This proceedings book features volumes gathered selected contributions from the International Conference on Engineering
1,688 134 103MB
English Pages 886 [899] Year 2021
Table of contents :
Contents
Keynote Addresses
Hardware Acceleration of Modern Data Management
Abstract
Electric Vehicle Development and LowCarbon Transport in Vietnam
Abstract
ICERA 2020 Main Track
A CommonGround SinglePhase Boost Inverter with Suppressed DoubleFrequency Ripple for Photovoltaic Applications
Abstract
1 Introduction
2 Proposed Topology
3 Simulation Results
4 Conclusion
Acknowledgment
References
A High Stepup DCDC Converter with Semiconductor Voltage Stress Reduction
Abstract
1 Introduction
2 Proposed SwitchedInductor DCDC Converter
2.1 A Configuration of Proposed SwitchedInductor
2.2 Circuit Analysis
3 Results
4 Conclusion
Acknowledgement
References
A Method to Partition Accuracy in Workspace for Robot Arms
Abstract
1 Introduction
2 Interpolated Model Using Shape Function
3 Partition of Accuracy in Workspace for Robot Arms Using Shape Function
4 Examples
5 Conclusion
Acknowledgments
References
A Novel Method for Shielding Problems with Taking Robust Correction Procedure into Account
Abstract
1 Introduction
2 Subdomain Technique
2.1 Definition of Coupled Subdomains
2.2 Magnetodynamic Problem
2.3 Shielding Structures
3 Sequence of a Weak Formulations
3.1 Magnetic Vector Potential Formulations
4 Application Test
5 Conclusions
References
A Review on Ultrasonic Stack Modelling
Abstract
1 An Overview of Ultrasonic Stack and Applications
2 Ultrasonic Horn
3 Ultrasonic Transducers
4 Research Gaps and Future Directions of Ultrasonic Stack Modelling
Acknowledgement
References
A Solution to Power Load Distribution Based on Enhancing Swarm Optimization
Abstract
1 Introduction
2 Mathematical Model of Load Distribution
3 Enhancing PSO with Multiobjective Function
3.1 Basic Particle Swarm Optimization
3.2 Multiobjective Optimization
3.3 Enhancing PSO Multiobjective
4 A Solution to Optimal Load Distribution
5 Experimental Results and Discussion
6 Conclusion
References
A Study for Determination of the Pressure Ratio of the V12 Diesel Engine Based on the Heat Flow Density to Cooling Water
Abstract
1 Introduction
2 Numerical Study
3 Experimental Study
3.1 Flow Measurement of Cooling Water
3.2 Test Facilities
4 Results and Discussion
5 Conclusion
References
A Study of Scissor Lifts Using Parameter Design
Abstract
1 Introduction
2 Kinematic Analysis
3 Kinetic Analysis
4 Conclusion
Acknowledgments
References
A Study on Prediction of Milling Forces
Abstract
1 Introduction
2 Developing Cutting Force Model Based on Cutting Force – Surface Roughness Relation
3 Validating the Accuracy of the Proposed Model for Predicting Cutting Forces
4 Conclusion
Acknowledgements
References
A Study on Qualitative and Quantitative Characterization of Machining Quality of Aerospace Composite Structures
Abstract
1 Introduction
2 Experimental Procedure
3 Results and Discussion
3.1 Evaluation of Machining Quality
3.2 Influence of Machining Parameters on the Surface Roughness
3.3 Influence of Machining Distance on the Surface Roughness
4 Conclusions
Acknowledgments
References
A Study on Prediction of Grinding Surface Roughness
Abstract
1 Introduction
2 The Model for Predicting Surface Roughness
3 Comparing Predicted Surface Roughness and Experimenting Results
4 Conclusion
Acknowledgements
References
A VisionBased Measurement and Classification System for Robot Arm Under Controlled Lighting Condition
Abstract
1 Introduction
2 Experimental System
3 Research Methodology
3.1 Image Processing
3.2 Calibration Algorithm and Method
4 Results and Discussion
4.1 Calibration Results
4.2 Measuring and Classification Results
5 Conclusions
References
About a Viewpoint of Calculating Spatial Dimensional Tolerance Chains According to Structure Group of a Parallel Robot
Abstract
1 Introduction
2 Tolerance Calculation for Parallel Robot
3 Tolerance Calculation for Parallel Robot Base on Structure Group
3.1 Two Branches of Dimensional Similarity
3.2 Two Branches of the Same Structure but not Similar
4 Case Study
4.1 Determining Tolerance for an Independent Robot
4.2 Determination of Tolerance Based on Structural Group
5 Conclusions
Acknowledgment
References
Adaptive Algorithm for Servo System Using Linear Electric Motor
Abstract
1 Introduction
2 Model of Servo System Using Linear Electric Motor
2.1 Mathematical Model of Linear Electric Motor
2.2 Model of Servo System Using Linear Electric Motor
3 Adaptive Regulator for Servosystem
3.1 Adaptive Algorithm
3.2 Building a Reference Model
3.3 Building State Observer for Servo System
3.4 Development of an Adaptive Mechanism for the System
4 Simulation Results
5 Conclusion
Acknowledgement
References
Adaptive Sliding Mode Control for a 2DOF Robot Arm in Case of Actuator Faults
Abstract
1 Introduction
2 Problem Preliminaries
3 Adaptive Sliding Mode Control Design
4 Simulation Results for a 2DOF Planar Robot Arm
5 Conclusions
References
An EnergyEfficient Combination of Sleeping Schedule and Cognitive Radio in Wireless Sensor Networks Utilizing Compressed Sensing
Abstract
1 Introduction
2 Network Model
3 The Proposed Algorithm
4 Energy Efficiency Analysis
5 Simulation Results
6 Conclusions and Future Work
Acknowledgements
References
An Enhancing Grasshopper Optimization for Efficient Feature Selection
Abstract
1 Introduction
2 Grasshopper Optimization Algorithm (GOA)
3 Strategies for Improving GOA (IGOA)
3.1 Nonlinear Decline Coefficient
3.2 Adaptive Weight Coefficient
3.3 Experimental Results and Analysis
4 A Solution to Feature Selection by Applied EGOA
5 Conclusion
Acknowledgment
References
An Evaluation of BSpline for Synthesis of Cam Motion with a Large Number of Output Conditions
Abstract
1 Introduction
2 BSpline Curve Description for Cam Motion
3 Control Point Determination
4 Result and Discussion
4.1 Motion Program
4.2 Fourier Analysis
5 Conclusion
Acknowledgment
References
An Experimental Study on VibrationDriven Locomotion Systems Under Different Levels of Isotropic Friction
Abstract
1 Introduction
2 Basic of the Study
2.1 Working Principle of the System
2.2 Objectives of the Study
3 Experimental Implementation
4 Results and Discussion
5 Conclusions
Acknowledgement
References
Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain
Abstract
1 Introduction
2 Dynamic Cutting Force Model
3 Experimental Setup
4 Simulated and Experimental Results
5 Estimation of Lyapunov Exponent
6 Conclusions
Acknowledgment
References
Analytical Study of the Power Parameters of Electric Traction Drive for Modern Vehicles
Abstract
1 Introduction
2 Methods
3 Research and Discussion
3.1 Data Collection
3.2 The Type of Power Plants Used
3.3 The Choice of the Power Plant
3.4 The Range of Powers of the Applied Power Plants by Classes
4 Conclusion
References
Automatic Extraction and Welding Feature Recognition from STEP Data
Abstract
1 Introduction
2 Methodology
2.1 Geometric Data Extraction from STEP File
2.2 Feature Recognition
2.3 Development of Module
2.4 Case Study
3 Result and Discussions
4 Conclusions
References
Characterization of Gelatin and PVA Nanofibers Fabricated Using Electrospinning Process
Abstract
1 Introduction
2 Nanospinning Process
3 Material Preparation and Methods
3.1 Polymer Solution Preparation
3.2 Nanospinning Process
4 Results and Discussion
4.1 Nanospun Fibers Fabrication
4.2 Dependence on Polymer Concentration
4.3 Nanofibers with Silver Nanoparticles
5 Conclusion
Acknowledgements
References
Choice of Selection Methods in Genetic Algorithms for Power System State Estimation
Abstract
1 Introduction
2 Power System State Estimation by GA
2.1 Formulation [1, 2, 19]
2.2 Application of GA for Power System State Estimation
3 Results
4 Conclusions
References
CollisionFree Path Following of an Autonomous Vehicle Using NMPC
Abstract
1 Introduction
2 Preliminaries
2.1 Bounded Convex Regions
2.2 Repulsive Potential Working Space
2.3 Hausdorff Distance
2.4 Inverse Activation Function
3 Safe Navigation and Path Following in NMPC Framework
3.1 Autonomous Surface Vessel Dynamic
3.2 Safe Navigation Through Repulsive Potential Activation
3.3 NMPC Implementation
4 Simulation Results
5 Conclusions
References
Compare the Efficiency of the Active Filter and Active Rectifier to Reduce Harmonics and Compensate the Reactive Power in Frequency Controlled Electric Drive Systems
Abstract
1 Introduction
2 Active Filter Design Method
2.1 Working Principle of Active Filter
2.2 The Required Compensation Current Determination Method
3 Active Rectifier Control Method
4 Results and Analysis
5 Conclusion
Acknowledgments
References
Comparing the Application of Gas Sensor Fabrication of Nanomaterials ZnO Fabricated by Hydrothermal and Chemical Vapor Deposition Method
Abstract
1 Introduction
2 Experimental
2.1 Hydrothermal Method
2.2 Chemical Vapor Deposition
2.3 Identification of Structural Morphology
2.4 Investigation of Gas Sensitivity Properties
3 Results and Discussion
3.1 Morphology and Structure of ZnO
3.2 Gas Sensitive Characteristics of ZnO Nanomaterials
4 Conclusions
References
Convergence Parameters for DType Learning Function
Abstract
1 Introduction
2 Main Results
2.1 Realization in Time Sequence
2.2 Optimal Realization
3 Illustrative Simulations
3.1 Simulation 1
3.2 Simulation 2
4 Conclusions
References
Current Harmonic Eliminations for SevenPhase Nonsinusoidal PMSM Drives applying Artificial Neurons
Abstract
1 Introduction
2 Modeling of a SevenPhase PMSM
3 Current Harmonic Analyses
4 Eliminations of Current Harmonics with ADALINEs
5 Experimental Results
6 Conclusion
Acknowledgment
References
Design and Some Experimental Results of the Robust Current Controller of DoublyFed Induction Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer
Abstract
1 Introduction
2 Design the Robust Controller of Doubly Fed Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer
2.1 Design the Robust Direct Current Controller of Doubly Fed Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer
2.2 Design the Robust Quadrature Current Controller of Doubly Fed Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer
3 Experimental Results
3.1 Experimental System
3.2 Experiment with the Robust Current Controller When Grid Voltage Is Collapsed with Remaining Voltage of 15% of Its Normal Value and the Time Lasts 8 Cycles, Ird = 1.5A, Irq = −2.7A
3.3 Experiment with the Current Controller Without Disturbance Observer When Grid Voltage Is Collapsed with Remaining Voltage of 15% of Its Normal Value and the Time Lasts 8 Cycles, Ird = 0A, Irq = − 2.7A
4 Conclusion
Acknowledgment
References
Design and Some Experimental Results of the UType Permanent Magnet ThreePhase Linear Motor Based Position Control System with the Backstepping Technique Based Disturbance Observer
Abstract
1 Introduction
2 Construction, the Working Principle and Mathematical Model of UPMLM
2.1 Construction of UPMLM
2.2 Working Principle of UPMLM
2.3 Mathematical Model of UPMLM
3 Design the UType Permanent Magnet ThreePhase Linear Motor Based Position Control System with the Backstepping Technique Based Disturbance Observer
3.1 The Overall Structure of the UPMLM’s Position Control System
3.2 The Overall Structure of the SPMLM’s Position Control System
3.3 Design the Disturbance ObserverBased Position Control System for SPMLM
4 Experiments and Discussion
4.1 DSP Based Experimental System for UPMLM
4.2 Results of Experiments
5 Conclusions
Acknowledgment
References
Detail Design of IPM Motor for Electric Power Traction Application
Abstract
1 Introduction
2 Specification of Electric Power Traction
3 Electromagnetic Performance Evaluation
4 Conclusion
Acknowledgment
References
Detecting Common Web Attacks Based on Machine Learning Using Web Log
Abstract
1 Introduction
2 Related Works
3 Proposed Detection Model for Common Web Attacks
3.1 Proposed Web Attack Detection Model
3.2 Experiments on HTTP Param Dataset
3.3 Experiments on Real Web Logs
4 Conclusion
References
Determination of Kinematic Control Parameters of Omnidirectional AGV Robot with Mecanum Wheels Track the Reference Trajectory and Velocity
Abstract
1 Introduction
2 Determination of Kinematic Control Parameters of Omnidirection AGV Robot with Mecanum Wheels
2.1 Establishment of Kinematic Equations of Omnidirectional AGV Robot with Mecanum Wheels
2.2 Determination of Kinematic Control Parameters by the Given Trajectories and Velocity
3 Design of Moving Trajectory of AGV Robots
4 Simulation Examples
5 Conclusion
Acknowledgement
References
Development of New Method for Choosing Standard Components Subject to Minimal Cycle Time and Minimal Sum of Purchasing Cost
Abstract
1 Introduction
1.1 Compensation of Cycle Time
1.2 Removal of the Bottleneck Station
2 Methods of Study
2.1 Describe the Operations of Assembly and Handling Function Carriers
2.2 Establish the Optimization Problem by Mathematical Formulas
3 Application Example
4 Conclusions
Acknowledgement
References
Dust Emission During Machining of CFRP Composite: A Calculation of the Number and Mass of the Thoracic Particles
Abstract
1 Introduction
2 Experimental Procedure
3 Results and Discussion
3.1 Influence of Cutting Parameter on the Number of Thoracic Particles
3.2 Influence of Cutting Parameter on the Mass of Thoracic Particles
4 Conclusions
Acknowledgments
References
Dynamic Surface Control of the AxialFlux Permanent Magnet Motor with Speed Sensorless Algorithm
Abstract
1 Introduction
2 Mathematical Model
3 Dynamic Surface Controls
4 HighGain Observer
5 Simulation and Result
6 Conclusion
References
EdgeBased Object Pose Estimation Using Differential Evolution Algorithm
Abstract
1 Introduction
2 Methodology
2.1 Chamfer Matching Maps from Query Images
2.2 Camera Model and Edges from CAD Model
2.3 Initial Pose Searching
3 Differential Evolution Algorithm
4 Experiment and Results
4.1 Experiment Setup
4.2 Results
5 Discussion and Conclusions
References
Effect of Changing Grounding Mode to Reduce Power Loss on Lightning Ground Wire by Induced Current  Northern Vietnam Overhead Power Transmission Line
Abstract
1 Introduction
2 Induced Current and Power Loss in Northern Vietnam Overhead Transmission Lines
2.1 Simulation the Induced Current and Power Loss in Lightning Ground Wires
2.2 Measurement of Induced Current on Lightning Gw’s
3 Impact Factors to Reduce the Power Loss on Lightning GW
3.1 Impact of Transposition and Changing the Number of Grounded Points
3.2 Impact of Sectionalization
4 Conclusion
References
Electromagnetic Design of Synchronous Reluctances Motors for Electric Traction Vehicle
Abstract
1 Introduction
2 Electromagnetic Performance Evaluation
3 Temperature and Mechanical Stress Verification
4 Conclusion
Acknowledgment
References
Enhancing Accuracy of Surface Roughness Model Using BoxCox Transformation in Surface Grinding AISI 5120 Alloy Steels
Abstract
1 Introduction
2 Grinding Test
2.1 Material Preparation
2.2 Machine Tool and Grinding Wheel
2.3 Design of Experiment
2.4 Roughness Tester
3 Results and Discussion
4 Developing Surface Roughness Model Using BoxCox Transformation
5 Conclusions
Acknowledgements
References
Ensemble of Deep Learning Models for InHospital Mortality Prediction
Abstract
1 Introduction
2 Related Works
3 Proposed Approach
3.1 MIMICIII Dataset
3.2 Feature Engineering
3.3 CNN Architecture
3.4 Training Deep Learning Models
3.5 Model Evaluation
3.6 Ensemble Models
4 Experiments and Discussions
5 Conclusion and Future Works
Acknowledgment
References
Evaluating the Impact of Demand Response in Planning Microgrids Considering Uncertainties
Abstract
1 Introduction
2 Planning Framework of Microgrids Integrated Demand Response
2.1 Structure of Microgrids
2.2 Demand Responses Modeling
2.3 Uncertain Parameters Modeling
2.4 Mathematics Model
3 Simulation Result and Discussion
3.1 Parameters and Assumptions of Test MicroGrid
3.2 Calculation Result
4 Conclusion
Acknowledgments
References
Evolutionary Tuning of PID Controllers for a Spatial CableDriven Parallel Robot
Abstract
1 Introduction
2 Dynamic of CableDriven Parallel Robots
3 PID Controller for a Spatial CableDriven Parallel Robot
4 The Objective Function for Optimization of PID Controllers
5 Implementation of EAs for PID Controller
6 Numerical Examples
7 Discussion
Acknowledgment
References
Experimental and Numerical Characterization of Mechanical Behavior for the Corrugated Cardboard
Abstract
1 Introduction
2 Experimental Characterization of the Behavior of Papers
2.1 Objective and Approach
2.2 Geometrical and Mechanical Characteristics of Papers
2.3 Behavior Law of ElastoPlastic Type for Papers of Cardboard
2.4 Inverse Identification of the Parameters of the Behavior Law for Papers of Corrugated Cardboard
3 Results and Discussion
4 Conclusions
Acknowledgments
References
Experimental and Numerical Investigations into Evaporation Rates of Some Fuels Utilized in Aviation Gas Turbine Engines
Abstract
1 Introduction
2 Mathematical and Experimental Models
2.1 Mathematical Modelling
2.2 Experimental Model
3 Results and Discussion
3.1 Model Prediction
3.2 Experimental Comparison
4 Conclusion
References
Experimental Evaluation of the Performance of OilBased Nanofluids in the Grinding of Ti6Al4V Alloy
Abstract
1 Introduction
2 Experimental Design
2.1 Experimental Materials
2.2 Experimental Equipment
3 Results and Discussion
3.1 Grinding Forces
3.2 Specific Grinding Energy
4 Conclusions
References
Fault Diagnosis for the ShortCircuit Fault of the Singlephase FiveLevel VIENNA Active Rectifier
Abstract
1 Introduction
2 ShortCircuit Fault in FiveLevel VIENNA Rectifier
3 Proposed Fault Tolerant Control Method and Diagnosis Algorithm Using Input Voltage
4 Simulation Results
5 Conclusion
Acknowledgment
References
Feedforward Based Dual Loop PI Controller for 400 Hz Ground Power Unit
Abstract
1 Introduction
2 Model of GPU
3 Feedforward Based Dual Loop PI Controller
3.1 The Design of Current Loop
3.2 The Design of Voltage Loop
4 Case Study
4.1 The Proposed GPU
4.2 The Simulation Results
4.3 The Experiment Results
5 Conclusion
Acknowledgements
References
ForceVelocity Relation of Dampers in Horizontal Washing Machines
Abstract
1 Introduction
2 Force  Velocity Relation of Dampers
3 Application of the F  V Relation of Dampers for the HWM’ Vibration Model
4 Conclusion
Acknowledgments
References
Gear Fault Classification Using the Vibration Signal Decomposition and Neural Networks
Abstract
1 Introduction
2 Vibration Signal Decomposition and Neural Networks
3 Experimental Demonstration
4 Conclusions
References
Genetic Algorithm Based Optimization of Cutting Parameters in CO2 Laser Beam Cutting of Cow Leather
Abstract
1 Introduction
2 Experiments and Methods
3 Results and Discussion
4 Conclusion
References
Influences of Cutting Parameters on Surface Roughness During Milling and Development of Roughness Model Using Johnson Transformation
Abstract
1 Introduction
2 Milling Process
2.1 Material Preparation
2.2 Experimental Set up and Measurement
2.3 Design of Experiment
3 Results and Discussion
3.1 Developing Prediction Model of Surface Roughness Using Response Surface Method
3.2 Proposing the Model to Predict Surface Roughness Using Johnson Transformation
3.3 Analysis of Model Errors
4 Conclusions
Acknowledgements
References
Influence of Random Fiber Length on Macroscopic Properties of Short Fiber Reinforced Composites Due to Microscopic Physical Uncertainty
Abstract
1 Introduction
2 Stochastic Homogenization Method
3 Numerical Results
3.1 Random Short Fiber Reinforced Plastic RVE Model
3.2 Stochastic Results
4 Conclusions
Acknowledgement
References
Kinematic Analysis of the Class 2 DegreeofFreedom Planar Parallel Mechanism via GRG2 Algorithm
Abstract
1 Introduction
2 Optimization Kinematic of the Class 2Dof PPM with Rotation and Prismatic Joints
2.1 Solving the IKP Problem of 5Bar Mechanism by GRG2 Optimization
2.2 Solving the FKP Problem of 5Bar Mechanism by GRG2 Optimization
3 Simulation
4 Experimental Validation
5 Conclusion
Acknowledgements
References
Material Removal Rate in Electric Discharge Machining with Aluminum Tool Electrode for Ti6Al4V Titanium Alloy
Abstract
1 Introduction
2 Experimental Methodology
3 Experimental Results and Discussion
4 Conclusion
Acknowledgements
References
Mathematical Modelling of Thermoacoustic Generator Systems and Simulation Studies
Abstract
1 Introduction
2 Mathematical Modelling of Thermoacoustic Generator (TAG) Systems
2.1 Physical Principle
2.2 Mathematical Modelling
3 Simulation Results
4 Conclusions
References
Measurement Setup for TemperatureDependent Electrical Property of ZnOBased Thermoelectric Thin Films
Abstract
1 Experimental Procedures
2 Results and Discussion
3 Conclusions
Acknowledgement
References
Modified QLearning Algorithm with Lifting Method for DiscreteTime Linear Periodic Systems
Abstract
1 Introduction
2 Problem Formulation and Preliminaries
2.1 Optimal Control Scheme for a DiscreteTime Linear Periodic Systems
2.2 Lifting Method
3 Modified QLearning Algorithm
3.1 Background
3.2 QFunction
3.3 Modified QLearning Algorithm
4 Simulation Results
5 Conclusion
Acknowledgements
References
Multi Response Optimization of Dressing Conditions for Surface Grinding SKD11 Steel by HaiDuong Grinding Wheel Using Grey Relational Analysis in Taguchi Method
Abstract
1 Introduction
2 Experimental Setup and Research Methodology
2.1 Experimental System
2.2 Experimental Design
3 Optimization Steps Using Grey Relational Analysis for S/N Ratio
4 Conclusions
Acknowledgements
References
Multiobjective Optimization of Process Parameters During Electrical Discharge Machining of Hardened 90CrSi Steel by Applying Taguchi Technique with Grey Relational Analysis
Abstract
1 Introduction
2 Experimental Preparation
2.1 Design of Experiments
2.2 Materials and Specimens
3 Grey Relational Analysis and Discussion on Experiment Results
4 Conclusions
Acknowledgements
References
Multiobjective Optimization of Surface Roughness and MRR in Surface Grinding of Hardened SKD11 Using GreyBased Taguchi Method
Abstract
1 Introduction
2 Experimental Procedures
3 Results and Discussion
4 Conclusions
Acknowledgements
References
Multiresponse Optimization in PMSEDM Process Using TaguchiGrey Method
Abstract
1 Introduction
2 Methodology
2.1 Experimental Setup
2.2 Design of Experiment
2.3 Multicriteria Optimization
3 Results and Discussion
3.1 Normalization of S/N Ratio and Computation of Grey Relational Grade
3.1.1 Normalization of S/N Ratio of Experimental Results for All Output Responses
3.1.2 Calculation of Grey Relational Coefficient
3.1.3 Calculation of Grey Relational Grade
3.2 Determining Optimum Input Parameters
3.3 Analysis of Variance (ANOVA)
3.4 Determining Predict Model
3.5 Evaluating the Predict Model
4 Conclusions
Acknowledgments
References
Numerical Identification of the Mechanical Behaviour of a Fluoroelastomer (FKM) Using Nanoindentation Test
Abstract
1 Introduction
2 Material
3 Experimental Nanoindentation Tests
4 Numerical Models
5 Comparison of the Numerical and Experimental Results
6 Conclusion
References
On RoomTemperature Electrodeposition of Cobalt from a Deep Eutectic Solvent: A Study of Electronucleation and Growth Mechanisms
Abstract
1 Introduction
2 Materials and Methods
2.1 Electrolyte Preparation
2.2 Electrochemical Experiments and Surface Characterization
3 Results and Discussion
3.1 Potentiodynamic Study
3.2 Nucleation and Growth Mechanisms of Co Electrodeposition
4 Conclusions
Acknowledgement
References
Optimal Design of Cab’s Isolation System for a SingleDrum Vibratory Roller
Abstract
1 Introduction
2 HalfVehicle Ride Dynamic Model
3 Optimization via Genetic Algorithm and Objective Function
4 Simulation and Discussion
5 Conclusions
Acknowledgment
References
Optimization of Cutting Parameters and Nanoparticle Concentration in Hard Milling for Surface Roughness of JIS SKD61 Steel Using Linear Regression and Taguchi Method
Abstract
1 Introduction
2 Experiment Setup
3 Results and Discussions
4 Conclusion
Acknowledgments
References
Optimization of Dressing Parameters in Surface Grinding SKD11 Tool Steel by Using Taguchi Method
Abstract
1 Introduction
2 Methodology
2.1 Experimental Machine and Equipment
2.2 Design of Experiment
3 Results and Discussion
3.1 Determination the Influence of Dressing Parameters
3.2 Determination the Influence of Dressing Parameters
3.3 Determination of Optimal Normal Cutting Force Value
3.4 Determining Confidence Interval
3.5 Determining the Influence of Dressing Parameters
4 Conclusions
Acknowledgements
References
Optimization of PMEDM Parameters for Improving MMR in Machining 90CrSi Steel  A Taguchi Approach
Abstract
1 Introduction
2 Experimental Procedure
3 Results and Discussions
4 Conclusion
Acknowledgments
References
Overshoot and Settling Time Assignment for SecondOrder Systems with Time Delay
Abstract
1 Introduction
2 Main Results
2.1 Proposed Filter PID Tuning Method for SecondOrder Systems
2.2 Proposed Controller for SecondOrder Systems with Time Delay
3 Numerical Simulations
4 Conclusions
References
Performance Ratio Analysis Using Experimental Combining Historical Weather Data for GridConnected PV Systems
Abstract
1 Introduction
2 Methods and Case Description
2.1 System Description
2.2 The Weather Data
2.3 Calculation Method
3 Result and Discussion
3.1 Reference Yield
3.2 Performance Ratio of GCPVS on a Sunny Day
3.3 Performance Ratio of GCPVS on a Scattered Cloudy Day
3.4 Performance Ratio of GCPVS on a Rainy Day
4 Conclusion
References
Power Control of AndronovHopf Oscillator Based Distributed Generation in GridConnected Microgrids
Abstract
1 Introduction
2 Analysis of the GridConnected AHOBased DG
2.1 AHO Control Model
2.2 Power Tracking Problem Determination
2.3 Proposed Simultaneous Power Tracking Control
3 Simulation Results and Discussion
4 Conclusion
Acknowledgement
References
Prediction of Cutting Force When Surface Milling Using Face Milling Tool
Abstract
1 Introduction
2 Cutting Force Model
3 Comparison of Cutting Force When Calculating, Testing and Predicting Using Regression Model
4 Conclusion
Acknowledgements
References
Research Method for Calculating Additional Power Losses, Considering the Asymmetric Loads in the LowVoltage Power Supply System Vietnam
Abstract
1 Introduction
2 Research Methods
3 Research Results and Discussion
4 Conclusions
References
Research to Improve the Quality Control for Drive System Tracking Electromechanical Takes into Account Nonlinear Undetermined Application in Industrial Production
Abstract
1 Introduction
2 The Model Drive System Tracking Electromechanical
2.1 The Building Speed Controller Model
2.2 The Design of Load Torque Observer
3 The Simulation and Experimental
3.1 The Simulation
3.2 The Experimental
4 Conclusions
References
Role of Electrolyte Media in the Exfoliation of MoS2 Nanosheets by Electrolysis PlasmaInduced Method
Abstract
1 Introduction
2 Results and Discussions
3 Conclusions
Acknowledgement
References
Simulated Annealing Algorithm for Modeling Large Deflection of Flexible Links in Complaint Mechanisms
Abstract
1 Introduction
2 Methodology for Modeling Large Deflection
3 Determination of Characteristic Radius Parameters and Stiffness Coefficients
4 Simulated Annealing for Solving Parameters
4.1 Simulated Annealing Algorithm
4.2 Simulated Annealing Algorithm for Solving Optimal Parameters
5 Results and Discussion
6 Conclusion
Acknowledgement
References
Study on Thermal Convective Gas Gyroscope Based on Corona Discharge Ion Wind and Coriolis Effect
Abstract
1 Introduction
2 Design and Working Principle
3 Modeling and Simulation
4 Result and Discussion
4.1 Simulation Result
4.2 Experimental Investigation
5 Conclusions
Acknowledgement
References
Studying Electron Transport Coefficients in C2H4SiH4 Mixtures Using Bolsig+ Program
Abstract
1 Introduction
2 Analysis
3 Results and Discussion
4 Conclusions
References
Synthesis of Automatic Motion Control Systems of an AUV Based on Fuzzy Logic Methods with Neural Network Setting of Parameters
Abstract
1 Introduction
2 Nonlinear Dynamic Model of the AUV
3 Algorithm of Motion Control of the AUV Based on Fuzzy Logic Methods with Neural Network Setting of Parameters
4 Configuration of the Control Algorithm
5 Simulation of the Algorithm of Neural Network Setting of Parameters
6 Conclusion
Acknowledgement
References
TaguchiDEAR Based MCDM Approach on Machining Titanium Alloy in AWJM Process
Abstract
1 Introduction
2 Experiments and Methods
3 Results and Discussion
4 Conclusion
References
The Characterization of Machined Damage of CFRP Composite: Comparison of 2D and 3D Surface Roughness Performance
Abstract
1 Introduction
2 Experimental Procedure
3 Results and Discussion
3.1 Damage and Characterization
3.2 Correlating 2D and 3D Surface Roughness to Compressive Strength
4 Conclusion
Acknowledgement
References
The Dimensional Synthesis of the FourBar Mechanism with a Symbiotic Organisms Search Algorithm
Abstract
1 Introduction
2 Problem Statement
2.1 Vector Loop Equation of FourBar Linkage
2.2 Objective Function
3 Symbiotic Organisms Search
3.1 Mutualism Phase
3.2 Commensalism Phase
3.3 Parasitism Phase
4 Numerical Examples
4.1 Case 1
4.2 Case 2
4.3 Case 3
5 Discussion
6 Conclusions
Acknowledgments
References
The Effect of Bonnet Skin and Bonnet Reinforcement Thickness on Pedestrian Head Injuries in Collisions
Abstract
1 Introduction
2 Pedestrian Headform Impactor to Bonnet Top Tests
2.1 Testing Procedure
2.2 Testing Simulations
3 Results and Discussions
4 Conclusions and Recommendations
References
The Effect of the Wheel Rotation Angle on the Braking Efficiency of the Tractor Semitrailer on the Wet Roundabout Route
Abstract
1 The 3D Dynamic Model
1.1 Dynamic Equations of the Tractor Semitrailer in the Plane (XOY)
1.2 Dynamic Equations of the Tractor Semitrailer in the Plane (XOZ)
1.3 Dynamic Equations of the Tractor Semitrailer in the Plane (YOZ)
1.4 Dynamic Equations of the Wheels
2 Investigation on the Braking Efficiency
3 Conclusions
References
The Effect of Welding Speed on the Mechanical Properties of the FSW Cu/Al
Abstract
1 Introduction
2 Experimental Procedures
3 Results and Discussion
4 Conclusions
Acknowledgment
References
The New Method to Control Twin Rotor MIMO System (TRMS)
Abstract
1 Introduction
2 Hedge Algebras and Fuzzy Logic [1]
3 Twin Rotor MIMO System (TRMS)
4 Conclusion
Acknowledgement
References
Theoretical and Experimental Study of Sound Transmission Loss Across Finite Clamped Composite Sandwich Plates
Abstract
1 Introduction
2 Theoretical Study
2.1 Geometry and Assumptions
2.2 Flexural Motion of the Plate
2.3 Boundary Conditions of the PlatePorous Coupling
3 Modeling the Poroelastic Material
4 Determination of Sound Transmission Loss
5 Validation
6 Numerical and Experimental Results
6.1 Test Setup
6.2 Results and Discussions
7 Conclusions
Acknowledgments
References
Tool Wear Rate Analysis of Uncoated and AlCrNi Coated Aluminum Electrode in EDM for Ti6Al4 V Titanium Alloy
Abstract
1 Introduction
2 Experimental Details
3 Results and Discussion
4 Conclusion
Acknowledgements
References
Tracking Control of Directed Acyclic Formation via Target Point Localization
Abstract
1 Introduction
2 Problem Formulation
3 Proposed Control Laws and Stability Analysis
3.1 Formation in TwoDimensional Space ( {\varvec d} = 2 )
3.2 Formation in ThreeDimensional Space ( {\varvec d} = 3 )
4 Simulation Results
5 Conclusion
Acknowledgments
References
Tracking Control of Rostock Delta Parallel Robot Based on the Dynamic Model
Abstract
1 Introduction
2 Dynamic Model of Rostock Delta Robot
3 PD Control Law for Tracking Control
4 Numerical Simulation of the Tracking Control for Rostock Delta Robot by PD Control Method
5 Conclusion
References
Trajectory Tracking Control of a Caterpillar Vehicle
Abstract
1 Introduction
2 System Modeling
3 Tracking Controller Design
4 Simulation Results
5 Conclusion
Acknowledgement
References
Truss Optimization Under Frequency Constraints by Using a Combined Differential Evolution and Jaya Algorithm
Abstract
1 Introduction
2 Problem Statement
3 Optimization Algorithm
3.1 Differential Evolution Algorithm
3.2 Jaya Algorithm
3.3 Combined Differential Evolution and Jaya Algorithm
4 Numerical Examples
4.1 Size Optimization
4.2 Shape and Size Optimization
5 Conclusion
Acknowledgment
References
Web Tension Observer Based Control for SingleSpan Roll to Roll Systems
Abstract
1 Introduction
2 Tension Observer Base on Backstepping Controller
2.1 Modeling of R2R with Single Span
2.2 Controller Design
2.3 Web Tension Observer
3 Simulation Results
4 Conclusion
Acknowledgement
References
Author Index
Lecture Notes in Networks and Systems 178
KaiUwe Sattler · Duy Cuong Nguyen · Ngoc Pi Vu · Banh Tien Long · Horst Puta Editors
Advances in Engineering Research and Application Proceedings of the International Conference on Engineering Research and Applications, ICERA 2020
Lecture Notes in Networks and Systems Volume 178
Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA; Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
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KaiUwe Sattler Duy Cuong Nguyen Ngoc Pi Vu Banh Tien Long Horst Puta •
•
•
•
Editors
Advances in Engineering Research and Application Proceedings of the International Conference on Engineering Research and Applications, ICERA 2020
123
Editors KaiUwe Sattler Department of Computer Science and Automation Ilmenau University of Technology (IUT) Ilmenau, Germany Ngoc Pi Vu Faculty of Mechanical Engineering Thai Nguyen University of Technology Thai Nguyen, Vietnam
Duy Cuong Nguyen Faculty of Electronic Engineering Thai Nguyen University of Technology Thai Nguyen, Vietnam Banh Tien Long Hanoi University of Science and Technology Hanoi, Vietnam
Horst Puta Institute for Automation and Systems Engineering Ilmenau University of Technology (IUT) Ilmenau, Germany
ISSN 23673370 ISSN 23673389 (electronic) Lecture Notes in Networks and Systems ISBN 9783030647186 ISBN 9783030647193 (eBook) https://doi.org/10.1007/9783030647193 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Contents
Keynote Addresses Hardware Acceleration of Modern Data Management . . . . . . . . . . . . . . KaiUwe Sattler Electric Vehicle Development and LowCarbon Transport in Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Le Anh Tuan
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ICERA 2020 Main Track A CommonGround SinglePhase Boost Inverter with Suppressed DoubleFrequency Ripple for Photovoltaic Applications . . . . . . . . . . . . . MinhDuc Ngo, QuynhVan Nong, ThuyNgan Ngo, HongQuang Nguyen, TanTai Tran, and SeonJu Ahn A High Stepup DCDC Converter with Semiconductor Voltage Stress Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HongQuang Nguyen, NgocAnh Tran, VanNghiep Dinh, VinhThuy Nguyen, MinhDuc Ngo, and JoonHo Choi A Method to Partition Accuracy in Workspace for Robot Arms . . . . . . HuuThang Nguyen, Long Pham Thanh, and JenTzong Jeng A Novel Method for Shielding Problems with Taking Robust Correction Procedure into Account . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vuong Dang Quoc and Dinh Bui Minh A Review on Ultrasonic Stack Modelling . . . . . . . . . . . . . . . . . . . . . . . . Ngo Nhu Khoa, Nguyen Thi Bich Ngoc, and Tran Duc Tai A Solution to Power Load Distribution Based on Enhancing Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TruongGiang Ngo, ThiThanh Tan Nguyen, ThiXuan Huong Nguyen, TrinhDong Nguyen, VanChieu Do, and TrongThe Nguyen
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A Study for Determination of the Pressure Ratio of the V12 Diesel Engine Based on the Heat Flow Density to Cooling Water . . . . . . . . . . Kien.Nguyen Trung
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A Study of Scissor Lifts Using Parameter Design . . . . . . . . . . . . . . . . . . AnhTuan Dang, DinhNgoc Nguyen, and DangHao Nguyen
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A Study on Prediction of Milling Forces . . . . . . . . . . . . . . . . . . . . . . . . Do Duc Trung, Tran Ngoc Giang, Tran Thi Hong, Bui Thanh Danh, Vu Van Khoa, Nguyen Dinh Ngoc, Nguyen Thanh Tu, and Vu Ngoc Pi
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A Study on Qualitative and Quantitative Characterization of Machining Quality of Aerospace Composite Structures . . . . . . . . . . . Nguyen Dinh Ngoc and Nguyen Thi Hue
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A Study on Prediction of Grinding Surface Roughness . . . . . . . . . . . . . 102 Do Duc Trung, Nhu Tung Nguyen, Hoang Tien Dung, Nguyen Van Thien, Tran Thi Hong, Tran Ngoc Giang, Nguyen Thanh Tu, and Le Xuan Hung A VisionBased Measurement and Classiﬁcation System for Robot Arm Under Controlled Lighting Condition . . . . . . . . . . . . . . . . . . . . . . 112 QuangCherng Hsu, NgocVu Ngo, ThanhLong Pham, QuocKhanh Duong, and DucVuong Vu About a Viewpoint of Calculating Spatial Dimensional Tolerance Chains According to Structure Group of a Parallel Robot . . . . . . . . . . . 120 Thuy Le Thi Thu, Trung Trang Thanh, HuuThang Nguyen, and Long Pham Thanh Adaptive Algorithm for Servo System Using Linear Electric Motor . . . 133 V. E. Kuznetsov, Phan Thanh Chung, Nguyen Thi Ha, and Nguyen Hoang Ha Adaptive Sliding Mode Control for a 2DOF Robot Arm in Case of Actuator Faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Le Ngoc Truc and Nguyen Phung Quang An EnergyEfﬁcient Combination of Sleeping Schedule and Cognitive Radio in Wireless Sensor Networks Utilizing Compressed Sensing . . . . 154 Minh T. Nguyen, Thuong T. K. Nguyen, and Keith A. Teague An Enhancing Grasshopper Optimization for Efﬁcient Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 TrongThe Nguyen, ShiJie Jiang, ThiKien Dao, TruongGiang Ngo, ThiThanhTan Nguyen, and TheVinh Do An Evaluation of BSpline for Synthesis of Cam Motion with a Large Number of Output Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Nguyen Thi Thanh Nga, Nguyen VanSy, Nguyen Thi Bich Ngoc, and Vu Thi Lien
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An Experimental Study on VibrationDriven Locomotion Systems Under Different Levels of Isotropic Friction . . . . . . . . . . . . . . . . . . . . . . 181 NgocTuan La, QuocHuy Ngo, KyThanh Ho, and KhacTuan Nguyen Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 MinhQuang Tran, MengKun Liu, and QuocViet Tran Analytical Study of the Power Parameters of Electric Traction Drive for Modern Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Aleksey Kolbasov, Kirill Karpukhin, Dmitry Sheptunov, Povalyaev Andrey, Nguyen Khac Tuan, and Nguyen Khac Minh Automatic Extraction and Welding Feature Recognition from STEP Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 Lan Phung Xuan and Linh Tao Ngoc Characterization of Gelatin and PVA Nanoﬁbers Fabricated Using Electrospinning Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 Cuong Nguyen Nhu, Nhung Vu Thi, Nam Nguyen Hoang, Thao Pham Ngoc, Trinh Chu Duc, Van Thanh Dau, and Tung Bui Thanh Choice of Selection Methods in Genetic Algorithms for Power System State Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 ThanhSon Tran and ThiThanhHoa Kieu CollisionFree Path Following of an Autonomous Vehicle Using NMPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 NgoQuocHuy Tran, Ionela Prodan, and NguyenDuyMinh Phan Compare the Efﬁciency of the Active Filter and Active Rectiﬁer to Reduce Harmonics and Compensate the Reactive Power in Frequency Controlled Electric Drive Systems . . . . . . . . . . . . . . . . . . 242 Le Van Tung, Pham Thanh Long, Ngo Van An, and Bogdan Vasilev Comparing the Application of Gas Sensor Fabrication of Nanomaterials ZnO Fabricated by Hydrothermal and Chemical Vapor Deposition Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Hoang Van Han, Dao Huy Du, and Do Anh Tuan Convergence Parameters for DType Learning Function . . . . . . . . . . . . 262 Cao Thanh Trung, Nguyen Thu Ha, Tran Kim Quyen, and Nguyen Doan Phuoc Current Harmonic Eliminations for SevenPhase Nonsinusoidal PMSM Drives applying Artiﬁcial Neurons . . . . . . . . . . . . . . . . . . . . . . . 270 Duc Tan Vu, Ngac Ky Nguyen, Eric Semail, and Thi Thanh Nga Nguyen
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Design and Some Experimental Results of the Robust Current Controller of DoublyFed Induction Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer . . . . . . . 280 C. X. Tuyen and N. T. Huong Design and Some Experimental Results of the UType Permanent Magnet ThreePhase Linear Motor Based Position Control System with the Backstepping Technique Based Disturbance Observer . . . . . . . 291 C. X. Tuyen and N. T. Huong Detail Design of IPM Motor for Electric Power Traction Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Bui Minh Dinh and Dang Quoc Vuong Detecting Common Web Attacks Based on Machine Learning Using Web Log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Xuan Dau Hoang Determination of Kinematic Control Parameters of Omnidirectional AGV Robot with Mecanum Wheels Track the Reference Trajectory and Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Trinh Thi Khanh Ly, Nguyen Hong Thai, Le Quoc Dzung, and Nguyen Thi Thanh Development of New Method for Choosing Standard Components Subject to Minimal Cycle Time and Minimal Sum of Purchasing Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Tan Nguyen Dang and Manh Cuong Nguyen Dust Emission During Machining of CFRP Composite: A Calculation of the Number and Mass of the Thoracic Particles . . . . . 341 Dinh Nguyen Ngoc, Thi Nguyen Hue, Bui Van Hung, and Vu Duy Duc Dynamic Surface Control of the AxialFlux Permanent Magnet Motor with Speed Sensorless Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 Manh Tung Ngo, Quang Dang Pham, Huy Phuong Nguyen, and Tung Lam Nguyen EdgeBased Object Pose Estimation Using Differential Evolution Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Ngoc Linh Tao and Lan Phung Xuan Effect of Changing Grounding Mode to Reduce Power Loss on Lightning Ground Wire by Induced Current  Northern Vietnam Overhead Power Transmission Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 Nhat Tung Nguyen and Xuan Phuc Nguyen Electromagnetic Design of Synchronous Reluctances Motors for Electric Traction Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Bui Minh Dinh, Do Trong Tan, and Dang Quoc Vuong
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Enhancing Accuracy of Surface Roughness Model Using BoxCox Transformation in Surface Grinding AISI 5120 Alloy Steels . . . . . . . . . 379 Do Duc Trung, Nguyen Dinh Ngoc, Tran Thi Hong, Bui Thanh Danh, Nguyen Thanh Tu, Tran Ngoc Giang, Nguyen Thi Quoc Dung, and Vu Ngoc Pi Ensemble of Deep Learning Models for InHospital Mortality Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Quang H. Nguyen and Quang V. Le Evaluating the Impact of Demand Response in Planning Microgrids Considering Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 V. V. Thang and N. H. Trung Evolutionary Tuning of PID Controllers for a Spatial CableDriven Parallel Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Sy NguyenVan, Diem Thi Thu Thuy, Nga Nguyen Thi Thanh, and Ngoc Nguyen Dinh Experimental and Numerical Characterization of Mechanical Behavior for the Corrugated Cardboard . . . . . . . . . . . . . . . . . . . . . . . . 425 Duong Pham Tuong Minh, Dao Lien Tien, and Nguyen Quang Hung Experimental and Numerical Investigations into Evaporation Rates of Some Fuels Utilized in Aviation Gas Turbine Engines . . . . . . . . . . . . 434 Nam V. T. Pham, Kien T. Nguyen, Thin V. Pham, and Phuong X. Pham Experimental Evaluation of the Performance of OilBased Nanoﬂuids in the Grinding of Ti6Al4V Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 Trung Kien Nguyen, Hung Trong Phi, Got Van Hoang, Tam Ngoc Bui, and Son Hoanh Truong Fault Diagnosis for the ShortCircuit Fault of the Singlephase FiveLevel VIENNA Active Rectiﬁer . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 Pham Thi Thuy Linh, Nguyen Ngoc Bach, and Doan Van Binh Feedforward Based Dual Loop PI Controller for 400 Hz Ground Power Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 Son Tran Que, Dich Nguyen Quang, Minh Y. Nguyen, Quy Do Ngoc, and Phu Do Ba ForceVelocity Relation of Dampers in Horizontal Washing Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Nguyen Thi Hoa and Ngo Nhu Khoa Gear Fault Classiﬁcation Using the Vibration Signal Decomposition and Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478 Nguyen Trong Du and Nguyen Phong Dien
