Box-Mounting Tool Load Case 1: Pitch box at ideal position (without any tilt). The loading and boundary conditions are the remote point at COG location (COG location is taken approximately and 200 mm vertically offset concerning global coordinate system). The remote force of 3924 N (400 kg * 9.81 m/s2 ) at COG of pitch box is applied. The bottom support surfaces are fixed in all degrees of freedom (DOF). Load Case 2: Pitch box at 25° tilt position with vertical. The loading and boundary conditions are the remote point at COG location after tilting at 25° (COG location after 25° tilt is taken approximately 93 mm horizontally offset concerning COG at the ideal position of pitch box). The remote force of 3924 N (400 kg * 9.81 m/s2 ) at COG of pitch box with a tilt position is applied. The bottom support surfaces are fixed in all degrees of freedom (DOF). The holes where pins with M6 screw are fitted to take the pitch box load at tilting position are constrained only in the Z-direction (inclined direction of top base plate). Load Case 3: Bottom support analysis. The loading and boundary conditions are force of 3924 N (400 kg * 9.81 m/s2 ) which is applied vertically upward at the surface of the bottom support. Top surfaces of resting pads are fixed in all degrees of freedom (DOF).

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Battery Box Support Bracket The loading and boundary conditions are Force of 3200 N (320 kg * 10 m/s2 ) is applied vertically downwards at the surface of the battery box support bracket and bolted holes are fixed in all degrees of Freedom (DOF). Battery Box-Mounting Tool The loading and boundary conditions are force of 3200 N (320 kg * 10 m/s2 ) which is applied vertically downward at the surface of the battery box-mounting tool, and cut surfaces are fixed in all degrees of freedom (DOF).

72.4 Results and Discussion 72.4.1 Pitch Box-Mounting Tool Analysis results for pitch box-mounting tool are documented for load case 1, load case 2 and load case 3 separately. A. Load Case 1: Pitch box at ideal position (without any tilt) In these section results of load, case 1 is documented. Total Deformation The pitch box-mounting tool having maximum deformation of 3.8 mm was observed at the ends of the top base plate location. Maximum deformation just above the bottom support is 2.5 mm, which is within the gap available of 10 mm between the base plate and bottom support as shown in Fig. 72.1. Hence, the top base plate will not collide with bottom support.

Fig. 72.1 Total deformation for pitch box-mounting tool—load case 1

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Fig. 72.2 Von Mises stress plot for pitch box-mounting tool—load case 1

Von Mises Stress The pitch box-mounting tool has maximum Von Mises stress of 138 MPa and is observed on the top base plate near the rubber pads as shown in Fig. 72.2. This stress is within the yield limit of the material, i.e. 322 MPa. Reaction Force Output reactions are closely matching with input load. B. Load Case 2: Pitch box at 25° tilt position with vertical In these section results of load, case 2 is documented. Total Deformation The pitch box-mounting tool having maximum deformation of 3.9 mm was observed at the ends of top base plate location maximum deformation just above the bottom support is 2.7 mm which is within the gap available of 10 mm between the base plate and bottom support as shown in Fig. 72.3. Von Mises Stress The pitch box-mounting tool has maximum Von Mises stress of 169 MPa observed on the top base plate pinhole location as shown in Fig. 72.4. This stress is within the yield limit of the material, i.e. 322 MPa. High stress at a constrained edge location, which may be spurious due to singularity, is ignored. M6 Screw Results In this section, the results of the M6 screw are documented. Reaction forces at constrained hole location are obtained from analysis, and equivalent shear stress is calculated for an M6 screw. Shear stress calculated from the analysis is compared with M6 screw allowable shear stress for validation. The maximum reaction force observed at the hole from the analysis is 896 N. Shear area of M6 screw is 28.26 mm2 . So shear stress for M6 screw is 896/28.26 = 31.7 MPa which is well below the

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Fig. 72.3 Total deformation for pitch box-mounting tool—load case 2

Fig. 72.4 Von Mises stress plot for pitch box-mounting tool—load case 2

shear stress limit of M6 screw. The ultimate strength of the M6 screw is 700 MPa, so shear stress limit will be around 0.55 of ultimate strength, i.e. 385 MPa. Hence, the M6 screw can withstand the pitch box load at a 25° tilt position of the pitch box from vertical. C. Load Case 3: Bottom support analysis

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In these section results of load, case 3 is documented. Total Deformation The bottom support having maximum deformation of 0.09 mm was observed at the ends which means bottom plate lifting by 0.09 mm due to the load, as shown in Fig. 72.5. Von Mises Stress The bottom support has maximum Von Mises stress of 38 MPa, which is at the contact region and can be neglected as shown in Fig. 72.6. In the bottom support plate, maximum stress observed is 29 MPa as shown in Fig. 72.7. Hence, stress is within the yield limit of the material, i.e. 322 MPa.

Fig. 72.5 Total deformation for bottom support—load case 3

Fig. 72.6 Von Mises stress plot for bottom support—load case 3

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Fig. 72.7 Von Mises stress plot for bottom support plate—load case 3

Battery Box Support Bracket Total Deformation Maximum deflection is 1.14 mm on the battery box support bracket as shown in Fig. 72.8. Von Mises Stress The maximum stress is 114 MPa, which is below the allowable yield limit of S235 material as shown in Fig. 72.9. Fig. 72.8 Total deformation for battery box support bracket

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Fig. 72.9 Von Mises stress for battery box support bracket

Battery Box-Mounting Tool Total Deformation Maximum deflection is 0.37 mm on the lever arm and 0.27 mm on the battery box-mounting tool, respectively, as shown in Fig. 72.10.

Fig. 72.10 Total deformation for battery box-mounting tool

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Fig. 72.11 Von Mises stress for battery box-mounting tool

Von Mises Stress The high stress of 70 MPa is observed at the contact region, which may be spurious and can be neglected. The maximum stress is 32 MPa, which is below the allowable yield limit of S235 material as shown in Fig. 72.11.

72.5 Conclusion The loads and boundary conditions considered for 2XM pitch box tools conclusion are documented as per below: Pitch Box-Mounting Tool • Deformation of top base plate is within 10 mm gap limit between top base plate and bottom support which means top base plate will not collide with the bottom plate due to pitch box weight of 400 kg including FOS 2. • Maximum stress on the top base plate and bottom support is within the limit of S355 material for all structural parts. • M6 screw can withstand the pitch box load at 25° tilt position of pitch box from vertical. Hence, pitch box-mounting tool is safe for pitch box weight of 400 kg with FOS 2 for above load cases. Battery box support bracket • Maximum stress is within the limit of S235 material. Hence, battery box support bracket is safe for battery box weight 320 kg with FOS 2.

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Battery box-mounting tool • Maximum stress is within the limit of S235 material.

72.6 Scope of Future Work The tool is currently used for one particular platform pitch boxes, so the next action plan is to upgrade the tool to utilize for multi-function operational purposes. Once the prototype operation is a success, the new development plan will be active. The development action plan is as follows: • Optimization of tool utilization. • Cost-effectiveness of the manufacturing of the lifting tool. • Ease the operational and dismantling of the tool.

References1 1. P. Ravikanth Raju et al., Design and analysis of crane hook. Int. J. Curr. Eng. Sci. Res. (IJCESR) 5(4) (2018). ISSN (PRINT): 2393-8374. (ONLINE): 2394-0697 2. P.N. Godage et al., Static structural analysis of engine lifting bracket. Int. J. Res. Appl. Sci. Eng. Technol. 6(VI) (2018). ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 6.887 3. N. Khan et al., Design and stress analysis of Ramshorn hook with different cross section using CAE tools, in 2nd International Conference on Engineering Technology, Science and Management Innovation (ICETSMI-2017), 15 Jan 2017. ISBN: 978-81-932712-3-0 4. R. Tarale et al., Structural and modal analysis of crane hook with different material using FEA. Int. Res. J. Eng. Technol. 04(06) (2017). e-ISSN: 2395-0056 5. A.R. Contractor et al., Design and analysis of ladder frame chassis considering support at contact region of leaf spring and chassis frame. IOSR J. Mech. Civ. Eng. (IOSR-JMCE) 12(2) (2015) (Ver. IV). e-ISSN: 2278-1684, p-ISSN: 2320-334X 6. M.N.V. Krishnaveni et al., Static analysis of crane hook with I-section using ANSYS. Int. J. Eng. Manag. Res. 5(3) (2015). ISSN (ONLINE): 2250-0758, ISSN (PRINT): 2394-6962 7. 2006/42/EC-Machinery Directive 8. EN 1993-1-1-Euro code 3: design of steel structures—part 1–1: general rules and rules for buildings 9. EN 1993-1-8-Euro code 3: design of steel structures—part 1–8: design of joints 10. IEC 61400-Turbine Safety Standards 11. EN ISO 14122-2-Safety of machinery—permanent means of access to machinery 12. EN13155-Cranes—safety—non-fixed load lifting attachments 13. RUD Product Catalogue from website link. https://www.rud.com/en/products/sling-lashingsystems/sling-systems/lifting-points/detail/vrs-starpoint-metric-thread.html 14. Spring Latches Details from website link. https://www.elesa-ganter.com/en/www/Indexing-ele ments--Spring-latches--GN416 15. C-cranes details from website link. https://www.cranes-uk.com/p/tkg-vh-manual-balancecrane-forks 1 The following journals and datasheets are referred during the design and development of the pitch

box-mounting tool.