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Genetic Algorithm Based Optimization of Cutting Parameters in CO2 Laser Beam Cutting of Cow Leather . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Thangaraj Muthuramalingam, Swaminathan Vasanth, Sanjeev Gupta, and Vu Ngoc Pi Inﬂuences of Cutting Parameters on Surface Roughness During Milling and Development of Roughness Model Using Johnson Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 Do Duc Trung, Nguyen Dinh Ngoc, Tran Thi Hong, Vu Van Khoa, Nguyen Thanh Tu, Tran Ngoc Giang, Nguyen Thi Quoc Dung, and Vu Ngoc Pi Inﬂuence of Random Fiber Length on Macroscopic Properties of Short Fiber Reinforced Composites Due to Microscopic Physical Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 TienDat Hoang, NhuKhoa Ngo, Dinh NgocNguyen, VanTruong Nguyen, Tuong Minh Duong Pham, Thi Thanh Nga Nguyen, and Viet Dung Luong Kinematic Analysis of the Class 2 DegreeofFreedom Planar Parallel Mechanism via GRG2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 ThanhTrung Trang, Yueming Hu, Weiguang Li, ThanhLong Pham, TuanAnh Nguyen, and ThiThuThuy Le Material Removal Rate in Electric Discharge Machining with Aluminum Tool Electrode for Ti6Al4V Titanium Alloy . . . . . . . . . . . . 527 Nguyen Huu Phan, Vu Ngoc Pi, Shailesh Shirguppikar, M. S. Patil, Mohsen Asghari Ilani, Le Xuan Hung, T. Muthuramalingam, and Tran Quoc Hung Mathematical Modelling of Thermoacoustic Generator Systems and Simulation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534 Trong Tan Do, Duy Tung Le, Phuong Nam Dao, Minh Dinh Bui, and Huy Du Dao Measurement Setup for TemperatureDependent Electrical Property of ZnOBased Thermoelectric Thin Films . . . . . . . . . . . . . . . . . . . . . . . 541 Trinh Quang Thong, Nguyen Anh Minh, Nguyen Trong Tinh, Trieu Viet Phuong, and Dao Huy Du Modiﬁed QLearning Algorithm with Lifting Method for DiscreteTime Linear Periodic Systems . . . . . . . . . . . . . . . . . . . . . . . 548 Ngoc Trung Dang, Tien Hoang Nguyen, and Phuong Nam Dao Multi Response Optimization of Dressing Conditions for Surface Grinding SKD11 Steel by HaiDuong Grinding Wheel Using Grey Relational Analysis in Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . 560 Tran Thi Hong, Ngo Ngoc Vu, Nguyen Huu Phan, Tran Ngoc Giang, Nguyen Thanh Tu, Le Xuan Hung, Bui Thanh Danh, and Luu Anh Tung
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Multiobjective Optimization of Process Parameters During Electrical Discharge Machining of Hardened 90CrSi Steel by Applying Taguchi Technique with Grey Relational Analysis . . . . . . . . . . . . . . . . . . . . . . . . 572 Tran Thi Hong, Nguyen Manh Cuong, Nguyen Dinh Ngoc, Luu Anh Tung, Tran Ngoc Giang, Le Thu Quy, Nguyen Thanh Tu, and Do Thi Tam Multiobjective Optimization of Surface Roughness and MRR in Surface Grinding of Hardened SKD11 Using GreyBased Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584 Tran Thi Hong, Do The Vinh, Tran Vinh Hung, Tran Ngoc Giang, Nguyen Thanh Tu, Le Xuan Hung, Bui Thanh Danh, and Luu Anh Tung Multiresponse Optimization in PMSEDM Process Using TaguchiGrey Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594 Tran Thi Hong, Nguyen Manh Cuong, Tran Ngoc Giang, Nguyen Anh Tuan, Le Thu Quy, Thangaraj Muthuramalingam, Nguyen Thanh Tu, and Do Thi Tam Numerical Identiﬁcation of the Mechanical Behaviour of a Fluoroelastomer (FKM) Using Nanoindentation Test . . . . . . . . . . . 607 Florent Chalon, Julie Pepin, Nathan Le Pennec, TienDung Do, Stéphane Meo, Clémence Fradet, Gaelle Berton, and Florian Lacroix On RoomTemperature Electrodeposition of Cobalt from a Deep Eutectic Solvent: A Study of Electronucleation and Growth Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 Thao Dao Vu Phuong, Hoang Thi Thanh Thuy, Phuong Dinh Tam, and Tu Le Manh Optimal Design of Cab’s Isolation System for a SingleDrum Vibratory Roller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619 Le Van Quynh, Nguyen Tien Duy, Nguyen Van Liem, Bui Van Cuong, and Le Xuan Long Optimization of Cutting Parameters and Nanoparticle Concentration in Hard Milling for Surface Roughness of JIS SKD61 Steel Using Linear Regression and Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . 628 ThanhDat Phan, TheVinh Do, ThanhLong Pham, and HuongLam Duong Optimization of Dressing Parameters in Surface Grinding SKD11 Tool Steel by Using Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 636 Tran Thi Hong, Nguyen Thanh Tu, Nguyen Anh Tuan, Tran Ngoc Giang, Nguyen Thi Quoc Dung, Le Xuan Hung, Bui Thanh Danh, and Luu Anh Tung
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Optimization of PMEDM Parameters for Improving MMR in Machining 90CrSi Steel  A Taguchi Approach . . . . . . . . . . . . . . . . . . . 648 Tran Thi Hong, Do Thi Tam, Do The Vinh, Luu Anh Tung, Le Thu Quy, Thangaraj Muthuramalingam, Vu Ngoc Pi, and Nguyen Manh Cuong Overshoot and Settling Time Assignment for SecondOrder Systems with Time Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 Nam Hoai Nguyen and Phuoc Doan Nguyen Performance Ratio Analysis Using Experimental Combining Historical Weather Data for GridConnected PV Systems . . . . . . . . . . . 665 Ngo Xuan Cuong, Nguyen Thi Hong, Do Anh Tuan, and Do Nhu Y Power Control of AndronovHopf Oscillator Based Distributed Generation in GridConnected Microgrids . . . . . . . . . . . . . . . . . . . . . . . 675 Tobias Heins, Trung Tran, David Raisz, and Antonello Monti Prediction of Cutting Force When Surface Milling Using Face Milling Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 688 Nguyen Van Thien, Do Duc Trung, Vilaivanh Xaixavang, Tran Thi Hong, Nguyen Thanh Tu, Tran Ngoc Giang, and Le Xuan Hung Research Method for Calculating Additional Power Losses, Considering the Asymmetric Loads in the LowVoltage Power Supply System Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 Pham Trung Son Research to Improve the Quality Control for Drive System Tracking Electromechanical Takes into Account Nonlinear Undetermined Application in Industrial Production . . . . . . . . . . . . . . . . . . . . . . . . . . . 708 Tran Duc Chuyen, Do Quang Hiep, and Dao Huy Du Role of Electrolyte Media in the Exfoliation of MoS2 Nanosheets by Electrolysis PlasmaInduced Method . . . . . . . . . . . . . . . . . . . . . . . . . 724 VanTruong Nguyen, TienDat Hoang, Nguyen Thi Kim Ngan, Pham Minh Tan, and Dang Van Thanh Simulated Annealing Algorithm for Modeling Large Deﬂection of Flexible Links in Complaint Mechanisms . . . . . . . . . . . . . . . . . . . . . . 729 Nguyen Thi Thanh Nga, Nguyen Thi Bich Ngoc, Nguyen VanSy, Nguyen DinhNgoc, Nguyen QuangHung, and Hoang Tien Dat Study on Thermal Convective Gas Gyroscope Based on Corona Discharge Ion Wind and Coriolis Effect . . . . . . . . . . . . . . . . . . . . . . . . . 741 Hang Nguyen Thu, Ngoc Tran Van, Cuong Nguyen Nhu, Van Thanh Dau, An Nguyen Ngoc, Trinh Chu Duc, and Tung Thanh Bui Studying Electron Transport Coefﬁcients in C2H4SiH4 Mixtures Using Bolsig+ Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 748 Pham Xuan Hien, Tran Thanh Son, and Do Anh Tuan
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Synthesis of Automatic Motion Control Systems of an AUV Based on Fuzzy Logic Methods with Neural Network Setting of Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755 Van Tuan Pham and Thi Ha Nguyen TaguchiDEAR Based MCDM Approach on Machining Titanium Alloy in AWJM Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764 T. Muthuramalingam, Vu Ngoc Pi, and Ammar H. Elsheikh The Characterization of Machined Damage of CFRP Composite: Comparison of 2D and 3D Surface Roughness Performance . . . . . . . . . 771 Nguyen Dinh Ngoc, Duong Pham Tuong Minh, Nguyen Van Sy, Luong Viet Dung, Nguyen Thi Thanh Nga, Nguyen Dang Hao, and Hoang Tien Dat The Dimensional Synthesis of the FourBar Mechanism with a Symbiotic Organisms Search Algorithm . . . . . . . . . . . . . . . . . . . 780 Sy NguyenVan, Ngoc NguyenDinh, P. T. M. Duong, Nguyen Quang Hung, and Thi Thanh Nga Nguyen The Effect of Bonnet Skin and Bonnet Reinforcement Thickness on Pedestrian Head Injuries in Collisions . . . . . . . . . . . . . . . . . . . . . . . . 792 VanLuc Ngo and Minh Khong The Effect of the Wheel Rotation Angle on the Braking Efﬁciency of the Tractor Semitrailer on the Wet Roundabout Route . . . . . . . . . . 798 Nguyen Thanh Tung and Vo Van Huong The Effect of Welding Speed on the Mechanical Properties of the FSW Cu/Al . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805 Tran Hung Tra, Quach Hoai Nam, Phi Cong Thuyen, Duong Dinh Hao, Truong Thanh Chung, Pham Trong Hop, Ho Huu Huy, Vu Lai Hoang, and Chu Hoang Duc Anh The New Method to Control Twin Rotor MIMO System (TRMS) . . . . . 810 Lai Khac Lai, Trinh Thuy Ha, Vu Nhu Lan, and Nguyen Tien Duy Theoretical and Experimental Study of Sound Transmission Loss Across Finite Clamped Composite Sandwich Plates . . . . . . . . . . . . . . . . 820 Tran Ich Thinh and Pham Ngoc Thanh Tool Wear Rate Analysis of Uncoated and AlCrNi Coated Aluminum Electrode in EDM for Ti6Al4 V Titanium Alloy . . . . . . . . . . . . . . . . . 832 Nguyen Huu Phan, Vu Ngoc Pi, Nguyen Quoc Tuan, Shailesh Shirguppikar, M. S. Patil, Mohsen Asghari Ilani, Le Xuan Hung, T. Muthuramalingam, and Tran Quoc Hung Tracking Control of Directed Acyclic Formation via Target Point Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 839 Dung Van Vu, Trung Thanh Cao, Minh Hoang Trinh, and HyoSung Ahn
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Tracking Control of Rostock Delta Parallel Robot Based on the Dynamic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846 Vu Le Huy, Le Thi Huyen Linh, and Nguyen Dinh Dzung Trajectory Tracking Control of a Caterpillar Vehicle . . . . . . . . . . . . . . 854 Do Trung Hai, Bui Thi Hai Linh, and Tran Ngoc Anh Truss Optimization Under Frequency Constraints by Using a Combined Differential Evolution and Jaya Algorithm . . . . . . . . . . . . . 861 Sy NguyenVan, Thi Thanh Nga Nguyen, Ngoc NguyenDinh, and Qui X. Lieu Web Tension Observer Based Control for SingleSpan Roll to Roll Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874 Thi Ly Tong, Minh Duc Duong, Danh Huy Nguyen, and Tung Lam Nguyen Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 883
Keynote Addresses
Hardware Acceleration of Modern Data Management KaiUwe Sattler(&) TU Ilmenau, Ilmenau, Germany [email protected]
Abstract. Over the past thirty years, database management systems have been established as one of the most successful software concepts. In todays business environment they constitute the centerpiece of almost all critical IT systems. The reasons for this success are manyfold. On the one hand, such systems provide abstractions hiding the details of underlying hardware or operating systems layers. On the other hand, database management systems are ACID compliant, which enables them to represent an accurate picture of a real world scenario, and ensures correctness of the managed data. However, the currently used database concepts and systems are not well prepared to support emerging application domains such as eSciences, Industry 4.0, Internet of Things or Digital Humanities. Furthermore, volume, variety, veracity as well as velocity of data caused by ubiquitous sensors have to be mastered by massive scalability and online processing by providing traditional qualities of database systems like consistency, isolation and descriptive query languages. At the same time, current and future hardware trends provide new opportunities such as manycore CPUs, coprocessors like GPU and FPGA, novel storage technologies like NVRAM and SSD as well as highspeed networks provide new opportunities. In this talk we present our research results for the use of modern hardware architectures for data management. We discuss the design of data structures for persistent memory and the use of accelerators like GPU and FPGA for database operations.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 3–3, 2021. https://doi.org/10.1007/9783030647193_1
Electric Vehicle Development and LowCarbon Transport in Vietnam Le Anh Tuan(&) Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Vietnam’s social and economic development achievements are remarkable. However a steep rise in income and economic growth has led to rapid motorization and high energy demand. The transport sector is a major consumer of energy in Vietnam and thus it is one of the key sectors which produces most emissions including green house gas (GHG). As a result, according the emissions per GDP, Vietnam is ranked the 13th most carbon intensive economy in the world, and 4th among the low and middle income countries in East Asia. GHG emissions from the transport sector are expected to triple by 2030, to nearly 90 million tons carbon dioxide equivalent (CO2e). In road transport sector, there are about 40 million vehicles, including about 35 million motorbikes, over around 96 million population. Almost all of road vehicles use internal combustion engines which emit high GHG and high toxic emissions. A pathway for lowcarbon transport is crucial, consisting the shift from conventional vehicles to electric ones. This talk addresses the global electric vehicle outlook, related technologies of electric vehicles, Vietnam current situation of automotive industry and contribution of lowcarbon transport scenarios, in general, and of electric vehicles, in particular, to GHG reduction for Vietnam. Keywords: Vehicle outlook transport
Electric vehicle development Lowcarbon
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 4–4, 2021. https://doi.org/10.1007/9783030647193_2
ICERA 2020 Main Track
A CommonGround SinglePhase Boost Inverter with Suppressed DoubleFrequency Ripple for Photovoltaic Applications MinhDuc Ngo1(&), QuynhVan Nong2, ThuyNgan Ngo3, HongQuang Nguyen1, TanTai Tran4, and SeonJu Ahn4 1
Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Thai Nguyen University of Education, Thai Nguyen, Vietnam 3 Ha Noi Metropolitan University, Hanoi, Vietnam 4 Chonnam National University, Gwangju 61186, Korea
Abstract. A lowfrequency current ripple is introduced at the DC side of the singlephase inverter topology decreasing efﬁciency in the photovoltaic (PV), and battery systems and degrading the lifetime of an energy storage device. In this paper, a commonground singlephase singlestage boost inverter for PV applications is presented. The introduced topology consists of three capacitors, one inductor, ﬁve switches, and four diodes. The introduced topology has the main features as the common ground between the DC input voltage source and AC output voltage, and voltage boost capability. Furthermore, the lowfrequency input current ripple is signiﬁcantly limited. Besides, the leakage current, which is one of the major problems in gridconnected PV applications, is limited in an introduced inverter. The operating principles, circuit analysis, Mathematical analysis, and PWM control strategy for the introduced inverter are discussed. The simulation results based on PSIM simulation are given at the end of the paper to conﬁrm the feasibility, performance and viability of the introduced topology. Keywords: DC–AC inverter Boost inverter Photovoltaic systems Doublefrequency ripple
1 Introduction In the recent period, because of global surface temperature and energy crisis, using solar energy has been receiving more and more attention from many researchers. The distributed generation systems combined with solar energy have been rapidly researched. The singlephase gridtied transformerless dc–ac inverters in PV applications are becoming more common worldwide [1]. However, a gridconnected system without a transformer generates a leakage current. This current flows through the parasitic capacitance to the ground, causing EMI problems, insecurity, and the low reliability of the gridtied transformerless PV inverters [2, 3]. To solve this problem, some research works have been presented recently to limit the leakage current [4–6]. By clamping the commonmode voltage during the freewheeling period, the leakage current is reduced © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 7–12, 2021. https://doi.org/10.1007/9783030647193_3
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as discussed in [5, 6]. Recently, the transformerless PV inverters were introduced in [7– 9] by using common ground conﬁgurations. In these topologies, the ground of the PV array and the utility grid is directly connected, so there is no leakage current. However, these topologies are a buck power conversion where the peak AC output voltage is smaller than the dc voltage. In light of the above, a commonground singlephase singlestage boost inverter is presented in this paper. The introduced topology has the main features as the common ground between DC input voltage source and AC output voltage, and voltage boost capability. In addition, the lowfrequency input current ripple is signiﬁcantly limited. The operating principles, circuit analysis, mathematical analysis, and PWM control strategy for the introduced inverter are given in Sect. 2. The simulation results based on PSIM simulation are given in Sect. 3.
i LB LB
CA
D1
S0
CB
Vg
S4
VCB
v dc D3
io
D2
Ll
a CC
S1
S2
S3
Rl
D4 b
Fig. 1. Proposed commonground singlephase singlestage boost inverter.
2 Proposed Topology The proposed commonground singlephase singlestage boost inverter, which is indicated as in Fig. 1, consists of ﬁve switches, four diodes, a boost inductor, three capacitors. The same ground between dc input and ac output is an interesting feature of the introduced topology. From Fig. 1, we can see that the negative polarity of the dc input is directly connected to the ac output. It is merit, especially for PV applications. Figure 2 shows operating states of the proposed commonground singlephase singlestage boost inverter. Figure 3 indicates the proposed PWM control method for the introduced topology. As shown in Fig. 3, a control waveform, Vcontrol and a ﬁxed voltage, VST are compared with a carrier waveform, vtri to generate control signals for switches from S0 to S4.
A CommonGround SinglePhase Boost Inverter
9
Fig. 2. Equivalent circuit of the introduced inverter. (a) Circuit in booststate, and (b)(d) Circuits in nonbooststate.
VP VST
0
T
Vcontrol
vtri
DT
S0 S1 S2 S3 S4 vab : Shootthrough State
Fig. 3. Proposed PWM control method for the introduced topology.
In the boost state as shown in Fig. 2(a), four switches S0, S1, S3, and S4 are turned on while the switch S2 is turned off. As a result, the boost inductor is charged from the input dc source, Vg. The time interval in this state is DT, where D and T represent the duty cycle and a switching period, respectively. 8 < L diLB ¼ V þ V B g CB dt : : VCA ¼ VCB
ð1Þ
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In the nonboost states as shown in Fig. 2(b), (c), and (d), the switch S0 is turned on. During the positive cycle as shown in Fig. 2(b), two switches S1, S4 are turned on while two switches S2, S3 are turned off. The output voltage, vab, in this case, equals VCC. During the negative cycle as shown in Fig. 2(c), two switches S2, S3 are turned on while two switches S1, S4 are turned off. The output voltage, vab, in this case, equalsVCC. Following the negative or positive cycle, two switches S2 and S4 are turned ON to generate a zero voltage. During the nonboost states, the boost inductor is discharged while the capacitor CB is discharged. The time interval in this state is (1–D)T LB
diLB ¼ Vg þ VCB dt
and
VCC ¼ VCA þ VCB
ð2Þ
From (1) and (2), we get VCC ¼ VCA þ VCB ¼
2 Vdc : 1 2D
ð3Þ
The boost factor of the introduced topology is deﬁned by B¼
Vbus VCC 2 1 ¼ ¼ 1 2D Vdc Vdc
ð4Þ
Table 1. Simulation parameters Parameters Input DC Voltage Inverter Output voltage Inductor (LB) Capacitors CA and CB CC Switching frequency Inductive Load Ll Rl
Values 96 V 220 Vrms/50 Hz 1 mH 1000 µF 2000 µF 30 kHz 30 mH 50 X
3 Simulation Results To prove the operating principle of the proposed commonground singlephase singlestage boost inverter, simulation results based on PSIM simulation are given. The simulation parameters for the introduced topology are given as in Table 1. Figure 4 shows the simulation waveforms of the proposed commonground single phase single stage boost inverter when the input voltage is 96 V. As shown in Fig. 4(a) and Fig. 4(b), we can see that the peak ac output voltage of the introduced topology is 310 V. The peak value of the load current is 6.2 A. As shown in Fig. 4(a), the lowfrequency current ripple is reduced signiﬁcantly. From Fig. 4(d), the peaktopeak inductor ripple current is 4 A. From Fig. 4(c) and Fig. 4(d), the stress voltage across
A CommonGround SinglePhase Boost Inverter
11
Fig. 4. Simulation results of the introduced inverter when input dc voltage is 96 V. From top to bottom, (a) input voltage, input current, output voltage of the inverter, current load; (b) FFT of output voltage of the inverter, current load; (c)(d) drainsource voltage of switches and zoom version of input current.
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four switches S1, S2, S3, and S4 are the same as dc bus voltage while the stress voltage across switch S0 is equal to half of dc bus voltage.
4 Conclusion This paper proposes the commonground singlephase singlestage boost inverter for PV applications. The circuit analysis, and mathematical analysis, operating principles of the introduced inverter are provided. A simple PWM control method is introduced to modulate the proposed inverter. The introduced topology has the main features as the common ground between DC input voltage source and AC output voltage, and the voltage boost capability. Furthermore, the lowfrequency input current ripple is signiﬁcantly limited. Besides, the leakage current, which is one of the major problems in gridconnected PV applications, is limited in the introduced inverter. Since the proposed commonground single phase single stage boost inverter has a high reliability, there is no leakage current and voltage boost capability, it is suitable for PV applications. The simulation results based on PSIM simulation are given at the end of the paper to conﬁrm the feasibility, performance and viability of the proposed topology. Acknowledgment. This research was supported by Research Foundation funded by Thai Nguyen University of Technology.
References 1. Kjaer, S.B., et al.: A review of singlephase gridconnected inverters for photovoltaic modules. IEEE Trans. Ind. Appl. 41(5), 1292–1306 (2005) 2. Ribeiro, H., Borges, B., Pinto, A.: Singlestage DC–AC converter for photovoltaic systems. In: Proceedings IEEE Energy Conversion Congress and Exposition, Atlanta, USA, pp. 604– 610. IEEE (2010) 3. Gonzales, R., Lopez, J., Sanchis, P., Marroyo, L.: Transformerless inverter for singlephase photovoltaic systems. IEEE Trans. Power Electron. 22(2), 693–697 (2007) 4. Stalter, O., Wellnitz, P., Burger, B.: Flying capacitor topology for grounding of singlephase transformerless threelevel photovoltaic inverters. In: 16th European Conference on Power Electronics and Applications, Lappeenranta, Finland, pp. 1–9, IEEE (2014) 5. Ji, B., Wang, J., Zhao, J.: Highefﬁciency singlephase transformerless PV H6 inverter with hybrid modulation method. IEEE Trans. Ind. Electron. 60(5), 2104–2115 (2013) 6. Xiao, H.F., Zhang, L., Li, Y.: An improved zerocurrentswitching singlephase transformerless PV H6 inverter with switching lossfree. IEEE Trans. Ind. Electron. 64(10), 7896–7905 (2017) 7. Vazquez, N., et al.: Integrating two stages as a commonmode transformerless photovoltaic converter. IEEE Trans. Ind. Electron. 64(9), 7498–7507 (2017) 8. Siwakoti, Y.P., Blaabjerg, F.: A novel flying capacitor transformerless inverter for singlephase gridconnected solar photovoltaic system. In: IEEE 7th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Vancouver, Canada, pp. 1–6. IEEE (2016) 9. Siwakoti, Y.P., Blaabjerg, F.: Commongroundtype transformerless inverters for singlephase solar photovoltaic systems. IEEE Trans. Ind. Electron. 65(3), 2100–2111 (2018)
A High Stepup DCDC Converter with Semiconductor Voltage Stress Reduction HongQuang Nguyen1, NgocAnh Tran1, VanNghiep Dinh1, VinhThuy Nguyen1, MinhDuc Ngo1(&), and JoonHo Choi2 1
Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Chonnam National University, Gwangju 61186, Korea
Abstract. Nowadays, switchedinductor and switchedcapacitor conﬁgurations are widely used for boost DCDC converters to improve high boost ability. By applying the principle operating of charging and discharging of inductor and capacitor elements in parallel or series connection, it is considered that four diodes, one high voltage rating switch, two inductors are validated to show as a conventional switchedinductor boost converter. In this research, a novel highboost DCDC converter based on the switchedinductor technique is introduced. The introduced converter is transformerless topology and determined by changing the conﬁguration of the conventional switched inductor structure and a semiconductor switch. As a result, the introduced converter can give low voltage rating active switches. Furthermore, the introduced converter is low in cost and achieve higher efﬁciency with simple topology. The operating analysis of the introduced converter is presented in detail. The simulation results with the output control are presented to verify the analysis. Keywords: Boost converter technique
Voltage stress reduction Switchedinductor
1 Introduction In recent years, we are facing pressure from environmental protection, global surface temperature and the energy sources are depleting. Researchers are paying more attention to environmental protection, energy conservation and emission reduction. The distributed generation systems combined with available renewable energy sources have been rapidly researched. These distributed generation systems are generated by some sources such as fuel cell and photovoltaic (PV) arrays in the world [1, 2]. Generally, the available renewable energies are wind, solar, and fuel cells are dependent on the weather conditions, and their output voltages are low and instability. This problem leads to develop high boost DCDC converters, which can generate high voltage DCbus for the input terminal of gridconnected DCAC inverter. Figure 1 presents a typical gridconnected renewable energy generation system. The high stepup DCDC converters have been researched and obtained a highvoltage gain in both isolated and nonisolated topologies. However, the isolated topologies have to use the highfrequency transformer, which leads to the high cost and low efﬁciency for the converter [3, 4]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 13–19, 2021. https://doi.org/10.1007/9783030647193_4
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Nowadays, the converters without transformer have more attention with highvoltage improvement, greater efﬁciency and lowcost design. The classical DCDC converter with simple topology by using one inductor, one capacitor, one diode and one switch has been widely used for both research ﬁeld and industrial applications. The research of classical DCDC converter has developed in [4]. However, the voltage gain of the classical DCDC converter is still low. Moreover, the cascaded [5], switchedcapacitor, switchedinductor, and interleaved [6] techniques are also used for the classical DCDC converter to achieve the high voltage boost ability. Most of them, the switched inductor technique is more effective, thanks to the improvement of the simple topology and the power density of the converter [7, 8]. The conventional switchedinductor converter with threediode and twoinductor is shown in Fig. 2(a). This is modiﬁed by replacing the boost inductor in the classical boost converter. When a highstepup voltage ability is required, a larger duty cycle should be used which leads to high voltage stress on the switches. To reduce the stress, the novel switchedinductor is introduced in this paper, which can reduce voltage stress on the semiconductor devices and improve the efﬁciency of the converter.
Renewable Energy Sources
DC/DC Converter
PV or Fuel Cell
Boost Converter
Applications Inverter
Load/ Utility
Fig. 1. The typical gridconnected renewable energy generation system.
2 Proposed SwitchedInductor DCDC Converter 2.1
A Conﬁguration of Proposed SwitchedInductor
Figure 2(b) depicts the introduced switchedinductor DCDC converter. It consists of a modiﬁed switchedinductor cell, two power switches, and the load. When the operating principle of the proposed circuit is considered in the continuous conduction mode, the following assumptions were made: i) All components are ideal and no loss; ii) the capacitance of the capacitors is large enough to maintain the constant capacitor voltage; and iii) the current of the inductor is changed linearly. 2.2
Circuit Analysis
Figure 3 introduced the key waveforms of the introduced switchedinductor converter operating in the continuous conduction mode. Both switches S1 and S2 are ONstate simultaneously with the time interval of DT. The output switch S0 is ONstate with the time interval of (1D)T. To analysis the operating principle, the following assumptions were made all semiconductor devices and passive components are set ideal; the
A High Stepup DCDC Converter with Semiconductor Voltage Da L1 Vi
Da
L2
D0
Db Dc
L1 Co
S
Vo
R
Io
L2
Io Db
S1
S0 Co
Vi
15
Vo
S2
(a)
(b)
Fig. 2. Topology of (a) conventional switchedinductor DCDC converter; (b) the proposed switchedinductor DCDC converter. T
VGS1,VGS2
S1, S2 S0
VGS0
vLx, iLx
VS1,VS2 VS0
t
iL1=iL2
t
vL1=vL2
t
0.5(VoVi) t
0.5(Vo+Vi) 0.5(VoVi)
VDa,VDb
Vi
t
Vo
t
DT
t
VD0
Fig. 3. Typical waveform in continuous conduction mode of the proposed switchedinductor DCDC converter.
capacitance is enough to generate the constant voltage; and the inductor current is linear. The operation of the introduced switchedinductor converter is divided into two modes, mode 1 is the circuit in ONstate when both switches S1 and S2 are turned on and S0 is turned off. Mode 2 is the circuit in OFFstate when both switches S1 and S2 are turned off and S0 is turned on. Mode 1: Both switches S1 and S2 are turned on and S0 is turned off. The diode Da is on and two inductors L1, L2 of the switchedinductor cell are connected in parallel.
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Two inductors are magnetized by an input voltage source, as shown in Fig. 4(a). The equations can be obtained Da L1 Vi
L2
Io
S1
L1 Co
S2
Io
L2
Vo
S0
Db
Co
Vi
(a)
Vo
(b)
Fig. 4. Equivalent circuit (b) Circuit in ONstate, and (c) Circuit in OFFstate.
8 < L diL1 ¼ L diL2 ¼ V 1 2 i dt dt : iin ¼ 2iL1 ¼ 2iL2 ;
ð1Þ
Mode 2: The converter operates in OFFstate. The switches S1 and S2 are turned off and S0 is turned on. The diode Db is on and two inductors L1, L2 of the switchedinductor cell are connected in series. The output capacitor is charged by two inductors and an input voltage source, as shown in Fig. 4(b). The equations can be given 8 diL1 diL2 > > ¼ L2 ¼ Vi L1 > > dt dt < diL1 diL2 þ L2 ¼ Vi Vo L1 > > dt dt > > : iin ¼ iL1 ¼ iL2 ;
ð2Þ
From (1)–(2), the voltage gain and input current can be obtained as 8 < G ¼ Vo ¼ 1 þ D Vi 1 D : iin ¼ iL1 ð1 þ DÞ;
ð3Þ
A High Stepup DCDC Converter with Semiconductor Voltage
17
3 Results The introduced switchedinductor DCDC converter is veriﬁed by using PSIM software simulation when Vi = 50 − 100 V, Vo = 300 V is shown at Figs. 5–6. The frequency of switching is 20 kHz. The simulation parameters for the converter is presented in Table 1. The introduced switchedinductor DCDC converter is veriﬁed by using PSIM software simulation when Vi = 50 − 100 V, Vo = 300 V is shown in Figs. 5–6.
Fig. 5. Simulation results of the introduced converter when Vi = 50 V.
Fig. 6. Simulation results of the introduced converter when Vi = 100 V.
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The simulation parameters for the converter is presented in Table 2. The obtained waveforms of output and input voltages and current, and voltage stress of semiconductor devices are shown in Fig. 5, when the input voltage is set 50 V and D = 0.72. The waveforms of output and input voltages, output and input currents are presented in Fig. 5. The peak values of voltage stress VS1, VS2, VS0, VDa, and VDb are 128 V, 178 V, 306 V, 128 V, and 50 V, respectively. Moreover, in the case of Vi = 100 V and D = 0.5, the observed peak values of voltage stress VS1, VS2, VS0, VDa, and VDb are also approximately 100 V, 200 V, 300 V, 100 V, and 100 V, respectively. It can be seen that the simulation results are matched with the principle analysis. The testing efﬁciencies at the input voltage of 50 V and 100 V are 94.9% and 96.7%, respectively. Table 1. Testing parameters Parameters Input DC range Output voltage operating Power rating Inductors Capacitor Switching frequency
Value 50–100 V 300 V 300 W 1 mH 110 µF 20 kHz
4 Conclusion The proposed switchedinductor boost converter is introduced with reduced voltage stress across the semiconductor devices. Compared to conventional switchedinductor converter, the proposed converter required less number of diodes and voltage stress across switches are low. Furthermore, the converter cost can be decreased due to the utilization of lower rating active switches. The detail continuous conduction mode operating principle and voltage gain is discussed. The waveform simulation results are presented which validated the theoretical analysis and functionality. Acknowledgement. This research was supported by Research Foundation funded by Thai Nguyen University of Technology.
References 1. Kjaer, S.B., Pedersen, J.K., Blaabjerg, F.: A review of single phase gridconnected inverters for photovoltaic modules. IEEE Trans. Ind. Appl. 41(5), 1292–1306 (2005) 2. AlGreer, M., et al.: Advances on system identiﬁcation techniques for DC–DC switch mode power converter applications. IEEE Trans. Power Electron. 34(7), 6973–6990 (2019)
A High Stepup DCDC Converter with Semiconductor Voltage
19
3. Salvador, M.A., et al.: High stepup DC–DC converter with active switchedinductor and passive switchedcapacitor networks. IEEE Trans. Ind. Electron. 65(7), 5644–5654 (2018) 4. Yang, L., et al.: Transformerless DC–DC converters with high stepup voltage gain. IEEE Trans. Ind. Electron. 56(8), 3144–3152 (2009) 5. Lakshmi, M., Hemamalini, S.: Nonisolated high gain DC–DC converter for DC microgrids. IEEE Trans. Ind. Electron. 65(2), 1205–1212 (2018) 6. Tang, Y., et al.: Multicell switchedinductor/switchedcapacitor combined activenetwork converters. IEEE Trans. Power Electron. 30(4), 2063–2072 (2015) 7. Dwari, S., Parsa, L.: An efﬁcient highstepup interleaved DC–DC converter with a common active clamp. IEEE Trans. Power Electron. 26(1), 66–78 (2011) 8. Bhaskar, M.S., Meraj, M., Iqbal, A., Padmanaban, S., et al.: High gain transformerless doubledutytriplemode DC/DC converter for DC microgrid. IEEE Access 7, 36353–36370 (2019)
A Method to Partition Accuracy in Workspace for Robot Arms HuuThang Nguyen1,2(&), Long Pham Thanh2, and JenTzong Jeng1 1
2
Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan [email protected] Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam
Abstract. This paper introduces a method to partition accuracy for robot arms. In this method, our technique divides workspace into small domains with different accuracy respectively and there are distinct boundaries between these domains. This will provide effective information for engineers to decide which tasks are able or unable to apply robot arms. The accuracy provided by manufactures is only the nominal value, but the real accuracy of robot arms fluctuated in the workspace and transformed according to times. Thus, the method in this paper is useful for determining the varied accuracy in the workspace and time of using industrial robot arms. Keywords: Accuracy Workspace
Robot arm Interpolation Shape functions
1 Introduction In machines, their catalogs usually provide information in terms of accuracy and repeated accuracy and they are only nominal values. These values are possible maximum distances between the tool center point (TCP) and the nominal position in all directions [1, 2]. For example, according to the catalogs of Robot ABBIRB 1520 and Kuka KR 180 R29002, they have a repeated accuracy of ± 0.005 mm. However, this is not true because the accuracy is different in the workspace and is changed over time. This means that the accuracy is decreased according to using time and its value also fluctuates in the workspace [3–7]. For engineers, regarding determination of using machines, relying only on the nominal values of accuracy in catalogs is not enough. For each contour which TCP goes through in the workspace, the accuracy in each position is possibly different, but the required accuracy of this value in each task is constant. For effective determination of using machines, the partition of accuracy is necessary. In this paper, the authors introduce an interpolated method to determinate the region in the workspace of the robot arm that has only one certain accuracy. Interpolation is approximation that the accuracy is depended on some following requirements: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 20–28, 2021. https://doi.org/10.1007/9783030647193_5
A Method to Partition Accuracy in Workspace for Robot Arms
21
• The shape function must be suitable • Number of data for keypoint must be large enough • Type and number of boundary conditions for modeling must be suitable In the interpolation model, if the calculation error due to rounding is neglected, the most signiﬁcant error is the error due to the incorrect assumption of shape function. Thus, some special techniques are needed to solve this problem. In research communities, some interpolated methods have been using, such as: Type1 Fuzzy interpolation, trilinear and spline interpolation [8, 9], BSpline interpolation, online fuzzy interpolation [10–12]. It clears that the accuracy of the interpolated method by using shape function is high, in this paper, the authors introduce a method to determinate the workspace of the robot arm with predeﬁned accuracy.
2 Interpolated Model Using Shape Function The necessary condition for interpolation of one quality is its continuous property, the error of the endeffector is satisﬁed with this condition. To partition the accuracy of the robot arm, data of error in the workspace is ﬁrst collected as shown in Fig. 1. As in Fig. 1, for determination of the position of the endeffector, there are two following independent monitors:
Fig. 1. A scheme for measuring error in the workspace of a robot arm by using cameras
• Using encoders e1, e2,…,en to get the coordinate of p px ; py ; pz which is the desired position and this information is shown in the robot interface. • Using a camera to get the real coordinate of p0 px dx ; py dy ; pz dz with the reference of O0 . The difference between the two coordinates is absolute error of the endeffector at point p and is dx ; dy ; dz . Assume that error at point pi has following 6 parts which are position and direction errors:
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dpi ¼ dix ; diy ; diz ; dihx ; dihy ; dihz
ð1Þ
Let’s consider a subspace of the triangular prism as shown in Fig. 3. In this space, the errors are measured at the tops of the prism and have 6 components as in Eq. (1) (Fig. 2).
E
F
D
NE ND
NF
Pi NC NB
NA
B C
A Fig. 2. Effect of sample points on the survey points by shape function
At each point pi in the triangular prism, we have some relationships as follows: 8 ðiÞ ðAÞ ðBÞ ðFÞ > < dx ¼ NA :dx þ NB :dx þ . . . þ NF :dx ... > : ðiÞ ðAÞ ðBÞ ðFÞ dhz ¼ NA :dhz þ NB :dhz þ . . . þ NF :dhz
ð2Þ
Where: ðiÞ ðiÞ ðiÞ ðiÞ ðiÞ dðiÞ x ; dy ; dz ; dhx ; dhy ; dhz are 6 errors at the point pi; NA ; NB ; NC ; ND ; NE ; NF are coefﬁcient of stopping of shape functions which have effects on the error of point pi from 6 tops of the triangular prism. ðAÞ ðAÞ ðAÞ ðAÞ ðAÞ ðFÞ ðFÞ ðFÞ ðFÞ ðFÞ ðFÞ dðAÞ x ; dy ; dz ; dhx ; dhy ; dhz . . .dx ; dy ; dz ; dhx ; dhy ; dhz are real errors in terms of position and orientation of 6 tops of the triangular prism (A…F). From Eq. (2), we always have coefﬁcients of stopping of shape function as follows: 2
3 2 ðAÞ NA dx 6 7 4...5 ¼ 4 ... ðAÞ dhz NF ðiÞ
... ... ...
3 dðiÞ x 6 7 ...7 . . . 5 :6 4 5 ðFÞ ðiÞ dhz d dðFÞ x
31
2
hz
ð3Þ
A Method to Partition Accuracy in Workspace for Robot Arms
23
h i ðiÞ T Where the matrix of dðiÞ is the error measured at the point pi in the x . . .dhz triangular prism and ½NA . . .NF TðiÞ are stopping values of 6 shape functions that have effects on the error of point pi. With data of n points, the results of n sets of stopping values of shape function are collected as follows: 2
NA
3
2
NA
3
2
NA
3
6 7 6 7 6 7 4 . . . 5 ; . . .; 4 . . . 5 ; . . .; 4 . . . 5 NF ð1Þ NF ðiÞ NF ðnÞ
ð4Þ
Thus, interpolation laws allow to determine shape function at the kth top as follows: ð1Þ
ðiÞ
ðnÞ
ðNk ; . . .; Nk ; . . .; Nk Þ ) f k ðx; y; zÞ ðk ¼ A; B; C; D; E; FÞ
ð5Þ
From Eq. (2), the error of arbitrary point (pi) inside the prism can be calculated. To have interpolated rectangular cuboid, we can assemble two triangular prisms and need to have two different models of Eq. (2) respectively.
3 Partition of Accuracy in Workspace for Robot Arms Using Shape Function Equation (2) allows us to determinate the error of arbitrary points in the workspace by using shape function. There are two problems based on this Eq. (2): Problem 1: Find out the error of position and orientation of point pi in the workspace when the coordinates of point pi (x,y,z) and shape function are known as follows: di ¼ f ðNA ; . . .; NF ; dA ; . . .; dF ; x; y; zÞ
ð6Þ
To ﬁnd the error, we can use shape function in Eq. (5) and coordinates of survey points to ﬁnd coefﬁcients of shape function. Substitution these coefﬁcients into Eq. (2), we can get the error of top pi. Problem 2: Partition of the accuracy of the robot arm in the workspace This is the opposite problem of Problem 1 with the shape functions in Eq. (5) are known, the coordinates that have the same errors di or are in the range of max di 2 ðdmin Þ, will form a domain inside the workspace and this domain is clearly i ; di distinguished with the rest. When this data is made for the entire workspace of robot arms, it can be the reference value for engineers. For example, these data help engineers decide whether or not a robot arm is suitable for a known task.
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Assume that the locus of points that have di ¼ a, from Eq. (6), we have: f ðNA ; . . .; NF ; dA ; . . .; dF ; x; y; zÞ ¼ a
ð7Þ
From [15], the previous problem can be transformed as follows: Min ½f ðNA ; . . .; NF ; dA ; . . .; dF ; x; y; zÞ a xmin x xmax ymin y ymax zmin z zmax
ð8Þ
The solution of Eq. (8) is one or more sets of the same precisions which are needed to be found. It should be noted that the boundary of this space must contain the entire trajectory of the predeﬁned precision a in Eq. (7), thus the robot will meet the required technical requirements, on the contrary, it needs other methods.
4 Examples The DenavitHartengerg (DH) table of the robot arm is shown as in Fig. 3, data from cameras at survey points are provided in Table 1. The question is how to determine the error in the workspace of the robot arm? The dimension of arm robots are:
Fig. 3. The DH table of robot arm
d1 = 335, a1 = 75, a2 = 270, a3 = 90, d4 = 295, d5 + d6 = 80 mm. Assume that the workspace of this robot arm is a quadrate as shown in Fig. 4. Pi is a point in the workspace of the robot arm. To evaluate the end effector error in this quadrate, the measurement and interpolation of shape function are performed for each triangular prism ABCDEF and AGCDHF (Table 2).
A Method to Partition Accuracy in Workspace for Robot Arms
25
Table 1. The accuracy of the robot arm by using Cognex 3D 5005 camera Point Desired position (mm) X Y Z A 300 275 200 B 300 175 200 C 200 275 200 G 200 175 200 D 300 275 300 E 300 175 300 F 200 275 300 H 200 175 300
Measured pose (mm) a12 −0.0996 −0.0996 −0.0996 −0.0996 −0.0996 −0.0996 −0.0996 −0.0996
a13 −0.2010 −0.2012 −0.2008 −0.2010 −0.2010 −0.2012 −0.2008 −0.2013
a23 −0.1000 −0.1000 −0.1000 −0.1000 −0.1000 −0.1000 −0.1000 −0.1002
a14 300.4288 300.4375 200.2540 200.2107 300.4797 300.5044 200.3056 200.1510
a24 275.5409 175.3727 275.5905 175.4230 275.5262 175.3645 275.5795 175.4189
a34 200.0781 200.0565 200.0506 199.9979 300.2378 300.2193 300.2131 300.0849
Fig. 4. The box for determination of error of the endeffector Table 2. The difference between the desired point and the measured point Point Desired position (mm) X Y Z A 300 275 200 B 300 175 200 C 200 275 200 G 200 175 200 D 300 275 300 E 300 175 300 F 200 275 300 H 200 175 300
Error measured pose (mm) Delta a12 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004 0.0004
Delta a13 −0.0010 −0.0012 −0.0008 −0.0010 −0.0010 −0.0012 −0.0008 −0.0010
Delta a23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Delta a14 0.4288 0.4375 0.2540 0.2107 0.4797 0.5044 0.3056 0.4288
Delta a24 Delta a34 0.5409 0.0781 0.3727 0.0565 0.5905 0.0506 0.4230 −0.0021 0.5262 0.2378 0.3645 0.2193 0.5795 0.2131 0.5409 0.0781
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The camera resolution according to x, y and z directions are 44 µm, 44 µm and 8 µm and the repeated accuracy is 6 µm. In this experiment, the RSM is adjusted to 0.005 mm. Based on the results, the shape functions are given as follows: The shape functions in the 1st triangular prism are given as follows: NA = −2167.6 + 8.3793 X + 8.4567 Y − 0.04736 Z − 0.006500 X*X − 0.006258 Y*Y + 0.000110 Z*Z − 0.019734 X*Y + 0.000024 X*Z − 0.000082 Y*Z NB =2433.7 − 9.4041 X − 9.4933 Y + 0.04798 Z + 0.007305 X*X + 0.007021 Y*Y − 0.000128 Z*Z + 0.022173 X*Y − 0.000027 X*Z + 0.000079 Y*Z NC = −246.09 + 0.95888 X + 0.96963 Y − 0.008368 Z − 0.000756 X*X − 0.000714 Y*Y + 0.000013 Z*Z − 0.002282 X*Y + 0.000005 X*Z + 0.000002 Y*Z ND = 2583.4 − 10.0065 X − 10.0840 Y + 0.06412 Z + 0.007776 X*X + 0.007461 Y*Y − 0.000135 Z*Z + 0.023592 X*Y − 0.000034 X*Z + 0.000078 Y*Z NE = −2602.4 + 10.0723 X + 10.1508 Y − 0.05637 Z − 0.007825 X*X − 0.007510 Y*Y + 0.000140 Z*Z − 0.023748 X*Y + 0.000032 X*Z − 0.000077 Y*Z NF = 0. The shape functions in the 2nd triangular prism are given as follows: NB = − 450.21 + 3.8303 X − 0.0976 Y + 0.3499 Z − 0.007855 X*X − 0.000150 Z*Z  0.000633 X*Z  0.000584 Y*Z NC = 388.36  3.3109 X + 0.0959 Y  0.3122 Z + 0.006804 X*X + 0.000129 Z*Z + 0.000554 X*Z + 0.000502 Y*Z NG = 48.534 − 0.39499 X + 0.00331 Y − 0.02586 Z + 0.000801 X*X + 0.000011 Z*Z + 0.000044 X*Z + 0.000043 Y*Z NE = 420.27 − 3.5691 X + 0.0872 Y − 0.3370 Z + 0.007329 X*X + 0.000144 Z*Z + 0.000612 X*Z + 0.000563 Y*Z NF = − 405.96 + 3.4447 X − 0.0888 Y + 0.3251 Z − 0.007080 X*X − 0.000135 Z*Z − 0.000576 X*Z − 0.000524 Y*Z NH = 0 From the previous shape function, the error in the subspace will be determined. The calculation will be performed and the errors at 2604 keypoints in the subworkspace are got. The errors of the endeffector in a range of 0.450.767 mm are shown as in Fig. 5. From the determination of error of each position in the subworkspace, this work can provide data to make the error of the endeffector equal zeros or make error smaller than a deﬁned error [13].
A Method to Partition Accuracy in Workspace for Robot Arms
27
Fig. 5. Error in the workspace of the robot arm
5 Conclusion The proposed accuracy partitioning method allows robot users to actively control the quality of equipment according to technical requirements. The accuracy itself as presented here cannot be represented by a nominal value, thus it is partitioned so that the description is most accurate, especially in tasks that require high precision. Over time, the precision changes and error models in terms of shape functions also need to be recalculated to match actual usage. The errors measured by the camera are also included the effects of robots and thermal deformation due to effects of the environment. Acknowledgments. This research was supported by the Thai Nguyen University of Technology (TNUT) of Vietnam.
References 1. Escande, C., Chettibi, T., Merzouki, R., Coelen, V., Pathak, P.M.: Kinematic calibration of a multisection bionic manipulator. IEEE/ASME Trans. Mechatron. 20, 663–674 (2015). https://doi.org/10.1109/TMECH.2014.2313741 2. Klimchik, A., Wu, Y., Abba, G., Garnier, S., Furet, B., Pashkevich, A.: Robust algorithm for calibration of robotic manipulator model. IFAC Proc. 46(9), 808–812 (2013). https://doi.org/ 10.3182/201306193ru3018.00449 3. Jing, W., Tao, P.Y., Yang, G., Shimada, K.: Calibration of industry robots with consideration of loading effects using ProductOfExponential (POE) and Gaussian Process (GP). In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4380– 4385. IEEE (2016) 4. Klimchik, A., Caro, S., Pashkevich, A.: Optimal pose selection for calibration of planar anthropomorphic manipulators. Precis. Eng. 40, 214–229 (2015). https://doi.org/10.1016/j. precisioneng.2014.12.001
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5. Wu, Y., Klimchik, A., Caro, S., Furet, B., Pashkevich, A.: Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments. Robot. Comput. Integr. Manuf. 35, 151–168 (2015). https://doi.org/10.1016/j.rcim.2015.03. 007 6. Bai, Y., Wang, D.: On the comparison of an interval Type2 Fuzzy interpolation system and other interpolation methods used in industrial modeless robotic calibrations. In: 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 1–6. IEEE (2016) https://doi.org/ 10.1109/civemsa.2016.7524323 7. Borrmann, C., Wollnack, J.: Calibration of external linear robot axes using spline interpolation. In: Proceedings of 2014 International Conference on Modelling, Identiﬁcation & Control, pp. 111–116. IEEE (2015) https://doi.org/10.1109/icmic.2014.7020737 8. Bai, Y., Smith, J.C., Zhuang, H., Wang, D.: Calibration of parallel machine tools using fuzzy interpolation method. In: IEEE International Conference on Technologies for Practical Robot Application, pp. 56–61. IEEE (2008) 9. Bai Y., Wang D.: Fuzzy logic for robots calibration — using fuzzy interpolation technique in modeless robot calibration. In: Bai Y., Zhuang H., Wang D. (eds.) Advanced Fuzzy Logic Technologies in Industrial Applications. Advances in Industrial Control. Springer, London (2006). https://doi.org/10.1007/9781846284694_20 10. Bai, Y., Zhuang, H.: On the comparison of modelbased and modeless robotic calibration based on the fuzzy interpolation technique. In: IEEE Conference on Robotics, Automation and Mechatronics, 2004, pp. 892–897. IEEE (2005) https://doi.org/10.1109/ramech.2004. 1438036 11. Bai, Y., Zhuang, H.: On the comparison of bilinear, cubic spline, and fuzzy interpolation techniques for robotic position measurements. IEEE Trans. Instrum. Meas. 54, 2281–2288 (2005). https://doi.org/10.1109/TIM.2005.858563 12. Ying Bai, Dali Wang, On the comparison of interpolation techniques for robotic position compensation. In: SMC’03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference ThemeSystem Security and Assurance (Cat. No. 03CH37483) 4, pp. 3384–3389. IEEE (2004) https://doi.org/10.1109/icsmc.2003. 1244412 13. Huu, T.N., Quoc, K.D., Thu, T.L.T., Thanh, L.P.: A solution to adjust kinetic of industrial robots based on alternative trajectories. In: Sattler, K.U., Nguyen, D.C., Vu, N.P., Tien Long, B., Puta, H. (eds.) ICERA 2019. LNNS, vol. 104, pp. 55–65. Springer, Cham (2020). https://doi.org/10.1007/9783030374976_6
A Novel Method for Shielding Problems with Taking Robust Correction Procedure into Account Vuong Dang Quoc(&) and Dinh Bui Minh School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam [email protected]
Abstract. This paper presents a robust correction procedure based on the perturbation method to reduce errors around edges and corners related to thin structures via a subdomain technique. The idea of the method is considered as several scenarios. A subdomain involving with stranded or massive inductors alone is initially considered. A shielding approximation that neglects end and border effects is then added with an impedancetype condition across a surface. A volume correction is ﬁnally introduced to improve the inaccuracies from the shielding approximation. But, this volume correction usually faces with cancellation errors in the calculation of the local ﬁelds around corners and curvatures. Thus, in order to treat this inconvenience, a robust correction procedure is developed to take cancellation errors into account. Each sequence of the method is considered separately on its own mesh and domain without depending on other meshes and domains. Keywords: Magnetic ﬁeld Shielding approximation power loss Subdomain technique
Eddy current Joule
1 Introduction Shielding structures developed by many authors [1, 2] are considered with a priori known 1D analytical distributions to ignore meshing volume thin regions and are proposed by interface conditions (ICs). However, the ICs usually cancel edges and border effects, which lead to inaccuracies in the simulation of the ﬁelds in the vicinity of regional discontinuities near curvatures and edges, and it will be increased with higher thicknesses. To overcome this inconvenience, many authors have recently implemented a subproblem method for removing errors around edges and corners occurring from the shielding approximation [4, 5]. However, these implementations have neglected robust improvement procedures appearing in the volume corrections, this leads to errors in conducting regions. In this research, a robust improvement procedure is introduced in a novel method (i.e. subdomain technique) to overcome the cancellation errors in magnetic materials that generally have not been treated in the volume correction [4, 5] yet, so far. The scenario of the method is based on the subproblem approach, allowing a full domain to divide into submeshes (SMs) with a sequence change of material properties [4, 5]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 29–37, 2021. https://doi.org/10.1007/9783030647193_6
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V. D. Quoc and D. B. Minh
The method is solved with four steps: • In step 1: A submesh (SM) or subdomain (SD) involving with stranded inductors alone is ﬁrst considered. • In step 2: A shielding approximation that neglects end and border effects is then inserted with an impedancetype condition across a surface without including any stranded inductors any more. • In step 3: A volume correction is ﬁnally given to overcome inaccuracies in step 2. Nevertheless, the volume correction is often sensitive to cancellation errors in conducting regions. • In step 4: A robust improvement procedure is presented to overcome cancellation errors in step 3. Each process of the method is solved with its own SM without depending on other SMs. The extended method is developed for the bconformal formulation and is validated on the international TEAM work problem 28 [11].
2 Subdomain Technique 2.1
Deﬁnition of Coupled Subdomains
Each problem is solved on the separate SM, without solving again a new complete for each change. In this development, each subdomain is constrained by sources (which can be either volume sources (VSs) or surface sources (SSs)), where VSs point out changes of material properties of volume thin regions and SSs present changes of ICs across the surfaces from previous SMs. 2.2
Magnetodynamic Problem
A canonical magnetodynamic problem i is performed in a domain Xi , with @Xi ¼ Ci ¼ Ch;i [ Ce;i . The studied domain is split into two parts, i.e. Xi ¼ Xc;i [ XCc;i , where stranded inductors belong to nonconducting region XCc;i . The eddy current is deﬁned in conducting regions Xc;i : The set of Maxwell’s equations, associated relations and boundary conditions (BCs) of the SD i are [4–6] curl hi ¼ ji ;
ð1aÞ
div bi ¼ 0;
ð1bÞ
curl ei ¼ @t bi
ð1cÞ
hi ¼ l1 i bi þ hs;i ;
ð2aÞ
ji ¼ rp ei þ js;i ;
ð2bÞ
A Novel Method for Shielding Problems
31
½n hi Ch;i ¼ jf ;i ;
ð3aÞ
½n bi Cb;i ¼ bf ;i ;
ð3bÞ
n hi jCh;i ¼ 0;
ð4aÞ
n bi jCb;i ¼ 0;
ð4bÞ
where bi is the magnetic flux induction (T), hi is the magnetic ﬁeld (A/m), ei is the electric ﬁeld (V/m), js;i is the electric current density (A/mm2), ri is the electric conductivity, lq is the magnetic permeability and n is the unit normal exterior to Xi . The source ﬁelds (hs;i and js;i ) in (2a–2b) are VSs, where the ﬁeld hs;i is expressed changes of the permeability, and the ﬁeld js;q is presented changes of the conductivity. Hence, the changes from SD u (lu and ru ) to SD q (lq and rq ), the ﬁelds hs;q and js;q are deﬁned [4–7] 1 hs;q ¼ ðl1 q lu Þbu ;
ð5aÞ
js;q ¼ rq ru eu :
ð5bÞ
The updated ﬁelds of h and j are written as h ¼ hq þ hu ¼ l1 q bq þ bu ;
ð6Þ
j ¼ jq þ ju ¼ rq eq þ eu :
ð7Þ
The surface ﬁelds jf ;q and kf ;q in (3a–3b) are SSs. These SSs can express as discontinuities across the negative and positive side of cq in Xq , with the notation ½cq ¼ jcqþ jcq [3–6]. 2.3
Shielding Structures
The constraints between SDs in the shielding model are considered as SSs. This has done for the magnetic vector potential formulation, i.e. [3] ½n hq cq ¼ rb@t 2ac;q þ ad;q ; n hq jcqþ
1 ¼ rb@t 2ac;q þ ad;q þ 2 1 ¼ rb@t 2ac;q þ ad;q þ 2
1 ad;q n hu jcqþ lb 1 ad;q ; jf ;q lb
ð8Þ
ð9Þ
where ac;q and ad;q are respectively the continuous and discontinuity components of aq . It should be note that is equal to zero on C ts;p of the shielding, that does not allow the
32
V. D. Quoc and D. B. Minh
magnetic flux to enter there. The discontinuity n hu jcpþ in (9) is considered as a SS which will be appearing in the weak formulation.
3 Sequence of a Weak Formulations 3.1
Magnetic Vector Potential Formulations
Starting from the weak form of the Ampere’s law (1 a), the weak form of magnetic vector potential formulation for problem q (i q) is written as i.e. [4–6] 0 0 0 0 ðl1 q curl aq ; curl a q ÞXq þ ðrq @t aq ; a q Þq þ ðrq grad mq ; a q ÞXc;q þ ðhs;q ; curl a q ÞXc;q 0 0 0 þ ðjs;q ; curl a q ÞXc;q þ n hq ; a qCh;q cq þ n hq ; a qCb;q þ ½n hq cq ; a0 qcq ¼ ðjs ; a0 q ÞXs;q ; 8a0 q 2 Fq1 Xq ;
ð10Þ where the function space Fq1 Xq deﬁned in Xq is contained the basis functions for aq 0
and the test function aq as well; notation ð:; :ÞXi is a volume integral in Xq and h:; :iCq is a surface intergal on Cq of the product of their vector ﬁeld arguments. The surface integral on Ch;q is given in (3a), usually zero. The shielding problem [3] indicated in the Eq. (12) is determined via term D
E ½n hcq ; a0 q ¼ rb@t 2ac;q þ ad;q ; a0 c;q c q cq Dh i E 1 þ 12 rb@t 2ac;q þ ad;q þ lb ad;q ; a0 c;1 jf ;q
ð11Þ
c1
Robust Correction Procedure Replacing Shielding Representation The shielding solution obtained by combining Eqs. (9), (10) and (11), is now adjusted by the volume correction (e.g. problem k) via VSs ðjs;q ; curl a0 q ÞXc;q ðhs;q ; curl a0 q ÞXc;q are given by (6) and (7). These VSs have to be transferred from the mesh of the shielding problem q to the mesh of problem k by a projection method [9]. Therefore, the weak formulation for the problem k is 0
1 þ ð l1 k lp
0 ðl1 þ ðrk @t ak ; ak ÞXc;k þ ðrk grad mk ; a0 k ÞXc;k k curl ak ; curl a k ÞX Dk E þ ðrk @t ap ; a0 k ÞXc;k ¼ 0; 8a0 k 2 Fk1 ðXk Þ: ðcurl ap ; curl a0 k ÞXk þ ½n hk ct;k ; a0 k ct;k
ð12Þ At the discrete level, the source ﬁeld ap solved in the mesh of shielding problem p is projected to the mesh of problem k in (12), where Xc;k is limited to the volume correction [9].
A Novel Method for Shielding Problems
33
As presented, the volume correction in (12) is often sensitive to cancellation errors in the computation of the ﬁeld ap [5]. In order to avoid the cancellation error, it needs to be combined a problem kn with 1 1 hkn ¼ l1 kn bkn þ ðlkn lp Þðbq þ bu Þ;
ð13Þ
considering a perfect magnetic region in Xc;k (lkn ¼ 1; l1 kn ¼ 0Þ, and a problem km with 1 1 hkm ¼ l1 km bkm þ ðlkm lkn Þ bkn þ bq þ bu ;
ð14Þ
presenting a change to the actual permeability (lkm ¼ lvolume ¼ lk Þ. The problem kn þ uses a SS only contributing on the positive side of Cc;k of Cc;k , that is ½n hkn Cc;k ¼ n hkn jC þ ¼ n hq jC þ ¼ jf ;kn ; c;k
c;k
ð15Þ
with hs;kn = 0 and bkn 6¼ 0: The trace n hq jC þ originally presents in (15) for the c;k
problem p limited to Cc;k ¼ Ct;q . It maybe also naturally presented via the volume in (15), that is D
½n hkn Cc;k ; a0 k
E Cc;k
D E ¼ n hq j C þ ; a 0 k
Cc;k
c;k
0 ¼ ðl1 p curl aq ; curl a k jCc;k ÞXTL ; ð16Þ
þ where XTL in (16) is only deﬁned in one single layer of FEs touching Cc;k , because it 0
0
0
0
contributes only to the trace n ak jC þ [5] (akn ¼ akm ¼ ak ). For the problem kb, the c;k
VS has 1 hs;km ¼ ðl1 km lkn Þ bkn þ bq þ bu ;
ð17Þ
with lkm ¼ lvol ¼ lk and l1 kn = 0. Both the problem ka and problem kb gain at being solved at the same time, with the SS jf ;k ¼ jf ;kn and the resulting relation hk ¼ hkn þ hkm ¼ l1 bq þ l1 bkm þ l1 bkn þ bq þ bu p km km 1 bkm þ bq þ ðl1 ¼ l1 k k lq Þðbq þ bu Þ
ð18Þ
As the same way, in this procedure, a projection of the source ﬁeld in the added magnetic region and in the layer of FEs surrounding this region need to be performed. 1 1 hkn ¼ l1 kn bkn þ ðlkn lp Þðbq þ bu Þ;
ð19Þ
34
V. D. Quoc and D. B. Minh
considering a perfect magnetic region in Xc;k (lkn ¼ 1; l1 kn ¼ 0Þ, and a problem km with 1 1 hkm ¼ l1 km bkm þ ðlkm lkn Þ bkn þ bq þ bu ;
ð20Þ
presenting a change to the actual permeability (lkm ¼ lvol ¼ lk Þ. The problem kn þ uses a SS only contributing on the positive side of Cc;k of Cc;k , that is ½n hkn Cc;k ¼ n hkn jC þ ¼ n hq jC þ ¼ jf ;kn ; c;k
c;k
ð21Þ
with hs;kn = 0 and bkn 6¼ 0: The trace n hq jC þ originally presents in (12) for the c;k
problem p limited to Cc;k ¼ Ct;q . It maybe also naturally presented via the volume in (15), that is D
½n hkn Cc;k ; a0 k
E Cc;k
D E ¼ n h q j C þ ; a0 k
Cc;k
c;k
0 ¼ ðl1 p curl aq ; curl a k jCc;k ÞXTL ð22Þ
þ where XTL in (22) is only deﬁned in one single layer of FEs touching Cc;k , because it 0
0
0
0
constributes only to the trace n ak jC þ [5] (akn ¼ akm ¼ ak ). For the problem kb, c;k
the VS has 1 hs;km ¼ ðl1 km lkn Þ bkn þ bq þ bu ;
ð23Þ
with lkm ¼ lvolume ¼ lk and l1 kn = 0. Both the problem ka and problem k_b gain at being solved at the same time, with the SS jf ;k ¼ n hq jC þ ¼ jf ;kn and the resulting c;k
relation 1 1 hk ¼ hkn þ hkm ¼ l1 p bq þ lkm bkm þ lkm bkn þ bq þ bu : 1 bkm þ bq þ ðl1 ¼ l1 k k lq Þðbq þ bu Þ
ð24Þ
As the same way, in this procedure, a projection of the source ﬁeld in the added magnetic region and in the layer of FEs surrounding this region need to be performed.
4 Application Test The application test is an international test problem (TEAM workshop problem 28) [10] (Fig. 1). It consists of two s inductors and an above shielding plate, with lr ¼ 200; rr ¼ 34 MS=m, f ¼ 50 Hz: The winding turns of the inner inductor is w1 = 960 turns, where the outer inductor is w2 = 576 turns. The excitation currents flowing in the inductors in opposite directions is a sinusoidal function, i.e. iðtÞ ¼ 20 sinð2pftÞð AÞ.
A Novel Method for Shielding Problems
35
Fig. 1. Geometry 2D of TEAM problem 2 8 (dimensions in mm) [10].
The problem is performed with four scenarios presented Sect. 1. The magnetic vector potential on ﬁrst problem is considered with stranded inductors alone shown in Fig. 2 (top left). The shielding solution that does not include inductors anymore is then added (Fig. 2, top right). A volume correction is introduced to replaces the shielding FEs with the actual volume covering the plates and their surrounding (Fig. 2, bottom left). The robust improvement procedure is ﬁnally presented to correct cancellation errors appearing from the volume correction (Fig. 2, bottom right). The relative errors
Fig. 2. Flux lines on the magnetic vector potential for the stranded inductor alones (top level), added a shielding model (second level), corrected volume (third level) and robust improvement procedures (bottom level) (d = 10 mm, f = 50 Hz, lr = 200 and r = 34 MS/m)
36
V. D. Quoc and D. B. Minh
16
Error of B on robust procedure (%)
Error of B on volume corretion (%)
on the volume correction and the robust improvement procedure of the magnetic induction along the shielding plate, with the different thicknesses, are pointed out in Fig. 3. The inaccuracy on the volume correction can reach 14.5% for the thickness d = 10 mm, or 7% for d = 5 mm, with lr = 200, r = 34 MS/m in both cases (Fig. 3, left). Signiﬁcant errors on the robust improvement procedure are depicted in Fig. 3 (right). It is lower than 6% for d = 10 mm, or lower than 3% for d = 5 mm.
d = 3 mm d = 5 mm d = 10 mm
14 12 10 8 6 4 2 0
0.06
0.04
0.02
0
0.02
0.04
Position along the shielding plate (m)
0.06
6
d = 3 mm d = 5 mm d = 10 mm
5 4 3 2 1 0
0.06
0.04
0.02
0
0.02
0.04
0.06
Position along the shielding plate (m)
Fig. 3. Relative inaccuracies on volume correction (top) and robust improvement procedure of the magnetic flux along the shielding plate, with the different thicknesses (f = 50 Hz, lr = 200 and r = 34 MS/m).
Joule power loss density 103(W/m)
The joule power loss density on the shielding solution, volume correction and robust improvement procedure along the shielding plate is presented in Fig. 4, for d = 3 mm, f = 50 Hz, lr = 200, r = 34 MS/m. The error between the shielding solution and the volume correction reaches 62% near edges and corners, and being lower than 20% between the volume correction and the robust improvement procedure as well. Finally, the robust improvement procedure is compare to be similar the reference solution in the computation of the traditional ﬁnite element method (FEM) [7, 8]. This is an agreement to demonstrate a very suitable validation of the robust improvement procedure presented in the SDM.
25
Reference Robust procedure Volume correction Shielding solution
20 15 10 5 0
0.06
0.04 0.02 0 0.02 0.04 Position along the shielding plate (m)
0.06
Fig. 4. Joule power loss densities for the shielding solution, volume correction, robust procedure and full/complete solution along the shielding plate (d = 3 mm, f = 50 Hz, lr = 200 and r = 59 MS/m).
A Novel Method for Shielding Problems
37
5 Conclusions In this paper, the magnetic vector potential for the robust improvement procedure has been successfully proposed via a SDM in four scenarios. The development allows to correct inaccuracies around the shielding plate and cancellation errors in the magnetic region of the volume correction. In particular, the calculated result of the method is compared with the reference solution in the calculation of the FEM [7, 8]. This is a very good illustration between the studied method and the FEM. The development has been successfully solved with the linear problem in the frequency domain. The extension will be further performed in a twoway coupling [11]. The proposed method has been applied to the international TEAM workshop problem 28 [10].
References 1. Tsuboi, T., Asahara, F.K., Misaki, T.: Eddy current analysis on thin conducting plate by an integral equation method using edge elements. IEEE Trans. Magn. 33(2), 1346–1349 (1997) 2. Geuzaine, C., Dular, P., Legros, W.: Dual formulations for the modeling of thin electromagnetic shells using edge elements. IEEE Trans. Magn. 36(4), 799–802 (2000) 3. Dular, P., Vuong, Q.D., Sabariego, R.V., Krahenbuhl, L., Geuzaine, C.: Correction of thin shell ﬁnite element magnetic models via a subproblem method. IEEE Trans. Magn. 47(5), 1158–1161 (2011) 4. Vuong, Q.D., Dular, P., Sabariego, R.V., Krahenbuhl, L., Geuzaine, C.: Subproblem approach for “thin shell dual ﬁnite element formulations. IEEE Trans. Magn. 48(2), 407–410 (2012) 5. Dular, P., Sabariego, R.V.: A perturbation method for computing ﬁeld distortions due to conductive regions with hconform magnetodynamic ﬁnite element formulations. IEEE Trans. Magn. 43(4), 1293–1296 (2007) 6. Dular, P., Sabariego, R.V., Geuzaine, C., Ferreira da Luz, M.V., KuoPeng, P., Krahenbuhl, L.: Finite element magnetic models via a coupling of subproblem of lower dimensions. IEEE Trans. Magn. 46(8), 2827–2830 (2010) 7. Koruglu, S., Sergeant, P., Sabarieqo, R.V., Dang, V.Q., De Wulf, M.: Influence of contact resistance on shielding efﬁciency of shielding gutters for highvoltage cables. IET Electr. Pow. Appl. 5(9), 715–720 (2011) 8. Meunier, G.: The Finite Element Method for Electromagnetic Modeling. Wiley, Hoboken (2008) 9. Geuzaine, C., Meys, B., Hernotte, F., Dular, P., Legros, W.: A Galerkin projection method for mixed ﬁnite elements. IEEE Trans. Magn. 35(3), 1438–1441 (1999) 10. Karl, H., Fetzer, J., Kurz, S., Lehner, G., Rucker, W.M.: Description of TEAM Workshop Problem 28: An Electrodynamic Levitation Device 11. Quoc, V.D., Geuzaine, C.: Twoway coupling of thin shell ﬁnite element magnetic models via an iterative subproblem method. COMPEL Int. J. Comput. Math. Electr. Electron. Eng. aheadofprint (2020). https://doi.org/10.1108/compel0120200035
A Review on Ultrasonic Stack Modelling Ngo Nhu Khoa1, Nguyen Thi Bich Ngoc1(&), and Tran Duc Tai2 1
2
Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] Thai Nguyen Vocational School, Vietnam General Confederation of Labor, Thai Nguyen, Vietnam
Abstract. In recent years, the main processing methods for difﬁculttomachine materials have focused on the ﬁeld of ultrasonicassisted processing. Ultrasonic stack, the key part in ultrasonic equipment, is composed of transducer, booster, and horn. The present literature review aims to provide a broad overview of the recent achievement on the modal assembly of the ultrasonic vibration stack and guide the future development of ultrasonic vibration assisted technologies. With advancement of computer control in ultrasonic machining, this technology can be used for any material in future to achieve world class manufacturing. Keywords: Ultrasonic vibration Ultrasonic horn/sonotrode Ultrasonic transducer Ultrasonic booster Ultrasonic assisted machining
1 An Overview of Ultrasonic Stack and Applications The ultrasonic vibration energy used in machining can be divided into two different trends. Firstly, an ultrasonic machining (USM), is based on abrasive principle of material removal in which the tool is in the exact shape to be ground in workpiece and is attached to a horn. The second approach is based on the conventional machining technologies – ultrasonic assisted machining [1]. USM is a nonconventional mechanical machining process generally associated with low material removal rates (MRR), especially its applications are not limited by the electrical or chemical characteristics of the workpiece materials. It is used for machining both conductive and nonmetallic materials; typically for those with low ductility [2–6] and hardness more than 40 HRC [7–11], e.g. inorganic glasses, silicon nitride, nickel/titanium alloys [12– 21]; machining holes with small diameter as 76 mm [22]; the depth to diameter ratio is about 3:1 [7–11]. Before investigating more details in ultrasonic stack modelling, let’s take a moment to overlook its components. Transducer, booster, horn and tool are subcomponents of stack assembly. Transducers is basically to convert the electrical energy into ultrasonic vibration, with low amplitude and high frequency. The vibration amplitude is then increased or decreased by a booster. The horn is functioned as the ampliﬁer of the ultrasonic vibration to the desired level. Figure 1 shows the schematic layout of the ultrasonic stack assembly. The present literature review aims to provide a broad overview of the recent achievement on the modal assembly of the ultrasonic vibration stack. This study starts © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 38–52, 2021. https://doi.org/10.1007/9783030647193_7
A Review on Ultrasonic Stack Modelling
39
Fig. 1. Schematic layout of ultrasonic stack assembly [23, 24]
with basic conﬁguration and design guidelines. The breadth of this report focuses on the background and practical modeling issues that affect simulation strategies.
2 Ultrasonic Horn The horn is variously referred to as an acoustic coupler, velocity/mechanical transformer, tool holder, concentrator, stub or sonotrode, the most critical and influential component in the ultrasonic stack that contacts the workpieces, is designed to provide the optimum contact area and amplitude of vibration to the workpieces [24]. In general, horns are located between ultrasonic transducers and workpieces. It works as a magniﬁer of the mechanical vibration produced by the ultrasonic transducer and transmits the vibration to the workpiece. This phenomenon is similar to the electric transformer in electric circuit. Therefore, apart from the role as mechanical displacement magniﬁcation, it also acts as mechanical impedance matching ultrasonic transducers and loads to achieve high energy transmission efﬁciency; and/or the isolator of the ultrasonic transducer from working environments of ultrasonic energy as high temperature, highly corrosive, and other extreme situations which are harmful to the ultrasonic transducers [25]. The two ends of the horn move in opposite directions to lengthen or shorten the horn at its resonant frequency. The critical point of horn design is that resonance frequency of horns must match the working frequency of transducers. Stress concentration is largest in the horn’s center or nodal area although no longitudinal motion occurs at this area. The horn design is based on the workpieces’ material, shape, size and proﬁle. The amplitude of vibration in a horn depends on the horn material. Such materials which have good acoustic properties and resistance to fatigue cracking could have maximum amplitude. The most often used metals are monel, titanium, stainless steel, heat treated steel and aluminum [1]. Titanium has the best acoustical properties
40
N. N. Khoa et al.
and high fatigue strength welladapt for withstanding high cycle rates at high amplitudes [26]. On the other hand, the shape of a horn can be classiﬁed in two different groups: longitudinal and transverse cross section. Longitudinal cross section can be classiﬁed as stepped, exponential, conical, catenoidal shape [27–30], while the transverse cross section includes round and rectangular [31]. Normally, the area of cross sections at the larger end and the smaller end are in the ratio 2:1 ratio for steel and in the ratio 3:1 for titanium [32]. Among the commonly used horns, stepped horns have the largest displacement ampliﬁcation [33]. Generally, the crosssection variation is given by different mathematical functions (exponential [34, 35], linear [36], catenoidal [36], tapered (or stepped) [37], Bezier [33], Gaussian [38], Fourier [39]). Compared with the commonly, horns with parametric curve proﬁles as Bezier, Gaussian, Fourier may offer higher displacement ampliﬁcation. Many studies investigated in the wave propagation and horns design [40–43]. A problem that is associated with the horns design refers to the shape optimization. The optimization procedure is related to design parameters as working frequency [43], signal amplitude [44, 45], load variation in the manufacturing area [46], the objective function [47, 48], the impact of different design variables on the objective function [49], combined signals transmitted in horns [50], etc. Some authors [51, 52] have suggested that at constant amplitude, MRR is proportional to the second order of frequency, f, of 400 Hz. At higher frequencies, up to 5 kHz, this is a linear relationship instead. Above an upper threshold value, MRR decreases speedily and is proportional to square root of f [4, 52]. Researchers in [7, 20] showed that, in practice, when static load climbs up from zero value, with other parameter values remain constant, MRR and load are roughly linear. Above an optimum value, the fact that MRR decreases will lead to the abrasive grains size reduction and insufﬁcient slurry circulation [7, 14, 51, 53–57]. It is indicated that when static load is smaller than optimal value, abrasive wear will be gradually decrease and tool life will increase. The performance of ultrasonic horn can be described by many parameters, which are resonance frequency, ampliﬁcation factor, shape factor, input force impedance and bending stiffness. An important aspect of horn design is the calculation of the correct resonant length, in which resonant length should usually be in multiples of half the wavelength of the system [35]. The half wavelength is generally used for ultrasonic metal welding to for material saving and reducing manufacturing cost. The magniﬁcation factor is the ratio of the displacement or velocity at the output end and the input end; the shape factor is a critical value to measure the maximum vibration velocity that horns can achieve. The bending stiffness is an issue to present flexible ability. The longer the ultrasonic horn is, the greater the flexibility is. Bending stiffness is also related to the geometry of the horn [58]. There are studies on theoretical design of horn as presented in [6–8, 59, 60]. Traditional methods of acoustic horn design are based on a differential equation which considers the equilibrium of an inﬁnitesimal element under the action of elastic and inertia forces, which is then integrated over the horn length to achieve resonance frequency [7, 8, 61]. Recently, numerical analysis based on FEM is more preferred for analysis of horn design than analytical and experimental methods because the solution of wave equation through analytical method is very difﬁcult and experimental approach
A Review on Ultrasonic Stack Modelling
41
is not reproducible and time consuming. The modelling programs used are ANSYS [27, 29, 35, 58, 62–66], ABAQUS [32, 67], CAD [31, 68], SOLIDWORKS [69], Elmer FEM [48] … A systematic methodology as shown in Fig. 2 is carried out for designing a stepped horn using FEA using ABAQUS with the following objectives: 1) Extracting mode shapes and corresponding natural frequencies of the horn. CAD/CAE is applied to model the proﬁle based on theory. 2) Determining the dynamic properties, design parameters as amplitude gain and von Mises stresses in the stepped horn. 3) The performance of horns by experiments and then compare them for optimum values. FEM has also been used to assess the working stresses to ensure safe stress limits [70]. The uniformity of the horn vibration limits the process to the cutting of small shapes typically less than 100 mm diameter. When the tool length is excessed to 2– 3 mm, the resonance normally reduces by approximately 0.5–1 kHz [71], but in case of drilling very deep holes, this loss can be solved by making the tool selfresonate [72]. A ratio of tool length to diameter less than 20:1 is practically preferred [26], but when the length is larger than 10 mm, the horn must be shortened by an amount equivalent to the weight of the tool [73]. Beside the fact that these researches were carried out theoretically, there are some assumptions applied to validate the modelling results as during the design, the width and thickness at the two ends of the horn are considered as a constant because of the dependence of the length horn and the ampliﬁcation factor. because of the tool’s light weight, it is not considered during the design process, and so, it does not affect the condition of resonance. Furthermore, the holes for attaching the horn to the transducer are also neglected during designing of a horn. As for the measurement errors, the following factors should be considered. First, the real material parameters of the horn are not exactly as in theories. Second, in experiments, to measure horn parameters, the central prestressed metal bolt, which is not considered in textbooks, is used to clamp the metal cylinders and the piezoelectric material. Moreover, the experimental horns are not completely free from external forces, and this is not consistent with the assumption that the load mechanical impedance is zero and the boundary of the horns is free.
3 Ultrasonic Transducers Ultrasonic transducers/Converter/Probe are the most signiﬁcant component of an ultrasonic system which mostly affect the precision and accuracy of the measurements. An ultrasonic transducer is to convert mechanical displacement to electrical voltage (direct piezoelectric effect) or vice versa (reverse piezoelectric effect). Therefore, it can be acted as either a transmitter or receiver for generating or detecting ultrasonic energy. Vibration transmit through a transducer in a longitudinal or compressive mode. A typical piezoceramic transducer is shown in Fig. 3 [74]. The piezoelectric material is placed between two electrodes and a backing material and functions to broaden the bandwidth and reduce ultrasound generation artifacts from the back side. The upper electrode is normally connected to ground to minimize noise from surroundings. An acoustic matching layer is located at the front of the transducer to compensate for the acoustic mismatch between the transducer and the medium.
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Selection of vibration frequency
Selection of type and materials
Calculation of wavelength
Calculation of theoretical dimensions
CAD Model of Sonotrodes Governing equation, boundary conditions, loads, material properties and element type FEM Model of sonotrodes
Modal and Harmonic analysis
Design requirements
Fabrication of sonotrodes
Experimental Trials
Conclusions
Fig. 2. Methodology for design of ultrasonic horn [32]
In mass production, a magnetostrictive [53, 65, 75] or a piezoelectric transducer is usually used. Magnetostrictive devices can be suitable for a wide frequency range (i.e. 17–23 kHz) and greater horn design flexibility, where the horn can be redesigned several times without critical amplitude errors [5, 76]. However, magnetostrictive transducers have high electrical losses and low energy efﬁciencies ( < Pi Ui Uj Gij cosdij þ Bij sindij j2i P ð5Þ Q U Uj Gij sindij þ Bij cosdij > i i : j2i
Qi;min Qi Qi;max ; i 2 Reactive power supply set
ð6Þ
Vi;min Vi Vi;max ; i 2 1; 2; . . .; N
ð7Þ
Sl Slmax ; i 2 1; 2; . . .; NL
ð8Þ
where, Qi;min , Qi;max are the upper and lower limits of reactive power output power; Vi;min , Vi;max are the upper and lower limits of voltage amplitude of load nodes; Sl is the power passing through branches; Gij and Bij are members of the matrix for nodal admissions; Slmax is the upper limit of line capacity; Vi is the voltage amplitude of the ith load node; N, NL are the number of system nodes and lines.
3 Enhancing PSO with Multiobjective Function 3.1
Basic Particle Swarm Optimization
There are N particles in the ddimensional search space [12]. The position of the ith particle is expressed as Xi and the velocity is Vi . The best position it has experienced is Pid , and the best position the whole population has experienced is Pgd . Each particle updates its own velocity and position according to the following Eq. (14) vtidþ 1 ¼ wvtid þ c1 randðÞ Ptid xtid þ c2 randðÞ Ptgd xtid tþ1 ¼ xtid þ vtid xid
ð9Þ ð10Þ
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where: t is the number of iterations; D is the ddimensional variable in the ddimensional search space; c1 and c2 are the weight factors; rand () is the random number generated in the [0,1] interval; w is the inertia weight coefﬁcient. The following formula is used for adaptive adjustment w ¼ wmax iðwmax wmin Þ=imax
ð11Þ
where imax is the maximum value of iteration, and i is the current number of iterations. PSO generally adopts real number coding [12], that its structure is relatively simple, and its running speed is very fast. However, it is easy to fall into local optimum, that is, premature convergence phenomenon. 3.2
Multiobjective Optimization
Due to the ease, randomness, and regularity of its variables, the premature phenomenon of the local optimal solution problem of chaos optimization algorithm may be dealt with by jumping out of the loop. Search space reduction process of optimization variables [15] is used to boost the performance of the search accuracy. The multiobjective optimization problem mathematical model is deﬁned as follows. X ¼ ðx1 ; x2 ; . . .; xn Þ
ð12Þ
minF ð xÞ ¼ ff1 ð X Þ; f2 ð X Þ; . . .; fk ð X Þg
ð13Þ
s:t X 2 S ¼ fXjgi ðXÞ 0;
i ¼ 1; 2; . . .; mg
ð14Þ
where: X is the ndimensional decision variable vector; F is the objective functions; gi ð X Þ is the ith constraint condition; S is the feasible region of the decision variable. Control variables u; v 2 S, if satisﬁed 8i 2 f1; 2; . . .; k g; fi ðuÞ fi ðvÞ
ð15Þ
9i 2 f1; 2; . . .; k g; fi ðuÞ fi ðvÞ
ð16Þ
Then u dominates v, u v. Let X 2 S, if and only if there is no X in S so that X x holds, then x is called the Pareto optimal solution of a multiobjective optimization problem [17]. All Pareto optimal solutions constitute the optimal solution set, which is also the global optimal solution set. 3.3
Enhancing PSO Multiobjective
Selection of Optimal Solution of PSO: The main idea of multiobjective PSO based on Pareto optimal concept is to form a nondominated set, and make the nondominated solution set to approach the Pareto optimal solution set through iteration, and ﬁnally achieve the optimization purpose [17, 18]. When the optimal global solution is selected, one individual is randomly selected as the optimal global solution according
A Solution to Power Load Distribution
57
to the probability; otherwise, the minimum distance between each individual in the elite solution and other individuals is calculated, and the largest individual is selected as the optimal global solution. Chaos Optimization of the Optimal Solution: The chaotic search space is determined by the position of the optimal global solution of PSO. Through the inverse mapping back to the original solution space, the ﬁtness value of each feasible solution experienced by the chaotic variable is calculated in the original solution space, and the best feasible solution is retained, and a particle in the current population is randomly replaced to improve the position of the particle so that the particle swarm can fly in a better direction and overcome premature convergence. Solving Non Dominated Sets: The fast non dominated sorting method is generally used to judge the dominating relationship of solutions [17]. However, when the proportion of nondominated individuals in the population is high, a slow chain may appear in the late sorting stage. Experiments show that this method can effectively solve the slow chain problem [17, 19]. Using Elite Archiving Technology: The elitist set is used to save the nondominated optimal solution found in the iteration, which is regarded as the optimal global candidate set of each particle in the particle swarm optimization. The ﬁnal solution retained by the external elite set is the result of the algorithm. After each external iteration, only some particles that dominate the elite set in the nondominated set or those that have no dominating relationship with all particles in the elite set are added to the elite set, and the dominated particles in the elite set are eliminated. Cluster Analysis is Used to Maintain the Capacity of Elite Set: The multiobjective optimization algorithm requires not only good convergence but also good distribution in the whole Pareto solution space. In this paper, the clustering algorithm is used to keep the distribution performance of the solution and ensure the capacity of the elite set. Since the clustering algorithm needs to calculate the distance between all particles, this paper adopts the hierarchical clustering algorithm based on the kernel like distance [17, 20]
4 A Solution to Optimal Load Distribution Treatment of Constraints: If the power flow calculated converges, the power balances condition Eq. (5) the equality constraint can be satisﬁed [3]; if the power flow does not converge, the ﬁtness value of the individual to the objective function is designated as a larger value, which makes it unable to dominate in the optimization process. The control variables are limited to the allowable range during initialization and each update, so the inequality constraint (3) can also be satisﬁed [19]. The real constraint condition to be considered is Eqs. (6)–(8) that is transformed into a penalty function and added to the objective function as an objective minimization function.