Chapter 73

Heat Transfer Enhancement in Automobile Radiator Through the Application of CuO Nanofluids M. Chandra Sekhara Reddy and Veeredhi Vasudeva Rao

Abstract It is experimentally demonstrated that the nanofluids prepared using CuO nano particles improved the radiator thermal performance when used as a coolant. A mixture of water and EG in the ratios of 80:20 by volume is employed as base fluid for the preparation of CuO nanofluids in the present investigation. Volume concentration of CuO is varied between 0.07 and 0.023% in the base fluid for heat transfer experiments. Experiments are conducted with a flow rate ranging between 600 and 900 LPH through the radiator. In this investigation, it is observed that the radiator thermal performance improved with the flow rate in the range considered. Augmentation of heat transfer rate up to 53% is observed in comparison to the base fluid in the range of flow rates considered in this investigation. Effect of flow rate of nanofluids on the Nusselt number is presented graphically taking the nanoparticle concentrations as a parameter.

73.1 Introduction Water is used as heat transfer fluid for many years by industry. To increase the range of operating conditions, mixture of ethylene glycol with water, transformer oils, engine oils, and propylene glycol is also being used in some applications. Thermophysical properties of the fluid namely specific heat, density, viscosity, and thermal conductivity dictate the thermal performance of the fluids when used for heat transfer applications. Any improvement in these properties is expected to directly improve the

M. Chandra Sekhara Reddy (B) Department of Mechanical Engineering, Sri Venkateswara College of Engineering, Karakambadi Road, Opp. LIC Training Center, Tirupati 517507, Andhra Pradesh, India e-mail: [email protected] V. Vasudeva Rao Department of Mechanical and Industrial Engineering, CSET Science Campus, University of South Africa (UNISA), Johannesburg 1710, South Africa e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_73

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performance of the thermal systems. A simple method to improve thermal conductivity of any fluid is by adding nanosize particles of conducting metals or their oxides in small quantity to the base fluid. The pioneering works of Choi [1] demonstrated that the fluid thermal properties in general and thermal conductivity in particular improve by dispersion of nanosize particles in to the fluid under consideration. The fluids thus prepared are named as nanofluids for the first time by him. Since then, many researchers followed to investigate the heat transfer characters of a variety of nanofluids. Enhancement of 8% heat transfer was reported by Hwang et al. [2] for 0.03% water based Al2 O3 nanofluids when the flow is considered to be laminar in a tube. Similarly, Fotukian and Esfahany [3] conducted experiments on water-based Al2 O3 nanofluids under turbulent flow conditions and they reported enhancements in heat transfer compared to water which is base fluid. Yu et al. [4] conducted experiments on ethylene glycol water based Al2 O3 nanofluids at Reynolds number equal to 2000. They reported enhancement of heat transfer rate of the order of 57% and 106% for 1.0% and 2.0% volume concentration of nanoparticles, respectively. Pak and Cho [5] investigated the performance of water-based Al2 O3 nanofluids with 2.78% volume concentration and reported only 75% enhancement in heat transfer when the flow is turbulent. Hojjat et al. [6] considered three different types of nanofluids prepared using CuO, TiO2, and Al2 O3 , all with a volume concentration of 1.5%. They observed enhancement of heat transfer up to 71%, 67%, and 68%, respectively, for CuO, TiO2, and Al2 O3 nanofluids. Nguyen et al. [7] conducted experiments on Al2 O3 nanofluids taking water as base fluid and reported 40% enhancement in the radiator performance with 6.8% volume concentration of Al2 O3. Peyghambarzadeh et al. [8] reported experimental results from an investigation conducted using pure water and water-based Al2 O3 nanofluids in radiator used in an automobile. The results on heat transfer showed an enhancement of 45% with Al2 O3 nanofluids when compared to pure water. Jung et al. [9] also conducted experiments on Al2 O3 nanofluids and found an enhancement of 32% in heat transfer with 1.8% volume concentration. Improvement of the order of 51% is reported in heat transfer by Ho et al. [10] for Al2 O3 nanofluid with 2.0% volume concentration. Enhancements up to 8% in the Nusselt number were reported by Lai et al. [11] for Al2 O3 nanofluid taking water as the base fluid for 1.0% volume concentration. Based on the focused literature review presented above, it can be concluded that there are several groups of researchers that conducted extensive experiments on nanofluids for the improvement of the thermal performance. There were no attempts to consider EG/water as a based fluid in combination with CuO nanoparticles. Hence, the present work focuses on to the determination of heat transfer enhancements with EG/water-based CuO nano fluids in an automobile radiator. CuO nanofluids prepared with 0.02%, 0.007% to 0.023% volume concentration are investigated for their performance. In the present investigation, experiments are conducted with a volume flow rate ranging from 600 to 900 LPH.

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73.2 Preparation of CuO Nanofluid A mixture of water and ethylene glycol is prepared with a ratio of 80:20 which forms a base fluid. Then the desired quantity of CuO nanoparticle is added to this liquid mixture. Equation (73.1) is used to determine the quantity of nanoparticles required to prepare nanofluids with known volume concentration. CuO nanopowder with 21 nm of average particle size is procured from international supplier Sigma-Aldrich chemicals, USA [12]. ⎡

⎤

Wparticle ρparticle

φ = ⎣ Wparticle ρparticle

+

Wfluid ρfluid

⎦ × 100

(73.1)

While preparing CuO nanofluid, surfactants such as oleic acid and C-TAB were used to ensure stable dispersion of nanoparticles. Thermo-physical properties are shown in Table 73.1 for base fluid and CuO at 30 °C. Estimated thermo-physical properties of the nanofluids using Eqs. (73.3)–(73.5) of Pak and Cho [5] are presented in Table 73.2. For completeness, the equations are presented below. ρn f = φρ p + (1 − φ)ρb f

(73.2)

Table 73.1 Properties of CuO and base fluid at 30 °C Material

Density (kg/m3 )

CuO

6510

540

Water

1000

4184

Specific heat (J/kg K)

Thermal conductivity (W/m K)

Viscosity (mPa s)

18

–

0.6130

0.000894

Air

1.1839

1005

0.024

0.000018

20:80% EG/W

1055.39

3502

0.4120

0.002260

Table 73.2 Properties of CuO nanofluid at different volume concentrations (EG/W)-based nanofluids volume concentration

Density (kg/m3 )

Specific heat (kJ/kg K)

Thermal conductivity (W/m K)

0.007

1088.33

3501.31

0.416

0.002261

0.013

1121.59

3500.62

0.423

0.002264

0.019

1154.34

3499.93

0.433

0.002266

0.023

1189.25

3488.8

0.443

00.002268

Viscosity (mPa s)

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   ρb f ρp C Pb f + φ C Pp ρn f ρn f  k p + (n − 1)kb f − φ(n − 1) kb f − k p  = k p + (n − 1)kb f + φ(n − 1) kb f − k p

C Pn f = (1 − φ) kn f

μn f = μb f (1 + 2.5φ)

(73.3)

(73.4) (73.5)

73.3 Experimental Setup and Procedure The test rig used in the present investigation is designed and developed in-house. The required heat exchanger (car radiator), hardware components, sub-assemblies, and instrumentation are procured as per the specifications. Then, the test rig is fabricated and assembled as per the schematic diagram shown in Fig. 73.1. The test setup designed and fabricated to conduct experiments is shown in Fig. 73.2. Front and the rear view of the heat exchanger are shown in Figs. 73.3 and 73.4, respectively. A pump suitable to operate at high temperature (up to 120 °C) is employed to ensure the flow of hot nanafluid through the coolant circuit and the radiator with flow rates between 600 and 900 LPH. To determine the effect of fluid flow rate on the heat transfer rate from the radiator, the fluid is circulated at 600, 700, 800, and 900 LPH through the circuit. The air velocity and temperature entering and leaving the radiator are measured with an anemometer and a thermocouple. To simulate the equivalent heat generation in the engine, an industrial hear is employed that operates in the

Fig. 73.1 Experimental setup-schematic representation

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Fig. 73.2 Experimental setup

Fig. 73.3 Radiator front view

temperature range of 39–95 °C. Six calibrated thermocouples (K-type) are employed to measure temperatures at various locations such as inlet tank, collecting tank, one at the entry and one at the exit of the radiator. The remaining two thermocouples are used for recording air inlet and outlet temperatures.

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Fig. 73.4 Radiator rear view

73.4 Experimental Data Reduction 73.4.1 Assessment of Heat Transfer Coefficient The following Eqs. (73.6)–(73.9) are used to estimate experimental Nu in the radiator. Q = h AT = h A(Tb − Tw )

(73.6)

Q = mC p T = mC p (Tin − Tout )

(73.7)

Nuexp =

h exp Dh mC p (Tin − Tout ) = k C p (Tin − Tout )

(73.8)

where Nu represents the average value of Nussle number for the radiator as a system, m represents the mass flow rate, C p represents the heat capacity of the fluid under consideration, A represents external heat transfer area on radiator tubes, Tin and Tout are the inlet and outlet temperatures of the fluid, respectively, Tb represents the bulk mean temperature which is the average of Tin and Tout , and Tw is tube wall temperature, k is thermal conductivity of the fluid, and Dh is the hydraulic diameter. Thermo-physical properties of the fluid were estimated at mean bulk temperature of the fluid.

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73.4.2 Estimation of Nussle Number for Single-Phase Fluids The correlation proposed Gnielinski [13] is as follows.

 f − 1000)Pr 2 (Re Nu =

0.5 2  Pr 3 − 1 1 + 12.7 2f

(73.9)

f = (1.58 ln(Re) − 3.82)−2 ; 2300 < Re < 5 × 106 ; 0.5 < Pr < 2000 The correlation proposed by Tam and Ghajar [14] is as follows.  Nu = 0.023Re Pr 0.8

3≤

0.385

L P

−0.0054 

μb μw

0.14 (73.10)

μb L ≤ 191; 7000 ≤ Re ≤ 49,000; 4 ≤ Pr ≤ 34; 1.1 ≤ ≤ 1.7 D μw

73.5 Experimental Results and Discussion 73.5.1 Base Fluid in Radiator To establish the credibility of the experimental data generated, it is necessary to validate the test rig first. This is achieved by conducting few calibration runs exclusively using base fluids. Figure 73.5 shows the test results for constant inlet temperature of 35 °C. The test results showed that the Nusselt number increases as a function of Reynolds number. The two well-known empirical correlations established by Gnielinski [13] and Tam and Ghajar [14] are taken as reference to validate the data generated in the present investigation. When comparisons are drawn, a maximum of 2.5% deviation was found between the theoretical values taken from open literature and present experimental data. Figure 73.6 shows variation of non-dimensional heat transfer coefficient, Nu, as a function of Re for different inlet temperatures of the base fluid in the range from 60 to 90 °C.

73.5.2 Nanofluid in Radiator Likewise, Fig. 73.7 shows the variation of Nu, as a function of Re for EG. Water-based CuO nanofluids for different concentrations in the range from 0.007 to 0.023%. The

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Fig. 73.5 Experimental Nusselt number of the base fluid as a function of Reynolds number (validation with the published literature)

Fig. 73.6 Variation of NuExp with volume flow rate at different temperatures

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Fig. 73.7 Variation of measured Nu with volume flow rate of CuO nanofluids for different concentrations of at 70 °C

nanofluid of various concentrations of CuO with flow rates of 600, 700, 800, and 900 LPH was used in the car radiator to determine heat transfer coefficient. In any cooling system, for a given set of operating conditions, if the temperature drop in a hot fluid is larger, then such a system is considered to be better and superior in terms of thermal performance. Such improvements are observed in the present investigation by the use of CuO nanofluids. Figure 73.7 shows the effect of replacement of base fluid with CuO nanofluids as performance indicator. It is observed that for all flow conditions, the Nu increased with flow rate and corresponding Re. It is also found that the Nu increases with the concentration of CuO in the nanofluid at all flow conditions. It is found that by the addition of only 0.023% CuO nanoparticles by volume to the base fluid, an enhancement of 53.62% in Nu was observed in comparison with the base fluid. This enhancement is observed at temperature of 70 °C. It can be seen from Tables 73.1 and 73.2 that the estimated values of thermo-physical properties of nanofluids are marginally different from that of the base fluid. It is can be observed that there is increment in the density, thermal conductivity, and viscosity whereas the specific heat decreased slightly in compare to base fluid. Increase in viscosity and reduction in specific heat of the nanofluid with reference to the corresponding base fluid are not preferred in the context of overall enhancement of heat transfer. However, the variations of the order of 4% are too small to support the enhancement of 53.6% in heat transfer observed in this study. For 0.007% volume concentration, improvement in heat transfer is observed to be 10.63%. The observed variation in the Nu is attributed to the change in thermophysical properties of the fluid due to the temperature. Improvement in thermal

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performance obtained, through the use of CuO nanofluid by replacing the base fluid in a radiator, results in reducing the working temperature to a lower level for a given set of engine operating conditions. It is possible to reduce the size of an automobile cooling system in general and radiator in particle by the use of nanoparticle of CuO dispersed in the base fluid. The benefit of size reduction in the cooling systems is much more prominent in heavy duty engines used on large trucks. This in turn contributes to reduction in the specific fuel consumption and reduced emissions.

73.6 Conclusions In the current experimental studies, overall heat transfer coefficient is determined for EG/ water-based CuO nanofluids flowing in a car radiators as a function of flow rate (600–900 LPH) and at various concentrations and temperatures. In the present investigation, it is established that the presence of CuO nanoparticles in the range of 0.007–0.023% by volume in 20:80% EG/W-based nanofluid has profound effect on the heat transfer rate. When compared to the base fluid, the improvement in heat transfer was found to be more for CuO nanofluids by 53.6% at a concentration of 0.023%. From this investigation, it is concluded that the use of CuO nanofluids in place of conventional radiator coolants is highly promising.

References 1. S.U.S. Choi, Enhancing thermal conductivity of fluids with nanoparticles, in Proceedings of the 1995 ASME International Mechanical Engineering Congress and Exposition. San Francisco, CA, USA (1995) 2. K.S. Hwang, S.P. Jang, S.U.S. Choi, Flow and convective heat transfer characteristics of waterbased Al2 O3 nanofluids in fully developed laminar flow regime. Int. J. Heat Mass Transf. 52, 193–199 (2009) 3. S.M. Fotukian, M. Nasr Esfahany, Experimental investigation of turbulent convective heat transfer of dilute γ-Al2 O3 /water nanofluid inside a circular tube. Int. J. Heat Fluid Flow 31, 606–612 (2010) 4. W. Yu, H. Xie, Y. Li, L. Chen, Q. Wang, Experimental investigation on the heat transfer properties of Al2 O3 nanofluids using the mixture of ethylene glycol and water as base fluid. Powder Technol. 230, 14–19 (2012) 5. B.C. Pak, Y.I. Cho, Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particles. Exp. Heat Transfer 11, 151–170 (1998) 6. M. Hojjat, SGh. Etemad, R. Bagheri, J. Thibault, Convective heat transfer of non-Newtonian nanofluids through a uniformly heated circular tube. Int. J. Therm. Sci. 50, 525–531 (2011) 7. C.T. Nguyen, G. Roy, C. Gauthier, N. Galanis, Heat transfer enhancement using Al2 O3 /water nanofluid for an electronic liquid cooling system. Appl. Therm. Eng. 27, 1501–1506 (2007) 8. S.M. Peyghambarzadeh, S.H. Hashemabadi, M. SeifiJamnani, S.M. Hoseini, Improving the cooling performance of automobile radiator with Al2 O3 /water nanofluid. Appl. Therm. Eng. 31, 1833–1838 (2011) 9. J.Y. Jung, H.S. Oh, H.Y. Kwak, Forced convective heat transfer of nanofluids in micro-channels. Int. J. Heat Mass Transf. 52, 466–472 (2009)