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minf ¼ k1
NG ND Nl X X X Vi Vilim Qi Qilim Sl Sllim þ k2 þ k3 ð17Þ Vimax Vimin Qimax Qimin SlmaX i¼1 i¼1 i¼1
where k1 , k2 and k3 are penalty factors. Vilim ; Qilim ; Sllim are deﬁned as 8 < Vimax ; Vi [ Vimax Vilim ¼ Vimin ; Vi \Vimin : Vi ; others
Qilim
8 < Qimax , Qi [ Qimax ¼ Qimin , Qi \Qimin : Qi , others
Sl ¼
Slmax ; Sl [ SlmaX Sl ; Sl \SlmaX
ð18Þ
ð19Þ
ð20Þ
Data Standardization: In the analysis of elitist disaggregation class, due to the different dimensions of coal consumption and network loss, using the original data directly may highlight the effect of those indexes with huge order of magnitude on clustering, which will completely change the clustering results once the indicators are changed. Therefore, it is necessary to standardize the original data to make each index value uniﬁed in common within the range of data characteristics. The original data are standardized twice to ensure the dispersion of the solution after clustering analysis. For the ﬁrst time, the standard deviation is used to normalize the original data according to Eq. (21), and then the range standardization is used to normalize the results obtained for the ﬁrst time according to Eq. (22). yij yj rj
ð21Þ
xij xjmin xjmax xjmin
ð22Þ
xij ¼ 0
xij ¼
where yij is the data of the original matrix; yj and rj are the mean value and standard 0 deviation of each column of the matrix; xij and xij are the results after two standardization treatments. Determination of Optimal Compromise Solution: After getting Pareto solutions, a satisfactory solution is usually needed to choose as the ﬁnal result, which is a decisionmaking mechanism. Moreover, the fuzzy membership function is used to represent the satisfaction of each objective function in each Pareto solution
A Solution to Power Load Distribution
ui ¼
8 < 1;
fi \fi;min fi;max fi fi;min \fi \fi;max ; : fi;max fi;min 0;
59
ð23Þ
fi;max \fi
where fi is the ﬁtness value of the ith subobjective, and the subscripts max and min are the upper and lower limits of the ﬁtness function. ui ¼ 0 is completely dissatisﬁed with the value of an objective function; ui = 1 means that it is completely satisﬁed. For each solution in the Pareto solution set, the following Eq. is used to solve the standardized satisfaction value u ¼ k
Nobj X i¼1
uki =
Nobj M X X
! uki
ð24Þ
k¼1 i¼1
where: uk is the standardized satisfaction value of each solution; Nobj is the number of objective functions to be optimized; M is the number of elite solutions. The optimal compromise solution is the solution with the maximum standardized satisfaction value. Figure 1 shows the flowchart of the proposed EPSO for dispatch of the PLD problem.
Fig. 1. Flowchart of the proposed EPSO for dispatch the PLD problem
The speciﬁc calculation steps of applying the EPSO algorithm based on Pareto solution set to solve the multiobjective economic load distribution of PLD problem are as follows: Step 1 Initialization: set the data of the power system Step 2 The parameter, e.g., iterations, i ¼ 1, solutions n are randomly generated in the range of control variables, the individual, and global optimal of each particle is the initial positions, and the elite set is set to null. Step 3 According to the control variable value of the particle position, the power flow of the whole system is calculated, and the adaptive costs of the three
60
Step 4
Step 5 Step 6
Step 7
T.G. Ngo et al.
objective functions, namely, the active power network loss, the coal consumption of the unit and the penalty term, under the optimal scheme represented by each particle, are calculated. According to the Pareto dominance, the advantages and disadvantages of particles are compared, and the non dominated solution set is constructed according to the improved fast non dominated sorting method, and it is added to the elite set to determine the individual optimal solution. The elite set is updated and maintained to determine the optimal global solution According to Eq. (9) (10), the velocity and position of particles are updated, and the current particle swarm optimization is carried out according to a certain probability. Judge whether the completion criteria are met, and the maximumiteration or the objective function value corresponding to the optimal solution are reached. When the variable is less than the given value within the given iteration steps, the optimization is stopped and the result is output; otherwise, the iteration times i ¼ i þ 1, return to step 3
5 Experimental Results and Discussion The IEEE57 bus system is used to test the proposed scheme performance [21]. The detail of the system includes 57 nodes, 80 branches, 50 loads, seven generators, and the reference power is set to Pr (in which Pr =100, 150, 200 MVA). The obtained outcomes of the proposed scheme are compared with the other plans, e.g., Genetic algorithm (GA) [9] and Particle swarm optimization (PSO) [14] for the PLD problem. The algorithm parameters are set as follows. The population size is 50; the maximum iteration times is 2000; the elite set size is set to 100 for the algorithms. The constants c1 and c2 are set 1.45 for the PSO and EPSO respectively. The parameters of the crossover and mutation rate are set to 0.4 and 0.05, respectively, for the GA algorithm. In the system, unit 1 is the balance node, assuming that it is a hydropower unit, and the rest units are thermal power units. After energysaving scheduling, it is determined that all units will be started and operated. Finally, a set of Pareto optimal solutions are obtained by repeating the experiment 50 times. Table 1 shows the results of the system in different optimization schemes, e.g. the proposed EPSO, PSO, and GA schemes for the PLD problems. It can be seen that the proposed EPSO provides the outstanding performance of the algorithms in comparison.
A Solution to Power Load Distribution
61
Table 1. The optimal power obtained outcomes for systems of six generators
Figure 1 depicts the obtained outcomes of the proposed scheme compared with the other plans, e.g., Genetic algorithm (GA) and Particle swarm optimization (PSO) for the PLD problem.
3000 2000 1000 0
PSO (a) Comparison of the obtained result of the proposed EPSO scheme with the GA and PSO for the objecve funcon
EPSO
(b) Comparison of the power load distribuon of objecve values of two schemes of EPSO and PSO methods
Fig. 2. The obtained outcomes of the proposed EPSO scheme are compared with the GA and PSO for the PLD problem
Figure 2(a) displays the comparison of the obtained result of the proposed EPSO scheme with the GA and PSO for the objective function of the PLD problem in terms of the convergence speed. It is clearly seen that the proposed system offers faster than the GA and PSO algorithms. Figure 2(b) compares the outcomes of the power load distribution of objective values of two schemes of EPSO and PSO methods. It can be seen that the proposed system produces less loss of power load than the GA and PSO algorithms for the power load distribution.
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Table 2. The results comparing of different optimization methods for IEEE 57 bus system PSOscheme MW MW MW Network loss/MW 18.4 34.0 26.4 Coal, fuel consumptions 2090 1503 1668
EPSOscheme MW MW MW MW 18.8 33.2 26.4 23.5 2087 1506 1658 1774
Table 2 shows the results of the system in different optimization schemes, e.g., the proposed EPSO, PSO, and GA schemes for the PLD problem. The projects of the EPSO, GA, and PSO schemes adopt the objective network loss minimization and accurate fuel consumption minimization under considering constraint conditions of the upper and lower limits of reactive power outputs. It can be seen that the proposed EPSO provides the outstanding performance of the algorithms in comparison.
6 Conclusion In this paper, a solution to the multiobjective optimal load distribution problem was suggested based on hybridizing enhanced particle swarm optimization (EPSO) with Pareto. Multiobjective load dispatch model for minimal active network loss and minimum coal consumption of generating sets has been modeled based on the traditional load distribution dispatch and combining energy conservation dispatching goals. The built model of energy conservation systems has optimized based on the developing Pareto optimumbased multiobjective EPSO and extended optimum multiobjective load dispatch. In the simulation section, the benchmark of IEEE 57bus was used to test the proposed scheme performance. The results compared with the other methods of the test power system show that the proposed method reduced the net loss of power system and coal consumption of generating sets and energy sources conserved while meeting the security constraints of the power system. The proposed method also made available to a community of Pareto optimal solution meaning more decisionmakers efﬁcient comparisons.
References 1. Tsai, C.F., Dao, T.K., Pan, T.S., Nguyen, T.T., Chang, J.F.: Parallel bat algorithm applied to the economic load dispatch problem. J. Internet Technol. 17, 761–769 (2016) 2. Chiang, C.L.: Improved genetic algorithm for power economic dispatch of units with valvepoint effects and multiple fuels. IEEE Trans. Power Syst. 20, 1690–1699 (2005) 3. Huneault, M., Galiana, F.D.: A survey of the optimal power flow literature. IEEE Trans. Power Syst. 6, 762–770 (1991) 4. Nguyen, T.T., Wang, M.J., Pan, J.S., Dao, T., Ngo, T.G.: A load economic dispatch based on ion motion optimization algorithm BT. In: Pan, J.S., Li, J., Tsai, P.W., Jain, L.C. (eds.) Advances in Intelligent Information Hiding and Multimedia Signal Processing, pp. 115–125. Springer, Singapore (2020)
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5. Nguyen, T.T., Pan, J.S., Dao, T.K.: A novel improved bat algorithm based on hybrid parallel and compact for balancing an energy consumption problem. Information 10 (2019) 6. Dao, T.K., Pan, T.S., Nguyen, T.T., Chu, S.C.: Evolved bat algorithm for solving the economic load dispatch problem. In: Advances in Intelligent System Computing, pp. 109– 119 (2015) 7. Wang, C.H., Nguyen, T.T., Pan, J.S., Dao, T.K.: An Optimization Approach for Potential Power Generator Outputs Based on Parallelized Firefly Algorithm (2017) 8. Dao, T., Yu, J., Nguyen, T., Ngo, T.: A hybrid improved MVO and FNN for identifying collected data failure in cluster heads in WSN. IEEE Access 8, 124311–124322 (2020) 9. Devaraj, D., Roselyn, J.P.: Genetic algorithm based reactive power dispatch for voltage stability improvement. Int. J. Electr. Power Energy Syst. 32, 1151–1156 (2010) 10. Chu, S.C., Dao, T.K., Pan, J.S., Nguyen, T.T.: Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classiﬁcation. Eurasip. J. Wirel. Commun. Netw. 52(1–16) (2020) 11. Nguyen, T.T., Dao, T.K., Kao, H.Y., Horng, M.F., Shieh, C.S.: Hybrid particle swarm optimization with artiﬁcial bee colony optimization for topology control scheme in wireless sensor networks. J. Internet Technol. 18, 743–752 (2017). https://doi.org/10.6138/jit.2017. 18.4.20150119 12. Shi, Y., Eberhart, R.: A modiﬁed particle swarm optimizer. In: 1998 IEEE Int. Conf. Evol. Comput. Proceedings. IEEE World Congr. Comput. Intell. (Cat. No.98TH8360), pp. 69–73 (1998). https://doi.org/10.1109/icec.1998.699146 13. Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Probl. Eng. 2015 (2015) 14. Pandya, S., Roy, R.: Particle swarm optimization based optimal reactive power dispatch. In: 2015 IEEE International Conference on Electrical Computer Communication Technology, pp. 1–5. IEEE (2015) 15. Nguyen, T.T., Pan, J.S., Dao, T.K.: An improved flower pollination algorithm for optimizing layouts of nodes in wireless sensor network. IEEE Access 7, 75985–75998 (2019). https:// doi.org/10.1109/access.2019.2921721 16. Zakariazadeh, A., Jadid, S., Siano, P.: Economicenvironmental energy and reserve scheduling of smart distribution systems: a multiobjective mathematical programming approach. Energy Convers. Manag. (2014). https://doi.org/10.1016/j.enconman.2013.10.051 17. Tripathi, P.K., Bandyopadhyay, S., Pal, S.K.: Multiobjective particle swarm optimization with time variant inertia and acceleration coefﬁcients. Inf. Sci. (Ny) 177, 5033–5049 (2007) 18. Dao, T.K., Pan, T.S., Nguyen, T.T., Chu, S.C.: A compact Articial bee colony optimization for topology control scheme in wireless sensor networks. J. Inf. Hiding Multimed. Signal Process. 6, 297–310 (2015) 19. Mostaghim, S., Teich, J.: Strategies for ﬁnding good local guides in multiobjective particle swarm optimization (MOPSO). In: Proceedings of 2003 IEEE Swarm Intelligent Symposium SIS 2003 (Cat. No. 03EX706), pp. 26–33. IEEE (2003) 20. Nebro, A.J., Durillo, J.J., GarciaNieto, J., Coello, C.A.C., Luna, F., Alba, E.: SMPSO: a new PSObased metaheuristic for multiobjective optimization. In: 2009 IEEE Symposium on Computing Intelligent MultiCriteria Decision, pp. 66–73. IEEE (2009) 21. Sinha, A.K., Hazarika, D.: A comparative study of voltage stability indices in a power system. Int. J. Electr. Power Energy Syst. 22, 589–596 (2000)
A Study for Determination of the Pressure Ratio of the V12 Diesel Engine Based on the Heat Flow Density to Cooling Water Kien.Nguyen Trung1,2(&) 1
Faculty of Vehicle and Energy Engineering, PHENIKAA University, Hanoi 12116, Vietnam [email protected] 2 PHENIKAA Research and Technology Institute (PRATI), A&A Green Phoenix Group JSC, No.167 Hoang Ngan, Trung Hoa, Cau Giay Hanoi 11313, Vietnam
Abstract. Improving engine power density by using supercharging system with exhaust gas energy recovery (called an exhaust gas turbocharger) is a trend of improving nonturbocharged base engines in Vietnam. These engines are still capable of being used for a considerable time, but the power generating is not big enough to meet the requirements of improving vehicle maneuverability, especially special vehicles such us Russian or Vietnamese tanks. To determine the maximum level of pressure ratio of the engine according to the engine thermal load criteria, we can use direct and indirect indicators. The content of the paper is to determine the heat flow density for cooling water in the V12 engine according to the different pressure ratios. From that, the maximum level of pressure ratio for the engine is 2.1, but the safety limit allowing the heat to flow to the engine coolant to be guaranteed. Keywords: Special vehicles V12 engine Turbocharger Heat flow density Pressure ratio Thermal load
1 Introduction The maximum power a given engine can deliver is limited by the amount of fuel that can be burned efﬁciently inside the engine cylinder. This is limited by the amount of air inducted into each cylinder each cycle. If this air is compressed to a higher density than ambient, prior to entry into the cylinder, the maximum power an engine of ﬁxed dimensions can deliver will be increased. This is the primary purpose of supercharging. Today, one of the important development trends of internal combustion engines is the trend of “downsizing” (reducing combustion chamber capacity but still maintaining high power of the engine). In order to accomplish this, one of the technologies that the engine manufacturers in the world pay attention to and apply is the technology of supercharging, especially turbocharging method, where a turbocharger (a compressor and turbine on single shaft) is used to boost the inlet air density. Energy available in the engine’s exhaust stream is used to drive the turbocharger turbine which drives the turbocharger compressor and raises the inlet fluid density prior to entry to each engine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 64–74, 2021. https://doi.org/10.1007/9783030647193_9
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cylinder [6, 7]. For old engines, increasing engine power by turbocharger is limited for two reasons: mechanical stress and thermal stress of the components surrounding the combustion chamber. Almost no work has mentioned the thermal load of the engine. In the theoretical documents there have been no issues of pressure ratio effects on the thermal load and quantiﬁcation of the relationship between the pressure ratio and the engine’s thermal load. Therefore, to determine the turbocharging limit of the used engine, it is necessary to study the effect of the pressure ratio to the engine’s thermal load [1, 5]. Documents published just talk very little about this matter, and there have not been studies to determine the relationship between the pressure ratio and the engine’s the thermal load. Therefore, to determine the allowed pressure ratio of the used engine, it is necessary to study the thermal load. To assess the thermal load of the internal combustion engine, in theory, we often use direct and indirect indicators. The direct speciﬁcations are the thermal stress of the components and the maximum permissible temperature at the featured surfaces. Indirect speciﬁcations include: the heat flow density to cooling water, assumed heat stress parameters, exhaust gas temperature and the temperature of the pistoncylinder group where possibly measured [2]. From these thermal load parameters, the paper will focus on the impacts of the pressure ratio on the heat flow density to cooling water when the engine turbocharges so that the maximum pressure ratio could be deﬁned. Besides, the oxygen necessary for the combustion is extracted from the air introduced into the working chamber. Therefore, with increasing fuel quantity added to the cylinder, naturally the amount of heat to be dissipated increases as well. The heat flows through the engine increase correspondingly. Additionally, at higher degrees of supercharging and without charge air cooling, the temperature of the charge air increases signiﬁcantly, which results in further increased engine thermal loads. Therefore, to deﬁne the optimum pressure ratio of the old engine we have to research that effect on the heat flow density to cooling water. For engine manufacturers, when a new engine is designed and manufactured, the turbocharging issue has been calculated clearly by the manufacturer. The type of conversion from nonturbocharging engines to turbocharged engines is not the main research direction of developed countries; this problem is only suitable for developing countries like Vietnam [1, 5].
2 Numerical Study The heat flow density to cooling water is the ratio of the heat rejection rate to coolant in 1 s with the entire surface area inside the parts in contact with the incylinder gases [2], [kW/m2]. q¼
Qcool F:i
ð1Þ
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where: + F  area of the entire internal surface of the parts in contact with the incylinder gases; F ¼ 2:
p:D2 p:D2 e:p:D:S þ pD:ðS þ HC Þ ¼ þ e1 4 2
ð2Þ
+ i  the number of cylinders; + Qcool  heat rejection rate to coolant, [kJ/s] and determined from the heat balance equation. The object of study selected for simulation and experiment was a V12 engine, which is a highspeed diesel engine, a periodic cooling water. The engine consists of 12 cylinders, arranged in a Vshape, with a 60° angle between their axis and equipped on Russian and Vietnamese tanks. The main speciﬁcations of V12 diesel engine are showed in Table 1.
Table 1. Speciﬁcations of V12 diesel engine under study [8, 9] Parameters Number of cylinders Engine type
The working order of the cylinders Compression ratio Max. power Engine speed at the max. power Max. torque Engine speed at the max. torque Speciﬁc fuel consumption
Symbol i V12
Value Unit 12 – Diesel, the vee (V) arrangement, the twelve cylinders are arranged in two banks of six, with a 60° angle between their axis. 1L6R5L2R3L4R6L1R2L5R4L3R e 15±0.5 Ne.max 520 [HP] 387.4 [kW] nN 2000 [rev/min] Me.max 2256.3±10 [N.m] nM 1200 [rev/min] Ge.min 265±5 [g/kW.h]
Simulation of the V12 engine was made by GTPower software as shown clearly in Ref [4], the heat flow density to the cooling water and the energy balance of the original V12 engine at speeds of 2000 [rev/min] and 1200 [rev/min] corresponding to with loads of 50%, 60%, 75% and 100% of full load are presented in Table 2 and Fig. 1.
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Table 2. Energy balance of the original V12 engine Load [%] n [rev/min] Energy water Qe [kW] 100 2000 388.00 1200 291.00 75 2000 290.00 1200 226.00 60 2000 238.00 1200 191.00 50 2000 191.00 1200 156.00
balance and the heat flow density to the cooling Qcool [kW] 244.74 158.54 190.66 115.42 152.95 87.45 142.56 68.99
Qeg [kW] 441.48 278.84 383.14 239.66 365.32 226.48 327.14 201.08
Qoil [kW] 82.72 56.03 66.38 44.82 58.22 38.83 50.82 33.13
Qmisc QT [kW] [kW] 23.61 1180.56 16.01 800.42 18.98 949.17 12.77 638.68 16.62 831.11 11.10 554.86 14.52 726.04 14.20 473.40
q [kW/m2] 161.66 104.72 125.94 76.24 101.03 57.76 94.17 45.57
Fig. 1. The heat flow density to cooling water of the original V12 engine at speeds of 2000 [rev/min] and 1200 [rev/min] corresponding to with engine load of 50%, 60%, 75% and 100%
According to [8, 9], the maximum allowed heat flow density to the cooling water is 211 [kW/m2]. With the engine energy balance shown in Table 2, the study of the ability to enhance the power density of the V12 engine can be completed by an exhaust gas turbocharger.
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3 Experimental Study 3.1
Flow Measurement of Cooling Water
To measure the circulating coolant flow in the engine, the water temperature range of 70 90 [°C] and 105 [°C] allows it to be maintained in a short time and requires no cut off or partially destroys the pipe. The author chooses the DYNAMETERS DMTFH ultrasonic flow sensor to do this experimental content. The installation diagram on the engine test platform is shown in Fig. 2.
Fig. 2. Flow measurement of cooling water
3.2
Test Facilities
The experiments were carried out in one of the engine test cells of the Factory Z153 the General Department of Techniques, Vietnam Ministry of National Defense. The computer systems were located in the control room of the test cells. When a stable working mode of the engine was established, measurement data on the display devices were collected.
4 Results and Discussion Results of the cooling water flow rate in the cooling system, inlet water temperature, outlet water temperature of the original V12 engine and the heat flow density (q) by experiment are shown in Table 3.
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Table 3. Results of the cooling water flow rate in the cooling system, inlet water temperature, outlet water temperature of the original V12 engine and the heat flow density according to the different loads and speeds by experiment Modes
100% 2000 1200 75% 2000 1200 60% 2000 1200 50% 2000 1200
[rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min]
The cooling water flow rate Gn [kg/s] Gn [m3/h] 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64
Cooling water temperature Tout [°C] Tin [°C] 95 88 92 85 85 80 83 78 78 74 75 71 73 69 70 67
q [kW/m2]
160.22 92.36 114.45 65.97 91.56 52.78 91.56 39.58
The objective of supercharging is to increase the charge air density of the working medium before it enters the work cylinder to precompress the charge. The density increase in the working medium leads to the increases in the power density. It can also be used to improve the combustion process with the aim to achieve lower exhaust gas and/or noise emissions. To describe the pressure ratio p2/p1, the ratio between start and end pressure of the compression, the symbol pk is frequently used: pk = p2/p1. To ensure the desired level of the pressure ratio, there are various measures used to adjust the end pressure of the compression, such as: removing air after compressor back to the suction line of the compressor, discharging exhaust gas before entering the turbine, etc. In this article, the author uses the method of changing the amount of fuel injected per cycle to obtain the desired level of the pressure ratio [4, 7]. The pressure ratio: " # k1 T3 p4 k ðpk Þ ¼ 1 þ :gTC : : 1 T1 p1 mC k1 k
:
mT
ð3Þ
:
with: mT  the mass flow through the turbine; mC  the mass air flow through the compressor; T3  the temperature of exhaust gas into the turbine; T1  the temperature of the air into the compressor; ηTC  the charger total efﬁciencies; p4/p3  turbine pressure ratio. First of all, a turbocharged engine model was built with GTPower software and veriﬁed the simulation model with experiment. Then, with this model, the author set up the optimum run {Run ! Optimizer (Direct)} in GTPower software to determine the amount of fuel injected per cycle (Dgct) with a level of desired pressure ratio (pk) in each survey mode [4, 10–12]. The setup runs optimally as follows: – Input parameter: the desired pressure ratio value – Optimal parameter: excess air coefﬁcient or the relative air/fuel ratio k – Parameter changed to obtain the optimal parameter: the amount of fuel injected per cycle (Dgct). – Speciﬁc results are presented in Fig. 3.
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Fig. 3. The amount of fuel injected per cycle according to the pressure ratios at the different engine loads and speeds
Results of the cooling water flow rate, the inlet water temperature, the outlet water temperature and the heat flow density to the cooling water of the turbocharged engine by experimental study are presented in Table 4. Table 4. The experimental results of the cooling water flow rate, the inlet water temperature, the outlet water temperature of the turbocharged V12 engine according to the pressure ratio pk = 1.8 and pk = 2.0 at the different loads and speeds Modes
pk = 1.8 60% 2000 1200 pk = 2.0 60% 2000 1200 pk = 1.8 50% 2000 1200 pk = 2.0 50% 2000 1200
[rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min] [rev/min]
The cooling water flow rate Gn [kg/s] Gn [m3/h] 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64 8.5 30.6 4.9 17.64
Cooling water temperature Tin [°C] Tout [°C] 92 86 90 84 96 90 93 86 90 85 87 82 94 89 91 85
q [kW/m2] 137.33 79.17 137.33 92.36 114.45 65.97 114.45 79.17
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For the original V12 engine, when comparing the heat flow density to cooling water between theoretical calculations and experimental results, we ﬁnd that the largest relative error is 13.47% at 75% load and engine speed 1200 [rev/min], the smallest relative error is 0.89% at 100% load and engine speed 2000 [rev/min] as shown in Fig. 4.
Fig. 4. The heat flow density to the cooling water of the original V12 engine at the different loads and speeds according to simulation and experiment
For the turbocharging engine, when comparing the heat flow density to cooling water between theoretical calculations and experimental results, we ﬁnd that the largest relative error is 9.70% at 50% load, engine speed 1200 [rev/min] and the pressure ratio pk = 2.0. The smallest relative error is 0.65% at 50% load, engine speed 1200 [rev/min] and the pressure ratio pk = 1.8 as shown in Fig. 5.
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Fig. 5. The heat flow density to the cooling water of the turbocharged V12 engine according to the pressure ratio pk = 1.8 and pk = 2.0 at the different loads and speeds
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The heat flow density to the cooling water, brake power and brake speciﬁc fuel consumption of the turbocharged V12 engine according to the different pressure ratio (pk) at different loads and speeds are shown in Fig. 6 and Fig. 7.
Fig. 6. The heat flow density to the cooling water of the turbocharged V12 engine according to the different pressure ratio (pk) at different loads and speeds
Fig. 7. The brake power, brake speciﬁc fuel consumption of the turbocharged V12 engine according to the different pressure ratio (pk) at different loads and speeds
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The above calculated results are completely consistent with the theoretical analysis presented in the literature on turbocharging engines and internal combustion engine. According to [8, 9], the maximum allowed heat flow density to the cooling water is 211 [kW/m2]. Therefore, in order to ensure that the V12 engine is stable according to the heat flow density to cooling water, the allowed maximum pressure ratio of the engine is 2.1.
5 Conclusion The following conclusions are drawn based on the simulation and experimental studies as given below: + It is observed from the simulated results that when the engine load increases and the pressure ratio is at the maximum brake power mode, the change of the heat flow density has a greater value than the others. + In order to ensure that the V12 engine is stable according to the heat flow density, the allowed maximum pressure ratio of the engine is 2.1. + With the maximum pressure ratio of 2.1, the maximum brake power increases nearly by 56.44% and 57,04%, while the speciﬁc fuel consumption decreases approximately by 6.6% and 4.3% at 2000 [rev/min] and 1200 [rev/min], respectively.
References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
Hà Quang, M., Lê Đình, V.: Tăng áp cho động cơ. Học viện Kỹ thuật Quân sự (2014) Lê Viết, L.: Lý thuyết động cơ Diezel. NXB Giáo dục (2000) Pham Minh, T.: Lý thuyết động cơ đốt trong. NXB Khoa học và Kỹ thuật, Hà Nội (2012) Nguyễn Trung, K.: Nghiên cứu ảnh hưởng của mức độ tăng áp đến phụ tải nhiệt của động cơ diesel. Luận án tiến sĩ kỹ thuật, Học viện Kỹ thuật Quân sự, Việt Nam (2016) Lê Đình, V.: Nghiên cứu cường hóa động cơ B2 trên xe tăng họ T54/T55 bằng tăng áp tuabin khí thải. Đề tài Khoa học Công nghệ Cấp Nhà nước (2015) Heywood, J.B.: Internal Combustion Engine Fundamentals, 2nd edn. McGrawHill International Editions, London (2018) Hiereth, H., Prenninger, P.: Charging the Internal Combustion Engine, Powertrain Edited by Helmut List, SpringerWien NewYork (2003) Двигaтeли B2 и B6. Texничecкoe oпиcaниe. M.: Boeннoe издaтeльcтвo (1975) Baншeйдт B.A. Cyдoвыe двигaтeли внyтpeннeгo cгopaния: Cyдocтpoeниe (1977) Gamma technologies, GTPower Tutorial, Version 7.3 (2012) Gamma technologies, Engine Performance, Version 7.3 (2012) Gamma technologies, Flow Theory Manual, Version 7.3 (2012)
A Study of Scissor Lifts Using Parameter Design AnhTuan Dang1, DinhNgoc Nguyen2(&), and DangHao Nguyen1 1
Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam 2 Faculty of International Training, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]
Abstract. Scissor lifts are applied for transporting or lifting various objects. Hydraulic cylinders are used to raise or lower platforms which have many ways to arrange. This study aims to determine appropriate dimensions in design 1X hydraulic scissor lifts. Using symbolic variables to control the dimensions, positions of the cylinder are calculated to ensure the effectiveness of working space and forces in the cylinders. Results obtained from the calculations indicate the practice of numerical methods and can be used to determine optimal dimensions for design 1X scissor lifts. Keywords: Cylinder, scissor lift
Kinetic analysis, parameter design
1 Introduction Scissor lifts are lifting devices, which employ scissors mechanism to raise or lower goods or people through relatively far distances. A choice of lift devices is made based on three main criteria: lifting height, lifting weight, and lifting equipment according to the drive devices. There have been many layouts of scissor lifts concerned with the arrangement of the cylinder. These dimensions involve the working space of lifts and the operation of cylinders; they play an important role in the platform movement. There have been studies dealing with studying scissor lifts, some of which focus on simulation software to evaluate the operation of the system or calculated strength of component lift to determine suitable design dimensions [1–8]. However, there have been few studies dealing with the arrangement of cylinders or construction of the speciﬁc equation to calculate thrust force for cylinders. This study analyzes a model of scissor lifts in the given ﬁgure (Fig. 1), from which the appropriate dimensions for the system can be calculated. Results from the study are shown in graphic charts covering the relationship between cylinder features and platform operations. Through the calculation, decisions can be made to select the appropriate dimension for design and manufacture similar devices.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 75–85, 2021. https://doi.org/10.1007/9783030647193_10
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A
B
Q
2
4 C P
M 1
3 D
Fig. 1. Sketch of the mechanism: 1. Ground frame, 2. Inner frame, 3. Outer frame, 4. Cylinder, 5. Platform (hoisting terrace).
2 Kinematic Analysis Studying the movement of scissor lifts with different dimensions, it can be concluded that the designate dimensions in Fig. 2 are the elements with the most influence on the lift structure (in both movement and strength calculation). When the cylinder changes its length from Lmin to Lmax, the platform raises from the lowest position (Hmin) to the highest position (Hmax). However, this variable (H) also depends on the length of the frame (AD, BC) and the angle c between frames. Therefore, it is difﬁcult for designers to choose the exact dimensions from the layout to simulate the system movement.
Fig. 2. Scissor lift with symbolic parameter
Using symbolic parameter, a = a*A, b = b*a, and lc = k*a (see Fig. 2) with 0.5 > a>0; 1 b > 0 and k > 0), the system containing the basic parameters is the length of frame A, and other parameters are related to the layout of the cylinder in the system (a, b and k). The kinetic analysis process will evaluate the position, velocity of joints, and links in the mechanism. During operation, the cylinder changes its length from kmin to kmax to raise the height of the platform from Hmin to Hmax, respectively. This height can be established by expressions:
A Study of Scissor Lifts Using Parameter Design
H ¼ A sin
c 2
77
ð1Þ
The angle between frames c is determined from the relation equation of triangular MPQ cos c ¼
a2 þ b2 l2xl a2 þ ðb:aÞ2 ðk:aÞ2 1 þ b2 k2 ¼ ¼ 2ab 2b:a2 2b
ð2Þ
Substituting to Eq. (1): sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ c 1 cos c k2 ð1 bÞ2 H ¼ A sin ¼ A ¼A 2 2 4b
ð3Þ
To maintain the balance of the structure, the maximum angle between frames of the scissor mechanism is chosen to range from cmin to cmax, i.e. cos cmin [ cos c ¼
1 þ b2 k 2 [ cos cmax 2b
ð4Þ
Substitute to Eq. (2): qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 1 þ b2 2b: cos cmax k 1 þ b2 2b: cos cmin
ð5Þ
The raising ratio when lifting the object from the lowest to the highest position is determined by the following equation: Hmax Hmin kH ¼ ¼ A A
sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ k2max ð1 bÞ2 k2min ð1 bÞ2 4b 4b
ð6Þ
Based on Eq. (6), it can be constructed in Fig. 3 describing the working area for the position of cylinder depending on the coefﬁcients b and k: From Fig. 3, it is observed that the appropriate factor for the design system can be chosen. For instance, with the given factor b = 0.6, and the designate range of scissor angle c varies from 15° to 120°, the corresponding parameter for cylinder k ranges from 0.45 to 1.55. It is noticed that the stretch ratio of the cylinder can be found as follows. kc ¼ kmax =kmin ¼ 1:55=0:45 ¼ 3:44
ð7Þ
Nevertheless, it is impossible to choose a cylinder appropriate with this ratio since most cylinders have a stretch ratio smaller than 1.8.
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Fig. 3. Working range of the angle frame c with variables of b and k
With b = 0.15, and the operation angle ranges from 15° to 135°, the new parameter for the cylinder is kmin = 0.85 and kmax = 1.11, and the stretch ratio now is kc = 1.11/0.85 = 1.31. This ratio makes it easier to select the available cylinders. It can be seen that the ratio H/A and the angle c have the same curve, which depends on b and k (c.f. Figure 1), so that we can combine the ratio into Fig. 1 and calculate the raising coefﬁcient for the system by using Eq. (6): kH ¼ 0:924 0:131 ¼ 0:793
ð8Þ
The meaning of this result is that if the lifting system uses scissor frame with the length of the frame A = 100 cm, the raising height of the system when it raises from the lowest position (c = 15°) to the highest position (c = 165°) is 0.793*100 = 7 9.3 cm. With the given structure of the system, if the value of b larger than 0.4, the initial value for c must be above 30° to maintain the value of kmin large enough and affect the dimension of the system. Some of the dimensions are selected to check the efﬁcient operation of the system, shown in Table 1: Table 1. Raising efﬁciency and operation angle of the system with different values of b and k Number 1 2 3 4 5
b 0.2 0.3 0.3 0.35 0.35
kmin 0.82 0.85 0.75 0.85 0.85
kmax = kmin*1.35 1.11 1.15 1.01 1.15 1.15
Operation angle u Raising efﬁcient kH 22°–119° 0.86−0.19 = 0.67 52°–113° 0.83−0.44 = 0.39 29°–83° 0.66−0.25 = 0.41 55°–104° 0.79−0.46 = 0.33 55°–107° 0.80−0.46 = 0.34 (continued)
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Table 1. (continued) Number 6 7 8 9 10
b 0.4 0.4 0.5 0.6 0.6
kmin 0.62 0.80 0.85 0.9 0.8
kmax = kmin*1.35 0.84 1.08 1.15 1.21 1.08
Operation angle u Raising efﬁcient kH 15°–55° 0.46−0.13 = 0.33 49°–91° 0.71−0.41 = 0.30 58°–94° 0.73−0.48 = 0.25 63°–95° 0.74−0.52 = 0.22 53°–81° 0.65−0.45 = 0.20
For example, with b = 0.4, if kmin = 0.62 (corresponding to c = 15°), value of kmax should not larger than 0.621.35 = 0.837 (corresponding to c = 55°) and the raising efﬁciency kH = 0.460.13 = 0.33, too small for the idea design. Even when we are raising the initial angle (c = 49°) to get kmin = 0.80 and kmax = 0.801.35 = 1.08 (c = 91°), the raising efﬁciency still equals to kH = 0.710.41 = 0.30. ! ! ! ! RC ¼ l2 þ l31 þ l32
ð9Þ
! ! ! ! RE ¼ l2 þ l51 þ l52
ð10Þ
and
3 Kinetic Analysis Forces acting on the mechanism at joints with magnitude and direction change during the operation of the system. For the system of which the loading PG is located at point G is shown in Fig. 4. At the lowest position of the platform, G is placed between hinges A and B, but when the platform raises, point B moves near A while the distance between A and G is unchanged. This will impact the structure balancing and the force magnitude acting on the frames. However, most of the recent research of the scissor systems only bases on creating the concept model to run the simulation but did not propose appropriate methods to analyze the stability and optimization of the frame dimensions. lG
A
B
PG
G Hmax
PG G
B
Hmin
A
Fig. 4. Position of load PG during the operation of the system.
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Based on the analysis of the kinetic model, the authors continue to use the model in Fig. 4 to calculate the reaction forces on joints and frames. Because the system is in a 3D model, but the calculation is based on a 2D model, the results after the calculation will be divided in correspondence with the structure on the 3rd dimensions (the number of cylinders, the number of scissor frames). Assuming the movement of the platform is slow enough to neglect the effect of inertia, the weight of the frames in the system, which is much smaller than the weight of the object, can be ignored. This study mainly investigates the effect of the weight of loading PG on the frames and pin joints.
Fig. 5. The freebody diagram of the ground frame (CD) and platform (AB).
Release reaction forces at the platform and ground (c.f Fig. 5) and calculate the magnitude of forces in pins A, B, E, F. FB ¼ FD ¼
PG :lG L
FA ¼ FC ¼ PG PB ¼ PG
ð11Þ PG :lG L
Release the scissor joint between frame AD and BC (c.f Fig. 6)
ð12Þ
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Fig. 6. The freebody diagram of frame AD and BC.
Note: Fcyl is the reaction of cylinder acted along the axis of the cylinder, using equilibrium of moment for AD at center M: FA þ M FD þ M Fcyl ¼ 0 MM ¼ M
ð13Þ
M Fcyl ¼ jM FA j FD þ M
ð14Þ
A c A c A c A c Fcyl :b: sin / ¼ FA : cos þ FD : cos ¼ ðFA þ FD Þ cos ¼ PG cos 2 2 2 2 2 2 2 2 Fcyl ¼
PG A2 cos 2c b: sin /
ð15Þ ð16Þ
The value of sinu can be obtained by the relation equation between angles c and u from triangular MPQ: a lxl a:k ¼ ¼ sin / sin c sin c sin / ¼
ð17Þ
sin c k
ð18Þ
Substitute into Eq. (16): Fcyl ¼
PG A2 cos 2c b:a:A: sink c
¼
PG k ¼ 4:a:b: sin 2c
4ab:
PG :k qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ¼ 2 2 k ð1bÞ 4b
PG :k rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ h i 2a: b k2 ð1 bÞ2
ð19Þ
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Use the vector addition method with forces on frame AD: ! ! ! ! ! FA þ FD þ Fcyl þ FMx þ FMy ¼ 0
ð20Þ
Project these forces on x and y directions: c PG k c : cos / þ ¼ FMx ¼ Fcyl : cos / þ 2 4:a:b: sin 2c 2
ð21Þ
c PG k c PG :lG ð22Þ FMy ¼ Fcyl : sin / þ FA þ FD ¼ PG þ 2: c : sin / þ 2 4:a:b: sin 2 2 L Substitute Eq. (2) to Eq. (22), the angle using parameters can be rewritten as: c c c c 2 c b1 c cos / þ cos / sin2 : cos ¼ cos /: cos sin /: sin ¼ cos ¼ 2 2 2 2 k 2 k 2 ð23Þ c c c c 2 c bþ1 c cos / þ cos2 : sin sin / þ ¼ cos /: sin þ sin /: cos ¼ sin ¼ 2 2 2 2 k 2 k 2 ð24Þ Replace to the equation of FMx and FMy: FMx
P G ð b 1Þ c PG ðb 1Þ : cot ¼ : ¼ 4:a:b: 2 4:a:b: 2
sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ðb þ 1Þ2 k2 k 2 ð 1 bÞ 2
ð25Þ
3
4lG 6ðb þ 1Þ 7 þ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ FMy ¼ PG 4 15 4:a:b ðb þ 1Þ2 k2 A b
ð26Þ
Total reactions on joint M can be obtained by using the equation: FM ¼
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2 þ F2 FMx My
ð27Þ
To evaluate the accuracy of the proposed method, the Working Model to simulate the mechanism and measure the reactions at joints is utilized (see Fig. 7). Results from this process are compared with those obtained by the Eq. (15), (23), and summarized in Table 2. Results in Table 2 prove the accuracy of the numerical methods in determining force magnitudes in the system. The maximum difference takes up only 0.2% because the weight of frames caused a small change in the simulation model.
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Fig. 7. Results of the system constructed in Working Model. Table 2. Results of force between the two methods a = 0.3; b = 0.3; lG = 0.4 FA (N) Fcyl(N) FM (N) 3101.2 k = 0.8 F1 286.2 3142 F2 286.2 3143 3101.7 D% 0 0 0 k = 1.0 F1 236.3 2131 2153.8 F2 236.2 2131 2153.8 D% 0 0 0 k = 1.2 F1 61.9 1873 2237.7 F2 61.8 1873 2238.2 D% 0.2 0 0 With F1: Result from Working Model F2: Result using calculation equation F 1  F 2 D ¼ F :100%: Difference between 1 A = 10 m PG = 500 N
a = 0.4; b = 0.25; lG = 0.6 FA (N) Fcyl(N) FM (N) 187.4 3591.1 3647.4 187.6 3592.1 3648.1 0.1 0 0 100 1889.8 2144.5 100 1889.8 2144.5 0 0 0 356.5 1599.2 2795.7 357.1 1601.3 2798.8 0.2 0.1 0.1
a = 0.5; b = 0.4; lG = 0.5 FA (N) Fcyl(N) FM (N) 224.9 1196.2 1233.4 224.8 1195.2 1232.7 0 0.1 0.1 177.3 988.2 1119.1 177.3 988.2 1119.1 0 0 0 61.5 912.9 1278.8 61.5 912.9 1278.8 0 0 0
the two methods
It can be seen that in Eq. (15) the factor lG is omitted. This means the position of loading PG does not affect the magnitude of the force from the cylinder during the operation. Parameter a < 1 (position of the cylinder on the structure) changes the magnitude of thrust force in cylinders: smaller cylinder endures bigger forces.