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10. C.J. Ho, L.C. Wei, Z.W. Li, An experimental investigation of forced convective cooling performance of a micro-channel heat sink with Al2 O3 /water nanofluid. Appl. Therm. Eng. 30, 96–103 (2009) 11. W.Y. Lai, B. Duculescu, P.E. Phelan, R.S. Prasher, Convective heat transfer with nanofluids in a single 1.02-mm tube, in ASME-International Mechanical Engineering Congress and Exposition, Heat Transfer, vol. 3 (Chicago, Illinois, USA, 2006), pp. 5–10 12. www.sigma-aldrich.com 13. V. Gnielinski, New equations for heat and mass transfer in turbulent pipe and channel flow. Int. Chem. Eng. 16, 359–368 (1976) 14. L.M. Tam, A.F. Ghajar, Transitional heat transfer in plain horizontal tubes. Heat Transfer Eng. 27, 23–38 (2006) 15. R.S. Vajjha, D.K. Das, D.P. Kulkarni, Development of new correlations for convective heat transfer and friction factor in turbulent regime for nano-fluids. Int. J. Heat Mass Transf. 53, 4607–4618 (2010)

Chapter 74

Positioning of Wind Turbine in a Wind Farm for Optimum Generation of Power Using Genetic Algorithm for Multiple Direction Khalid Anwar and Sandip Deshmukh Abstract The objective of wind farm layout optimization (WFLO) is to maximize the power generation with less cost. This paper proposes a program based on genetic algorithm for positioning turbines in a wind farm and studies the effect of wind direction on WFLO. Wind speed is measured at 28 locations in two southern states in India. GIS approach is used to identify the ideal location for a wind farm. Two different scenarios are taken for study; the first is constant wind speed with single direction and the second is constant wind speed with multiple wind directions. A wind farm of 2 km × 2 km is divided into grids of 10 × 10; each grid can have one or no turbine. The wind data of the past three years is taken for the optimization problem. The best solution would accommodate 19 turbines which can generate an average power of 183.55 MW with maximum 343.15 MW in November and a minimum 29.8 MW in May. A case study of wind farm layout optimization along with economical aspect is done in India.

74.1 Introduction Wind energy has gained significant attention from the researchers worldwide as wind energy conversion systems have integration ability with other energy resources. Wind technology is developing rapidly in the direction of high reliability and cost efficiency. Wind technology can be used as grid-connected mode (where generated power can be supplied to the main grid), stand-alone mode (where the isolated region can be electrified) or recurrently hybrid mode (where integration with different combinations of distributed energy resources can be done) [1]. Wind turbines generated about 487 GW by the end of 2016 worldwide with an increase of 12.5% from 2015. India K. Anwar (B) · S. Deshmukh Department of Mechanical Engineering, BITS Pilani—Hyderabad Campus, Hyderabad, Telangana 500078, India e-mail: [email protected] S. Deshmukh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_74

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is at the fourth position in the world for an installed capacity of wind turbine with 28.665 GW in 2016 and still growing at a reasonable speed. For optimum use of available wind energy potential, it is critical to position the wind turbine in a wind farm. Therefore, researchers are taking benefit of the numerical technique with a high-end computer [2]. The problem becomes more complex and nonlinear due to uncertainties associated with wind speed and aerodynamic behavior of wind [3]. When high wind potential is available with less power requirement, power generation becomes economical and competitive with conventional power generation. Through for multimegawatt generation or with limited access to wind, a good number of wind turbines have to be used, and efficiency of a wind farm is hugely depended on the positioning of the turbines. Researchers are worldwide focusing on wind farm layout optimization using various techniques for better positioning of turbine and harness wind power more efficiently [4, 5]. Montoya et al. addressed the necessitating choosing two dissimilar wind turbines to minimalize the standard deviation of the energy generated for the day while exploiting the total energy generated by the wind farm [6]. A method based on multi-population genetic algorithm (MPGA) was reported for wind turbine layout optimization which explains the algorithm used for extraction of the highest power in the minutest input cost [7]. Turner et al. have worked on Jensen’s wake decay model to characterize multi-turbine effects and improve mixed optimization constructions [5]. Parada et al. use a method to resolve the wind farm arrangement optimization problem designed using a Gaussian wake wind turbine model [8]. The Gaussian model algorithm has an exponential function which is used to determine the drift in the velocity. Gonzalez et al. proposed an algorithm to calculate the yearly income as a result of the sale of the total generated electricity considering the individual wind turbine loss of generation due to wake decay effects [9]. In [10], a dispersed genetic algorithm is employed to pursue the best number and sites of wind turbines in big wind farms. Still, the wind farm layout optimization is needed for more complex terrain and real wind scenario to study the balance between cost and generation of wind turbines. In the present work, effect of wind speed and direction on WFLO is studied. Moreover, wind potential and generation cost are evaluated. Here, algorithms were developed for wind farm performance evaluation and optimization for two different scenarios; (i) constant wind speed with single direction and (ii) constant wind speed with multiple wind directions. A wind farm of 2 km × 2 km is divided into grids of 10 × 10. Each grid can have one turbine or no turbine. The wind profile is measured on the ground station to get real-world data over 28 locations over two states of India. The measured data is mapped using the GIS approach to find a suitable location for a wind farm with high wind potential. The investment cost and the total power extracted were the variables optimized. This study uses a genetic algorithm for optimization of wind farm layout.

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74.2 Turbine Modeling In the present study, the wake model is considered, which was established by Jensen [11]. Wake model was used by numerous researchers to address the wind farm layout optimization problems [12]. This model is influenced by overall momentum conservation in the wake formed along the wind direction behind the turbine. The study ignores the near field present just after the wind turbine, and hence, the resulting wake is designed to be as a turbulent wake or negative jet. Since it neglects the influence of tip vortices on the wind parameters, this wake model is suitable for designing problems considering that they are only in the far wake region. Some assumptions have been made in the analyses performed to make the entire wake model easier and lucid. The wake at the input of the proposed turbine has a radius of r 0 . The design of the turbine is such that the wake radius is proportionately increased along the downstream distance x, as the wave moves forward. This concurs with the help of Blitz theory. It has been already proved by studies that the net total efficiency of a turbine would drastically decrease due to wake effect, if the turbine placed closed to each other or behind the other. Turbine blades get a part of the energy from the motion of the wind inside the turbine. This is a kinetic energy of wind, which is being transferred to the blades. Consequently, the blades tend to lower the velocity of the wind. As a result of which it will undergo a volumetric expansion about the mass accretion before the blades. The wake effect will tend to surge when multiple wakes happen to apply to the only one wind turbine. In this model, it is assumed that the momentum is constant throughout inside of the wake. This is to treat the subsequent wake produced by a wind turbine as a turbulent wake if the near field behind the turbine is disregarded completely. Also, in this regard, it has been validated by different independent studies that the traditional Jensen’s wake model is more accurate than other ones at calculating the wake loss. Schematic of wake model of a wind turbine is given in Fig. 74.1. The power produced by a wind turbine is calculated with the help of given equations. U i can be decided.

Fig. 74.1 Schematic of wake model of a wind turbine

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pi =

1 ρ AUi3 C p 2

(74.1)

  Aoverlap Ui = Uo 1 − Udef A U0 =

U zj ln k zo

(74.2) (74.3)

where U i is the free stream velocity before wind turbine i, zj being the roughness of turbine i, and the von Karman constant (k) has been assumed to be 0.4. Based on practical values, the surface roughness length z0 is approximated to be 0.3 in this paper as suggested in the previous studies. The given equation can be used to evaluate velocity loss U def : 2a  Udef =  1 + α rxr .2

(74.4)

where ‘a’ called as a factor of axial induction and ‘x’ is the downstream distance from the wind turbine that causes the generation of the wake. Wake radius ‘r 1 ’ is found to be associated with entrainment constant ‘a’ and distance ‘x’. When several wakes join together, the resulting velocity ‘u’ can be calculated by equating the kinetic energy difference of the mixed wake to the summation of kinetic energy differences of every individual wake at any point. α = entrainment ratio =

0.5 z ln zoj

r1 = αx + rr  rr = r

(74.5) (74.6)

1−a 1 − 2a

(74.7)

C T = 4ax(1 − a)

(74.8)

   N  u 2  Ui 2 1− 1− = Uo Uo i=1

(74.9)

Maximum P =

N 

Pi

(74.10)

i=1

When a turbine is enclosed incompletely in a wake model, the forces on the turbine blades are irregular. Consequently, the whole turbine will face unstable operation and

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high turbulence. Here, it is assumed that the unstable operation due to incomplete coverage will have a negligible effect on the power output of a wind farm. Meanwhile, the velocity of the free stream at lower turbines is smaller than that at higher turbines, which may decrease power generation.