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Fig. 8. Reaction on pin M and thrust force on cylinder with different variables of b and k (with constant values of A = 10 m, PG = 500 N, a = 0.5, lG = 0.5)
From the results in Table 2, it can be noticed that the lower the platform is, it requires bigger force from the cylinder Fcyl to operation, and so does the reaction force at pin M. Combining the results from Table 1 with those from Table 1, the relationship between Fcyl and FM according to the increase of height (H) is shown in Fig. 8: Results from the ﬁgure show that the smaller of b can improve the operation of the system (platform can raise to higher positions) but increase the force in the cylinder. Even with the small loading of PG = 500 N, the reaction in joint M and thrust force from cylinder Fcyl for the system to operate are still too big ( 6 times). These forces will affect the frames in the system, such as causing bending or even breaking the function of the structure. In some cases, the change of Fcyl (blue line) is so small that it nearly becomes a constant, making the selection of appropriate cylinders easier.
4 Conclusion The applicability of the numerical methods using a variable in design lifting devices using scissormechanisms is analyzed in this study. The following conclusions can be made: • The propitiate dimensions of the hydraulic cylinder and the whole system can be purposely selected. The suitable values of the system can enhance the working effectiveness. The maximum stroke can be up to 80 cm when the frame length and angle between two lifts are 100 cm and 135° respectively.
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• The selection process of a hydraulic cylinder can be executed thanks to the calculated effective loading. • The proposed method in this study can allow us to establish force equations at the limiting loaded places on the system. In this way, the design recommendations for dimension and structure can be made. Acknowledgments. The authors wish to thank Thai Nguyen University of Technology for supporting this work.
References 1. Hongyu, T., Ziy, Z.: Design and simulation based on Pro/E for a hydraulic lift platform in scissors type. Procedia Eng. 16, 772–781 (2011) 2. Dong, Ren G., et al.: An investigation on the dynamic stability of scissor lift. Open J. Saf. Sci. Technol. 2, 8–15 (2012) 3. Zhang, W., Zhang, C., Zhao, J., Du, C.: A study on the static stability of scissor lift. Open Mech. Eng. J. 9, 954–960 (2015) 4. Stawinski, L., Kosucki, A., Morawiec, A., Sikora, M.: A new approach for control the velocity of the hydrostatic system for scissor lift with ﬁxed displacement pump. Arch. Civ. Mech. Eng. 19(4), 1104–1115 (2019) 5. Zhang, W., Zhang, C., Zhao, J., Du, C.: A study on the static stability of scissor lift. Open Mech. Eng. J. 9, 954–960 (2015) 6. Hongyu, T., Ziy, Z.: Design and simulation based on Pro/E for a hydraulic lift platform in scissors type. Procedia Eng. 16, 772–781 (2011) 7. Dong, Ren G., et al.: An investigation on the dynamic stability of scissor lift. Open J. Saf. Sci. Technol. 2, 8–15 (2012) 8. Manoharrao, S.A., Prof. Jamgekar R.S.: Analysis and Optimization of Hydraulic Scissor Lift. IJEDR, 4(4) (2016)
A Study on Prediction of Milling Forces Do Duc Trung1, Tran Ngoc Giang2, Tran Thi Hong3, Bui Thanh Danh4, Vu Van Khoa5, Nguyen Dinh Ngoc2, Nguyen Thanh Tu2, and Vu Ngoc Pi2(&)
3
5
1 Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen City, Vietnam [email protected] Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 4 University of Transport and Communications, Hanoi, Vietnam National Research Institute of Mechanical Engineering, Ha Noi City, Vietnam
Abstract. The aim of this study is to develop a milling force predicting model which is based on the relation between surface roughness and cutting force resulting from surface milling. Based on the analysis of the available models, the roughness model which has more advantages than others is selected. The newly proposed milling force model is developed based on the roughness model. Eight influential parameters are included in the model such as cutting edge radius, feed rate, side cutting edge angle, end cutting edge angle, radial depth of cut, axial depth of cut, tool diameters and number of cutting edge. Experimental tests by milling C45 steels are conducted to validate the predicted results as well as the realiability of the model. The results show that the maximum percentage errors between prediction and experiments is an average of 16.7%. Keywords: Milling operation milling
Surface roughness Milling force Surface
1 Introduction Milling is one of the most important machining processes to cut redundant materials for getting the ﬁnal shapes. This process is able to machine various geometries of workpiece. Cutting forces of milling process are typically selected as a representative reflecting the degree of the energy consume (or cutting capacity) [1–5]. Minimizing milling forces has been carried out by research community so far. Most of available studies have been dealing with exploring the influence of machining parameters on the milling forces. Hoang T.D et al. [6] when conducting milling of SKD61 steels revealed that cutting force values increase with increasing in depth of cut and feed rate. JhyCherng T et al. [7] performed milling process of Inconel 718 using face mill cutter using the hard alloy cut piece coating TiAlN. It was said that spindle speed, federate per cutting, and edge depth of cut have crucial influence on the cutting forces in which the ﬁrst two parameters are more important. However, the effect of depth of cut was minor. This © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 86–93, 2021. https://doi.org/10.1007/9783030647193_11
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result is opposed to that documented by WenHsiang L [1] where the influence of depth of cut on cutting forces was analyzed. The results showed that the depth of cut has the strongest effects on the cutting forces, i.e. an increase in depth of cut leads to an increase in cutting forces. Md. Anayet U Patwar et al. [8] when doing the end milling operation provided some conclusions that feed rate has more important impacts on the cutting forces than axial depth of cut and cutting speed which has the smallest influence. Furthermore, the cutting forces grow with a rise in feed rate and depth of cut, while the influence of cutting speed is unclear. Erol Kilickap et al. [4] studied milling of Ti6242S alloy and detailed that feed rate, cutting speed, and depth of cut signiﬁcantly impact on the cutting forces. Moreover, it was shown that the cutting forces rise when the depth of cut and feed rate expand or the cutting speed reduces. Gökkaya H [9] conducted the milling process of AA2014 (T4) alloy. It was exhibited that the tfeed rate has the most effect on the cutting forces, e.g. an increase in feed rate leads to an increase in cutting forces. Inversely, the cutting speed has little impact. For example, when cutting speed varies form 200 m/min to 500 m/min, the stable cutting force values are observed. Pathak et al. [10] carried out milling operation of Al1Fe1V1Si and Al2Fe1V1Si alloy. Their results showed that cutting parameters have important effect. The cutting forces grow when there is an increase in depth of cut and feed rate, and/or a decrease in cutting speed is executed. Based on the previously analyzed studies, it is known that milling forces are impacted by various parameters such as cutting parameters, hardness of workpiece materials, cutting tool materials, tool geometries, tool wear, and lubricant conﬁguration. It is difﬁcult to include the mentioned influential parameters at the same time within a speciﬁc study. On the other hand, experimental results cannot meet all requirements in practice. Hence, applying process of studied results for milling is less effective. In order to solve the existing problem, there have been cutting force models established by research community. In this study, a cutting force model based on the surface roughness model will be proposed.
2 Developing Cutting Force Model Based on Cutting Force – Surface Roughness Relation Cutting force – surface roughness relation has been documented in several studies in literature. In this study, a new model will be suggested based on those presented in [10] and [11]. It is noticed that when carrying out the milling process by face cutting cutters, the relation between cutting force and surface roughness can be described by Eq. (1). Ra ¼ 0:0254
593 293 Fc
2 ð1Þ
Regarding the surface roughness model, there have been studies considering the development of roughness model in both approaches, theoretical and experimental. Because of the weakness of models based on experiment, roughness model has been developed by researchers according to theoretical approach.
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Cieloszyk J et al. [13] proposed the equation as: Rt ¼
fz2 8re
ð2Þ
Boothroyd G et al. [14] suggested the roughness model: Ra ¼
0:0321fz2 re
ð3Þ
Junelia B.L et al. [15] reported the equation as: Ra ¼
fz2 32re
ð4Þ
Miko E et al. [16] introduced the roughness model: Ra ¼
0 fz tankr tankr 4 tankr þ tankr0
ð5Þ
Palanisamy P et al. [17] developed the equation as: Ra ¼
318fz2 4Dc
ð6Þ
Montgomery D et al. [18] suggested the roughness model: 5 pﬃﬃﬃ 2 Ra ¼ 9
sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ fz4 fz2 ¼ 0:60415 150 D2c Dc
ð7Þ
Stępiński L [19] recommended the roughness model: Ra ¼
f2 Dc z fz z 32 2 p
ð8Þ
In another study, Miko E et al. [16] presented the roughness model: sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ fz2 tankr tankr0 2 3 2 tankr tankr0 2p Ra ¼ þ e 1þ 1 cos 8 z 18 tankr þ tankr0 tankr þ tankr0 Lukasz Nowakowski [20] developed the equation as:
ð9Þ
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fz4 2hm r e hm Ra ¼ pﬃﬃﬃ þ pﬃﬃﬃ 1 þ fz2 18 3re 9 3
89
ð10Þ
Where:Fc is denoted for cutting force; Ra is the arithmetical mean roughness value; Rt is the total height of the roughness proﬁle; fz is the feed rate; re is the cutting edge radius; kr is the side cutting edge angle, kr0 is the end cutting edge angle; z is the number of cutting edge; Dc is the tool diameter. It can be said that each available roughness model is only true for limited testing conditions. Hence, it is necessary to carefully analyze the advantages of these models to continuously develop the new ones which can well predict roughness. From the previously mentioned models, some conclusions can be made as following; Model (1), (2), and (3) are simple because they contain only two influential parameters, e.g. feed rate and cutting edge radius. Model (5) takes into account three factors such as feed rate, side cutting edge angle, and end cutting edge angle. Model (6) and (7) have only two factors which are feed rate and tool diameter. Meanwhile, feed rate and the number of cutting edge, and tool diameter are considered in model (8). Consequently, model (1) to model (8) have weakness due to containing limited factors. Model (9) takes into account four influential parameters involving feed rate, number of cutting edge, side cutting edge angle, and end cutting edge angle. Model (10) has three parameters such as feed rate, cutting edge radius, and unreformed chip thickness parameter which is an important parameter affecting the surface roughness. It is realized that model (9) and (10) are better than those from (1) to (8) in terms of variable numbers. There is the fact that the influential parameters in model (9) and (10) are not similar, hence this study will propose a new model based on the mentioned models for predicting cutting forces. A newly proposed model is combined with model (1), (9), and (10) as follows: Fc ¼ 293 94:5 2
6 fz4 hm r e hm 6 436pﬃﬃ3ﬃr þ 9pﬃﬃ3ﬃ 1 þ f 2 e z
vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ31=2 u 0 2 u fz2 tankr tankr u 7 72 tankr þ tankr0 7 þu 0 t 5 tank tank r 3 2 þ 32 e 1 þ tank þ tankr 0 1 cos 2p z r
r
ð11Þ The unreformed chip thickness parameter is deﬁned by Eq. (12) hmin ¼
180 sinkr ae fz e p Dcap arcsin Dacap
ð12Þ
Where: ae is the radial depth of cut, Dcap is the effective diameter [21]; ap is the axial depth of cut.
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Dcap ¼ Dc
2 ap tankr
ð13Þ
(11), (12), and (13) are combined to get the new Eq. (14) which can be used to predict cutting forces for various levels of eight parameters, i.e. cutting edge radius, feed rate, side cutting edge angle, and end cutting edge angle, radial depth of cut, axial depth of cut, tool diameters and number of cutting edge. 8 vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ312 2 u > 0 2 > fz2 tankr tankr u > > 4 u 0 7 6 fz > h r h 72 tank þ tank > m e m r r > Fc ¼ 293 94:5 6 þu > 7 t 0 > 5 436pﬃﬃ3ﬃr þ 9pﬃﬃ3ﬃ 1 þ f 2 > tank tank r 3 2 > e z > þ 32 e 1 þ tank þ tankr 0 1 cos 2p < z r r > > > > > > > > > > > > :
hm ¼
180sinkr ae fz pDcap arcsin
ae Dcap
2a
Dcap ¼ Dc tankpr
3 Validating the Accuracy of the Proposed Model for Predicting Cutting Forces Milling tests are conducted by using face mill cutters with hard piece. The specimens made of steels have the dimension of 50 mm 50 mm 50 mm corresponding to the length, the width, and the thickness. Cutting speed is ﬁxed by 395 rpm (or 99.3 m/min). Other information of machining parameters can be seen in Table 1.
Table 1. Cutting parameters. Parameter Feed rate Cutting edge radius Side cutting edge angle End cutting edge angle Radial depth of cut Axial depth of cut Number of cutting edge Tool diameter
Symbol fz re kr kr0 ae ap z Dc
Value 0.3; 0.4; 0.5; 0.6 and 0.7 0.4 45 45 50 0.4; 0.5; 0.6; 0.7; and 0.8 1 80
Unit mm/tooth mm Degree Degree mm mm Tooth mm
The current can be measured before and during machining process. The cutting forces (Fc) and the cutting power (Pcut) can be determined by the following equation [22].
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Pcut ¼ U I Fc ¼
91
ð14Þ
Ncut v
ð15Þ
The values of cutting forces given by experiments and prediction can be seen in Tables 2 and 3. Table 2. Cutting forces for different feed rate. mm No. fz tooth PðKW Þ FcðmeasuredÞ Fcðcalculated Þ Deviation of Fc (%) 1 0.3 2 0.4 3 0.5 4 0.6 Mean
0.417 0.415 0.380 0.327
251.76 250.76 229.61 197.38
231.49 210.46 189.14 167.56
8.05% 16.07% 17.63% 15.11% 14.21%
It is observed that the predicted values of the cutting forces listed in Table 2 and Table 3 are close to those given by the experiments. When the feed rate varies, the maximum percentage error between prediction and experiment is 14.21%. The corresponding value in case of varying depth of cut is 19.6%.
Table 3. Cutting forces for different axial depth of cut. mm PðKW Þ FcðmeasuredÞ Fcðcalculated Þ Deviation of Fc (%) No. fz tooth 1 0.4 2 0.5 3 0.6 4 0.7 5 0.8 Mean
0.423 0.415 0.382 0.358 0.347
255.79 250.76 230.61 216.52 209.47
200.82 189.14 183.56 180.37 178.37
21.49% 24.57% 20.40% 16.70% 14.85% 19.60%
4 Conclusion Cutting force is an important parameter which have signiﬁcant impacts on the machining quality, lifetime of cutting tool, and energy consume during milling. Studying the methods to predict milling forces has been attracted by research community. This study successfully develops a new milling force model which can predict the milling forces. The model takes into account eight influential parameters including cutting edge radius, feed rate, side cutting edge angle, and end cutting edge angle, radial depth of cut, axial depth of cut, tool diameters and number of cutting edge. The reliability of the proposed model is validated by comparing experiment and prediction. The mean percentage error between predicted and experimental values of milling forces
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is around 16.7%. Applying the results in this study in industry can contribute to enhance the effectiveness of milling operation. Acknowledgements. This work was supported by Thai Nguyen University of Technology.
References 1. Lai, W.H.: Modeling of cutting forces in end milling operations. Tamkang J. Sci. Eng. 3(1), 15–22 (2000) 2. Fua, Z., Yanga, W., Wanga, X., Leopold, J.: Analytical modelling of milling forces for helical end milling based on a predictive machining theory. In: 15th CIRP Conference on Modelling of Machining Operations, Procedia CIRP, vol. 31, pp. 258–263 (2015) 3. Sekuli, M., Jurkovi, Z., Had Istevi, M., Gostimirovi, M.: The influence of mechanical properties of workpiece material on the main cutting force in face milling. METABK 49(4), 339–342 (2010) 4. Kilickap, E., Yardimeden, A., Çelik, Y.H.: Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artiﬁcial neural network and response surface methodology in milling of Ti6242S. Appl. Sci. 7(1064) (2017). https://doi. org/10.3390/app7101064 5. Okokpujie, I.P., Okonkwo, U.C.: Effects of cutting parameters on surface roughness during end milling of aluminum under minimum quantity lubrication (MQL). Int. J. Sci. Res. 4(5), 2937–2943 (2013) 6. Hoang, T.D., Nguyen, N.T., Tran, Đ.Q., Van Nguyen, T.: Cutting forces and surface roughness in facemilling of SKD61 hard steel, Strojniški vestnik. J. Mech. Eng. 65(6), 375– 385 (2019) 7. Tsai1, J.C., Kuo1, C.Y., Liu1, Z.P., Hsiao, K.H.H.: An investigation on the cutting force of milling Inconel 718. In: MATEC Web of Conferences, vol. 169 (2018) 8. Patwari, A.U., Nurul Amin, A.K.M., Faris, W.F.: Prediction of tangential cutting force in end milling of medium carbon steel by coupling design of experiment and response surface methodology. J. Mech. Eng. 40(2), 95–103 (2009) 9. Gökkaya, H.: The effects of machining parameters on cutting forces, surface roughness, builtup edge (BUE) and builtup layer (BUL) during machining AA2014 (T4) alloy. Strojniški vestnik J. Mech. Eng. 56, 584–593 (2010) 10. Pathak, B.N., Sahoo, K.L., Mishra, M.: Effect of machining parameters on cutting forces and surface roughness in Al(12) Fe1 V1Si alloys. Mater. Manuf. Process. 28, 463–469 (2013) 11. Cus, F., Zuperl, U.: Model referencebased machining force and surface roughness control. J. Achieve. Mater. Manuf. Eng. 9(2), 115–122 (2008) 12. Hadi, Y.: Prediction of surface roughness for periodic end mill tool holder. Appl. Mech. Mater. 330, 262–268 (2013) 13. Cieloszyk, J., Olszak, W., Skrodzewicz, E., Sobkowiak, E.: Stan geometryczny powierzchni frezowanej czołowo z dużymi posuwami. Materiały Konferencji I Forum Prac Badawczych ‘‘Kształtowanie części maszyn przez usuwani materiału”. Koszalin, pp. 46–55 (1994) 14. Boothroyd, G., Knight, W.A.: Fundamentals of machining and machine tools. Marcel Dekker, New York (2006) 15. Junelia, B.L., Sekhon, G.S.: Fundameltals of Metal Cutting And Machine Tools, 1st edn. Wiley Eastern Limited, New Delhi (1987)
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16. Miko E.: Konstytuowanie mikronierówności powierzchni metalowych obrabionych narzędziem o zdeﬁniowanej stereometrii ostrzy. Monograﬁe, studia, rozprawy 46. Kielce (2004) 17. Palanisamy, P., Rajendran, I., Shanmugasundaram, S.: Optimization of machining parameters using genetic algorithm and experimental validation for endmilling operations. Int. J. Adv. Manuf. Technol. (2006) 18. Montgomery, D., Altintas, Y.: Mechanism of cutting forces and surface generation in dynamic milling. ASME J. Eng. Ind. 160–168 (1991) 19. Stępiński L.: Wpływ przemieszczeń względnych w układzie: narzędzie przedmiot obrabiany, na chropowatość powierzchni frezowanej walcowo. Rozprawa doktorska, AGH. Kraków (1982) 20. Nowakowski, L.: Models for Prediction of Ra Roughness Parameters of Milled Surfaces (2015). https://doi.org/10.17814/mechanik.2015.89.414 21. Nowakowski, L., Skrzyniarz, M., Miko, E., Takosoglu, J., Blasiak, S., Laski, P., Bracha, G., Pietrala, D., Zwierzchowski, J., Blasiak, M.: Influence of the cutting parameters on the workpiece temperature during face milling. In: EPJ Web of Conferences, vol. 143, p. 02082 (2017). https://doi.org/10.1051/epjconf/201714302082 22. Son, N.H.: Effect of cutting parameters on cutting force and surface roughness of workpiece when milling 40Cr steel using PVDcoated cutter. Int. J. Sci. Eng. Investigat. 9(96), 13–18 (2020)
A Study on Qualitative and Quantitative Characterization of Machining Quality of Aerospace Composite Structures Nguyen Dinh Ngoc1(&) and Nguyen Thi Hue2 1
2
Faculty of International Training, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] Faculty of Fundamental Science, Thai Nguyen University of Technology, Thai Nguyen, Vietnam
Abstract. Machining CFRP composites generally create defects in machined surfaces which are more irregular than those given by metal machining. Minimizing and controlling the occurrence of damage is a crucial task. To archive that, quantifying damage and correlating it to cutting parameters, as well as mechanical behavior is signiﬁcant. This study investigates the influences of machining parameters such as spindle speed and feed speed on the tenpoint max, Rz. This roughness parameter is recommended to adopt for quantifying the machining quality of CFRPs instead of Ra. Machining defects are identiﬁed by SEM observation. Rz values are determined in both longitudinal and perpendicular directions to the machined surfaces. The results show that the combination between high spindle speed and low feed speed can seriously create damage levels. Moreover, it is proved that Rz can well reflect damage evolution, qualitatively identiﬁed by SEM observation. This can be concluded that Rz can be a good indicator to quantify machining damage of composite materials instead of using expensive systems which are difﬁcult to be afforded whenever. Keywords: SEM
Characterization of machining quality Tenpoint max Rz
1 Introduction Carbon ﬁber reinforced plastics (CFRP) composites is one of the advanced composite materials. It is frequently attracted by aerospace industry, automobiles, shipbuilding, etc. Typically, CFRPs are fabricated nearnet shape to economize preparing time and enhancing productivity. However, machining operations are crucially required to meet required tolerances and surface integrity for assembly. The machined surface of CFRP structures are generally irregular because of induced defects such as delamination, matrix/ﬁber debonding, ﬁber pullout, thermal degradation of matrix [1–3]. The irregular surface can create stress concentrations at the positions of the defects induced. This can reduce the working ability of CFRP structure during services. Accordingly, the characterizing machining quality is crucially essential. The characterization normally involves both qualitative and quantitative techniques. For the qualitative technique, there have been effective methods applied so far. These are scanning electron © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 94–101, 2021. https://doi.org/10.1007/9783030647193_12
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microscope (SEM), Cscan, optical [4], confocal 3D [5, 6], Xray tomography [5, 7]. Among these methods, the last provides dimensional information in machined surface and subsurface. The remaining techniques aim to identify the degree of damage, but little provide a quantitative evaluation. The different standards used to describe surface texture fall into three main categories: average roughness heights (deterministic method), statistical method, and random process method [8]. The ﬁrst has been widely utilized in community and industry research. Recently, Ghidossi. P et al. [9] suggested two new parameters, ‘‘percentage of damaged surface’’, and ‘‘depth of subsurface cracking’’ and correlated mechanical behaviors. It is showed that although these new parameters are promisingly adopted, they are not able to suit for all experimental results. Another new parameter has been proposed by several researchers [5, 6, 10]. For this method, the proposed parameters are based on a laser confocal microscopy through measurements of crater volumes. NguyenDinh et al. [5] conducted the trimming process of CFRP composite and investigate the influences of machining parameters on the machining quality characterized using crater volumes. It was said that machining with high cutting speed and low feed speed can generate a high level of tool wear due to induced temperatures. Hence, machining damage severely occurrences for this combination of machining parameters. Wang F et al. [10] quantify machining quality by measuring the depths and volumes of craters along with the position of 135° ﬁber directions, and correlate machining damage to cutting parameters. Their results show that an increase in feed per tooth leads to an increase in the average depth of craters and the average volume of the cavity. Nevertheless, crater volumes can better quantify machining defects compared to the crater depth parameter. It can be said that although the new parameters as previously presented are promising, the price of the measurement system is difﬁcult to equip. Hence, utilization of roughness parameters has been widely accepted by industry, as well as recommended by researchers [3, 8]. Ramulu M et al. [11] documented that the parameters of Ry and Rz are better descriptors of the machined surface compared to common roughness parameters of Ra and Rq. J. SheikhAhmad et al. [3] when correlating the tensile strength of CFRP composite specimens to machining quality concluded that the roughness parameter of Rz is bettercorrelated defects to tensile strength than the roughness parameter of Ra. This paper attempts to provide more detailed investigations regarding the effects of cutting parameters (spindle speed and feed speed) on surface roughness (Rz) and the impact on the tool wear during machining CFRP materials. SEM observation will be selected to qualitatively evaluate the machining quality. Surface roughness determination is carried out in both longitudinal and perpendicular directions to machined surfaces.
2 Experimental Procedure The CFRPs are laminated by P2352 prepregs according to the stacking sequence of [90°/90°/−45°/0°/45°/90°/−45°/90°/45°/90°]s and have the dimensions of 300 mm 15 mm 5.2 mm. The ﬁber volumes are approximately 59%. The mechanical properties of machined specimens are listed in Table 1. A full factorial design of cutting
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conditions including three levels of feed speed, Vf, (500 mm/min, 1000 mm/min, 1500 mm/min), and two levels of spindle speeds, N, (8000 rpm and 13300 rpm) is utilized. A radial depth of cut of 2 mm is constantly kept for all cutting conditions. New polycrystalline diamond cutters (PCD) with two straight flutes and a diameter of 8 mm are selected for all cutting conditions (Ref. Figure 1). Three 300 mm long specimens corresponding with six faces were machined for each cutting condition. No coolant was utilized during the machining process. Trimming tests are carried out on a CNC center 3axis milling machine (FANUC). The acquisition of cutting forces is recorded using a dynamometer (Kistler 9257BA). The experimental devices used trimming tests are schematically shown in Fig. 2. The machining quality will be characterized by SEM observations, 3D confocal laser technique (Alicona) which can also provide the 2D roughness information through extracting proﬁle. The surface roughness is evaluated with a cutoff length of 0.8 mm and a transverse length is 4.0 mm. The surface roughness representative for each cutting condition is averagely calculated by three times.
Fig. 1. Milling cutter made PCD with two straight cutting edges. Table 1. Mechanical properties of P2352 prepreg. Density (g/cm3) 2.63
Longitudinal shear modulus (GPa) 6.21
Longitudinal Young’s modulus (GPa) 162
Compressive strength (MPa) 1552
Tensile strength (MPa) 2844
Poisson’s ratio 0.34
Fig. 2. Schematic view showing the machining conﬁguration: axes, ﬁber orientation and the machining directions.
3 Results and Discussion 3.1
Evaluation of Machining Quality
After machining, the machined surfaces of specimens are identiﬁed by using SEM observations. The results reveal that damage induced has some forms such as burrs in
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free edge, failure ﬁbers, ﬁbermatrix debonding. In the positions of 135° ﬁber directions, defects particularly mostly have the shape of craters/cavities, matrix smearing/cracking next to craters and failure ﬁbers. However, the frequent occurrences of defects crucially depend on the cutting parameters and the cutting tool state, wear phenomenon. Figure 3 presents the SEM images of specimens resulting from cutting conﬁguration of N = 13300 rpm and Vf = 500 mm/min at short cutting distance of 0.2 m (Fig. 3.a) and 1.2 m (Fig. 3.b). This cutting condition is the combination of high spindle speed and low feed speed. In this cutting condition, due to the high tool wear rate, the damage level becomes severe after shorting cutting distance. This means that the tool wear accelerates just short machining length. The results show that at ﬁrst machining quality is good, little damage even in the positions of −45° ﬁber directions. Nevertheless, when cutting distance reaches approximately 400 mm, defects induced signiﬁcantly appear in which defects in shapes of cavities/craters are dominantly visualized. Matrix smearing covers across machined surfaces. The machining temperature exhibits, in this case, is still inferior to the glass transition temperature (Tg). Hence, thermal degradations of the matrix are not observed. It is realized that SEM observation cannot provide a quantitative evaluation. In the next section, machining damage will be quantitatively estimated and correlated to machining parameters.
Craters
No damage
Matrix smearing
(a)
600 µm
(b)
600 µm
Fig. 3. SEM images of the machined surface with a feed speed of 500 mm/min and a spindle speed of 13300 rpm at a cutting distance of (a) 0.2 m and (b) 1.5 m.
3.2
Influence of Machining Parameters on the Surface Roughness
The studies in the literature [3, 11] have recommended that tenpoint mean (Rz) can better describe machining damage than arithmetical mean surface roughness (Ra). In this study, the machined surface will be evaluated by using Rz instead of Ra. Values of
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Rz are identiﬁed in both longitudinal and transverse directions to machined surfaces in Fig. 4.a and 4.b respectively. From the results, it can be said that Rz values in the longitudinal direction are typically inferior to those measured in the perpendicular direction. For example, considering the combination of 8000 rpm and Vf = 500 mm/min, Rz of 4.2 µm is recorded in the longitudinal direction, while the corresponding Rz of 7.5 µm in transverse. This discrepancy is attributed to the fact that in the transverse direction the craters dominantly appearing in −45° ﬁber positions is generally taken into account the average values of Rz. On the contrary, these crates are whether included or not depending on the positions of measurements or extracted proﬁle.
Fig. 4. Evolution of Rz in (a) longitudinal and (b) transverse direction as a function of cutting parameters.
It is noticed that in both directions the domination of Rz given by the combination of N = 13300 rpm and Vf = 500 mm/min, namely severe condition, is obviously seen. This well reflects the damage level as shown in Fig. 3, e.g. high wear rate can accelerate due to a higher level of friction between the workpiece and cutting tool.
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However, Rz recorded in the transverse direction (22 lm) is more important than that in the longitudinal direction (11 lm). Except for severe conditions, it is observed that an increase in feed speed leads to an increase in Rz. Nevertheless, spindle speed has an unclear impact on Rz. The recorded Rz shown in Fig. 4 and Fig. 5 are average values of 6 measurements of each cutting condition. Thus, the tool wear information is not profoundly described. In the next section, the influence of tool wear, presented in terms of cutting condition variation, on Rz will be analyzed. 3.3
Influence of Machining Distance on the Surface Roughness
Figure 5 shows the evolution of Rz versus tool wear when Rz is measured in the transverse direction. It is seen that except for severe conditions the influence of cutting distance or tool wear is not clear. This is due to the tool wear rate is small. It means that the Rz parameter can similarly describe the evolution of damage level forward tool wear as 3D roughness parameters (Sa), and newly proposed parameters (crater volume – Cv, and a maximum depth of defect – D) documented in [5]. The stability of the damage level can be conﬁrmed by the SEM images at two different cutting conditions in Fig. 6 in which the sizes of craters in both cutting distances are almost similar. The ﬁgure exhibits the cuttingedge shape when N = 13300 rpm and Vf = 1500 mm/min is utilized. It is observed that after a cutting distance of 1.7 m, the radius increases, namely rounded cutting edge. However, it seems that the variation range of radius is not still reached the limiting value which can generate serious damage as that given by severe condition.
Fig. 5. Evolution of Rz vs cutting distance for (a) spindle speed of 8000 rpm and (b) spindle speed of 13300 rpm.
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Fig. 6. SEM images with N = 8000 rpm and feed speed of 1500 mm/min at a cutting distance of (a) 200 mm and (b) 1500 mm.
Fig. 7. Fig. 7. SEM images of cutting edge with N = 13300 rpm and feed speed of 1500 mm/min.
4 Conclusions In this study, machining defects are analyzed in both qualitative and quantitative evaluation. The tenpoint max parameter, Rz, is selected due to better describing defects induced. It is determined in the longitudinal and perpendicular direction of machined surfaces. The following conclusions can be made: • Rz is evaluated along the longitudinal and perpendicular direction of machined surfaces. The results show that Rz values in the longitudinal direction are typically more important to those in the transverse direction. This is attributed to the fact that
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the existence of severe defects at −45° ﬁber position which is considered of Rz average values in longitudinal directions, but not in perpendicular direction. • Tool wear accelerates when machining is conducted with a severe condition (N = 13300 rpm and Vf = 500 mm/min). This leads to serious appearance defects after a short cutting distance of 400 mm. Temperatures induced are lower than Tg, hence thermal damage is not observed. • Rz can well describe the evolution of machining damage level when correlating it to machining parameters. Therefore, the analysis process aims to prove that Rz can be a good indicator to characterize machining quality of composite machining instead of using expensive technology, i.e., 3D confocal noncontacting laser, optical topography. Contacting Acknowledgments. The authors wish to thank Thai Nguyen University of Technology for supporting this work.
References 1. Hintze, W., Hartmann, D., Schütte, C.: Occurrence and propagation of delamination during the machining of carbon ﬁbre reinforced plastics (CFRPs) – An experimental study. Compos. Sci. Technol. 71(15), 1719–1726 (2011) 2. Haddad, M., Zitoune, R., Eyma, F., Castanié, B.: Machinability and surface quality during high speed trimming of multi directional CFRP. Int. J. Mach. Mach. Mater. (2013) 3. Shahid, A.H., SheikhAhmad, J.: Effect of edge trimming on failure stress of carbon ﬁber polymer composites_Jamal SheikhAhmad_Hay dung bai nay (2013) 4. Duboust, N., et al.: An optical method for measuring surface roughness of machined carbon ﬁbrereinforced plastic composites. J. Compos. Mater. 51(3), 289–302 (2016) 5. NguyenDinh, N., et al.: Surface integrity while trimming of composite structures: Xray tomography analysis. Compos. Struct. 210, 735–746 (2019) 6. Hejjaji, A., et al.: Surface and machining induced damage characterization of abrasive water jet milled carbon/epoxy composite specimens and their impact on tensile behavior. Wear 376–377, 1356–1364 (2017) 7. Saoudi, J., et al.: Critical thrust force predictions during drilling: analytical modeling and Xray tomography quantiﬁcation. Compos. Struct. 153, 886–894 (2016) 8. Ramulu, M., Wern, C.W., Garbini, J.L.: Effect of ﬁbre direction on surface roughness measurements of machined praphite epoxy composite.pdf. Compos. Manufact., 4 (1993) 9. Ghidossi, P., Mansori, M., Pierron, F.: Influence of specimen preparation by machining on the failure of polymer matrix offaxis tensile coupons. Compos. Sci. Technol. 66(11–12), 1857–1872 (2006) 10. Wang, F., et al.: Influences of milling strategies and process parameters on the cavity defect generated during milling of carbon ﬁber reinforced polymer. Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci. (2020) 11. Ramulu, M.: Machining and surface integrity of ﬁbrereinforced plastic (1997)
A Study on Prediction of Grinding Surface Roughness Do Duc Trung1, Nhu Tung Nguyen1, Hoang Tien Dung1, Nguyen Van Thien1, Tran Thi Hong2, Tran Ngoc Giang3, Nguyen Thanh Tu3, and Le Xuan Hung3(&) 1
2
3
Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi City, Vietnam Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen City, Vietnam [email protected]
Abstract. This paper presents a study on the prediction of surface roughness in surface grinding. Based on the results of several previous studies, the relation between the abrasive grain tip radius and the standard systems of grinding wheels and the surface roughness were predicted. Also, the surface roughness when surface grinding SKD11, SUJ2, and 3X13 steel by Al2O3 and CBN grinding wheels was anticipated. The predicted surface roughness values were found to be close to the experimental values. In addition, the average deviation between the predictive results and the experimental results was 15.11% for the use of Al2O3 grinding wheels and 24.29% for the case of using CBN grinding wheels. Keywords: Surface roughness Surface grinding Grinding Standard system
1 Introduction Surface quality when grinding is assessed through many parameters: hardness, surface layer residual stress, surface roughness, etc. In particular, surface roughness is one of the parameters that greatly affect the workability and lifetime of machine details and it is often chosen as a parameter to evaluate the grinding process. To study the grinding roughness surface, experimental methods are often employed, however, they have some disadvantages that the results obtained in the experimental process can usually be applied only under similar conditions and the experimental cost is large [1]. To overcome the above limitations, studies of modeling simulating the grinding process to predict surface roughness have been carried out by various researchers. Typical studies include building models to predict surface roughness when grinding based on the analysis models of cutting thickness [2]; predicting the surface roughness when grinding with the assumption that the grits are uniformly distributed on the grinding wheel surface [3–5]; predicting the surface roughness when grinding through © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 102–111, 2021. https://doi.org/10.1007/9783030647193_13
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determining the average value of the cutting depth into the machining surface of the abrasive grains [6]; applying probability theory when analyzing the cutting process of abrasive grains to predict surface roughness [7]; estimating surface roughness with the assumption that the grinding process is a mechanicalthermal equilibrium process [8]; constructing the relationship between surface roughness and unformed chip thickness with the assumption that the cross section of each cut is made by an abrasive on the surface of the workpiece with different geometric shapes (triangle, semicircular, curved, hyperbole) [1, 9–12]. However, applying the results of these studies is rather demanding because of the microstructure of the grinding wheel surface [1]. Therefore, it is necessary to build the correlation between the microstructure of the grinding wheel surface and its parameters, which will serve as the basis for predicting the achieved surface roughness by grinding.
2 The Model for Predicting Surface Roughness In surface grinding, the surface roughness can be found by the following formula [13–15]: Ra ¼
0; 25 VW0;4 tf0;6 0;2 0;2 KC0;4 ðVG þ VW Þ0;4 n0;4 g De qg
ð1Þ
In which, VW is the workpiece velocity; tf is the depth of cut; KC is the chip generation coefﬁcient (the calculation of this parameter value is often difﬁcult, in most cases it is possible to choose KC = 0,9) [13]; VG is the grinding wheel velocity; ng is the number of abrasive particles per unit area of the grinding wheel surface; De is the equivalent abrasive grains’ diameter; qg is the radius of the abrasive grain’s top. The determination of these parameters will be discussed in detail. The equivalent grinding wheel diameter is calculated as follows [16, 17]: De ¼
DG DW DG DW
ð2Þ
In which, DG and DW are the grinding wheel and the workpieces diameters, respectively. The plus sign (+) in formula (2) is used when external grinding, while the minus sign (−) is used for internal grinding. When grinding flat, DW ! 1, so that De = DG. The depth of cut is determined as follows [13–15]: sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 13; 55 VW t pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ tf ¼ 0; 739 t þ 0; 546 t2 þ KC ðVG VW Þ nG De qg
ð3Þ
Where, t is the depth of the layer of metal removed after grinding; The plus sign (+) in formula (3) is used when external grinding, minus sign (−) is used for internal grinding.
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Regarding the abrasive grain tip radius qg, this parameter depends on the limited size of abrasive grain. However, the standard systems for grinding wheel do not indicate the abrasive grain tip radius or the limit size of abrasive grain. Therefore, it is needed to ﬁnd a solution to determine the abrasive grain tip radius from the Standard marking systems of the grinding wheel. Currently, two commonlyused standard systems for grinding wheels are ISO 84861.2: 1996 (E) and GOST R 364780. According to ISO 84861.2: 1996 (E), the grain size is denoted by a number and by the letter F. Table 1 displays the limit size values of abrasive grain and the abrasive grain tip radius determined in some experimental studies. Table 1. The values of the limit size of abrasive grain Bg and the abrasive grain tip radius qgaccording to the experimental studies of the denoted grinding wheel according to ISO 84861.2: 1996 (E). Denoted Grain size, F 16 25 32 40 50 63 80 The limit size of abrasive grain, µm 160 240 315 400 500 630 800 The abrasive grain tip radius, µm – – 26 – – 45 – 13 19 – 28 – – – 11 17 25 – 41 – – – 19 – 30 – – 68 14 21 – 30 – – – – 18 26 – 43 – – 12 – – – – 48 – 13 19 27 28 38 – 60 Average 12,6 19 26 29 39,5 48 64
Ref. 100
125
160
200
1000 1250 1600 2000 – – 76 – – 80 – –
– – – 97 – 91 93 –
117 114 – 115 – – 119 –
– – – 130 – 138 149 –
76
95
115,3 139,5
[16] [18] [19] [20] [21] [22] [23] [24]
Based on the data in Table 1, the relationship between the abrasive grain tip radius qg and the limited size of abrasive grain is shown in Eq. (4). Alternatively, the relationship between the abrasive grain tip radius qg and denoted grain size is presented in Eq. (5). qg ¼ 0; 0535 B0;955 g
ð4Þ
qg ¼ 0; 8754 F 0;9659
ð5Þ
Or:
Table 2 presents the abrasive grain tip radius when calculated using (4), (5) and the values in experimental studies (data from Table 1).
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Table 2. Comparing the abrasive grain tip radius according to the predicted results and experimental results of the denoted grinding wheel according to ISO 84861.2: 1996 (E) standard. Denoted grain size, F 16 25 32 40 50 63 80 The limit size of abrasive grain, µm 160 240 315 400 500 630 800 The abrasive grain tip radius, µm 12.6 19 26 29 39.5 48 64 12.8 19.4 24.5 30.7 38.1 47.6
1.59 2.11 5.77 5.86 3.54 0.83
12.7 19.6 24.9 30.9 38.3 47.9
0.79 3.16 4.23 6.55 3.04 0.21
How to determine 100
125
160
200
1000
1250 1600 2000
76
95
115.3 139.5 Average experimental value 59.6 74.3 2.4 115.4 143 The calculated values by formula (4) 6.88 2.24 2.74 0.09 2.51 % Deviation from experimental values (4) 60.3 74.8 92.8 117.8 146.1 The calculated values by formula (5) 5.78% 1.58% 2.32 2.17 4.73 % Deviation from experimental values (5)
The data in Table 2 reveal that the values of the abrasive grain tip radius when calculated according to formulas (4) and (5) are greatly close to the experimental values. The values of deviation ranges 0.09–5.77% for formula 4, and 0.79–6.55% for formula 5. Therefore, it is drawn that one of the two formulas can be used to calculate the value of the abrasive grain tip radius for each grinding wheel according to Standard marking system ISO 84861.2: 1996 (E). Another standard system for grinding wheels is GOST R 364780. According to this standard, the size of abrasive grain is expressed through the number of sieve holes per square inch of sieve used for grading and it is denoted as M. Table 3 presents the abrasive grain tip radius of some grades. From the data in Table 3, the relationship between the abrasive grain tip radius and the M index is built in formula (6). qg ¼ 1245; 5 M 1;132
ð6Þ
Table 4 presents the calculated abrasive grain tip radius using Eq. (6) and experimental value (data from Table 3).
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Denoted grain The limit size of size, M abrasive grain, µm 10 2000–2500 12 1600–2000 16 1250–1600 20 1000–1250 24 800–1000 30 630–800 36 500–630 46 400–500 54 315–400 60 250–315 70 200–250 80 160–200 100 125–160 120 100–125 150 80–100 180 63–80 230 50–63 280 40–50 320 28–40
The average limit size of abrasive grain, µm 2250 1800 1425 1125 900 715 565 450 357.5 282.5 225 180 142.5 112.5 90 71.5 56.5 40 34
The abrasive grain tip radius, µm 85.1 68.7 55.0 43.9 35.5 28.5 22.7 18.3 14.7 11.7 9.4 7.6 6.2 5.4 4.2 3.7 2.4 2.0 1.7
Table 4. Comparing the abrasive grain tip radius according to the predicted results and experimental results of the denoted grinding wheel according to GOST R 364780 standard. Denoted grain size
Experimental value in Table 3
10 12 16 20 24 30 36 46 54 60 70 80 100 120
85.1 68.7 55.0 43.9 35.5 28.5 22.7 18.3 14.7 11.7 9.4 7.6 6.2 5.4
The abrasive grain tip radius The calculated values Deviation from by formula (6) experimental values (%) 91.9 7.99 74.8 8.88 54.0 1.82 41.9 4.56 34.1 3.94 26.5 7.02 21.6 4.85 16.3 10.93 13.6 7.48 12.1 3.42 10.2 8.51 8.7 14.47 6.8 9.68 5.5 1.85 (continued)
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Table 4. (continued) Denoted grain size 150 180 230 280 320
Experimental value in Table 3 4.2 3.7 2.4 2.0 1.7
The abrasive grain tip radius The calculated values Deviation from by formula (6) experimental values (%) 4.3 2.38 3.5 5.41 2.6 8.33 2.1 5.00 1.8 5.88
It is observed from the data in Table 4 that the calculated abrasive grain tip radius by formula (6) is very close to the experimental value. The deviation ranges from 1.82% to 14.7%. Hence, formula (6) can be used to estimate the calculated abrasive grain tip radius for each grinding wheel according to the GOST R 364780. The number of abrasive grains per unit area of grinding wheel surface is determined by the following formula [16]: ng ¼
6 2 p B2g
ð7Þ
In formula (7) 2 is the percentage of the grain volume in the grinding wheel. For the conventional grinding wheel, it is determined by the formula (8) [16] with the index of the structure number of the grinding wheel. For the diamond and CBN grinding wheels, the values of the grinding wheels are investigated according to the index showing the concentration of abrasive particles, as shown in Table 5 [25]. 2 ð% Þ ¼ 2 ð32 SÞ
ð8Þ
Table 5. Relationship between 2 and index showing the abrasive concentration of diamond and CBN grinding wheels [25] Concentration, C 25 50 75 100 125 150 175 200 2 ð%Þ 6.25 12.50 18.75 25.00 31.25 37.50 43.75 50.00
The data in Table 5 disclose that if the index denotes the concentration of abrasive grains of diamond and CBN grinding wheels, their values are determined by formula (9). 2 ð% Þ ¼ 0; 25 C
ð9Þ
In the case of a medium structure grinding wheel, for simple denotation of grinding wheel, some grinding wheel manufacturers do not show the structure of the grinding wheel in their standard systems. In this case, for a conventional grinding wheel, it is possible to choose a structure S ¼ 8, but for diamond or CBN grinding wheel, choose C ¼ 100 [16].
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Formulas (1) to (9) show that it is possible to predict the grinding surface roughness according to each speciﬁc case of the parameters of the grinding wheel, the dimension of workpiece and the parameters of cutting mode. In particular, if the grinding wheel is denoted according to ISO 84861.2: 1996 (E), the abrasive grain tip radius is calculated by the formula (4) or (5), and if the grinding wheel is denoted according to with GOST R 364780, the abrasive grain tip radius is calculated by formula (6). The percentage of the abrasive grits in a grinding wheel is calculated by Eq. (8) with a conventional grinding wheel or by the formula (9) with the CBN or diamond grinding wheel.
3 Comparing Predicted Surface Roughness and Experimenting Results In this work, the experimental results in the study [26] in which Al2O3 and CBN grinding wheels were used for grinding SKD11, SUJ2 and X13M materials on the flat grinding machine are used to compare with the values obtained in this study. The grinding wheel and technological regime parameters used during the experiment of the study [26] are presented in Table 6 and 7 for cases of using Al2O3 grinding wheel and CBN grinding wheel, respectively. The experimental surface roughness values when grinding different materials and the calculated surface roughness values by formulas (1) to (9) are also included in these tables. From the data in Tables 6 and 7, the roughness comparison charts were built as shown in Figs. 1 and 2 for grinding by Al2O3 and CBN grinding wheels, respectively. Table 6. Surface roughness values in [26] and by calculating when using Al2O3 grinding wheel. Grinding wheel: 36A60LV, grain size M = 60, number of structure S = 8, diameter of grinding wheel DG ¼ 180, cutting speed: VG ¼ 26 m/s No. tðmmÞ VW Surface roughness, Ra ðm=minÞ The experimental surface roughness Ra(calculated) values [26] Ra(ExpSKD11) Ra(ExpSUJ2) Ra(Exp3X13) 1 0.05 8 0.45 0.41 0.46 0.54 2 0.035 8 0.42 0.48 0.32 0.43 3 0.05 15 0.48 0.55 0.50 0.69 4 0.035 15 0.55 0.55 0.62 0.56
Tables 6, 7 and Figs. 1, 2 show that in the case of using Al2O3, the calculated surface roughness values are close to the experimental surface roughness values for all three steel types tested. The average deviation calculated for all three steel types tested is 15.11%. Regarding the case of using CBN grinding wheel, the average deviation between the calculated results and experimental results is approximately 24.29%. These results show that the method of surface roughness presented in this study can be used to predict surface roughness in speciﬁc cases.
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Table 7. Surface roughness values in [26] and by calculating when using CBN grinding wheel. Grinding wheel: 3 HY100#, grain size M = 100, number of structure C = 100, diameter of grinding wheel DG ¼ 180, Cutting speed: VG ¼ 26 m/s No. t V Surface roughness, Ra ðmmÞ ðm=minÞ The experimental surface roughness Ra(calculated) values [26] Ra(ExpSKD11) Ra(ExpSUJ2) Ra(Exp3X13) 1 0.01 5 0.50 0.32 0.23 0.39 2 0.02 5 0.63 0.38 0.32 0.59 3 0.01 15 0.82 0.65 0.6 0.60 4 0.02 15 1.06 0.55 0.64 0.92
Fig. 1. Comparison of surface roughness in [26] and by calculating when using Al2O3 grinding wheel.
Fig. 2. Comparison of surface roughness in [26] and by calculating when using CBN grinding wheel.
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4 Conclusion Based on the published results of surface roughness modeling when grinding, this study presents a method to determine the abrasive grain tip radius using the different standard marking systems of the grinding wheel. Also, formula for calculating the percentage of grinding grain volume for diamond and CBN grinding wheels was introduced. Additionally, the methods to predict the surface roughness when grinding with conventional, diamond or CBN grinding wheels were clariﬁed. The accuracy of these methods has been veriﬁed by comparing the roughness values resulting from calculation and experiments. The results show that the obtained roughness values in both cases were very compatible with each other. Consequently, the results of this study can be used to predict the grinding surface roughness to reduce the machine time as well as to improve the efﬁciency of the grinding process. Acknowledgements. This work was supported by Thai Nguyen University of Technology.
References 1. Hecker, R.L., Liang, S.Y.: Predictive modeling of surface roughness in grinding. Int. J. Mach. Tools Manufact. 43, 755–761 (2003) 2. Lal, G.K., Shaw, M.C.: The role of grain tip radius in ﬁne grinding. J. Eng. Ind., 1119–1125, August 1975 3. Nakayama, K., Shaw, M.C.: Study of ﬁnish produced in surface grinding, part 2. In: Proceeding of the Institution of Mechanical Engineers, vol 182 (1968) 4. Sato, K.: On the surface roughness in grinding. Technol. Rep., Tohoku Univ. 20, 59–70 (1955) 5. Yang, C., Shaw, M.C.: The grinding of titanium alloys. Trans. ASME 77, 645–660 (1955) 6. Zhou, X., Xi, F.: Modeling and predicting surface roughness of the grinding process. Int. J. Mach. Tools Manufact. 42, 969–977 (2002) 7. Basuray, P., Sahay, B., Lal, G.: A simple model for evaluating surface, roughness in ﬁne grinding. Int. J. Mach. Tool Des. Res. 20, 265–273 (1980) 8. Steffens, K.: Closed loop simulation of grinding. Annals CIRP 32(1), 255–259 (1983) 9. Sanjay Agarwal, P., Rao, V.: A probabilistic approach to predict surface roughness in ceramic grinding. Int. J. Mach. Tools Manufact. 45, 609–616 (2005) 10. Sanjay Agawal, P., Rao, V.: Surface Roughness Prediction Model for Ceramic Grinding, pp. 1–9. International Mechanical Engineering Congress and Exposition, Orlando, Florida USA (2005) 11. Khare, S.K., Agarwal, S.: Predictive modeling of surface roughness in grinding. In: CIRP Conference on Modelling of Machining Operations, vol. 31, pp. 375–380 (2015) 12. Saxena, K.K., Agarwal, S., Das, R.: Surface roughness prediction in grinding: a probabilistic approach. In: MATEC Web of Conferences, vol. 82 (2016). https://doi.org/10.1051/ matecconf/20168201019 13. Novoselov, Y.: The Dynamics of Formation of Surfaces in Abrasive Machining. Publ. SevNTU, Sevastopol (2012)
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14. Bogutsky, V., Yu, N., Bratan, S.: Analysis of relation between grinding wheel wear and abrasive grains wear. In: 2nd International Conference on Industrial Engineering Procedia Engineering, vol. 150, pp. 809814 (2016) 15. Bogutsky, V., Yu, N., Shron, L.: Forecasting the surface roughness of the workpiece in the round external grinding. In: International Conference on Modern Trends in Manufacturing Technologies and Equipment, Web of Conferences, vol. 129 (2017). https://doi.org/10.1051/ matecconf/201712901080 16. Malkin, S., Guo, C.: Grinding Technology  Theory and Application of Machining with Abrasives, 2nd edn. Industrial Press, New York (2008) 17. Marinescu Loan, D., Uhlmann, E., Brian Rowe, W.: Handbook of Machining with Grinding Wheels, CRC Press Taylor & Francis Group (2006) 18. Bajkalov, A.K.: Introduction to the Theory of Grinding Materials. Naukova dumka, Kiev (1978) 19. Maslov, E.N.: Theory of Grinding Materials. Mashinostroenie, Moscow (1974) 20. Murdasov, A.V., Wolff, A.M.: Peculiarities of Working of Grinding Wheels from Abrasive Grains of Different Shapes, (Abrasives and diamonds: scientiﬁc technical abstract collection vol 4) (Moscow: NIIMASH), pp. 6569 (1967) 21. Vakser, D.B.: The Influence of the Geometry of Abrasive Grains on the Properties of the Grinding Wheel. Mashgiz, Moscow (1960) 22. Shaw, C.M.: Principles of Abrasive Processing. Oxford University Press, Oxford Series on Advanced Manufacturing New York (1996) 23. Korolev, A.V., Novoselov, Y.: Theoretical and Probabilistic Basis of Abrasive Treatment. Saratovsk. unt, Saratov (1987) 24. ElHofy: Fundamentals of Machining process: Conventional and Nonventional Processes, CRC Press, Boca Raton (2006) 25. https://www.noritake.co.jp/eng/catalog_type/download/ 8aa6080c86465c93cfebd53c07689c7f.pdf 26. Trung, D.D., Son, N.H.: An experimental study on prediction of surface roughness in grinding. Int. J. Mech. Prod. Eng. Res. Dev. 10(1), 47–58 (2020)
A VisionBased Measurement and Classiﬁcation System for Robot Arm Under Controlled Lighting Condition QuangCherng Hsu1, NgocVu Ngo2(&), ThanhLong Pham2, QuocKhanh Duong2, and DucVuong Vu2 1 National Kaohsiung University of Science and Technology, 415 Chien Kung Road, Sanmin District, Kaohsiung 80778, Taiwan, ROC 2 Thai Nguyen University of Technology, No. 666, 3/2 Street, Thai Nguyen City, Vietnam [email protected]
Abstract. This paper presents development of a measurement and classiﬁcation system for robot arm using machine vision under controlled lighting environment. The proposed system uses a single camera as a sensor for measuring and classifying objects which are bolts and nuts. Using image processing and analysis, characteristics of objects was extracted and area of blob in binary image also was calculated for classiﬁcation process. For coordinate calibration process, the quadratic transformation and regression analysis were used to determine relationship between image coordinate and the world coordinate. Experiment results showed that the proposed system can measure and classify the components exactly of 100% from all samples tested and measurement errors are suitable with the system which applied to a robot arm. Keywords: Machine vision Classiﬁcation Robot arm Calibration Camera
1 Introduction One of the techniques for automating robots is the machine vision [1, 2]. With this technique, robots are equipped with a humanlike perception that can distinguish the shapes and colors or locations of objects to perform operations according to human. Machine vision uses one or more cameras to collect images of objects, then image analysis and processing algorithms will be used to identify and locate objects. There have been many researches applying machine vision to develop automatic identiﬁcation and sorting systems integrated with robots in production lines. In a study of Tsarouchi et al. [3], the authors used a machine vision system to identify and locate objects in the world coordinate. This system used a 2MP camera which is Basler A641FC. The camera is located and ﬁxed on the top of conveyor belt. Their research used MATLAB software to implement image processing algorithms, identify and convert image coordinates to 2D real coordinates. This system has been integrated with robots to perform operations in production lines. In another research of Phansak et al. [4], the authors have developed a flexible automated assembly system for © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 112–119, 2021. https://doi.org/10.1007/9783030647193_14
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robotic arm. They created assembly parts to perform the experiments. These parts include triangles, squares and circles that can be assembled with the corresponding holes. Thus, to identify and classify the images above, they used a camera to capture images, then the image processing and analysis was carried out. After classifying the above objects, the positions of the shapes and the holes are determined to perform the assembly. An important work in machine vision system for industrial robots is coordinate calibration process. Coordinate calibration is to transform image coordinates to the world coordinates. There have been many studies related to coordinate calibration algorithms to enhance accuracy of this process. In the study of Ngo et al. [5], a 3D coordinate calibration method has been proposed. This method used six deﬁned points in a coordinate system in real space. With these points, the authors can identify all internal and external parameters of the double cameras used in the study and thereby determine the relationship between image coordinates and the world coordinates in three dimensions (3D). In another research by Hsu et al. [6], a calibration 2D coordinate method was investigated. This study used a quadratic transformation and regression analyze for the calibration process. This method has been compared with Matlab’s calibration tool and experimental results showed that it is accurate and suitable for their proposed visual system. Base on previous researches, in this study, the authors have developed a system that can classify and measure objects in twodimensional space (2D). In order to obtain good images for processing and analysis, the research used controlled lighting source. The quadratic transformation and regression analysis algorithms were used for coordinate calibration process and accuracy of this process also was investigated.
2 Experimental System The visionbased measurement and classiﬁcation system in this study is shown in Fig. 1. This system used a camera CMOS which amount on the top of the workshop. Measured objects are metal components including bolts and nuts. They are random on the top of the lighting source. A computer was used to analyze images of desired objects and perform measuring and classiﬁcation algorithms. To locate objects in the world coordinate system, calibration process was implemented using a calibration sample with the quadratic transformation algorithm and regression analysis. And this system was integrated with a robot arm via a controller. In fact, metallic components are very sensitive to ambient light. When light intensity changes, the degree of light reflection on the surface of the components also changes. This greatly affects the image processing. Therefore, to overcome this problem, this research used the controlled lighting source. With this light source, components are placed in the space between the camera and the light source [7]. Therefore, the boundary of the objects is exactly extracted, so that the resulting image can easily be processed by adjusting the binary threshold. The layout of this light source is also shown in Fig. 1. The objects in this study consist of 7 nuts and 5 bolts placed randomly above the light source, as shown in Fig. 2.
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Fig. 1. Experimental system
Fig. 2. Metallic components
3 Research Methodology 3.1
Image Processing
Extracting and processing objects in an image is an important work in a vision system. With this work, camera will capture objects and record into a computer. Here, threshold operation is performed to obtain the best binary image. In image processing, morphological operations are often used to recognize and enhance the quality of image and then extract characteristics of objects [8]. Morphological operations normally are performed onto the binary and gray images. With each image, the output values of each pixel are based on the input values of pixel, respectively, and their neighbor values. Here, the algorithm will give each pixel value with gray level in the range of 0–255. The threshold operation uses the highest and smallest threshold value in the range of
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0–255 to ﬁnd the most suitable threshold for image, as shown in Eq. (1). In addition, the morphological method is also used to reduce noise in the image and smooth the boundaries of the objects of interest, thereby extracting the characteristics of the best object. 8 < 255 f ðx; yÞ K1 f 0 ðx; yÞ ¼ f ðx; yÞ\K2 ð1Þ : 0 others Where f’(x,y) is the new gray level at point I(x,y). f(x,y) is a 2D light intensity function. K1 is the low limit threshold value (0 K1 K2). K2 is the high limit threshold value (K1 K2 255). x, y are the image coordinates in the X and the Y directions, respectively. 3.2
Calibration Algorithm and Method
Calibration Algorithm. To determine relationship between image coordinate and the world coordinate, this study used quadratic transformation and regression analysis based on Eq. (2), as follows:
X ¼ a1 x2 þ b1 xy þ c1 y2 þ e1 x þ f1 y þ g1 Y ¼ a2 x2 þ b2 xy þ c2 y2 þ e2 x þ f2 y þ g2
ð2Þ
Where a1, b1, c1, e1, f1, g1, and a2, b2, c2, e2, f2, g2 are transformation coefﬁcients and x, y are image coordinates; X, Y are the world coordinates The regression analysis can be applied for Eq. (1) to determine transformation coefﬁcients when image and the world coordinates of six points are known. Whereby, the summation of the least error square in X and Y direction is described in Eq. (3), as follows: 8 n 2 P > > Xi ða1 x2i þ b1 xi yi þ c1 y2i þ e1 xi þ f1 yi þ g1 Þ < SX ¼ i¼1 n 2 P > > Yi ða2 x2i þ b2 xi yi þ c2 y2i þ e2 xi þ f2 yi þ g2 Þ : SY ¼
ð3Þ
i¼1
Where SX and SY are the summation of the error square, n is number of calibration points. To determine the transformation coefﬁcients in Eq. (3), partial derivatives of SX and SY with each the transformation coefﬁcient were performed, as shown in Eq. (4): @SX @SX @SX @SX @SX @SX ¼ 0; ¼ 0; ¼ 0; ¼ 0; ¼ 0; ¼0 @a1 @b1 @c1 @e1 @f1 @g1 @SY @SY @SY @SY @SY @SY ¼ 0; ¼ 0; ¼ 0; ¼ 0; ¼ 0; ¼0 @a2 @b2 @c2 @e2 @f2 @g2
ð4Þ
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Calibration Method. This study used a coordinate calibration sample, as shown in Fig. 3 (a). This calibration sample consists of 156 black circles with a diameter of 11 mm. The distance between two successive circles is 28 and 22 mm in the X and Y directions, respectively. First, the vision system will collect the image of the calibration sample as an input data. And then, this image will be binary by adjusting the binary threshold.
(a)
(b)
Fig. 3. Coordinate calibration sample
To determine the center of the circles and their order, the Blob analysis was used. With this method, closing and opening functions are performed to eliminate noise and improve the quality of blobs in binary images. Then, the system will calculate the number of Blobs corresponding to the black circles in binary image to be determined and eventually give the image coordinates of the entire center of the calibration circles as well as their order, as shown in Fig. 3 (b). By using the calibration method, these coordinates will be interpolated to the world coordinates in 2D space and the transformation coefﬁcients in Eq. (2) will be determined and used for calculating coordinates of other objects in the workspace of the system.
4 Results and Discussion 4.1
Calibration Results
According to the calibration results, as shown in Fig. 4, the highest positive deviations of the calibration process are 0.63 mm and 0.72 mm in X and Y directions, respectively. The highest negative deviations are −0.67 mm and −0.80 mm in X and Y directions, respectively. Base on the calibration deviation graph, it can be seen that the number of calibration points with an outside deviation of 0.6 mm is small in both negative and positive ones. Most deviations are within the range of 0.4 mm to 0.5 mm in both negative and positive deviations. These deviations can be improved by enhancing the camera resolution and image quality. While the resolution of the proposed vision system was 0.56 mm/pixel and 0.6 mm/pixel for image size in X and Y directions, respectively. Besides, this vision system is applied to a robot arm, these
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deviations can be compensated by gripper of robot arm. Therefore, robot arm can still pick and place exactly all objects in workspace.
Calibration deviation 0.8 0.6 0.4 0.2 0 0.2 0.4 0.6 0.8 1
1
12
23
34
45
56
67
78
OX axis
89 100 111 122 133 144 155 OY axis
Fig. 4. Calibration deviation chart
4.2
Measuring and Classiﬁcation Results
Image processing for bolts and nuts is shown in Fig. 5. To achieve the best binary image quality, the intensity threshold values were controlled and selected. After obtaining the best binary images, Blob analysis was conducted to classify bolts and nuts. To distinguish these two types of components, the area of Blob (white color) in binary images was calculated and compared. The larger Blobs were identiﬁed as bolts and the nuts were the Blobs which have smaller area with black hole, and the system automatically assigned labels and names to the corresponding components. In classiﬁcation process, nuts are nondirectional distinction, so no need to identify their angle. However, the bolts need to be determined the direction and head. Direction of bolts is angle between center line of bolts and OX axis in counterclockwise. To identify the head of bolts, image of bolts was found both the left and right side of the center of bolts. Comparison of number of pixels between left side and right side of bolts was performed. The larger image region that number of pixels is bigger is head of bolt. After the classiﬁcation process, the system showed labels and the center coordinates of the objects, as shown in Figs. 6 and 7. Through calibration process, the center coordinates of the objects were determined in real space 2D. The classiﬁcation results show that the nuts are more precisely deﬁned than the bolts because the shape of the nut is quite simple while the shape of the bolts are more complicated, so their positions are skewed. This is a difﬁcult problem in image processing and machine vision and it can be improved by improving the resolution of the camera and adjusting the lighting condition.
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a. Gray image
b. Binary image
Fig. 5. Gray and binary image
Fig. 6. Measuring and classiﬁcation result
Fig. 7. Interface of proposed system
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5 Conclusions This research has successfully developed a system to automatically measure and classify metallic parts under controlled lighting conditions for robot arm. In addition, the coordinate calibration method using the quadratic transformation and regression analysis algorithm has been used to determine the world coordinates of 2D objects with errors that match the resolution of the system. By using the controlled light source, the influence of light on the image quality for classiﬁed objects has been signiﬁcantly reduced. The proposed system can automatically measure and classify the metal components exactly of 100% and measurement deviations are suitable with the system. In the next studies, the authors will continue to develop this system in a realistic light environment to better ﬁt the systems and production lines in the factory to increase flexibility for machine vision system.
References 1. Peter, C.: Robotics, Vision and Control: Fundamental Algorithms in Matlab. Springer, New York (2013) 2. Fu, K.S., Gonzalez, R.C., Lee, C.: Robotics: Control, Sensing, Vision, and Intelligence. McGrawHill Book Company, New York (1987) 3. Tsarouchi, P., Matthaiakis, S.A., Michalos, G., Makris, S., Chryssolouris, G.: A method for detection of randomly placed objects for robotic handling. CIRP J. Manufact. Sci. Technol. 14, 20–27 (2016) 4. Phansak, N., Pichitra, U., Kontorn, C.: Using machine vision for flexible automatic assembly system. In: The 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2016, York, United Kingdom (2016) 5. Ngo, N.V., Hsu, Q.C., Hsiao, W.L., Yang, C.J.: Development of a simple three dimensional machine vision measurement system for inprocess mechanical parts. Adv. Mech. Eng. 9, 1– 11 (2017) 6. Hsu, Q.C., Ngo, N.V., Ni, R.H.: Development of a faster classiﬁcation system for metal parts using machine vision under different lighting environments. Int. J. Adv. Manufact. Technol. 100(9–12), 3219–3235 (2019) 7. Malamas, E.N., Petrakis, E.G., Zervakis, M., Petit, L., Legat, J.D.: A survey on industrial vision systems, applications and tools. Image Vis. Comput. 21(2), 171–188 (2003) 8. Frank, Y.: Shih: Image Processing and Mathematical Morphology: Fundamental and Applications. CRC Press, Taylor & Francis Group, Boca Raton (2009)
About a Viewpoint of Calculating Spatial Dimensional Tolerance Chains According to Structure Group of a Parallel Robot Thuy Le Thi Thu1(&), Trung Trang Thanh1,2, HuuThang Nguyen1,3(&), and Long Pham Thanh1 1
Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam {hanthuyngoc,nguyenhuuthang}@tnut.edu.vn 2 School of Mechanical and Automotive Engineering, South China University of Technology, Guangdong, China 3 Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Abstract. In robotics technology, tolerance design is a part of mechanical design and determines the ﬁnal product quality. The spatial dimensional chain problem of the parallel structure with component links having an active assembly and the closed link having a given error is a complex one. Determining the relationship between the end effector tolerances and component link tolerances of each leg in general form is the basis for digitizing the problem. Calculating the initial approximation of tolerance for other structures in the group based on the sample solution has the meaning of deciding time and quality of calculation. This paper proposes a hypothesis that it is always possible to determine a reasonable initial approximation based on a sample solution. Therefore, the process of calculating tolerances of the component kinematic parameters of parallel industrial robots is signiﬁcantly shortened. Keywords: Parallel robot Endeffector accuracy
Tolerance Kinematics Structure similarity
1 Introduction After having the nominal dimensions, the structures should have tolerances to decide the processing technology measures, the cost and methods of assembly. Usually, the design process will give the accuracy and repeatability accuracy at the end of the actuator link, and the engineer needs to determine the component tolerances of the two objects are the tolerance of the generalized coordinates and the fabrication dimensions. Generalized coordinate tolerances are often used to select the resolution of electronics such as motors or sensors, while structural tolerances are used to make structural components. The problem is complicated when considering execution objects with the form of parallel structures (Fig. 1). A parallel robot is a machine with a special form. The links and joints connect together to form a closed chain. In these closed chains, the moving platform links with © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 120–132, 2021. https://doi.org/10.1007/9783030647193_15
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Fig. 1. Several types of parallel robots.
the base platform via legs for end actuator working with very high load capacity, rigidity and precision. There have been studies on the accuracy and tolerance design of these parallel robots, but the number is limited. Some researches have tried to use different methods to ﬁnd the relationship between the endeffector kinematic deviation with the link and joint tolerances, and then design, determine the suitable tolerance values. Each method of building a mathematical model is applied to certain types of parallel robots [1–6]. In this paper, the authors propose a procedure for calculating tolerances of kinematic parameters of parallel robots based on considering each branch acting as an independent serial robot. The calculation process goes through two clear steps: determining the preliminary tolerances as initial approximations and checking, adjusting these preliminary tolerances. In particular, the tolerance design process based on a parallel robot structure group relationship to shorten the calculation process is considered.
2 Tolerance Calculation for Parallel Robot Consider a general structure as shown in Fig. 2a, in which the E link is the working link. The accuracy of this link is given. At the design analysis step, it is necessary to indicate the errors of the active joints (the tolerances of the generalized coordinates) and the errors of its component links so that the given ﬁnal link errors are guaranteed in the entire working area. The ith branch (ith leg) of the structure can form the following equation: fi ðO1 ; O0 ; qi ; di Þ ¼ pðjÞ
ð1Þ
With i ¼ 1; . . .; n n is the number of legs and is the degrees of freedom of the robot. The complete set of equation systems as Eq. (1) describes the kinematics of n legs and will fully describe the kinematics of the parallel robot.
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Fig. 2. Components of a parallel robot
p (j): pose of the endeffector O0: Basic coordinate system O1: local coordinate system mounted on the moving platform di: dimension parameter of the ith link on each leg ðaÞ ðbÞ ðaÞ qi: joint parameter, in which qi is divided into two types: qi ¼ ðqi ; qi Þqi are ðbÞ
active joint variables (generalized coordinates);qi are extra parameters. These parameters are not as active as the generalized coordinates, they act as cosine directions of the legs. Equation (1) will be rewritten in full form as follows: ðaÞ
ðbÞ
fi ðO1 ; O0 ; qi ; qi ; di Þ ¼ pðjÞ
ð2Þ
To characterize the accuracy or limit the ﬁnal error in the workspace, a sphere with center pi and radius d is used. In which, Pi is the desired target position and radius d describes the permissible deviation limit at the position Pi. As shown in Fig. 3, with the center of the sphere as the desired position, the space in the sphere is the actual end position achieved and within the desired precision limits. Therefore, to determine the outside permissible region inside permissible region
permissible error radius r
Endeffector
real position desired position
Fig. 3. The sphere limits the permissible error of the endeffector
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allowable deviations of the link and joint parameters of the robot, the following kinematic problems were established: ðaÞ
ðbÞ
ðjÞ
ð3Þ
ðaÞ
ðbÞ
ðjÞ
ð4Þ
ðaÞ
ðbÞ
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ðaÞ
ðbÞ
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ð6Þ
ðaÞ
ðbÞ
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ð7Þ
ðaÞ
ðbÞ
ðjÞ
ð8Þ
fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p1 ðxp þ d; yp ; zp Þ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p2 ðxp d; yp ; zp Þ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p3 ðxp ; yp þ d; zp Þ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p4 ðxp ; yp d; zp Þ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p5 ðxp ; yp ; zp þ dÞ fi ðoj ; o0 ; qi ; qi ; di Þ ¼ p6 ðxp ; yp ; zp dÞ ðjÞ
ðjÞ
Where, p1 p6 is six representative points, surveying the allowed deviation around the position p (j) of the endeffector. J = 1… m; m is the number of survey points in the workspace. The inverse problem from (3) – (8) solved by [7] gives the allowed displacement of all parameters in Eq. (2) as follows: – With generalized coordinates: ðaÞ
2 ðqimin ; qimax Þ ¼¼ [ dqi
ðbÞ
2 ðqimin ; qimax Þ ¼¼ [ dqi
qi
ðaÞ
ðaÞ
ðaÞ
ð9Þ
ðbÞ
ðbÞ
ðbÞ
ð10Þ
– With extra parameters: qi – With link dimensions: di 2 ðdimin ; dimax Þ
¼¼ [ ddi
ð11Þ
This is a preliminary set of tolerances. To get the ﬁnal tolerances, a retest is required to meet the accuracy required by the expression: ðaÞ
ðaÞ
ðbÞ
ðbÞ
fi ðO1 ; O0 ; qi dqi ; qi dqi ; di ddi Þ pðkÞ d
ð12Þ
Because the forward kinematic problem is applied to parallel robots, it is always polynomial. For the forward kinematic problem to repeat at the exact point of the survey without giving new points due to its polynomial, it is necessary to provide the ðaÞ ðbÞ ðaÞ value of qi ; qi ; di in Eq. (12), not only the extrapolation coordinates qi as when regular solving. The results of calculation of tolerance after satisfactory inspection at Eq. (12) are used as follows:
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The deviation range of the generalized coordinates (in Eq. (9)) is used to select the sensors and the robot drive motors; The deviation range of the extra parameters (in Eq. (10)) is to determine the manufacturing and assembly quality for structures which produce the corresponding extra parameters; The deviation range of link dimensions (in Eq. (11)) is the tolerances written on the fabrication drawing to manufacture the links.
3 Tolerance Calculation for Parallel Robot Base on Structure Group Considering the problem, there are two parallel robots with the same structure, in which one robot already has tolerances or its tolerances calculated as Sect. 2. The remaining parallel robot needs to determine initial tolerance. The whole process of the calculation is divided into two clear steps: determining the ﬁrst approximation tolerance according to the structure group and checking the endpoint accuracy response to adjust the ﬁrst approximation to the ﬁnal completed tolerance value (Fig. 4).
Fig. 4. Example of two parallel robots with the same structure type
Let A and B be two robots of the same structure type. A is a sample robot with links and joints tolerances already in place. B is a robot that needs to ﬁnd tolerance. aAi ; diA and aBi ; diB are respectively the nominal dimensions of the ith link of robots A and B, daAi ; ddiA and daBi ; ddiB are their tolerances.r A ; r B are the radius of the sphere that limits the permissible error at the endeffector of robots A and B respectively. dA
rA
Call k ¼ diB the dimensional similarity ratio between two robots A and B, kr ¼ rB is i
the accuracy ratio between A and B.
About a Viewpoint of Calculating Spatial Dimensional
125
Fig. 5. The discrete branches of the parallel structure from the root structure are described as the open chain
Robot kinematic equations are built on each leg (Fig. 5) to form separate closed loops. Then, each branch will be equivalent to an openchain robot and can apply the theory presented in [8]. Some situations occur as follows: 3.1
Two Branches of Dimensional Similarity
Suppose A and B are two similar branches of two parallel robots of the same structural form. Robot A has an endeffector accuracy and a kinematic tolerance given. At the time of the survey, branch A has a generalized coordinate set of A1 (q1, q2, q3)(1A) and the tolerance of the link parameters is ðdai ; ddi Þð1AÞ corresponding to that state. B is the same as branch A at that position, that is, has the same generalized coordinate set as A1 is B1 (q1, q2, q3)(1B). – Consider case 1: A and B are similar, k = kr. Having the following similarity relationship: ðai ; di ÞðAÞ ¼ kðai ; di ÞðBÞ
ð13Þ
Because the two branches satisfy the similar relationship, it is always possible to establish the derivative relation of eq. (14) for the ith survey point of the working area as: ðdai ; ddi ÞðiAÞ ¼ kðdai ; ddi ÞðiBÞ
ð14Þ
–  Consider case 2: A and B are similar, but k 6¼ kr. Considering the case of two arms that are similar but the similarity ratio differs from the accuracy ratio (k 6¼ kr), that is, the accuracy is not zoomed in the same proportion as the link dimension of the branch:
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k¼
di
rA or k 6¼ kr rB
6¼
ðBÞ di
ð15Þ
Call B’an intermediate branch: B’ has the conﬁguration of B, that is k ¼
ðBÞ
di
ðB0 Þ di
¼ 1,
and the endeffector accuracy rB’ satisﬁes ðAÞ
k¼
di
ðB0 Þ di
¼
rA i:ek ¼ kr rB 0
ð16Þ
Thus, the tolerance of intermediate branch Bis found as in case 1. From here, we determine the tolerance of branch B according to B′. Suppose that the tolerance of tolerance B′ determined is ðB0 Þ ðda1 ; ::; dan ; dd1 ; ::; ddn Þ . The establishment of component relationships between the permissible error of the endeffector and the tolerances of the component links according to the rates is as follows: ð
rB 0 rB0 rB0 rB0 ðB0 Þ ; ::; ; ; ::; Þ da1 dan dd1 ddn
ð17Þ
Assume that this is also true for branch B. It is possible to apply the above ratio to distribute the permissible error of the endeffector rB for the tolerances of the component links ðda1 ; ::; dan ; dd1 ; ::; ddn ÞðBÞ to accelerate the computational speed. Let m be the common divisor of rB and r’B and j1 ¼
rB0 rB ; j2 ¼ m m
ð18Þ
Then, the relationship of the unit displacement between the joint space and the workspace of robot B′ is assumed to be ðB0 Þ
ðB0 Þ
ðB0 Þ
rB0 da dan dd1 ! ð 1 ; ::; ; j1 j1 j1 j1
ðB0 Þ
ddn ; ::; Þ j1
ð19Þ
If this ratio is applied to branch B based on similarity, the tolerances of the corresponding links will be ðB0 Þ
ð
ðB0 Þ
ðB0 Þ
da1 dan dd :j2 ; ::; :j2 ; 1 j1 j1 j1
ðB0 Þ
:j2 ; ::;
ddn j1
:j2 ÞðBÞ
ð20Þ
The reallocation of the tolerances of the joint variables of B also uses the above rule:
About a Viewpoint of Calculating Spatial Dimensional ðB0 Þ
ð
ðB0 Þ
127
ðB0 Þ
dq1 dq dqn :j2 ; ::; 2 :j2 ; ::; :j2 ÞðBÞ j1 j1 j1
ð21Þ
Thus, this method saves time because it does not have to recalculate the tolerance of B from the beginning. The ratio distributing the upper and lower deviations of the tolerance of B is taken from that of B’ as a sample. 3.2
Two Branches of the Same Structure but not Similar
Consider the case where the branches designed have the same structural form as a sample branch but not identical. Assume branch A has kinematic parameters: aAi ; diA i ¼ 1. . .n with the corresponding tolerances daAi ; ddiA i ¼ 1. . .n . Joint variable qi, i = 1… n, having dqi (rad). The sphere limiting permissible error has radius rA (mm); Let B be a branch of the same structural form but not similar as A, that is, the ratio between their respective links is not equal: diB diBþ 1 6¼ diA diAþ 1
ð22Þ
The positioning accuracies of the endeffectors rA and rD are different. The kinematic tolerance of robot B is found through robot A and intermediate robot C in two phases, as shown below: – Phase 1: Find rC for intermediate branch C – Phase 2: Find the tolerance of branch B based on branch C being similar to B Phase 1: Find rC for intermediate branch C Let C be a robot with a similar structure to robot B and in the same structure group but not similar to robot A, that is, diC diCþ 1 6¼ diA diAþ 1
ð23Þ
With the technique introduced in Sect. 2 and applied to branch A, the reasonable ratio between the tolerance and the corresponding link length of this type of robot structure was determined as
ddiA . diA
The natural kinematics of the robot according to this
ratio can be reasonable. Without loss of generality, suppose that robot C in the same group A uses the same ratio for link consideration, that is, it has the following relationship: ddiA ddiC dC ¼ C ! ddiC ¼ ddiA : iA A di di di
ð24Þ
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When dCi changes its nominal length compared with dA i , its tolerance is recalculated by this ratio. After the tolerance of links ddiC is deﬁned as Eq. (24), rC is found by using the forward kinematic equation to sweep the combinations of C. The joint tolerances dqCi of C stay the same as those of robot A. The parameters are passed to the computational software to determine the radius of the spherically permissible region for all combinations and to ﬁnd the maximum possible spherical radius rC = max(rCi). The parameter used for phase 2 is rC; therefore, after determining this value, phase 1 ends. Phase 2: Find the tolerance of branch B based on branch C being similar to B Branch B is in the same group as sample branch A and has an identical conﬁguration as the intermediate branch C. We need to ﬁnd the kinematic parameter tolerances of B according to the requirement from the endeffector accuracy rB. Since B is a branch with the same conﬁguration as branch C rC 6¼ rB , the procedure is similar to problem 1 with case 2 presented above.
Fig. 6. The planar parallel robot 3RRR
4 Case Study Consider the planar parallel robot structure 3RRR as shown (Fig. 6) The planar parallel robot 3RRR has three legs structure forming three closed loops. The moving platform of the manipulator is a tripod ABC working in the 2D plane. Each leg is formed by two links ai and bi, linked together and with the base platform, the moving platform through three hinge joints. Here, the position and direction of the moving platform are determined by the center of the tripod O1 (xO1, yO1) and the direction angle u. Active joint variable ϴi, passive joint variable ai (i = 1, 2, 3 corresponds to 3 legs of the robot).
About a Viewpoint of Calculating Spatial Dimensional
4.1
129
Determining Tolerance for an Independent Robot
Planar parallel robot A has link dimensions a1 = a2 = a3 = 40 cm; b1 = b2 = b3 = 30 cm; limit of position deviation at the centre of the moving platform due to the robot dimension error is rA = 0.1 mm. The kinematic equations of the three legs of the robot are as follows:
xO1 yO1
xO1 yO1
xO1 yO1
# " hpﬃﬃ3 : cosðu þ 30Þ b1 : cosðh1 þ a1 Þ a1 : cosðh1 Þ 3 þ þ hpﬃﬃ3 ¼ a1 : sinðh1 Þ b1 : sinðh1 þ a1 Þ : sinðu þ 30Þ
3
b : cosðh2 þ a2 Þ c a : cosðh2 Þ þ 2 þ ¼ þ 2 a2 : sinðh2 Þ b2 : sinðh2 þ a2 Þ 0
¼
"
c b3 : cosðh3 þ a3 Þ a3 : cosðh3 Þ 2 ﬃﬃ p c 3 þ a : sinðh Þ þ b : sinðh þ a Þ þ 3 3 3 3 3 2
# pﬃﬃ h 3 : cosðu þ 30Þ 3 ﬃﬃ p h 3 3 : sinðu þ 30Þ
"
# pﬃﬃ h 3 3pﬃﬃ: sinðu þ 30Þ h 3 3 :cosðu þ 30Þ
Two separate problems are performed: determining link tolerances and determining joint tolerances. Based on solving the kinematic problem, the joint variables hi ; ai are certain values, calculating the allowed deviations ai, bi at the boundary positions of the permissible error region of the endeffector with r = 0.05 mm. Making statistics of errors, tolerances of the links are determined as dai ¼ 0:35 mm, dbi ¼ 0:32 mm.
Fig. 7. Simulation on the software of checking the design tolerances at a position in the working space of robot A
Similarly, with the radius controlling endeffector error r = 0.05 mm, the joint tolerances are determined as dqi ¼ 0:00034 rad.
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The calculated data are input into the specialized software with interchangeable plans the links and joints in the selected tolerance range. Test results show that the tolerances above are satisﬁed (Fig. 7). 4.2
Determination of Tolerance Based on Structural Group
a. Two parallel robots are similar and k = kr Robot B that needs to ﬁnd tolerances has structural parameters: a1 = a2 = a3 = 20 cm; b1 = b2 = b3 = 15 cm; limit of position error at the center of the moving platform due to the kinematic dimension error of robot is rB = 0.05 mm. Thus, this is a case of determining the tolerance of a robot based on a similar relationship and the similarity ratio equal to the accuracy ratio. According to the theory presented in 3.1, the tolerance of this robot is determined as follows: dai ¼ 0:175 mm, dbi ¼ 0:16 mm, dqi ¼ 0:00017 rad (i = 1, 2, 3 corresponds to 3 branches (3 legs) of the robot). b. Two parallel robots are similar and k 6¼ kr Robot B has a1 = a2 = a3 = 20 cm; b1 = b2 = b3 = 15 cm. Assuming that in this case, the accuracy at the endeffector of robot B needs to be rB = 0.07 mm. Thus, the similarity of the structure and accuracy of the endeffector have different zoom ratios or k 6¼ kr. In this situation, using intermediate robot B’and found the tolerance for this intermediate 0 robot (at this time B’ is robot B in Sect. 4.1.a): r B ¼ 0:05 mm, r B ¼ 0:07 mm. The common divisor m = 0.01 j1 ¼
rB 0 rB ¼ 5; j2 ¼ ¼ 7 m m
So, the tolerance of robot B to ﬁnd is: 0
0
0
daB dbB dqB dai ¼ i j2 ¼ 0:245mm; dbi ¼ i j2 ¼ 0:224dqi ¼ i j2 j1 j1 j1 ¼ 0:000238rad c. Two parallel robots have the same type of structure but not the similarity Robot B, which needs to ﬁnd tolerance, has kinematic parameters: a1 = a2 = a3 = 40 cm; b1 = b2 = b3 = 35 cm, the endeffector accuracy should be achieved rB = 0.1 mm. It is easy to see that sample robot A and robot B have the same structure as the planar parallel manipulator 3RRR but not similar in size. How to solve the problem is done according to theory Sect. 3.2 as follows: Call C an intermediate robot conﬁgured as the robot that needs to ﬁnd tolerance B. With the internal ratio showing the reasonable relationship between the tolerance and aC
the link length of robot A, apply this ratio to robot C: daCi ¼ daAi : aiA ¼ 0: 175 mm, i
bC
dbCi ¼ dbAi : biA ¼ 0: 187 mm i
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Using the forward kinematic problem combined with statistics, the allowable error of the center of the moving platform caused by the link and joint error is determined as rC = 0.12 mm.
Fig. 8. Checking design tolerances for robot B
At the end of phase 1, the intermediate robot has the same conﬁguration as the robot B that needs to ﬁnd tolerance, has the link and joint tolerance and endeffector accuracy different from that of B. Therefore, the relationship between robot C and B is similar to the case of two robots of the same conﬁguration but different from the endeffector accuracy resolved above (rB = 0.1 mm, rC = 0.12 mm). The ﬁnal result, the tolerance of the kinematic parameters of robot B be found as follows:dai ¼ 0:145 mm, dbi ¼ 0:17 mm, dqi ¼ 0:00017 rad The veriﬁcation process based on the interchange done on the software proved that tolerance values found by the above method are perfectly reasonable and satisfactory for the given endeffector accuracy (Fig. 8).
5 Conclusions Tolerance calculation for a parallel robot is a very complex problem. The splitting each branch (leg) of the robot and applying the hypothesis of the existence of the initial approximation tolerance in the structural group as we presented above shows that this is the correct direction to speed up the calculation process. Starting from any robot in the group that has a known tolerance, the initial approximation tolerances are determined quickly and they will be checked for a combination of factors on the specialized software for the ﬁnal reasonable tolerance values. The tolerance technique for an independent robot is different from the tolerance technique for a serial robot that we mentioned earlier in [8] because the subject here is a parallel robot. The initial approximation tolerance is applied to each branch without counting once for all branches, i.e. all links such as chain robots. The above computational efﬁciency has shown that this technique deserves to be applied in parallel robot tolerance design process.
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Acknowledgment. The authors want to thank Thai Nguyen University of Technology (TNUT), Vietnam for sponsoring this research.