74.3 Optimization Function Optimization techniques are employed to govern the size of each constituent that is participating in the system problem so that the final load can be frugally satisfied. For this motive, the control variables are to be designed so that they could characterize the size or rating of all the influential system constituents. The objective function is chosen to be minimalized the cost of generating one kilowatt-hour of electricity. The magnitude of system dependability is expressed by the expected energy not served within every sub-period of operation. The cost model can be designed now for minimization. The investment cost of the wind turbines is decided in such a way that merely, the number of turbines needs to be deliberated in computing the total cost disregarding other parameters. The total cost per year for the complete wind farm can be communicated as follows [13]:  costtotal = costgen × N ×

2 1 −0.00174N 2 + e 3 3

 (74.11)

where costgen is the cost of running a turbine for a year. For a wind system, a wind turbine generator produces electrical power when the wind speed V is above than the specified cut-in speed V ci and is dysfunctional when V is higher than the cut-out speed V co . However, when V r < V < V co , (V r being the rated wind speed), a wind turbine generator generates the mentioned rated power Pi without any increase in power even though wind velocity increases in that range. If V ci < V < V r , the output of a wind turbine is seen to follow the cube law stated in Eq. (74.13). The following equations are summarized wind turbine equations for its operation around the mentioned specific speeds and are considered while designing the optimization problem in our study. Pw = 0 if V < Vci Pw = aV 3 − b Pr

if Vci < V < Vr

(74.12) (74.13)

Pw = Pr if Vr < V < Vco

(74.14)

Pw = 0 if Vco > V

(74.15)

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where a=

Vr3

V3 1 Pr , b = 3 ci 3 and Pr = C p ρ AV 3 3 2 − Vci Vr − Vci

(74.16)

C p , p and A are the power coefficient, density of the medium (here, it is air) and area of the rotor, respectively. The optimization coefficients to be used here are as follows. Total rotor area A is the control variable for the wind turbine model considered. This value is controlled by the availability of space and also the allotted budget for the development. Numerically, ‘A’ can be found, and its generation requires linear programming algorithm. However, it is the work of the turbine modeler to dispense A, among numerous machines. Furthermore, the following mandatory studies are necessary for any wind farm analysis before setting it up at a site—variation of the wind in direction, velocity and intensity. The wind farm has to be modeled such that maximum energy per installation cost can be generated. To optimize for the best solution, knowing the limitations and assumptions of the problem statement is necessary (i.e., the total extent of the designated terrain, the study of wind distribution for different periods, the type of wind turbines accessible and their installation and maintenance costs, etc.) which leads to design the performance of a wind farm conformation, estimate its optimality and suitably achieve an optimum by discovering a minor number of feasible configurations.

74.4 Genetic Algorithm Characteristic optimization problem statements are typically deciphered by using a mountaineering process-based predominantly on local gradients of a quantified cost function requiring optimization. Conversely, a representative shortcoming of this method is the possibility of evaluating just a local optimum and the unfeasibility of determining a global optimum commencing from the identical structure. The wind farm position determination problem is a different characteristic problem and is infeasible to solve precisely, where a simple gradient-based technique cannot be used. A grid of 10 × 10 potential wind turbine positions, even if for every grid point deliberation, is constrained only to the two likelihoods of having or not having a turbine at each site, which means 2100 possibilities to scrutinize which far surpasses the competence of any prevalent processor. A genetic search algorithm is one possible method that can be applied to this problem statement to approach the solution. The fundamental backbone of the method is that of the biological evolution through meek modifications of a genetically coded confirmation. As per in the natural processes of selection, as in biological reproduction, two sets of chromosomes with genetic codes is generated from two parents. Since the best species have traits suitable for adaptation and evolution, they survive and are optimized to fit in society. The basic procedure that can happen in the building of fresh chromosomal strings is an arbitrary mutation

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of a gene chromosome, an interchange of genetic data among the breeding parents and a reversal of the entire chromosomal string. In the subsequent study, only the first two are to be considered, explicitly mutation and crossover as the means of genetic modifications to the next generation. The selection of the individuals is subjected to the breeding procedure, namely crossover and mutation are haphazardly made, allocating a probability to be removed to each individual comparative directly to the fitness.

74.5 Results and Discussion 74.5.1 Selection of Wind Farm Location The location for the wind farm is decided after obtaining wind speed data within Andhra Pradesh and Telangana, India. Monthly, mean wind speed has been obtained from India Meteorological Department, Pune. Based on wind speed profile, land availability, and government recommendation, Anantapur (14.5833°, 17.6333°) found to be the best location for a wind farm. The wind data for Anantapur was considered for the past three years from Jan 2007 to Dec 2009. The yearly average wind speed for the region was 8.95 kmph. This wind speed was considered in the optimization function. The input properties are given in Table 74.1. The variation of monthly average wind speed for Anantapur is shown in Fig. 74.2.

Fig. 74.2 Monthly average wind speeds at Anantapur

Input properties

Data

Input properties

Hub height

60 m

Ground roughness

0.3 m

Rotor diameter

40 m

Axial induction factor

0.33

Thrust coefficient

0.88

Entrainment factor

0.094

Wind speed (kmph)

Table 74.1 Input properties for Anantapur, India

Data

20 15 10 5 0

1 2 3 4 5 6 7 8 9 10 11 12 Months

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74.5.2 Wind Farm Layout Optimization for Single Direction In the case of constant wind speed with single direction, it is assumed that the wind is coming only from the front of the turbines. The proposed algorithm is used to optimize wind farm of size 2 km × 2 km. There are a total of 100 grids (10 × 10) in 4 km2 . Each grid is of 200 m × 200 m and can have only one turbine installed which means that there are possibilities of 100 turbines to be installed. The simulation results are obtained for yearly average wind speed. The cost objective function to be minimized will study the function of the turbine investment, operation and maintenance cost per kW installed. Figure 74.3 shows the behavior of the fitness curve as a function of the generation for a yearly average wind data. It can be noted that the fitness value converges before 400 generations. It was found in the studies that for the single wind direction, the best solution would accommodate less than 19 turbines. Optimized wind farm layout is presented in Fig. 74.4. Fig. 74.3 Fitness curve with respect to generation

Fig. 74.4 Optimization result for wind farm layout for single direction wind

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Fig. 74.5 Optimization result for wind farm layout for multi-direction wind

74.5.3 Wind Farm Layout Optimization for Multiple Wind Directions In the case of constant wind speed with multiple directions, wind is coming in diagonal to the farm. Therefore, the wind has a component in both the directions, parallel and transverse to the turbine. For multiple wind direction, yearly, average wind speed is taken for wind farm optimization problem. As it is evident from single direction wind speed, there is not much difference in numbers of turbine or power production. It is observed that even in this case, a population of 200 individuals can be appropriate to reach a well-optimized configuration even starting from a random distribution. The behavior of the fitness function becomes stable and converges before 400 generations. The optimum number of wind tubine is calculated as 16 for the multiple wind direction. The multidirectional optimized wind farm layout at Anantapur with annual average wind speed of 4 km2 is shown in Fig. 74.5.

74.6 Conclusions Genetic algorithm was instrumental in designing the wind farms at a location in India where wind farms were proposed to be set. A wind farm of 2 km × 2 km is considered for the optimization problem. A program based on genetic algorithm was used to determine the optimal locations of the wind turbine in wind farm two scenarios; scenario-1 is constant wind speed with single direction and scenario-2 is constant wind speed with multiple wind directions. Optimum number of wind turbine was found to be 19 and 16 for the first and second scenario, respectively. Positioning of the turbine in the wind farm is also optimized. For the 19 turbines in the wind farm, the average theoretical power generated will be 183.55 MW with a wind speed of 8.95 kmph, maximum 343.15 MW in November with a wind speed of 14.62 kmph and minimum 29.8 MW in May with a wind speed of 4.88 kmph.

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References 1. K. Anwar, S. Deshmukh, S. Mustafa Rizvi, Feasibility and sensitivity analysis of a hybrid photovoltaic/wind/biogas/fuel-cell/diesel/battery system for off-grid rural electrification using homer. J. Energy Resour. Technol. 142(6), 1–12 (2020) 2. G. Bekele, G. Tadesse, Feasibility study of small hydro/PV/wind hybrid system for off-grid rural electrification in Ethiopia. Appl. Energy 97, 5–15 (2012) 3. K. Anwar, S. Deshmukh, Parametric study for the prediction of wind energy potential over the southern part of India using neural network and geographic information system approach. Proc. Inst. Mech. Eng. Part A J. Power Energy 234(1), 96–109 (2020) 4. M. Abbes, M. Allagui, Centralized control strategy for energy maximization of large array wind turbines. Sustain. Cities Soc. 25, 82–89 (2016) 5. S.D.O. Turner, D.A. Romero, P.Y. Zhang, C.H. Amon, T.C.Y. Chan, A new mathematical programming approach to optimize wind farm layouts. Renew. Energy 63, 674–680 (2014) 6. F.G. Montoya, F. Manzano-Agugliaro, S. López-Márquez, Q. Hernández-Escobedo, C. Gil, Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms. Expert Syst. Appl. 41(15), 6585–6595 (2014) 7. X. Gao, H. Yang, L. Lin, P. Koo, Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshore. J. Wind Eng. Ind. Aerodyn. 139, 89–99 (2015) 8. L. Parada, C. Herrera, P. Flores, V. Parada, Wind farm layout optimization using a Gaussianbased wake model. Renew. Energy 107, 531–541 (2017) 9. J.S. González, A.G. Gonzalez Rodriguez, J.C. Mora, J.R. Santos, M.B. Payan, Optimization of wind farm turbines layout using an evolutive algorithm. Renew. Energy 35(8), 1671–1681 (2010) 10. I. Katic, J. Højstrup, N.O. Jensen, A simple model for cluster efficiency, in EWEC’86. Proceedings, vol. 1 (1987), pp. 407–410 11. N.O. Jensen, A note on wind generator interaction. Report 87-550-0971-9 (1983) 12. J.F. Herbert-Acero, O. Probst, P.-E. Réthoré, G.C. Larsen, K.K. Castillo-Villar, A review of methodological approaches for the design and optimization of wind farms. Energies 7(11), 6930–7016 (2014) 13. H.S. Huang, Distributed genetic algorithm for optimization of wind farm annual profits, in 2007 International Conference on Intelligent Systems Applications to Power Systems, Nov 2007, pp. 1–6

Chapter 75

Eco-Efficiency and Business Performance Evaluation—Lean and Green Manufacturing Approach R. Kishore, R. Pradeep, Suyash Roy, K. Ravi Teja, M. S. Narassima, K. Ganesh, and S. P. Anbuudayasankar Abstract The environmental issues have been a serious talk lately from the manufacturing industry’s point of view. The purpose of this study is to analyse the factors that influence the eco-efficiency and the business performance of a company towards lean and green techniques. The enablers/criteria were identified as the factors influencing lean and green and were surveyed in manufacturing industries to evaluate their performance. Analytic hierarchy process was used to calculate the weights and prioritize the enablers. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) prioritizes the enablers based on lean, green, sustainable factors and is improved based upon the ranks. The driving and the dependency power of the enablers were determined using interpretive structural modelling (ISM). The supply chain operations reference (SCOR) method categorizes the enablers under major factors like plan, source, make, delivery, return and attributes such as reliability,

R. Kishore · R. Pradeep · S. Roy · K. R. Teja · M. S. Narassima · S. P. Anbuudayasankar (B) Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India e-mail: [email protected] R. Kishore e-mail: [email protected] R. Pradeep e-mail: [email protected] S. Roy e-mail: [email protected] K. R. Teja e-mail: [email protected] M. S. Narassima e-mail: [email protected] K. Ganesh Head Supply Chain Management—Center of Competence, McKinsey & Company Inc., Chennai 600113, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3_75

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responsiveness, agility, cost and assets management. The overall business performance value of an industry is determined using SNORM De Boer Normalization Process Method (SNORM) values and the weights obtained from analytic hierarchy process (AHP). This study indicates that industries are shifting gradually towards lean and green techniques. The decision-makers can understand the areas to concentrate, concerning lean and green, according to the performance value of the criteria and choose the appropriate performance model.

75.1 Introduction The present generation is longing for an eco-friendly and sustainable system that enhances the growth and efficiency for the upcoming generations to meet their demands [1]. The lean and green techniques practised in the manufacturing industries do not maintain a balance over environmental and business performance. The lean manufacturing technique helps in reducing costs by reducing wastes in the manufacturing industries and thus helps in the economic factor of the industry [2]. Global warming and depletion of the resources have been the most serious problems in the industrial point of view. The important problem faced by the industrialist is that they could not perform continuous performance effectively. The green manufacturing technique helps in the emission control and reduction in resource depletion in the manufacturing industry, thus helping in the environmental conservation of the industry [2]. As per the Green Technology Master Plan in Malaysia, the lean and green techniques should be the key for the environmental and business development of a company and to develop green products, process and energy [3]. For the past years, we have been focusing on the lean and the green techniques independently, but as to maintain the balance, it is indispensable to integrate lean green and sustainable strategies [4]. The lean and green as a combination helps in the improvement of environmental concerns and develops the overall performance measures of the company. Multiple criteria decision-making (MCDM) tools play a vital role in weighting and ranking the criteria or the enablers when there is more complexity or more criteria [5]. There are several MCDM tools like analytic hierarchy process(AHP), Technique for Order of preference by Similarity to Ideal Solution (TOPSIS), preference ranking organization method for enrichment evaluation (PROMETHEE), interpretive structural modelling (ISM) and ELimination Et Choix Traduisant la REalité (ELECTRE) to rank and evaluate the criteria. Analytic hierarchy process (AHP) is a structural method for organizing and analysing complex decisions. It is a pairwise comparison method to evaluate the criteria [6]. AHP converts the criteria evaluations to numerical values that help in determining the best set. The major advantages of AHP are they are flexible, intuitive and no bias in the decision making [7]. TOPSIS compares the attributes with the criteria and then evaluates them. It converts the criteria dimensions to non-dimensional criteria as similar to the ELECTRE method [8, 9]. PROMETHEE is an outranking method to support decisions in the urban planning and regeneration

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process. It compares the attributes with the criteria to evaluate the ranks. The criteria are ranked in the order of decisions [8, 10]. The ISM helps in determining the driving and dependency power of different enablers/ criteria and gives the complex relationship between the enablers [10]. ISM compares the criteria with each other and ranks them. It is widely applied in identifying and summarizing relationships among the factors. The business performance is used to improve the overall performance of a company towards lean and green to compete with others in the market. In order to measure the performance, the supply chain operation reference (SCOR) method is used to calculate the key performance indicators (KPIs). Designing the system based on the SCOR model helps in the measurement of the business performance of the company and suggests the improvements that must be made [11, 12]. The performance measurement system will be carried out with the Snorm de Boer method to obtain performance values [11].

75.1.1 Lean and Green Manufacturing The literature on lean and green manufacturing is found to be interesting to explore. But, at the same time, the analysis of the enablers towards lean and green is not many. To mention a few: Mittal et al. [2] study the adoption of lean–green–agile strategies for manufacturing systems of 10 enablers. The main aim of the study is to integrate lean–green–agile techniques to improve performance [2]. The development of the lean and green index helps as the benchmark tool for the industrialists. This study uses analytic hierarchy process (AHP) to prioritize the criteria and using the application of the backpropagation optimization method to enhance the lean–green models [13]. Many of the industries still do not follow lean techniques; this brings the impact of an increase in waste production. The combination of lean, green and agile practices does not always show positive impact towards environmental waste reduction [14]. Lean techniques should be practised by the workers as well as should be implemented in the industries [15]. To reduce the wastage from the consumed resources and to obtain friendly environment, lean techniques are first implemented by Toyota Motors Co. in Japan. Using these techniques can change global awareness and brings profits to the industries [16].

75.1.2 Multi-criteria Decision Making (MCDM) Identification of key performance indicators (KPIs) using the supply chain operation reference (SCOR) method is the one important aspect of this study. The identified KPI is evaluated and ranked by AHP. The AHP gives out the weights of the attributes/criteria [11]. The literature supports that the TOPSIS and PROMETHEE as the best method to get a better design alternative [17]. The sustainable means of

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public transport and railways have been the most important aspect and expansion in China. But many factors hinder the effectiveness in sustainability [18]. Greening of the supply chain management (SCM) plays an important role in reducing the waste and usage of resources, and it also helps in conserving the raw materials and helps in bringing the ecological balance. Many decision-makers use this method for finding the performance as it can easily answer any question. Among the other MCDM methods, AHP is mostly used in determining the right decision and helps in consistency checking [19].