References 1. Takematsu, R., Satonaka, N., Thasana, W., Iwamura, K., Sugimura, N.: A study on tolerances design of parallel link robots based on mathematical models. J. Adv. Mech. Des. Syst. Manuf. 12(1), 2018. https://doi.org/10.1299/jamdsm.2018jamdsm0015 2. Sun, T., Wang, P., Lian, B., Liu, S., Zhai, Y.: Geometric accuracy design and error compensation of a onetranslational and threerotational parallel mechanism with articulated traveling plate. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 232(12), 2083–2097 (2018). https://doi.org/10.1177/0954405416683433 3. Huang, T., Chetwynd, D.G., Mei, J.P., Zhao, X.M.: Tolerance design of a 2DOF overconstrained translational parallel robot. IEEE Trans. Robot. (2006). https://doi.org/10. 1109/TRO.2005.861456 4. Huang, T., Bai, P., Mei, J., Chetwynd, D.G.: Tolerance design and kinematic calibration of a fourdegreesoffreedom pickandplace parallel robot. J. Mech. Robot. 8(6), 061018 (2016). https://doi.org/10.1115/1.4034788 5. Ni, Y., Shao, C., Zhang, B., Guo, W.: Error modeling and tolerance design of a parallel manipulator with fullcircle rotation. Adv. Mech. Eng. 8(5), 1–16 (2016). https://doi.org/10. 1177/1687814016649300 6. Hassan, M., Notash, L.: Optimizing fault tolerance to joint jam in the design of parallel robot manipulators. Mech. Mach. Theor. 42(10), 1401–1417 (2007). https://doi.org/10.1016/j. mechmachtheory.2006.10.001 7. Trang, T.T., Li,. W.G., Pham, T.L.: A new method to solve the kinematic problem of parallel robots using general reduce gradient algorithm. J. Robot. Mech. 28N03 (2016) 8. Thu, T.L.T., Thanh, L.P.: Tolerance calculation for robot kinematic parameters to ensure endeffector errors within a predetermined limit area. Int. J. Eng. Res. Technol. 12(9), 1567–1574 (2019)
Adaptive Algorithm for Servo System Using Linear Electric Motor V. E. Kuznetsov1, Phan Thanh Chung1, Nguyen Thi Ha2(&), and Nguyen Hoang Ha3(&) 1
Saint Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam 3 Institute of Mechanical Engineering, Ha Noi, Vietnam [email protected]
Abstract. This article aims to build the construction of a mathematical model of a linear motor for moving the valve of an electrohydraulic drive. First, an analytical description of its nonlinear traction characteristic is given as well as the resulting reaction from the action of electromagnetic force and the elastic force of the center spring. Second, the Adaptive algorithm of servo control based on the Lyapunov theorem is built and an analysis of the effectiveness of the adaptive algorithm with the change of the servo system’s parameters. Simulation results on the model of the servo system with the controller in the MATLABSimulink software package are presented to demonstrate the effectiveness of the proposed approach in this paper. Keywords: Linear electric motor
Servo system Adaptive control
1 Introduction The advancement of new types of aircrafts makes steering systems operate with low displacement amplitudes, which makes requirements for the dynamic properties of steering actuators and for the stability of their properties in the area of small signals even more demanding. In the area of small signals, there is a number of factors that decrease the dynamic and static properties of the system, namely essential nonlinearity in properties linear force motor (LM) and hydraulic valves [1, 2]. The peculiarity of these nonlinear properties is determined by steepness values of static parameters depending on the value of the input signal. Electrohydraulic system parameters changes depend on the changes in temperature and pressure of the service fluid, reduction in reserved control channels quantity, and the presence of external disturbances [3]. In this case, the dynamic properties of a servo system can vary in a wide range. Increasing the sensitivity and stability of an electrohydraulic steering system in the ﬁeld of small signals under the given conditions can be efﬁciently achieved by means of adaptive control [2, 3].
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 133–142, 2021. https://doi.org/10.1007/9783030647193_16
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2 Model of Servo System Using Linear Electric Motor 2.1
Mathematical Model of Linear Electric Motor
Under the assumptions pertaining to the description of electromagnetic process [5], a complete mathematical model of a linear electric motor was obtained, which is described by the following system of differential equations:
di LðFi ; xÞ dt ¼ u Ri Ce ðFi ; xÞ dx Fi ¼ wi; dt ; 2 ðC C ð x ÞÞx fL ; mlm ddt2x ¼ KFI ðxÞi blm dx M dt
ð1Þ
u ðUÞ; iðIÞ; R; w is correspondingly, voltage, current, resistance and number of turns of the LM’s winding; x is the absolute displacement of the armature of the LM; mlm is mass of the moving part; blm is coefﬁcient of viscous friction; C stiffness of the centering mechanical spring, fL is the external force. LM is subject to a signiﬁcant impact of internal nonlinear properties [2, 5]: – Static property of LFM power The nonlinear static mechanical characteristic of the force LM KFI ðxÞ ¼
M þ 1 þ x2 Þw 2kFM ðR ; M þ 1 x2 Þ2 ðR
ð2Þ
1 k ¼ 2l0 Sf R2x0 , l0 are magnetic constant, Sf being square area of the pole tips; Rx0 ¼ d l0 Sf , x ¼ x=d, d are air gap with motor in the mid position, the magneto M ¼ RM =Rx0 ; the resistance of permanent motive force of permanent magnets FM ; R magnets RM ¼ RM1 ¼ RM2 . Сlm ( x ) x, kgf
100
С=128 kgf/mm С=100 kgf/mm С=70 kgf/mm С=45 kgf/mm
50 0 50
1
0.5
0
0.5
x , mm
Fig. 1. 1. View of nonlinear resulting elastic force of a magnetic and mechanical spring at various stiffness values C
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– The resulting force and the electromagnetic force and of the elastic force The resulting force of the electromagnetic force and the elastic force fR ¼ Clm ðxÞx !
2 4kFM
Clm ðxÞx ¼ ½CM ðxÞ C x ¼
M þ 1 x2 Þ d ðR 2
C x;
ð3Þ
The most impact is done by the resulting elastic force Clm ðxÞx, the property of which is given in Fig. 1. Its steepness changes depending on the stiffness of mechanical spring C and has two extreme points, which—when reached—make LM reduce its static stiffness. When LM’s operating gap is reduced down to 1/3—where the gradient of tilt angle change is insigniﬁcant—the property is linear. – The coefﬁcient of linear force motor h i 2 2 2 2 R w ð þ 1 Þ x M wFM ðRM þ 1 þ x Þ Ce ðxÞ ¼ M þ 1 x2 Þd ; L Fy ; x ¼ 2R M þ 1 x2 Þ M ðR Rx0 ðR
2.2
ð4Þ
Model of Servo System Using Linear Electric Motor
kui Uss 
kе

ki 1 Тi S
fL
kFi(x) ku Тu S + 1  
1 LS
fT
I

kvx 1 Vlm 1 xlm mlmS S
1 S
xss
blm
R UEM Се(х) kux
Сlm ( x)
Fig. 2. Mathematical model of a servo taking into account nonlinear properties of a LM.
Block diagram of a servo system with a completed nonlinear model of linear force motor that takes into account all the abovementioned nonlinear factors is shown in Fig. 2. The diagram shows: ke is gain ratios, position error and error correspondingly; ki ; Ti are gain and time constant of current regulator; kvx being speed performance; ku ; Tu are gain and time constant of power ampliﬁer; kui ; kux are coefﬁcients of current sensor and position.
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3 Adaptive Regulator for Servosystem 3.1
Adaptive Algorithm
According to the above analysis, the system is a system of sufﬁciently high order, including nonlinear coefﬁcients: Ce ; kFI ; Clm ; kvx . The system can be described as equations in state space: x_ ¼ Aðx; tÞx þ Bðx; tÞu; y ¼ Cx
ð5Þ
гдe x n– dimensional state vector; y – dimensional output vector; u m – dimensional control vector; Aðx; tÞ; Bðx; tÞ – nonstationary matrices (they depend on work zone and time), C– constant output matrix. The essence of applying adaptive control to a system is to design the regulator so that the system achieves the characteristics of the reference model (the desired mathematical model). Assuming that the reference model has the form (6), we have the goal of the control problem (7) x_ M ¼ AM xM þ BM u;
y ¼ CxM
ð6Þ
Where xM n dimensional state vector of the reference model AM ; BM ; C: constant matrices of the reference model we have the goal of the control problem (7) lim e ¼ lim ðx xM Þ ¼ 0
t!1
ð7Þ
t!1
Equation (5) can be written by form x_ ¼ AM x þ BM u þ r;
y ¼ Cx
ð8Þ
Where r ¼ ½Aðx; tÞ AM Þx þ ½Bðx; tÞ BM u. When adding an adaptive signal z ¼ zðtÞ to the control object (8) satisﬁes condition (7), then Eq. (5) can be written: x_ ¼ AM x þ BM ðu þ zÞ þ r; y ¼ Cx. Consider a vector error: e ¼ x xM . We have: e_ ¼ x_ x_ M ¼ AM x þ BM ðu þ zÞ þ r ðAM xM þ BM uÞ ¼ AM ðx xM Þ þ BM Z þ r ¼ AM e þ BM z þ r
;
ð9Þ
Choose Lyapunov function: VðeÞ ¼ eT Pe, where P is a symmetric positive deﬁnite matrix determined from the Lyapunov equation PAM þ ATM P ¼ Q, where Q is a diagonal positive deﬁnite matrix. VðeÞ [ 0 ; 8e 6¼ 0 We can see: VðeÞ ¼ 0 ; e ¼ 0
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With the Eq. (9) we have: _ VðeÞ ¼ x_ x_ M ¼ eT ATM P þ PAM e þ 2eT PBM z þ 2eT Pr With condition zðtÞ ¼ h sign BTM Pe ; h [ BM krk, _ VðeÞ eT Q1 e 2eT PBM jBMþ r hj 0; 8e 6¼ 0 We have _ VðeÞ ¼ 0; e ¼ 0 thus, the system is asymptotic stability. According to the above base, in order to build adaptive algorithms for systems, the following steps must be performed: – building a reference model – building a system state observer – developing an adaptive mechanism for the system 3.2
Building a Reference Model
The reference model has the desired characteristics to meet the requirements of the system. The servo system is linearized and then added a modal controller to become a reference model. The linearized model is shown in Fig. 3, The model is linearized at the working points of the characteristics. The diagram in Fig. 3 contains a current loop with PI controller.
fL USS
fT
i=x4 ke E
1 kui
1
Di
kFi
Tems
1 ki k u
Xlm=x2
x3 1 mlm s
1 s
kvx
1 XSS=x1 s
blm Ce Clm kux
Fig. 3. Linear system
Where Di ¼ kui ki ku r 1 , Uss : task servo system x1 ¼ xss –output position of the servo system; x2– changing the armature of the LM; x3 – armature speed of LM; x4– current running in the winding of LM. State equations of the linearized system: x_ ¼ A0 x þ B0 u; y ¼ C0 x ;
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where 2 6 6 A0 ¼ 6 4 h B0 ¼ 0
0
0 0 0
kvx 0 mClmlm
0 1 mblmlm
kkuxuikTeemDi
0
rTCeme
0
ke D i kui Tem
iT
0 0 mkFIlm
þ 1Þ ðDiTem
; C0 ¼ C ¼ ½ 1
0
3 7 7 7; 5
0
0 :
Matrices A0 ; B0 are the result of system linearization. Reference model: x_ M ¼ AM x þ BM u; y ¼ CxM ; where AM ¼ A0 BK ; BM ¼ B0 ; K ¼ ½ k1 k2 k3 k4 . K – matrix coefﬁcient of modal regulator. To determine the modal regulator coefﬁcients, the distribution of the roots of the characteristic equation using the standard Butterworth or Newton form is taken. H0 ðpÞ ¼ a0 p4 þ a1 x0 p3 þ a2 x20 p2 þ a3 x30 p þ a4 x40 where a0, a1, a2, a3, a4 are coefﬁcients of Butterworth or Newton polynomials, x0 is desired cutoff frequency of system. 3.3
Building State Observer for Servo System
To obtain information on the state of the system in the absence of sensors, an observer is used. Equations of observer: ^x_ ¼ A0^x þ B0 u þ Gðy ^yÞ; ^y ¼ C^x, where ^x is vector of state variable estimates; G – is vector coefﬁcients feedback of observer. Matric G can be determined by the equation: det½pI A þ GC ¼ Hg ðpÞ, where: Hg ðpÞ ¼ a0 p4 þ a1 xg p3 þ a2 x2g p2 þ a3 x3g p þ a4 x4g , xg ¼ ð3 5Þx0 3.4
Development of an Adaptive Mechanism for the System
For system (5), the signal adaptation algorithm is as follows 2.1: ua ðtÞ ¼ uðtÞ þ zðtÞ; zðtÞ ¼ hsgnðBTM PeÞ where e ¼ xM ^x; uðtÞ – control input, zðtÞ – signal adaptation algorithm, h – coefﬁcient gain, characterizing amplitude (restriction) relay control, h ¼ const > 0. P – symmetric ðP ¼ PT [ 0Þ positive deﬁnite ðn nÞ, determined from the Lyapunov equation PAM þ ATM P ¼ Q. The speciﬁc values of the elements of the matrix Q are selected in accordance with the method, which is considered in [2]. A program of optimal choice of the values of the matrix Q is built [6], which determines the values of its diagonal elements based on the fulﬁllment of the condition of minimizing the ratio between the largest and the lowest eigenvalues of the matrix P.
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4 Simulation Results The system was simulated with the following parameters: Table 1. Parameters of the system simulation Parameters ke ki0 Ti0 r Tem kvx
Units Value – 1.5 – 3 – 0.1 Ohm 12 – 0.12 – 450000
Parameters kFi0 Ce0 blm mlm Clm0
Units Value – 0.12 – 162 – 1.99 kg 0.017 – 21820
During the research of the servo system, the following variables are compared: the position of the original servo system (ORG), the reference model (Ref), and the position of the servo system with an adaptive controller (AC), force load is added at the time t = 0.06 s.
1.2
x1, mm
1 0.8 0.6 ORG АC Ref
0.4 0.2 0
0.02
0.04
0.06
0.08
t, s 0.1
Fig. 4. Transient processes of the servo system when system at maximum speed (ki = 3, C = Clm)
The system in Fig. 4 has a setting of the Ref in speed equal to the speed of the speed part of the servo system (current loop ki = 3). The operation of algorithms AC leads to a satisfactory coincidence of the movement of the servo system to the Ref.
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1.2
x1, mm
1 0.8 0.6
ORG АC Ref
0.4 0.2 0
0.02
0.04
0.06
0.08
t, c 0.1
Fig. 5. Transient processes of the servo system at reduced speed (ki = 0.5, C = Clm)
With a decrease in the speed of the current loop (an increase in its constant time) (ki = 0.5), the transient processes are grouped around the motion of the Ref (Fig. 5). The work of AC is satisfactory.
1.2
x1, mm
1 0.8 0.6 ORG АC Ref
0.4 0.2 0
0.02
0.04
0.06
0.08
t, c 0.1
Fig. 6. Transient processes of the servo system when changing the parameters of the elastic part of the LM (ki= 2, C = 0.5Clm)
With the increasing speed of the mechanical part of the servo system (Fig. 6). LM resonance frequency is 2 times more than in the initial one. The adaptive controller still brings movement of the servo system close to the Ref.
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x1, mm
1 0.8 0.6
ORG АC Ref
0.4 0.2 0
0.02
0.04
0.06
0.08
t, c 0.1
Fig. 7. Transient processes of the servo system when changing the parameters of the elastic part of the LM (ki = 2, C = 2Clm)
By reducing the frequency of the resonance of LM in half (Fig. 7) adaptive controller brings the movement of the servo system closer to the reference. If the subsystem is correctly adjusted by the speed of the servo system (the current loop has a reduced gain factor ki = 2). The servo system has the necessary stability reserves in amplitude and phase, which allows the improvement its dynamic properties by means of an adaptive controller.
5 Conclusion From the mathematical proofs and simulation results, we see that with the optimal setting of the speed of the servo system and the dynamics of the reference model, adaptive regulators ensure a good convergence of the output variable of the system to the reference model when changing the parameters of the servo system: the speed of the LM current loop and the resonance of the mechanical part of the LM. Acknowledgement. The work described in this paper was supported by Thai Nguyen University of Technology for a scientiﬁc project.
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References 1. Kuznetsov, V., Konstantinov, S., Polyakhov, N., and others.: Electrohydraulic actuators of aircraft flight surface with adaptive control. St. Peterburg, Russia: Publishing House of the ETU “LETI”, p. 513 (2011) 2. Bortsov, YuA., Polyakhov, N.D., Putov, V.V.: Electromechanical systems with adaptive and modal control, p. 216. Energoatomizdat, Leningrad, USSR (1984) 3. Kuznetsov, V.E., Polyakhov, N.D.: Adaptive control of technical plants based on exomodel. In: (2015) Proceedings of International Conference on Soft Computing and Measurements, SCM 2015, art. no. 7190425, pp. 106–108, https://doi.org/10.1109/scm.2015.7190425 4. Kuznetsov, V.E.: Adaptive control of the steering servo system based on exomodel/Problems of improvement of robotic and intelligent aircraft systems: Collection of reports of the X AllRussian Scientiﬁc Technical Conference. – 26.6.2015, MAI – Moscow.2015. – C.204–208 5. Kuznetsov, V.E., Chung, P.T., Lukichev, A.N., Ha, N.H.: A Linear Electric Motor Servo System with the Adaptive Controller Based on Exomodel. (EIConRus), 28 Jan 2019, pp. 584–589. IEEE (2019) 6. Kuznetsov, V.E., Chung, P.T.: The program for calculating the parameters of the signal adaptive algorithm for the servo electrohydraulic system/ Certiﬁcate of state registration of computer programs № 2018664091, Moscow 12.11 (2018)
Adaptive Sliding Mode Control for a 2DOF Robot Arm in Case of Actuator Faults Le Ngoc Truc1(&)
and Nguyen Phung Quang2
1
2
Hung Yen University of Technology and Education, Hung Yen 17817, Vietnam [email protected] Hanoi University of Science and Technology, Hanoi 11615, Vietnam
Abstract. The paper presents an adaptive sliding mode controller for a 2DOF robot arm suffering actuator faults. The type of actuator faults considered in this study is the proportional degradation of torque. The robot model is set up with unknown factors representing the degree of the actuator torque fault. Based on this model, an adaptive sliding mode methodology is designed to tolerate the faults. The system stability is guaranteed according to the Lyapunov approach. The adjustable controller coefﬁcients can be adapted to greater values of unknown bounds, which satisﬁes the Lyapunov criterion. A performance comparison between the proposed control fashion and a popular robot controller is carried out via the MATLAB/Simulink simulation environment. The results show that the proposed controller can satisfactorily steer the joint responses following the desired trajectories in the faulty state with reasonable degrees of torque loss. Keywords: Adaptive control
Sliding mode control Robot Actuator fault
1 Introduction Nowadays, due to the sustained needs of using robots in production lines and/or in hazardous environments, the fault tolerance capability of a robot system has received signiﬁcant interest. Usually, robot control systems respond to almost faults by halting the system or accommodating for the faults. The fault problem considered in this study is actuator torque degradation. The actuator faulty may be kept operating, or be put into a safe mode to avoid unnecessary damages. In general, topics related to fault investigation are detection, isolation, identiﬁcation, diagnosis, and faulttolerant control design. Many contributions regarding to the ﬁrst four topics are presented. In [1], a nonlinear observer based on the robot dynamics to identify actuator faults is provided. The study in [2] proposes a fault identiﬁcation scheme for robot actuators through a linear robust observer of fault time proﬁles. An approach to detect and isolate robot actuator faults through the adoption of SupportVectorMachines is introduced in [3]. For actuator fault diagnosis of robot manipulators, a discretetime framework processed by a decisionmaking system is devised in [4], and a performance analysis of a space manipulator in case of a single freeswinging joint is presented in [5]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 143–153, 2021. https://doi.org/10.1007/9783030647193_17
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Several schemes have been proposed in the design of faulttolerant controllers. In [6], an embedded adaptation scheme is synthesized to compensate the event of actuation degradation and to maintain the robot performance. The study in [7] provides a robust adaptive fault tolerant control for a class of Lipschitz nonlinear systems with actuator failure and bounded disturbances. Therein, for tuning adaptive parameters, two algorithms based on LMIs using the Lyapunov criterion are developed. An adaptive faulttolerant control based on boundary estimation is designed for a space robot under partial loss of actuator effectiveness and uncertain parameters [8]. For a class of unknown singleinput singleoutput nonlinear systems, a robust adaptive faulttolerant control for partial loss of actuator effectiveness is developed in [9]. For uncertain multipleinput singleoutput nonlinear systems with actuator failures, several compensation control approaches are: two adaptive backstepping control schemes with unknown loss of actuator effectiveness [10], combination of a twolayer neuralnetwork adaptive law and an automatic switching function mechanism [11], adaptive compensation with eventtriggered input by estimating the bound of the actuator failure parameters [12]. Motivated by these results, this study introduces an adaptive sliding mode methodology for a nonredundant 2DOF robot arm in case of actuator faults. The type of actuator faults put into investigation is the proportional degradation of torque. The switching gains of the proposed slidingmode controller are adjustable by an adaptive law, and the unknown partial loss of actuator effectiveness can be tolerated with acceptable performance. The system stability is guaranteed based on the Lyapunov method. This paper is organized as follows: Sect. 2 gives a 2DOF robot model and the description of actuator fault problem. The adaptive slidingmode control design for the tolerance of actuator faults is presented in Sect. 3. The efﬁciency of the proposal method is expressed via simulation for a 2DOF planar robot arm in Sect. 4. And in the ﬁnal section, some important conclusions will be mentioned.
2 Problem Preliminaries Let us consider a 2DOF robot dynamic model as follows M€ q þ Cq_ þ g ¼ sa
ð1Þ
where q 2 R2 is the joint variable, sa 2 R2 is the actual torque generated by actuators, M :¼ MðqÞ 2 R22 is the general inertia matrix, C :¼ Cðq; q_ Þ 2 R22 is the Coriolis/centrifugal matrix, g :¼ gðqÞ 2 R2 is the gravity term. Robot actuators are subjected to be the fault type: Proportional Degradation of Torque (PDT), where the actuator torque sa can be replaced by sa ¼ Usc
ð2Þ
where sc is the control torque, U ¼ diagðuÞ is the 2by2 diagonal matrix, u ¼ ½u1 ; u2 T is the torque degradation coefﬁcient vector, ui 2 ½0; 1, i ¼ 1; 2. Actuator i has either no torque fault if ui ¼ 1 or a PDT fault if 0\ui \1. Especially, if ui ¼ 0,
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the torque of actuator i will be the total loss and joint i becomes freeswinging. Matrix M is invertible because of its symmetric and positive deﬁnite properties [13]. Hence, the dynamic Eq. (1) can be written as € ¼ M1 ðCq_ þ gÞ þ M1 Usc q
ð3Þ
€ ¼ f þ Bsc q
ð4Þ
f :¼ M1 ðCq_ þ gÞ; B ¼ M1 U
ð5Þ
or by the compact form
where
Due to the degradation coefﬁcient U is unknown, matrix B is not also known ^ þB ~ where: B ^ is the nominal values of B, and B ~ is precisely. But we can have B ¼ B 2 the estimation errors. In the next section, with the desired joint trajectory qd 2 R and under the presence of actuator torque faults, an adaptive sliding mode controller is designed to steer q to qd and make the tracking error e ¼ qd q convergent to zero.
3 Adaptive Sliding Mode Control Design The sliding manifold s ¼ ½s1 ; s2 T is chosen as s ¼ Ae þ e_
ð6Þ
where A ¼ diagðaÞ is the 2by2 diagonal matrix with a ¼ ½a1 ; a2 T , ai [ 0 (i ¼ 1; 2) is chosen such that sliding surface si ¼ ai ei þ e_ i has stable dynamics. Taking the time derivative of (6) yields €d q € s_ ¼ A_e þ €e ¼ A_e þ q €d ðf þ Bsc Þ ¼ A_e þ q ^ þ BÞs ~ cÞ €d ðf þ ðB ¼ A_e þ q
ð7Þ
^ c Þ Bs ~ c €d ðf þ Bs ¼ A_e þ q The total loss of actuator torque is not considered in this study. Coefﬁcient ui [ 0 ^ are invertible. The sliding mode control law is designed as therefore U, B as well as B sc ¼ sno þ sdis 1
^ ðA_e þ q €d fÞ sno ¼ B 1
^ ^ wsgnðsÞ sdis ¼ B
ð8Þ
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where sno is the nominal control term, sdis is the adaptive discontinuous control term, h iT ^ ^ is the 2by2 diagonal matrix with w ^¼ w ^ ;w ^ ¼ diag w being the adjustable W 1 2 constant vector, sgnðsÞ :¼ ½sgnðs1 Þ; sgnðs2 ÞT is the vector of signum function sgnðsi Þ deﬁned as 8 > < 1 if si [ 0 sgnðsi Þ ¼ 0 if si ¼ 0 > : 1 if si \0
ð9Þ
Substituting (8) into (7) gives ^ s_ ¼ WsgnðsÞ p
ð10Þ
e c ¼ ½p1 ; p2 T is the combined uncertain vector. It is an assumption that where p :¼ Bs there exists a constant vector w ¼ ½w1 ; w2 T satisfying wi [ jpi j (i ¼ 1; 2). Therefore, if ^ convergent to W ¼ diagðwÞ, every onebyone element of an adaptive law can adjust W s_ will have opposite sign with that of s. We deﬁne the adaptive law as ^_ ¼ di jsi j ¼ di si sgnðsi Þ w i
ð11Þ
where di [ 0 (i ¼ 1; 2) is the chosen positive coefﬁcient. The adaptation speed is determined by di . The larger di , the faster matching convergence. The matrix and vector e ¼ w w, b respectively. Let us choose a e WW b and w of adaptation error are W¼ vectortype of Lyapunov function candidates as 1 1 ~ ~w V ¼ Ss þ D1 W 2 2
ð12Þ
where S ¼ diagðsÞ and D ¼ diagðdÞ are the 2by2 diagonal matrices, d ¼ ½d1 ; d2 T , 1 T [ 0 is the inverse matrix of D. Thus, Lyapunov function D1 ¼ diag d1 1 ; d2 ~2 candidate Vi ¼ 12 s2i þ 12 d1 i wi [ 0 (i ¼ 1; 2). Taking the time derivative of V obtains ~_ ~w V_ ¼ S_s þ D1 W
ð13Þ
Substituting (10) into (13) gives ~_ ^ ~w V_ ¼ SðWsgnðsÞ pÞ þ D1 W
ð14Þ
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~_ ¼ w_ w ^_ ¼ w ^_ and deduce Because W is a constant matrix, w ^_ ^ ^ w V_ ¼ SWsgnðsÞ Sp D1 ðW WÞ
ð15Þ
From the adaptive law (11) we have ^_ ¼ DSsgnðsÞ w
ð16Þ
Substituting (16) into (15) yields ^ ^ V_ ¼ SWsgnðsÞ Sp D1 ðW WÞDSsgnðsÞ ^ ^ ¼ SWsgnðsÞ Sp D1 WDSsgnðsÞ þ D1 WDSsgnðsÞ
ð17Þ
^ and D are diagonal, therefore they satisfy the commutative Matrices S, W, W, property of matrix multiplication. Thus, ^ ^ V_ ¼ SWsgnðsÞSpWSsgnðsÞ þ SWsgnðsÞ ¼ SpWSsgnðsÞ
ð18Þ
Considering the i th element of vector V_ is V_ i ¼ si pi wi si sgnðsi Þ ¼ si pi wi jsi j
ð19Þ
jsi jjpi j wi jsi j ðjpi j wi Þjsi j 0 The result in (19) shows that the stability of the proposed adaptive sliding mode ~ and control system is proven. The convergence to zero of both adaptation error w i sliding variable si is veriﬁed by the Lyapunov criterion. Hence, tracking error ei converges to zero within ﬁnite time.
4 Simulation Results for a 2DOF Planar Robot Arm The efﬁciency of the proposed adaptive sliding mode control law for a 2DOF robot under actuator torque degradation will be veriﬁed through an application example. Let us consider a 2DOF planar robot arm depicted in Fig. 1 where li is the length of link i, ri is the length between joint i and the centroid of link i, mi is the mass of link i, O0 x0 y0 z0 is the base frame, Oi xi yi zi is the link frame attached at link i, y0 axis is aligned with gravitational acceleration vector in the opposite direction.
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y2 x2 l2
y1
x1
l1
y0
r1 q1
r2
q2 x0
Fig. 1. A 2DOF planar robot arm with attached frames.
The general inertia matrix, Coriolis/centrifugal matrix, and gravity term vector in the dynamic model for the 2DOF robot arm (Fig. 1) are as follows: Mð1; 1Þ ¼ ðl21 þ 2l1 r2 cosðq2 Þ þ r22 Þm2 þ m1 r12 þ I1zz þ I2zz Mð2; 1Þ ¼ Mð1; 2Þ ¼ l1 m2 r2 cosðq2 Þ þ m2 r22 þ I2zz Mð2; 2Þ ¼ C¼ g¼
l1 m2 r2 q_ 2 sinðq2 Þ l1 m2 r2 q_ 1 sinðq2 Þ
m2 r22
ð20Þ
þ I2zz
l1 m2 r2 ðq_ 1 þ q_ 2 Þ sinðq2 Þ 0
gððl1 m2 þ m1 r1 Þ cosðq1 Þ þ m2 r2 cosðq1 þ q2 ÞÞ gm2 r2 cosðq1 þ q2 Þ
ð21Þ ð22Þ
where I1zz , I2zz are the principal moments of inertia of link 1, link 2 with respect to zaxis, respectively. For simulations, the robot’s parameters are m1 ¼ 4.0 kg, l1 ¼ 0.40 m, r1 ¼ 0.16 m, I1zz ¼ 110 103 kg m2 , m2 ¼ 2.0 kg, l2 ¼ 0.32 m, r2 ¼ 0.15 m, I2zz ¼ 56:5 103 kg m2 ; and the gravitational acceleration is 9.807 m=s2 . The robot’s initial conﬁguration is set as qð0Þ ¼ q_ ð0Þ ¼ ½0; 0T . The desired joint trajectory qd ¼ ½q1d ; q2d T is given by q1d ¼ 0:5 þ 2 sinð2ptÞ ðradÞ; q2d ¼ 0:5 þ sinð2ptÞ ðradÞ
ð23Þ
The robot will be subject to PDT faults in two cases of torque degradation coefﬁcient u ¼ ½u1 ; u2 T . Case 1: u1 ¼ u2 ¼ 0.9 corresponds to 10% loss of PDT, and case 2: u1 ¼ u2 ¼ 0.5 corresponds to 50% loss of PDT. In each case, with the same robot’s parameters, several simulations are implemented in turn with two schemes for performance comparison: a computedtorque controller with PD outerloop (CTCPD),
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and the proposed adaptive sliding mode controller (ASMC). To reduce the chattering phenomenon of sliding mode control system, saturation functions sat(si /ai ) described in (24) can be used instead of signum functions sgn(si ) in simulation. 8 if si ai < 1 satðsi =ai Þ ¼ ðsi =ai Þ if ai \si \ai ð24Þ : 1 if si ai Firstly, by using CTCPD without anticipating the PDT fault, the controller is given as _ q_ þ gðqÞ qd þ Kd e_ þ Kp eÞ þ Cðq; qÞ sc ¼ MðqÞð€
ð25Þ
where Kd ¼ diagð½20; 20Þ and Kp ¼ diagð½100; 100Þ. Secondly, by using the proposed ASMC with anticipating the PDT fault (Fig. 2), the controller parameters are chosen as: sliding manifold with a1 ¼ a2 ¼ 10, saturator functions with a1 ¼ a2 ¼ 0.5, adaptation speed coefﬁcients d1 ¼ 200, d2 ¼ 1500. The simulations are conducted by MATLAB/Simulink using solver ode5 with sample time of 0.001 s. The system performances in case 1 and case 2 are depicted in (Fig. 3, Fig. 4) and (Fig. 5, Fig. 6), respectively.
Fig. 2. Simulation diagram of adaptive sliding mode control for the 2DOF robot arm.
In case 1, under 10% loss of PDT faults in both joints (Fig. 4): Fig. 3 shows that the robot’s joint responses by using CTCPD controller start to deviate from the desired trajectories with tracking errors e1 , e2 oscillating in [− 0.108, 0.081], [ 0.012, 0.123], respectively; whereas the ASMC controller gives better joint responses with tracking errors e1 , e2 varying in the much smaller bounds of [ 0.002, 0.001], [ 0.0002, 0.001], respectively. In case 2, when the percentage of actuator torque loss increases to 50% in both joints (Fig. 6), the performance of CTCPD robot control system becomes signiﬁcantly worse with large tracking errors (Fig. 5). The joint angles cannot follow the desired trajectories, especially at joint 2. Meanwhile, by using the ASMC controller, the joint responses still track the desired paths with acceptable tracking errors fluctuating in the ranges of [− 0.013, 0.010], [− 0.001, 0.011] for e1 , e2 , respectively.
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Fig. 3. Reference, response(left), and tracking error(right) of the 2DOF robot using CTCPD and ASMC under PDT faults of 10% actuator torque loss in both joints (case 1).
Fig. 4. Control torque and actuator torque of the 2DOF robot using CTCPD (left) and ASMC (right) under PDT faults of 10% actuator torque loss in both joints (case 1).
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Fig. 5. Reference, response(left), and tracking error(right) of the 2DOF robot using CTCPD and ASMC under PDT faults of 50% actuator torque loss in both joints (case 2).
Fig. 6. Control torque sc and actuator torque sa of the 2DOF robot using CTCPD (left) and ASMC (right) under PDT faults of 50% actuator torque loss in both joints (case 2).
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Through comparison with a CTCPD controller, it is obvious that better robot’s joint responses can be achieved by using the ASMC controller despite the existence of actuator faults, i.e., PDT faults. In other words, the proposed ASMC methodology can tolerate the PDT fault event in a robot system.
5 Conclusions The paper aims at the topic of robot control design in case of actuator faults. The investigated actuator fault type is the proportional degradation of torque. An adaptive sliding mode control law is synthesized for 2DOF robots to tolerate the mentioned fault. The system stability is guaranteed with the Lyapunov method in the control design procedure. The effectiveness of the proposed controller is veriﬁed by a comparison with a popular CTCPD controller through simulation. The simulation results clearly express that the faulttolerant capability of the proposed ASMC is considerably better than that of the CTCPD controller. In case of using the proposed ASMC, the robot responses can track the reference trajectories with sufﬁciently small errors when the percentage of torque loss is up to about 10%. Although, the larger the torque loss is, the worse the tracking performance becomes, the robot responses are still acceptable even if the torque loss increases to 50%. However, robot parameter uncertainties and disturbances have not been considered in this study. These conditions will be addressed in our next work.
References 1. McIntyre, M.L., Dixon, W.E., Dawson, D.M., Walker, I.D.: Fault identiﬁcation for robot manipulators. IEEE Trans. Robot. 21(5), 1028–1034 (2005) 2. De Luca, A., Mattone, R.: An identiﬁcation scheme for robot actuator faults. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Edmonton, Alta., Canada, pp. 2613–2617. IEEE (2005) 3. Caccavale, F., Cilibrizzi, P., Pierri, F., Villani, L.: Actuators fault diagnosis for robot manipulators with uncertain model. Control Eng. Pract. 17(1), 146–157 (2009) 4. Caccavale, F., Marino, A., Muscio, G., Pierri, F.: Discretetime framework for fault diagnosis in robotic manipulators. IEEE Trans. Control Syst. Technol. 21(5), 1858–1873 (2013) 5. Domski, W., Mazur, A.: Emergency control of a space 3R manipulator in case of one joint failure. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, pp. 384–389. IEEE (2017) 6. Liu, G.: Control of robot manipulators with consideration of actuator performance degradation and failures. In: 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, pp. 2566–2571. IEEE (2001) 7. Azmi, H., Khosrowjerdi, M.J.: Robust adaptive fault tolerant control for a class of lipschitz nonlinear systems with actuator failure and disturbances. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 230(1), 13–22 (2015) 8. Lei, R., Chen, L.: Adaptive faulttolerant control based on boundary estimation for space robot under joint actuator faults and uncertain parameters. Def. Technol. 15(6), 964–971 (2019)
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9. Zhang, J.X., Yang, G.H.: Robust adaptive faulttolerant control for a class of unknown nonlinear systems. IEEE Trans. Ind. Electron. 64(1), 585–594 (2017) 10. Wang, W., Wen, C.: Adaptive actuator failure compensation control of uncertain nonlinear systems with guaranteed transient performance. Automatica 46, 2082–2091 (2010) 11. Yang, Q., Ge, S.S., Sun, Y.: Adaptive actuator fault tolerant control for uncertain nonlinear systems with multiple actuators. Automatica 60, 92–99 (2015) 12. Xing, L., Wen, C., Liu, Z., Su, H., Cai, J.: Adaptive compensation for actuator failures with eventtriggered input. Automatica 85, 129–136 (2017) 13. Tourassis, V.D., Neuman, C.P.: Properties and structure of dynamic robot models for control engineering applications. Mech. Mach. Theor. 20, 27–40 (1985)
An EnergyEfﬁcient Combination of Sleeping Schedule and Cognitive Radio in Wireless Sensor Networks Utilizing Compressed Sensing Minh T. Nguyen1(&) , Thuong T. K. Nguyen1, and Keith A. Teague2 1
Thai Nguyen University of Technology (TNUT), Thai Nguyen, Viet Nam {nguyentuanminh,nguyenthikimthuong}@tnut.edu.vn 2 Oklahoma State University (OSU), Stillwater, USA [email protected]
Abstract. Conventional wireless sensor networks (WSNs) have been well exploited with many applications and also numerous techniques for improvements. Recently WSNs employ mutimedia services that require more resources including frequency bandwidth and transmission rate. This encourages more exploration to support the networks to approach the increasing demand in quality of service. This paper shows an investigation to combine some techniques to meet some requirements. Cognitive radio (CR) have been known to use frequency band effectively. Compressed sensing (CS) applied in WSNs reduces the data transmission in the networks. Sleeping schedules for sensor nodes in such networks are also considered to save energy while still provide enough data needed. This work is a combination that provide analysis of network models, simulation results and shows promise for future WSNs. Keywords: Wireless sensor works Cognitive radio Compressed sensing Data reconstruction
Sleeping schedule
1 Introduction Wireless sensor networks (WSNs) have been explored for years with many leading applications in different ﬁelds [1]. Sensors/actuators are usually deployed randomly in sensing areas to collect data or to detect events [2]. The sensing data is transmitted between sensors or from sensors to the basestation (BS) for different purposes. Since the sensors often work on hash condition that people cannot access, precharged batteries may not be enough for the network to provide their services for a long time. Energy consumption for both sensing and communicating in such network is always a critical problem that researchers approach to solve. There are many existing research papers proposing novel methods to improve batteries, routing schemes to prolong the network lifetime [3, 4]. This paper investigates a combination between Cognitive radio (CR) [5] and sleeping schedule [6] in WSNs deploying Compressed sensing (CS) [7– 9]. As mentioned above, CS techniques support sleeping schedules for sensors to save energy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 154–160, 2021. https://doi.org/10.1007/9783030647193_18
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Fig. 1. 1000 sensors are deployed in the sensing area; There are only 50 sensors are active at a time. The rest falls into sleeping mode to save energy.
The active sensors would be the ones creating a CS measurement. Another time for another group of sensors, one more CS measurement is created. With CR, each group of sensors takes turn to use the licensed bandwidth. CR based sensors can adapt to varying channels conditions and increase transmission efﬁciency that reduce energy consumption for communications. The remainder of this paper is organized as follows. Section 2 presents the network model and the background of technical problems that combine all the techniques mentioned above. Problem Formulation including the data collection algorithm combining with sleeping schedules and CS is addressed in Sect. 3. Energy efﬁciency is analyzed in Sect. 4. Simulation results are provided in Sect. 5. Conclusions and future work are ﬁnally mentioned in Sect. 6.
2 Network Model We assume that a WSN has N static sensor nodes randomly deployed in a sensing area as shown in Fig. 1, which are all equipped with single omnidirectional antennas. There exists a basestation (BS) to collect the data from all the sensors. We also assume that all the sensor nodes have the same communication range Rc. In practise, in wireless networks, the packets transmitted by a sensor may be received by all the nodes within the communication range. Therefore, interference may occur among these nodes due to the broadcast nature of the wireless medium. For simplicity, noise is not considered for either analysis or simulation sections. All the sensors are programmed to have sleeping schedules frequently. They are also integrated with CR technology to be CRWSNs. The sensors are able to detect unused spaces (white spaces) by the incumbents in the spectrum bands.
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Sleeping Schedule: The N sensors pick a probability, denoted as P, to take turn to be active for collecting data. The rest of sensors can fall in sleep status for energy saving purpose. If the number of sensors active a time is A, then the probability is P = A/N. There are (NA) sensors fall to sleep as scheduled. In the sleeping mode, sensors spend much less energy than all others in the active mode. So, this mode should be combined with other methods to be able to achieve more energy efﬁcient manners. Cognitive Radio (CR): The technique has the capability of sensing the spectrum and determining the vacant bands [10]. These CR techniques can operate in licensed bands as well as in unlicensed bands. Working in licensed bands with a speciﬁc license, while wireless sensing users are communicating over the allocated band, i.e., the primary user (PU), has the priority to access the channel. Secondary users (SU), in CR networks, can access the channel if they do not cause interference to the PU. Based on the problem mentioned in the sleeping schedule, k sensors at ﬁrst can be PU and after that another group of k sensors will take turn to be PU to be able to use the frequency band. Compressed Sensing (CS): The techniques are wellknown to offers many paradigms to sample and to reconstruct sparse signals with a certain number of CS measurements [11, 12]. The requirements to be able to apply CS the compression process is the sensing data should be sparse or compressible in proper domains.
3 The Proposed Algorithm The combination algorithm has three phases that can be addressed as follows. Phase 1. Network Setup: • All N sensors are given an active mode schedule: Probability P support A = PN active sensors on average at a time; • CR techniques are integrated in sensors to be able to use multiple channels for communications; • A greedy routing tree algorithm [8, 9] is given; • Vector X 2 RN X ¼ ½x1 x2 . . .xN T represents all unknown data in the sensing area; • Vector Y 2 RM Y ¼ ½y1 y2 . . .yM T represents all M CS measurements collected at the BS; • U: the measurement matrix in which each row has A nonzero elements representing A active sensors contributing to the corresponding CS measurement; • Cognitive Radio techniques are integrated into sensors. Phase 2. Network Data Collection: • Active sensors connect to others and forward their sensory readings to the BS based on CR; M measurements are collected as: Y ¼ UX; • A random seed is stored to be sent along with CS measurements. This is the hidden key for security and privacy purposes; • M periods of time deﬁnes M sampling times.
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Phase 3. Data Reconstruction: • The measurement matrix U is updated and created based on the CS measurements at the BS; • An appropriate W (scarifying matrix) is chosen as DCT or Wavelet; ^ is recovered as: • All sensing data X
^ ¼ Arg Min ^ ^ and X ^ ^ ¼ UWH; ^ ¼ UWH; H
H
subject to Y 1
4 Energy Efﬁciency Analysis As shown in Fig. 1, there are only 50 active sensors working at a time of sampling. The energy consumption for the whole network can be reduced signiﬁcantly. Compared to existing work with all 1000 sensors being active all the time, this sleeping schedule can save up to 90% energy consumption among all sensors. And, the networks still satisfy their monitoring tasks. In general, energy consumed for transmitting and receiving data WSNs is considered as PTx and ERx which are respectively formulated as: ETx = ET0 + EA(d, N) and ERx = ER0 . Where ET0 and ER0 are the energy consumed at electrical elements for data processing, modulation or data coding. We do not considered these elements since they do not depend on transmitting distances (d) in our networks. Through out this work, only the consumed energy of the power ampliﬁer EA(d, N) that includes d is considered. N is the number of sensor nodes representing all values of sensor readings, speciﬁed as scalar values. Hence, total energy consumption for data communication in the networks has two parts, sensor energy consumption transmitting over distance d and the data packets including N scalar values as: EA(d, N) = PA(d)N. In conventional WSNs, BS needs to collect all N sensor readings that cost a lot of energy. In our proposed WSNs, the number of CS measurements need to be able to reconstruct N scalar values is much smaller (M N). So, this transmission method can also reduce signiﬁcantly energy consumption.