75.1.3 Business Performance Measures Hasibuan et al. [20] study the case study in a company to implement the competition strategy to produce the goods/products that is more qualified and cheaper. Supply chain operation reference (SCOR) method has been used to identify the attributes under plan, source, make, delivery and return. The performance value is calculated using the Snorm de Boer so as to improve the business performance of the company [20]. Supply chain performance analysis of a company can improve the overall business performance value and helps to compete in the market [21].

75.2 Methodology 75.2.1 Flowchart The methodology for calculation of the business performance is shown in Fig. 75.1.

75.2.2 Identifying Enablers A proper analysis of the factors can reduce the problems faced by many suppliers in the manufacturing industries. The three main criteria for green manufacturing include green energy utilization, renewable goods manufacturing and green technologies for operation [22, 23]. The waste management and improvement plan play an important role in the economic, social and environmental factors of a company [24]. The enablers/criteria that are considered as the factors influencing the eco-efficiency and business performance of a company are identified [25] (Table 75.1).

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Fig. 75.1 Methodology for calculating performance value

Table 75.1 Enablers for lean and green manufacturing S. no.

Enablers

S. no.

Enablers

1

Quality

8

Cost efficiency

2

Product design

9

Financial capabilities and benefits

3

Environmental compliance

10

Achieving production target

4

Inventory management

11

Make span

5

Market competition

12

Lead time

6

Technological resources

13

On-time delivery

7

Fulfilment of raw materials

14

Supplier–customer relationship

15

Effective communication throughout supply chain

75.2.3 Evaluation Using Analytic Hierarchy Process (AHP) The evaluation of the criteria using the MCDM methods helps in the ranking, and thus, we obtain the weights for calculating the performance values. We use AHP in

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this study to prioritize the criteria. AHP is performed with two types of measurement relative and absolute [26]. AHP helps in identifying the intensity between the rows and the columns of the decision matrix [12, 27]. The questionnaire for the survey from an expert in the manufacturing industry was prepared and distributed. With the help of the grades, the criteria are prioritized for the further calculations.

75.2.4 Identifying Ranks Using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) It is a type of MCDM system having its pros and cons. It is very important to check the priorities calculation with other methods because of the approximations in the MCDM methods. In TOPSIS, the criteria are graded on the Likert scale (five-point scale). This method compares the criteria with the attributes.

75.2.5 Calculating Driving and Dependency Using Interpretive Structural Modelling (ISM) This is a very important tool since it calculates the dependencies of criteria on each other. Unlike other tools, ISM uses linguistic grading than numerical grading. The linguistic grading for ISM is graded in V, A, X, O scale.

75.2.6 Business Performance Measure From the large- to small-scale industry, the main aim of the industry is to have the best quality at economic terms [20]. Plan, make, source, return and delivery are five factors that are considered under the supply chain operation reference (SCOR) model. This framework provides a variety of performance measure variances for evaluating supply chains, and there are five performance attributes in this model, namely responsiveness, reliability, cost, agility and asset management [12]. The enablers are graded by the survey from the experts in the industry. The flow process for Snorm de Boer method is shown in Fig. 75.2. The business performance is calculated using the Snorm de Boer method. For calculating the performance score value, Larger is better: SNORM = [(Si − Smin)/(Smax − Smin)] ∗ 100.

(75.1)

Smaller is better: SNORM = [(Smax − Si)/(Smax − S min)] ∗ 100.

(75.2)

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Fig. 75.2 Snorm de Boer process

Si = Average value (Si is the actual value that is achieved for particular criteria in a company); Smax = Achieving the best performance value (Smax is the maximum value for particular criteria in a company); Smin = Achieving the worst performance value (Smin is the minimum particular criteria in a company).

75.3 Results and Discussion AHP is a pairwise comparison system that helps in the identification of the key factors, and allocation of the resources based upon the results [18]. The grades for the criteria got from the survey in manufacturing industries are taken as input values into the AHP matrix. The ranks and the average weights of five companies for criteria are determined as shown in Table 75.2. From the matrix, it is seen that it is very less time consuming and simple where qualitative data are used for decision making. The results from AHP show that the industries are performing well towards quality, product design, environmental compliance and criteria. The enablers in lower ranks must be given more consideration. The weights obtained from AHP are further used in SNORM method. Using the SCOR method, the KPI is obtained for the business performance based on the lean and green factors [21]. By the Snorm de Boer analysis, the overall business performance value is calculated with the help of average weights of five companies obtained from AHP. The calculated values give the performance value of each enabler, and the summation gives the overall business performance of the company (Table 75.3). The ranks are identified for each enabler with respect to lean, green and sustainable factors through TOPSIS. The ranks obtained from TOPSIS are shown in Table 75.4.

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Table 75.2 Calculation of ranks and weights using AHP Enablers

Avg. weights

Enablers

Rank

Avg. weights

Quality

1

0.1667

Cost efficiency

13

0.0436

Product design

2

0.1251

Financial capabilities and benefits

14

0.0459

Environmental compliance

3

0.1214

Achieving production target

4

0.0578

Inventory management

5

0.0886

Make span

6

0.0498

Market competition

12

0.06

Lead time

8

0.0388

Technological resources

11

0.0503

On-time delivery

7

0.0465

9

0.0483

Supplier–customer relationship

10

0.0264

Effective communication throughout supply chain

15

0.0309

Fulfilment of raw materials

Rank

Table 75.3 Performance score using Snorm de Boer process Enablers

Value

Enablers

Quality

7.99

Financial capabilities and benefits

Value 0.87

Product design

4.30

Achieving production target

2.47

Environmental compliance

3.34

Make span

9.44

Inventory management

1.52

Lead time

7.23

Market competition

1.34

On-time delivery

1.64

Technological resources

0.92

Supplier–customer relationship

1.00

Fulfilment of raw materials

1.77

Effective communication throughout supply chain

1.19

Cost efficiency

0.97

Performance score

46.07

The ISM model helps in developing the relationship between the criteria. In this study of ISM, it compares the core attributes, plan, source, make and delivery. The graph showing the relation is shown in Fig. 75.3. The graph is divided into four quadrants: Quadrant 1 shows weak driving and dependency power. Quadrant 2 shows weak driving and strong dependency power. Quadrant 3 shows strong driving and dependency power. Quadrant 4 shows strong driving and weak dependency power. In our study, the delivery and return have strong dependency but weak driving power. Make has strong driving and dependency power; plan and source have strong driving but weak dependency power.

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Table 75.4 Calculating ranks using TOPSIS Enablers

Scores

Quality

0.7363

Performance index 1

Product design

0.5

4

Environmental compliance

0.6468

2

Inventory management

0.3532

7

Market competition

0.3532

7

Technological resources

0.5

Fulfilment of raw materials

0

4 11

Cost efficiency

0.5

4

Financial capabilities and benefits

0.2474

9

Achieving production target

0.6234

3

Make span

0.5

4

Lead time

0.5

4

On-time delivery

0.6234

3

Supplier–customer relationship

0.3532

7

Effective communication throughout supply chain

0.2385

10

Fig. 75.3 Plotted graph using ISM

75.4 Conclusion Many companies nowadays are more concerned about environmental problems due to the non-eco-friendly practices in the industries. In this research, five industries like pump manufacturing industry, paper manufacturing industry, industrial pumps and motors manufacturing industry, air vacuum purifier manufacturing industry and die-casting industry are surveyed and evaluated for their performance. The enablers are ranked based on the companies’ perspective towards lean and green practices. It shows that the overall performance values of the industries are 47.61, 40.78, 45.61, 40.51 and 37.63. The value shows that the industries are moderately performing towards lean and green techniques; still, more improvements have to be done. Support

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from top management is given more priority for green practices. Apart from focusing only on green factors, other factors like cost efficiency and effective communication throughout the supply chain should be given consideration. The areas like technological resources, financial capabilities, benefits and supplier–customer relationship must be improved to achieve environmentally friendly and good business performing company. The major factors that depend on the industry’s performance are geographical location, logistics and availability of minimum waged workers. The performance of the industry can be precisely measured by calculation periodically. By implementing new technological resources, improvements can be made in lean and green techniques.

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Author Index

A Aabid, Abdul, 675 Abhyankar, Ved, 307 Abraham, Kiran Mathews, 195 Adithya Sharma, K., 77 Ajay Kumar, V., 239 Alam, Shahood S., 571 Al-Khalifah, Turki, 675 Allamraju, K. Viswanath, 687 Amaljith, K. B., 343 Amaresh Kumar, D., 745 Anand, Mukul, 55 Anbuudayasankar, S. P., 779 Anita, M., 147 Anjali, T., 443 Anjum, Asraar, 675 Ansari, Mubashshir A., 571 Anto, S., 357 Anwar, Khalid, 769 Aparna, T. S., 443 Archana, M. S., 529 Arun, M., 285 Asik, Faysal Hasan, 217

B Babu, Penugonda Suresh, 113 Balaji, R., 619 Bansal, Pratosh, 317 Barik, Debabrata, 285 Basha, Mir Mohammed Junaid, 529 Behera, Santi Kumari, 467 Bhaisare, Aasawari, 667 Bhandarkar, Rekha, 325 Bharj, Rabinder Singh, 93

Bhavya Sree, B., 475 Bindu, G. R., 101 Bindumol, E. K., 65 Biradar, Srikumar, 251 Birajdar, Priyanka, 389 Biswas, Preesat, 467 Boche, Abhijeet, 601 Bonthala, Bhanu Prakash, 23 Brahma Reddy, A. L. S., 723

C Chandra Sekhara Reddy, M., 757 Charoo, M. S., 659 Chattopadhyaya, Somnath, 13, 135 Chaudhari, Jitendra P., 425 Chauhan, Pranay, 317 Chhibber, Sharan, 407 Chitra, K., 185, 609, 697 Corrales, Byron P., 1

D Darshan, M. L., 251 Das, Alok Kumar, 13, 135 Datta, Arijit, 609 Deepa, M. U., 101 Dennis, Bino Prince Raja, 125 Deshmukh, M. K., 559 Deshmukh, Mona, 375 Deshmukh, Sandip S., 559, 667, 769 Dhamangaonkar, P. R., 165 Dodda, Jhansi Reddy, 519 Donthamsetty, Suneel, 113 Dora, Nagaraju, 45

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. N. R. Reddy et al. (eds.), Intelligent Manufacturing and Energy Sustainability, Smart Innovation, Systems and Technologies 213, https://doi.org/10.1007/978-981-33-4443-3

791

792 Duggi, Niveditha, 455 Durga Prasad, M. V. R., 147

F Freire, Luigi O., 1, 509

G Gadekar, Aditya, 619 Gadhade, Yash, 619 Ganesh, K., 779 Gangalakurti, Laxminarayana, 227 Gangwar, Pawan Singh, 333 Garg, Nikhil, 55 Gnaneshwar, G., 579 Gogulapati, Sai Rohan, 77 Gopalakrishnan, Ramanan, 125 Grampurohit, Sneha, 485 Gupta, Rupali, 549 Gupta, Satish Kumar, 549 Gurugubelli, P. S., 55

H Hanief, M., 659, 715 Hari Thiagarajan, N. S., 579 Hasan, Abir, 207 Hasija, Yasha, 333 Hiwarkar, Abhishek, 667

I Ismail, Rakshana, 637

J Jeevitha, A., 591 Joshi, Divya, 261 Jyothsna, S., 609

K Kadoli, Ravikiran, 601 Kakkeri, Shrishail, 745 Kakulapati, V., 299 Kala, Namrata, 705 Kamatchi Kannan, V., 185 Kanakam, Radhika, 227 Kanakaraj, Darwins Anantha, 125 Kandwal, Sandeep, 261 Kapil, Harsh, 549 Katekar, Vikrant, 667 Katiresan, Supradeepan, 55

Author Index Kazmi, Syed Qamrul, 397 Keerthikeshwar, Mamilla, 357 Khadherbhi, Reshmi, 499 Khaled, Nassim, 275 Khan, Sher Afghan, 675 Kiran, P., 407 Kishore, R., 779 Kodati, Sarangam, 417 Kosta, S. P., 425 Kulkarni, Suyog, 165 Kumar, Surender, 93

L Lakshmanan, M., 185 Lakshman Narayana, V., 499

M Maheswari, K. Uma, 697 Malyadri, T., 147 Manjula Gururaj Rao, H., 539 Manoj, Aluri, 239 Mathur, Harsha, 275 Meera, M., 443 Megha, N., 343 Milan, K. John, 85 Mishra, Ruby, 649 Mohan, Chhabra Inder, 227 Mohapatra, Manoranjan, 649 Momin, Bashirahamad, 389 Moon, Mahjabin, 217 Mulay, Parthsarathi, 705 Muppalaneni, Naresh Babu, 367 Muraleedharan, C., 85 Murtoza Morshed, Shah, 207 Mushtaq, Zahid, 715 Muthukumaran, N., 33 Muthukumaraswamy, Senthil, 637

N Nagaraja, G. S., 539 Naik, Santosh Shiddaling, 23 Narassima, M. S., 779 Narayanan, Gayathri, 443 Narayanan, Lakshmi, 579 Narender, Maddela, 239 Navarrete, Luis M., 1 Neelima, N., 475 Nikitha, A. P., 529 Nimje, Akash, 667

Author Index O Osman, Mohamed Al-Mujtaba Ali Idris, 195

P Pal, Saurabh, 397 Pandey, Bitti, 467 Pandey, Chanki, 467 Panigrahi, Millee, 467 Parakh, Laukik, 619 Parameswaran, A. N., 325 Parasher, Mayank, 55 Parvathy, A., 443 Parveez, Bisma, 675 Patel, Gaurang K., 425 Patel, Satyanarayan, 175 Patil-Tekale, Rajkumar Ashok, 619 Porras, Jefferson A., 1 Pradeep, R., 779 Prakasha, K. S., 745 Prathima, Ch., 367 Pushpa, B. R., 343

Q Qamareen, Arees, 571

R Raghunath, Navneet, 559 Rahman, Md. Zishanur, 13, 135 Rajeev, T., 733 Rajendran, Ajith Raj, 125 Rajpoot, Sharad Chandra, 467 Rajput, Isha, 549 Rajula, Swaminadhan, 455 Rakhi, J. S., 733 Ram Kumar Reddy, D., 433 Ramsai, Ch, 45 Ramya Shree, A. N., 407 Rane, Twinkle, 705 Ravi, G., 417 Ravishankar, K. S., 251 Reddy, A. N. R., 723 Reddy, Balem Rahul, 519 Reddy, Kumbala Pradeep, 417 Reddy, Mallapi Debashree Gayatri, 649 Rezaul Karim, Md., 207 Rohinikumar, B., 85 Roshin, K. R., 65 Roy, Suyash, 779

793 S Sabareesh, V., 85 Sakhare, Aniket, 667 Sandhya Krishna, P., 499 Sanjay, K., 77 Santhi Sri, K., 499 Saxena, Akshay, 55 Sekar, T., 33 Selokar, Ashish, 619 Senthil Kumar, D., 579 Sethy, Prabira Kumar, 467 Shakthivel, R., 609 Sharmin, Israt, 217 Shawon, Sabbir Hossain, 207 Shivaprakasha, K. S., 325 Singhal, Sarthak, 549 Singh, Munindra Kumar, 397 Singh, Satyendra, 261 Soori, Prashant Kumar, 195 Sreekanth, Nara, 417 Sridhar, K. P., 285 Srikanth, G. S., 591 Srinivasan, S., 185 Srinivasa Rahul, Ch, 45 Srinivasulu, N. V., 519 Srivastava, Prateek, 375 Sudheesh, P., 433 Sundara Subramanian, G., 579 Suresh, Nikhil, 609

T Tapdiya, Rushikesh, 307 Tariq, Juairiya Binte, 207 Teja, K. Ravi, 779 Thakur, Pawan, 667 Thosar, Archana, 705

V Vacacela, Segundo G., 509 Vaibhav, Saket, 609 Varadharajula, Atchaih Naidu, 227 Varun Raj, N., 77 Vasudeva Banninthaya, K., 591 Vasudeva Rao, Veeredhi, 757 Venkata Ramana, P., 77 Venkata Subba Reddy, O. Y., 723 Venkatesh, V., 723 Venugopal Reddy, K., 227 Vignesh, G., 285 Vijayakumar, K., 33 Vijayakumar, M. N., 529

794 Vijay, M., 33 Viji, K., 697 Virmani, Rahul, 549

W Waddamwar, Amol, 165

Author Index Wasnik, Unmesh, 667

Y Yadav, Ajay Kumar, 23 Yashwanth Bharadwaj, V., 475 Yerra, Yashwant, 433