5 Simulation Results In this section, we deploy 1000 sensors in a square sensing area with dimension of 100 100 unit square, as shown in Fig. 1. We schedule for only 50 sensors to be active at a sampling time that can save up to 95% energy. As shown in Fig. 2, temperatures are various but highly correlated between sensors. It means that the sensors which are close to each other measure quite similar values. These 1000 scalar values are dense in the data vector X 2 RN . However, this dense vector are sparse in some frequency domains as DCT or Wavelet. Figure 3 shows transformed coefﬁcients achieved by both DCT and Wavelet. This can be applied CS techniques to sample and to reconstruct data. In Fig. 4, the numbers of CS measurements are chosen from 150 to 350.
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The normalized reconstruction errors reduce as the number of CS measurements increase. Both DCT and Wavelet bases work well with the CS recovery process.
Fig. 2. 1000 scalar values of temperatures collected from 1000 sensors in the sensing ﬁeld
Fig. 4. Normalized reconstruction errors versus the number of CS measurements in the CS recovery process
Fig. 3. 1000 transformed coefﬁcients transformed by both DCT and Wavelet basis
Fig. 5. All sensing data from 1000 nodes are reconstructed with 250 CS measurements vs. the original data
We ﬁx the number of CS measurements as 250 to reconstruct 1000 sensing values from the area. Figure 5 depicts both the original data from the sensing area and the reconstructed data. They look quite close to each other. This also depends on the QoS required of speciﬁc applications. At this point the data transmission in such network utilizing our novel combination signiﬁcantly is reduced.
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6 Conclusions and Future Work In this paper, an energyefﬁcient data collection method in wireless sensor networks that combines sleeping schedules, cognitive radio and compressed sensing is proposed. We analyze all the possibilities of exploiting the combination and choose some appropriate parameters for the system model. The system shows promising points that reduce data transmission in the networks, use spectrum effectively, and reduce the network energy consumption signiﬁcantly based on the sleeping schedules. Simulation results prove that the system can work well to reconstruct sensory data based on a certain number of measurements. In the future work, we consider to optimize the total energy consumption in the networks. The tradeoff between the number of active sensors and the communication range could be exploited for further energy saving. Acknowledgements. The authors would like to thank Thai Nguyen University of Technology (TNUT), Viet Nam for the support.
References 1. Nguyen, M.T., Tran, H.V., Nguyen, G.T., Do, K.H.: Wireless communication technologies and applications for wireless sensor networks: a survey. In: ICSES Transactions on Computer Networks and Communications (ITCNC), vol. 5, pp. 1–15, April 2019 2. Nguyen, M.T., Teague, K.A., Rahnavard, N.: CCS: Energyefﬁcient data collection in clustered wireless sensor networks utilizing blockwise compressive sensing. Comput. Netw. 106, 171–185 (2016) 3. Nguyen, M.T., Teague, K.A., Rahnavard, N.: Intercluster multihop routing in wireless sensor networks employing compressive sensing. In: 2014 IEEE Military Communications Conference, pp. 1133–1138, October 2014 4. Nguyen, M.T., Teague, K.A.: Random sampling in collaborative and distributed mobile sensor networks utilizing compressive sensing for scalar ﬁeld mapping. In: 2015 10th System of Systems Engineering Conference (SoSE), pp. 1–6, May 2015 5. Akan, O.B., Karli, O.B., Ergul, O.: Cognitive radio sensor networks. IEEE Netw. 23, 34–40 (2009) 6. Lai, W., Paschalidis, I.C.: Routing through noise and sleeping nodes in sensor networks: latency vs. energy tradeoffs. In: Proceedings of the 45th IEEE Conference on Decision and Control, pp. 2716–2721, December 2006 7. Nguyen, M.T., Rahnavard, N.: Clusterbased energyefﬁcient data collection in wireless sensor networks utilizing compressive sensing. In: MILCOM 2013  2013 IEEE Military Communications Conference, pp. 1708–1713, November 2013 8. Nguyen, M.T., Teague, K.A.: Treebased energyefﬁcient data gathering in wireless sensor networks deploying compressive sensing. In: 2014 23rd Wireless and Optical Communication Conference (WOCC), pp. 1–6, May 2014
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9. Nguyen, M.T., Teague, K.A.: Neighborhood based data collection in wireless sensor networks employing compressive sensing. In: 2014 International Conference on Advanced Technologies for Communications (ATC 2014), pp. 198–203, October 2014 10. Yeoh, P.L., Elkashlan, M., Kim, K.J., Duong, T.Q., Karagiannidis, G.K.: Cognitive mimo relaying with multiple primary transceivers. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 1956–1961, December 2013 11. Nguyen, M.T.: Data Collection Algorithms in Wireless Sensor Networks Employing Compressive Sensing. Ph.D thesis, Oklahoma State University (2015) 12. Candes, E., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theor. 52, 489–509 (2006)
An Enhancing Grasshopper Optimization for Efﬁcient Feature Selection TrongThe Nguyen1, ShiJie Jiang1, ThiKien Dao1, TruongGiang Ngo2, ThiThanhTan Nguyen3, and TheVinh Do4(&) 1
3
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China 2 Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam Information Technology Faculty, Electric Power University, Hanoi, Vietnam 4 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]
Abstract. Grasshopper optimization algorithm (GOA) is a new swarm intelligence algorithm that simulated a model of locust swarm behavior for optimization problems. Still, it has drawbacks, e.g., falling into a local optimum and slow convergence. This paper suggests a method enhancing GOA optimization (namely EGOA) based on an adaptive weight coefﬁcient and nonlinear parameters to balance the global exploration and local development capabilities, to promote the convergence speed, and to avoid trapping of falling into a local optimum the algorithm. In the simulation experiment, four benchmark test functions and the feature selection problem are used to prove that the improved strategy used in the proposed method can effectively enhance the precision and convergence speed of the GOA. The experimental results on seven UCI datasets show that the proposed method can effectively provide the best selection features for increasing classiﬁcation accuracy. Keywords: Enhancing grasshopper optimization Swarm computation, feature selection Optimization
1 Introduction Feature selection is considered as a critical link in the process of data preprocessing in machine learning [1]. It is not only to reduce the data dimension and improve the learning efﬁciency of the algorithm but also from the data set out on the performance of classiﬁer to classify most useful characteristics, to promote the classiﬁcation accuracy [2]. The conventional methods of feature selection can be roughly divided into categories such as ﬁlter type, package type, and embedded type [3]. The types of feature selection will learn performance as an evaluation standard of feature subset, so the way is also the most beneﬁcial for learning of choosing the best feature subset [4]. However, with the data contains a large number of features, the package type would be used speciﬁc search feature subset that is difﬁcult to achieve [5]. Therefore, how to make valid feature selection becomes a difﬁcult problem. In recent years, many search methods [6, 7] of swarm intelligence optimization algorithms have been used as the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 161–172, 2021. https://doi.org/10.1007/9783030647193_19
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search mechanism of wrapped feature selection, including the Particle Swarm Optimization (PSO) algorithm [8], Ant Lion Optimization (ALO) algorithm [9], and Whale Optimization (WOA) algorithm [10]. Grasshopper Optimization Algorithm (GOA) [11] is a new power swarm intelligence algorithm that puts forward a model of locust swarm behavior in nature to solve the optimization problems. GOA algorithm has been considered as a robust, proven effect algorithm in practical problemsolving for engineering ﬁelds [12]. However, similar to another approach of intelligent optimization algorithms, GOA still has some drawbacks. e.g., slow convergence speed, easy to fall into optimal local problems [13]. This paper suggests a solution to the problems of low accuracy of GOA optimization and slow convergence speed. In the proposed method, we introduce an adaptive weight coefﬁcient to change the update method of the locust position to improve the optimization accuracy, and we use nonlinear parameters to balance the global exploration and local development capabilities, to promote the convergence speed, and to avoid trapping of falling into a local optimum the algorithm. In the simulation experiment, four benchmark test functions and the feature selection problem are used to prove that the improved strategy used in the proposed method can effectively enhance the precision and convergence speed of the GOA. The experimental results on seven UCI datasets show that the proposed method can effectively provide the best selection features for increasing classiﬁcation accuracy.
2 Grasshopper Optimization Algorithm (GOA) The GOA algorithm is a new swarm intelligence optimization algorithm derived from simulating the swarm characteristics of grasshopper [11]. It means that the GOA simulates the behaviors of grasshoppers that often prey and migrate in largescale gatherings. The grasshoppers move at a low speed when larval stages, but adult grasshoppers can move quickly in a large area. These behaviors’ actions are mathematical modeled as follows. Xi ¼ Si þ Gi þ Ai
ð1Þ
where, Xi represents the position of the ith grasshopper in the grasshopper population, Si is the interaction between the i th grasshopper and other individuals in the population, Gi is the gravity of the ith grasshopper, and Ai is the wind force of the ith grasshopper. Considering the influence of random factors, Eq. (1) can be rewritten as: Xi ¼ r1 Si þ r2 Gi þ r3 Ai
ð2Þ
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where r1 , r2 and r3 are random numbers between [0,1], and the expression of Si is as follows: Si ¼
N P
j¼1 j 6¼ 1
s dij d^ij
ð3Þ
where, dij is the distance between the ith individual and the jth individual, d^ij is the unit x x vector from the ith individual to the jth individual, and d^ij ¼ jdij i , N is the number of individuals in the population, and s is the function deﬁning the interaction between individuals: r
sðr Þ ¼ fe l er
ð4Þ
where f indicates the strength of attraction, l is the attraction scale range. In this paper, f = 0.5, l = 1.5, through s can be the individual grasshopper space divided into attraction area, exclusion area, and comfort area. However, when the distance between individuals is greater than 10, the value of function s is close to 0, at this time no longer produces force to the individual, so this paper limits the position of individuals in the population within the range of [1, 4]. When the individual in the population is in the comfort area, the individual no longer has position updates, at which point the individual in the population is surrounded by the optimal solution, rather than all gathered in the location of the optimal solution. Therefore, the model of the formula (2) cannot be used directly to solve the optimization problem, which can be rewritten as: 0 Xid
1
B N C B P d xj xi C ubd lbd d B ^ c 2 s xj xi dij C ¼cB C þ Td @j ¼ 1 A j 6¼ i
ð5Þ
where ubd and lbd are upper and lower bounds of Ddimensional search space respectively, T^d is the optimal individual in the current population, regardless of the influence of gravity and assuming that the wind always points to the location of the optimal solution, c is the linear decline coefﬁcient, and the expression is as follows: min c ¼ cmax l cmax c L
ð6Þ
where l represents the current iteration number of the algorithm, L represents the maximum iteration number; in this paper, cmax = 1, cmin = 0. 00001. Based on the above model, the main steps of the grasshopper optimization algorithm are as follows: (1) (2) (3) (4)
population and parameter initialization. select the optimal solution with the highest ﬁtness in the current population. update parameter c according to Eq. (6). adjust the distance between individuals and update the position as Eq. (5).
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(5) check whether the individual has exceeded the search boundary after the update and if so, return to the position before the update. (6) update the optimal solution in the population. (7) determine whether the maximum number of iterations has been reached: no, then steps (3) to (6) of the cycle; yes, then the algorithm ends and returns the optimal solution.
3 Strategies for Improving GOA (IGOA) Several strategies of adjusting the mathematical model of the GOA to enhance the optimization accuracy and convergence speed of the algorithm (namely EGOA): e.g., the nonlinear parameters, adjusting position weight coefﬁcient, and combining the limit threshold and proposing optimization. 3.1
Nonlinear Decline Coefﬁcient
Some characteristics of grasshoppers, e.g., the attractiveness, repulsion, and agent’s search range of grasshoppers, are adjusted by decreasing the coefﬁcient c. Refer to Eq. (5), the parameter c acting on the inside of the bracket can help reduce the repulsive force and attraction between individuals proportional to the number of iterations of the algorithm. This action outside of the bracket can lessen the search coverage range of individuals with an increase in the number of repetitions. Therefore, GOA uses parameter c to balance the global exploration and local exploitation capabilities of the algorithm during iteration. Refer to Eq. (6), c decreases linearly as the number of iterations increases, which will slow the convergence rate of the algorithm and make it easy to fall into the local optimization. Therefore, we use nonlinear parameters instead of the original linear decreasing coefﬁcients and rewrites Eq. (6) as follows: qﬃﬃi h c ¼ 1 sin 12 p Ll cmax l cmaxcmin L
ð7Þ
where, l represents the current number of iterations, and L is the maximum number of iterations. The nonlinear decreasing coefﬁcient c can decrease at a faster rate in the early stage of algorithm iteration so that the individuals in the population can get closer to the objective quickly, and the convergence rate of the algorithm can be improved. However, in the later stage of algorithm iteration, the decreasing speed of c slows down, which enables individuals to search the surrounding space carefully and prevents the algorithm from falling into local optimization. Therefore, the use of a nonlinear decreasing coefﬁcient can better balance the global exploration and local exploitation capability of the algorithm in different iteration periods. 3.2
Adaptive Weight Coefﬁcient
When all the individuals in GOA [11] are falling in the comfort zone, e.g., optima local, they will no longer update their positions. At this time, the individuals are not clustered
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to reach the location of the optimal solution. This issue makes the algorithm prone to premature convergence. Refer to Eq. (5), the update of individual grasshopper position not only depends on other individuals in the population but also depends on the optimal solution in the current community. Therefore, the location of the optimal solution has an essential influence on the movement of other individuals. We should consider different periods of algorithm iteration, to ﬁnd the place of the optimal global solution, individuals in the population have different dependence on the optimal solution in the current community. In this situation, nonlinear parameters are introduced as the weight coefﬁcient of the optimal solution of the current population, which is deﬁned as follows. w ¼ 1 sin
1 2
p
qﬃﬃ
ð8Þ
l L
And we rewrite Eq. (5) as follows: 0 Xid
1
B N C B P ub lb d C d xj xi C d d B c 2 s xj xi dij C þ w T^d ¼ cB @j ¼ 1 A j 6¼ i
ð9Þ
where l represents the current number of iterations, and L is the maximum number of iterations. w is the weight coefﬁcient deﬁned in this paper, and the w value decreases nonlinearly with the number of iterations, that is, with the algorithm iteration, the influence of the optimal solution in the population on the location updates of other individuals also changes. At the beginning of the algorithm iteration w value is large, and the individual updates the position according to the optimal solution and the position information between the individuals. With the iteration of the algorithm, in order to avoid the individual in the population gradually in their own comfort area, the dependence of individuals in the community on the optimal solution position should be reduced with a lower w value, so that the individual in the population can move near the optimal solution, so as to avoid all the individuals stay around the optimal solution of the problem. In this way, the local exploitation ability of the algorithm can be enhanced, and the accuracy of the optimization can be improved. Moreover, the limits threshold setting is one of the ways used to judge whether the algorithm trapped in a local optimum. The number of stagnations caused the decreased quality of the optimal solution in the population. The limit threshold should be set up based on the speciﬁc issue if it needs to jump out of local optimal, the threshold would be set high level; however, it would be slow conveyance. If the limit is set too low, it would frequently be a random disturbance of individuals in the population, affecting the average ﬁtness of the community. In order to ensure that the individuals in the population are close to the optimal solution with the algorithm iteration, the position update before the limit is set to 15 in the experiment section.
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Experimental Results and Analysis
In order to evaluate the potential performance of the proposed enhancing GOA (called EGOA), we use ﬁve selected benchmark test functions from the CEC 2013 [14] for experiments test. The obtained results of the proposed EGOA algorithm are compared with the variety of previous algorithms of GOA [11, 15]. The setting parameters for the algorithms: the population size Np is to 40, MaxIter is set to 1500, the boundaries and diminution are set to the same listed in Table 1. Table 1. Selected benchmark functions Function name Schwefel 2.22 Schwefel 1.2 Schwefel 2.21 Rastrigin Ackley
Test functions F1 ¼
D im P
jxi j þ
i¼1
F2 ¼
Dimensions Ranges Dim Q
jx i j
5/30
[−10,10]
Target function 0
5/30
[−100,100]
0
5/30
[−100,100]
0
5/30
[−5.12,5.12] 0
5/30
[−32,32]
i¼1
Dim P
i P
i¼1
j1
!2 xj
F3 ¼ maxi fjxi j; 1 i Dg F4 ¼ x2i 10 cosð2pxi Þ þ 10 sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ! Dim P 2 1 xi F5 ¼ 20exp 0:2 Dim
0
i¼1
Dim P 1 cosð2pxi Þ þ 20 þ e exp Dim i¼1
Table 1 shows the comparison of the proposed EGOA algorithm with the GOA and OBLGOA algorithms. Observed, the proposed approach produces the obtained optimization results is better accuracy and faster of convergence speed than the GOA and OBLGOA algorithms Table 2. Table 2. Comparison of algorithm optimization performance Function Dim 5 Mean F1 Std. Dev 30 Mean Std. Dev F2 5 Mean Std. Dev 30 Mean Std. Dev
GOA 2.36E 2.88E 1.68E 1.91E 8.27E 2.51E 2.60E 1.67E
+ 000 + 000 + 001 + 001 − 006 − 005 + 003 + 003
OBLGOA 1.49E + 000 2.06E +000 3.64E −010 3.63E − 010 7.17E − 008 2.38E − 007 6.78E − 016 9.99E − 016
EGOA 3.85E − 019 5.83E − 020 2.70E − 018 4.66E − 019 4.96E − 035 4.58E − 035 1.02E − 033 1.17E − 033 (continued)
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Table 2. (continued) Function Dim 5 Mean F3 Std. Dev 30 Mean Std. Dev F4 5 Mean Std. Dev 30 Mean Std. Dev F5 5 Mean Std. Dev 30 Mean Std. Dev
GOA 1.71E 2.71E 1.50E 4.05E 1.11E 7.57E 9.45E 3.30E 1.04E 2.52E 5.50E 1.76E
− 004 − 004 + 001 + 000 + 001 + 000 + 001 + 001 + 000 + 000 + 000 + 000
OBLGOA 1.17E − 006 3.92E − 006 1.93E − 010 2.15E − 010 7.85E + 000 5.19E + 000 0.00E + 000 0.00E + 000 7.42E −001 1.08E + 000 2.06E − 010 2.03E − 010
EGOA 2.82E − 018 8.14E − 019 3.89E − 018 4.29E − 019 0.00E + 000 0.00E + 000 0.00E + 000 0.00E + 000 8.88E − 016 0.00E + 000 8.88E − 016 0.00E + 000
Fig. 1. Comparison of the results of the EGOA algorithm with the GOA and OBLGOA algorithms for the ﬁrst and second functions.
Figure 1 shows the comparison of the results of the EGOA algorithm with the GOA [11] and OBLGOA [15] algorithms for the ﬁrst and second functions. It can be seen that in the search spaces of different dimensions, the convergence speed of the EGOA on the benchmark test functions is signiﬁcantly better than the varieties of GOA algorithms.
4 A Solution to Feature Selection by Applied EGOA The problem of feature selection is considered as a multiobjective optimization problem, that is, selecting as few feature numbers as possible for the classiﬁer to obtain the optimal classiﬁcation accuracy. In this section, the proposed EGOA algorithm is used to solve this practical optimization problem. An EGOAbased feature selection method is recommended; the speciﬁc algorithm flow is shown in Fig. 2. In the feature
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selection problem, the EGOA population is represented as a set of feature combinations in the dataset that can be called the feature subset. The individual dimension is determined by the original number of features in the dataset, and the agent vector is composed of 0 and 1, 1 indicates that the corresponding feature attribute is selected, and 0 indicates that the feature attribute is not selected.
Begin Import dataset IGOA inializaon
Compute and Update feature subset by IGOA
Fitness evaluaon Stop criteria?
Provide the opmal soluon Output best feature subset and classiﬁcaon accuracy End
Fig. 2. Flow chart of applying EGOA for the feature selection
The EGOA population initialized values of individual dimensions are random numbers of [0,1], with its individual vectors in binary the population 0 and 1. Here if individuals are higher than 0.65, they are set as 1; otherwise, they are set to 0. In order to obtain the highest possible classiﬁcation accuracy with as few feature numbers as possible, the ﬁtness function for evaluating individual good or bad needs to consider both these factors, so the ﬁtness function is deﬁned as follows. jRj Fitness ¼ a cD R ðDÞ þ b jN j
ð10Þ
where, cR ðDÞ is the classiﬁer error rate (this paper uses the KNN classiﬁcation algorithm to evaluate the advantages and disadvantages of the feature subset (take K = 5)), jRj is the number of features contained in the current individual, jN j is the number of original features in the dataset, a and b are the coordination parameters that balance the classiﬁcation accuracy and the length of feature subset, and b ¼ 1 a; a 2 ½0; 1, this paper takes a = 0.99. The accuracy rate of the classiﬁer is used to evaluate the advantages and disadvantages of the proposed EGOA for feature selection, the number
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of feature selections, and the feature selection rate as the measurement indicators. A parameter of classiﬁcation accuracy is used as a deﬁnition as follows. TN Accuracy ¼ TPP þ þN
ð11Þ
where, TP, TN, P, and N represent the real positive example, true negative model, positive and negative sample number, respectively. The deﬁnition of feature selection rate is given as follows. FsRatio ¼ M1
M P sizeð^gi Þ i¼1
D
ð12Þ
where M is the running times of feature selection algorithm, D is the number of original features in the dataset, ^gi is the optimal feature subset obtained by each run of the algorithm, and sizeð xÞ is the number of elements 1 in the vector x. In order to prove the effectiveness of the EGOAbased feature selection method proposed in this paper, the algorithm is tested on seven data sets [16] shown in Table 3. First, the EGOAbased feature selection method (EGOA FS) and traditional locust optimization are compared. The performance of the algorithm’s feature selection method (GOA⁃FS) [17] and the algorithm training with full features. Set the population size to 30, the maximum number of iterations of the algorithm to 100, and all algorithms to run independently ten times. The number of features is selected to evaluate the performance of the algorithm. The test results are shown in Table 4 (the bold type in the table is the optimal value in the compared algorithms). Table 5 depicts the comparison of the proposed EGOAFS with the other methods, such as the Sine Cosine Algorithm (SCA) [19], Whale optimization algorithm (WOA) [20], and Ant Lion Approaches (ALO) [18] for feature selection on the medical dataset. It can be seen that the number of highlights in the table has belonged to the proposed EGOAFS scheme. It means that the proposed method can be a robust altered competitor. Table 3. Description of the experimental dataset D1 D2 D3 D4 D5 D6 D7
Datasets Feature number Instances Number Breast Cancer EW 30 569 Zoo 16 101 Heart 12 270 Parkinson 22 197 Congress 16 435 Wine 13 178 Colon 2000 62
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Table 4. Comparison of feature selection performance of the algorithm on seven data sets Dataset D1 Accuracy Features D2 Accuracy Features D3 Accuracy Features D4 Accuracy Features D5 Accuracy Features D6 Accuracy Features D7 Accuracy Features
FULL 0. 951 30 0. 961 16 0. 763 12 0. 908 22 0. 940 16 0. 944 13 0. 677 2000
GOAFS 0. 959 11.2 0. 931 6.6 0. 768 6. 6 0.949 8. 9 0. 945 5. 5 0. 951 6. 2 0. 745 675.2
Proposed EGOAFS 0.976 13.5 0.963 7.1 0.801 6.4 0.949 8.4 0.970 3.3 0.960 5.8 0.833 691.9
Table 5. Comparison of performance of the proposed scheme EGOA with other algorithms Data set ALO [18] D1 0.930 D2 0.909 D3 0.826 D4 – D5 0.929 D6 0.911 D7 –
CSA [19] 0.903 0.937 0.788 0.908 – – –
WOA [20] Proposed EGOA 0.971 0.976 0.980 0.963 0.807 0.801 – 0.949 0.956 0.970 0.959 0.960 0.909 0.833
5 Conclusion In this paper, we proposed an approach to enhance the Grasshopper optimization algorithm (namely EGOA) based on an adaptive weight coefﬁcient and nonlinear parameters for the optimization and feature selection problems. Although the Grasshopper optimization algorithm (GOA) is a robust swarm intelligence algorithm for optimization problems, it has drawbacks, e.g., falling into a local optimum and slow convergence. The global exploration and local development capabilities of the swarm intelligence algorithm could be improved by adjusting the parameters such as adaptive weight coefﬁcient and nonlinear. We applied the customized strategies updating equations to promote the convergence speed, and to avoid trapping of falling into a local optimum the algorithm. In the simulation experiment, four benchmark test functions and the feature selection problem are used to prove that the improved strategy used in the proposed method can effectively enhance the precision and convergence
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speed of the algorithm. The compared results on selected test functions and datasets show that the proposed method can effectively provide the best selection features for increasing classiﬁcation accuracy in comparison with those other approaches in the literature. Acknowledgment. The authors would like to thank Thai Nguyen University of Technology for the support of this research.
References 1. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40, 16–28 (2014) 2. Molina, L.C., Belanche, L., Nebot, À.: Feature selection algorithms: a survey and experimental evaluation. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings, pp. 306–313. IEEE (2002) 3. Dao, T., Nguyen, T., Pan, J., Qiao, Y., Lai, Q.: Identiﬁcation failure data for cluster heads aggregation in WSN based on improving classiﬁcation of SVM. IEEE Access. 8, 61070– 61084 (2020). https://doi.org/10.1109/ACCESS.2020.2983219 4. Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: 2014 Science and Information Conference, pp. 372–378. IEEE (2014) 5. Chu, S.C., Dao, T.K., Pan, J.S., Nguyen, T.T.: Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classiﬁcation. Eurasip J. Wirel. Commun. Netw. 1, 1–15 (2020) 6. Nguyen, T.T., Pan, J.S., Dao, T.K.: An improved flower pollination algorithm for optimizing layouts of nodes in wireless sensor network. IEEE Access. 7, 75985–75998 (2019). https:// doi.org/10.1109/ACCESS.2019.2921721 7. Nguyen, T.T., Qiao, Y., Pan, J.S., Chu, S.C., Chang, K.C., Xue, X., Dao, T.K.: A hybridized parallel bats algorithm for combinatorial problem of traveling salesman. J. Intell. Fuzzy Syst. 38, 5811–5820 (2020). https://doi.org/10.3233/jifs179668 8. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 International Conference on Neural Networks, pp. 1942–1948 (1995) 9. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015). https://doi.org/10. 1016/j.advengsoft.2015.01.010 10. Mirjalili, S., Lewis, A.: The whale Optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.008 11. Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017) 12. Simon, B., Gulyás, G.G., Imre, S.: Analysis of grasshopper, a novel social network deanonymization algorithm. Period. Polytech. Electr. Eng. Comput. Sci. 58, 161–173 (2014) 13. Abualigah, L., Diabat, A.: A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Comput. Appl, 1–24 (2020) 14. Liang, J.J., Qu, B.Y., Suganthan, P.N., HernándezDíaz, A.G.: Problem deﬁnitions and evaluation criteria for the CEC 2013 special session on realparameter optimization. Comput. Intell. Lab. Zhengzhou Univ. Zhengzhou, China Nanyang Technol. Univ. Singapore, Tech. Rep. 201212, (2013)
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15. Ewees, A.A., Abd Elaziz, M., Houssein, E.H.: Improved grasshopper optimization algorithm using oppositionbased learning. Expert Syst. Appl. 112, 156–172 (2018). https://doi.org/10. 1016/j.eswa.2018.06.023 16. Bay, S.D., Kibler, D., Pazzani, M.J., Smyth, P.: The UCI KDD archive of large data sets for data mining research and experimentation. ACM SIGKDD Explor. Newsl. 2, 81–85 (2000) 17. Zakeri, A., Hokmabadi, A.: Efﬁcient feature selection method using realvalued grasshopper optimization algorithm. Expert Syst. Appl. 119, 61–72 (2019) 18. Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary ant lion approaches for feature selection. Neurocomputing. 213, 54–65 (2016) 19. Taghian, S., NadimiShahraki, M.H.: Binary Sine Cosine Algorithms for Feature Selection from Medical Data (2019). arXiv:1911.07805 20. Mafarja, M.M., Mirjalili, S.: Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260, 302–312 (2017)
An Evaluation of BSpline for Synthesis of Cam Motion with a Large Number of Output Conditions Nguyen Thi Thanh Nga1(&), Nguyen VanSy1, Nguyen Thi Bich Ngoc1, and Vu Thi Lien2 1
Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Phenikaa University, Hanoi, Vietnam
Abstract. In the cam design process, cam motion functions play an important role due to affect not only kinematics but also the dynamics of the camfollower systems. However, the selection of cam motion functions has been poor so far. This study aims to evaluate Bspline for the synthesis of cam motion with a large number of output conditions. The procedure for determining the cam motion was investigated. In addition, the comparison of the motion diagram between the cubic and quintic Bspline was also proposed. Moreover, Fourier analysis was used to examine the response of the kinematics in camfollower systems. The results demonstrate that using quintic Bspline can be given good characteristics of the cam motion. Keywords: Camfollower systems Bspline function dynamics of camfollower systems Fourier analysis
Kinematics and
1 Introduction Kinematics and dynamics are importantly characterized by camfollower systems. Many researches focus on the optimization of cam systems such as controlling the follower vibrations to reduce undesired dynamics [1], the optimization of cam proﬁle for the camfollower systems [2, 3], the dimension optimization of cam systems with translating roller follower [4], and cam optimization of highspeed cam for reducing the vibration [5]. Regarding the cam motion, the basic functions such as the harmonic, cycloidal, trapezoidal, polynomials used for designing cam mechanisms in kinematic diagrams were proposed in [6]. Kiran [7] presented the simulation and design of the cam proﬁle using piecewise polynomials. Each piecewise polynomial is used by the 23 and 345 polynomials for designing motion programs. The synthesis of constantbreadth cams with flatface double translating and oscillating was investigated by using Bezier functions [8]. Spline functions have been widely used in cam design. There have been many researches working on optimization problems for establishing the cam motion diagrams by using spline functions such as optimal synthesis for springactual cam systems [9], applying the genetic algorithm for optimizing the cam crosssection [10], © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 173–180, 2021. https://doi.org/10.1007/9783030647193_20
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designing the spline for motion program [11, 12], optimizing motion curves with spline to reduce kinematics of the cam systems [13–16]. The above studies have been a lack of evaluating the spline function for the synthesis of motion programs. This paper proposes the evaluation of Bspline used for cam motion with a large number of output motions. A systematic method for describing the motion curve with Bspline was set up. The third and ﬁfth degrees of Bspline were examined for motion programs. Finally, the Fourier analysis was utilized to evaluate the response of kinematic characteristics.
2 BSpline Curve Description for Cam Motion The displacement motion of cam mechanisms described by using Bspline curve can be expressed in the interval [a, b] S¼
n P
Ni;p ðhÞPi ; for h 2 ½a; b
i¼1
i ¼ 1; 2; . . .; n
;
ð1Þ
Where n is the number of boundary conditions in displacements, velocities, accelerations, and jerks; p is representative for the degree of displacement; h presents the angle of camshaft; Pi is control points. The displacement curve is calculated based on Bspline basic functions that can be described as Ni;p ðhÞ ¼
hi þ p þ 1 u h hi Ni;p1 ðhÞ þ Ni þ 1;p1 ðhÞ hi þ p þ 1 hi þ 1 hi þ v hi þ p
ð2Þ
In Eq. (2), hi are representative of elements in the knot vector U. The knot vector can be written as U ¼ fh1 ; h2 . . .; hm g
ð3Þ
In which, m is the number of elements in the knot vector, m = n + p + 1. All elements are nondecreasing values, i.e., hi < hi+1, i = 1,2,…, n − 1. These elements are in the interval [a, b]; values of p + 1 the ﬁrst elements are equal to and values of p + 1 the end elements are equal to b. The velocity, acceleration and jerk motions can be respectively expressed as VðhÞ ¼
AðhÞ ¼
n dSðuÞ X 1 ¼ Ni;p ðhÞPi ; dh i¼1
ð4Þ
n d 2 SðuÞ X 2 ¼ Ni;p ðhÞPi ; 2 dh i¼1
ð5Þ
An Evaluation of BSpline for Synthesis of Cam Motion
JðhÞ ¼
3
d SðuÞ ¼ dh3
n X
3 Ni;p ðhÞPi :
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ð6Þ
i¼1
1 2 3 In Eqs. (4–6), Ni;p ðhÞ; Ni;p ðhÞ; and Ni;p ðhÞ can be computed by the following equation
k Ni;p ¼
k p! X ak;j Ni þ j;pk ðhÞ; ðp kÞ! j¼0
ð7Þ
The coefﬁcients ak,j, can be calculated as a
; a0;0 ¼ 1; ak;0 ¼ ui þ pkk1;0 þ 1 ui ak1;j ak1;j1 a ak;j ¼ ui þ p þ jk þ 1 ui þ j ; ak;k ¼ ui þ p þk1;k1 1 ui þ k
ð8Þ
With j = 1, …, k − 1.
3 Control Point Determination In order to determine the motion curves as shown in Eqs. (1), (4), (5) and (6), control points Pi must be found. The procedure for calculating control points includes the steps as follows Step 1. Computing the knot vector U Based on the angle of the camshaft of boundary conditions, the knot vector can be computed as the uniform spaced method as found in [17]. Step 2. Calculating the basic functions as shown in Eq. (2) with degree p = 3 and p = 5. Step 3. Determining the values of the displacements, velocities, acceleration, and jerks at the boundary conditions. The values of displacements, velocities, accelerations, denoted as Vij (i = 1, 2, …, n and j = 1, 2, …, n), are respectively computed from the displacement, velocity, acceleration curves as shown in Eqs. (1), (4), and (5) at the points where the constraints are deﬁned. The ﬁrst and the second indexes in Vij imply the constraints and the Bspline basis functions. The constraint values are denoted as (i = 1, 2, …, n). The linear systems of equations can be written as P1 V11 .. . P1 Vn1
þ þ
P2 V12 .. . P2 Vn2
þ þ þ þ
Pn V1;n .. . Pn Vn;n
¼ C1 .. .
ð9Þ
¼ Cn
Step 4. Obtaining the control points by solving the linear systems (9). From these steps, the displacement, velocity, acceleration, and jerk curves are simply determined.
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4 Result and Discussion 4.1
Motion Program
We consider here 27 output conditions in displacements of the cam as found in [18]. Based on the fundamental law in cam design, the displacement, velocity, acceleration must be continuous to avoid the inﬁnitive values at the discontinuous points. Therefore, 2 boundary conditions in velocity and 2 boundary conditions in acceleration at the ﬁrst and the end points are added and they are equal to zero. The boundary conditions can see in star signs. In order to evaluate the Bspline for motion programs of a large number of output conditions, the cubic and quintic Bspline curves are selected. Based on the procedure of calculation as discussed in Sect. 3, the control points are obtained. The motion curves are thus established as shown in Eqs. (1), (4), (5) and (6). Figure 1 and Fig. 2 present the motion diagrams for a large number of output conditions using the cubic and quintic Bspline curves. It can be seen in Fig. 1a and Fig. 2a, the control points are adjusted in order to ﬁt the Bspline curves with given output conditions. Referring the extreme value of the curves as shown in Fig. 1 and Fig. 2, the maximum values of the velocity, acceleration, and jerk curves in case of Bspline with p = 3 are 7.7621 (mm/rad), 58.015 (mm/rad2), and 846.1806 (mm/rad3), respectively. For the Bspline with p = 5, these values are orderly 7.2891 (mm/rad), 48.3205 (mm/rad2), and 1703.9 (mm/rad3).
Fig. 1. Motion diagram for large number of boundary conditions with degree p = 3: (a) Displacement, (b) Velocity, (c) Acceleration, and (d) Jerk
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Fig. 2. Motion diagram for large number of boundary conditions with degree p = 5: (a) Displacement, (b) Velocity, (c) Acceleration, and (d) Jerk
4.2
Fourier Analysis
The Fourier analysis indicates the harmonic content. Figure 3 shows the frequency of the acceleration using Bspline with degree p = 5 and p = 3. It can be observed from this ﬁgure that almost amplitudes of the acceleration at each number in the case of Bspline with p = 3 (see yellow color in Fig. 3) are much larger than that in the case of Bspline with p = 5. Due to this, the inertial force response of the camfollower systems can be affected. Similarly, the Fourier analysis of jerk is depicted in Fig. 4. It can be clearly seen that the amplitudes of jerk at highorder harmonic components using Bspline with degree p = 3 are greatly enormous compared to the amplitudes of Bspline with degree p = 5. As known that the amplitudes of the harmonic content of jerk are affected by the vibration response of the camfollower systems. Therefore, it should use the smaller amplitudes for highspeed camfollower system.
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Fig. 3. Fourier analysis for acceleration
Fig. 4. Fourier analysis for jerk
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5 Conclusion In this work, the evaluation of the cubic and quintic Bspline for synthesizing the cam motion with a large number of output conditions was proposed. The methodology of computing cam motion using Bspline was established based on the boundary condition of the output motions. The conclusion of this paper can be drawn as follows – A general computation of cam motion with Bspline was exhibited. This computation can be applied for any output motions. – In comparison between two cases of Bspline, the motion diagrams in velocity and acceleration using the quintic Bspline obtain the good characteristics – The amplitudes of acceleration and jerk for almost numbers in Fourier analysis have the smaller values when using the quintic Bspline compared to those with the cubic Bspline. Acknowledgment. The work described in this paper was supported by Thai Nguyen University of Technology for a scientiﬁc project.
References 1. Gatti, G., Mundo, D.: On the direct control of follower vibrations in camfollower mechanisms. Mech. Mach. Theory 45, 23–35 (2010) 2. Ouyang, T., Wang, P., Huang, H.: Cam proﬁle optimization for the delivery system of an offset press. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 231, 4287–4297 (2017) 3. Xiao, H., Zu, J.W.: Cam proﬁle optimization for a new cam drive. J. Mech. Sci. Technol. 23, 2592–2602 (2009) 4. DasGupta, A., Ghosh, A.: On the determination of basic dimensions of a cam with a translating rollerfollower. J. Mech. Des. Trans. ASME 126, 143–147 (2004) 5. Liang, Z., Huang, J.: Design of highspeed cam proﬁles for vibration reduction using command smoothing technique. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 228, 3322– 3328 (2014) 6. Chen, H., Nguyen, T.T.N., et al.: Application of a cam workbench for education in mechanical engineering. In: New Advances in Mechanisms, Mechanical Transmissions and Robotics, pp. 177–186 (2017) 7. Kiran, T., Srivastava, S.K.: Analysis and Simulation of Cam Follower Mechanism Using Polynomial Cam Proﬁle, pp. 211–215 (2013) 8. Cardona, S., Zayas, E.E., Jordi, L., Català, P.: Synthesis of displacement functions by Bézier curves in constantbreadth cams with parallel flatfaced double translating and oscillating followers. Mech. Mach. Theory 62, 51–62 (2013) 9. Kim, J.H., Ahn, K.Y., Kim, S.H.: Optimal synthesis of a springactuated cam mechanism using a cubic spline. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 216, 875–884 (2002) 10. Lampinen, J.: Cam shape optimisation by genetic algorithm. CAD Comput. Aided Des. 35, 727–737 (2003) 11. Mandal, M., Naskar, T.K.: Introduction of control points in splines for synthesis of optimized cam motion program. Mech. Mach. Theory 44, 255–271 (2009)
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12. Nguyen, T.T.N., Hüsing, M., Corves, B.: Motion Design of Cam Mechanisms by Using NonUniform Rational BSpline Bewegungskurven von Kurvengetrieben unter Verwendung von NonUniform Rational BSpline (2018) 13. Nguyen, T.T.N., Kurtenbach, S., Hüsing, M., Corves, B.: Improving the kinematics of motion curves for cam mechanisms using NURBS. Mech. Mach. Sci. 52, 79–88 (2018) 14. Nguyen, T.T.N., Kurtenbach, S., Hüsing, M., Corves, B.: A general framework for motion design of the follower in cam mechanisms by using nonuniform rational Bspline. Mech. Mach. Theory 137, 374–385 (2019) 15. Sateesh, N., Rao, C.S.P., Janardhan Reddy, T.A.: Optimisation of camfollower motion using Bsplines. Int. J. Comput. Integr. Manuf. 22, 515–523 (2009) 16. Sateesh, N.: Camfollower systems using NURBS, pp. 15–21 (2014) 17. Nguyen, T.T.N., Kurtenbach, S., Hüsing, M., Corves, B.: Evaluating the knot vector to synthesize the cam motion using NURBS. Mech. Mach. Sci. 50, 209–216 (2018) 18. Kurtenbach, S., Wieja, F., Müller, I., Neidlin, M., Sonntag, S.J., Goetzenich, A., Hatam, N., Bruns, P., Chuembou Pekam, F., de la Fuente Klein, M., Radermacher, K., Hopmann, C., Autschbach, R., Steinseifer, U., Hüsing, M., Corves, B.: Motion analysis of the left ventricle of a human heart for realization in a cardiovascular mockloop. Mech. Mach. Sci. 48, 17–29 (2018)
An Experimental Study on VibrationDriven Locomotion Systems Under Different Levels of Isotropic Friction NgocTuan La1, QuocHuy Ngo2, KyThanh Ho2, and KhacTuan Nguyen2(&) 2
1 Vinh University of Technology Education, Vinh, Vietnam Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]
Abstract. This paper shows experimental analysis results of the study on vibrationdriven locomotion system, which can be applied in capsule robots. The experimental apparatus provided a capacity of varying friction force when keeping the weight of the whole system unchanged. Twelve experimental sets with 16 runs for each set were implemented, providing a deep insight of the system behavior, both in progression rate and the relative motions of the masses. The experimental data revealed that, the force ratio between the excitation magnitude and friction level would not be totally correct to present the excitation effects in modeling the system. The level of friction force may have a signiﬁcant effect on not only how fast the system move, but also which direction of the progression. The new ﬁndings would be useful for further studies on the design and operation of vibration driven locomotion systems. Keywords: Vibrationdriven locomotion
Capsule robots Isotropic friction
1 Introduction Conventional mobile robots usually consist of external propulsion mechanisms, such as wheels, legs or paddles. Hence, such systems would face with several boundaries in terms of mechanical complexity, controllability, physical size, failure of moving parts, and causing hazard to surrounding environment. Recently, the development of mobile devices employing vibration for automotive motion has become a very promising solution for encapsulate locomotion systems [1–3]. The principle of this solution, pioneered by Chernousko [4], is that the forward and backward progression of the system can be obtained in the presence of dry friction combined with a periodically driven internal mass interacting with the main body of the system. An earlier model using vibroimpact driven mechanism was proposed by Pavlovskaia et al. [5]. On the one hand, the friction force is usually considered as a resistance preventing the moving trend of the system, and thus should not be too large. On the other hand, the presence of friction plays an important role of external resistance to provide the locomotion of the system in desired direction [1, 6]. The system working under small friction would not be able to progress in the desired direction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 181–191, 2021. https://doi.org/10.1007/9783030647193_21
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Generally, the internal mass has been designed to have one of the two motion principles: 1) the mass moves periodically without having impact with the body and 2) the mass moves periodically and has impact (vibroimpact) with the body. In the former case, the relative motion of the internal mass must follow a specially designed multiphase accelerationcontrolled (see [7–10] for example). When the inertial force, caused by acceleration changes, exceeds the friction threshold, the body locomotion is generated. In many previous studies [1–10], the friction force was usually assumed to be isotropic, i.e. the friction forces in both forward and backward motion of the frame body have the same value. The latter choice of selfpropelled design is vibrationimpact driven locomotion. In this system, the internal mass oscillates and periodically collides with an obstacle block. This results in a jumpup of the inertial force, making the system move. The friction force was usually assumed to be isotropic [5, 11–16]. The system working under anisotropic friction, i.e. the friction in forward direction is different from that in backward direction, was also examined. However, the examined system requires a special control of the internal mass motion (See [17] for example) or can only move in the direction with smaller friction (downward of an inclined chute [18]). The effectiveness of the robots has typically been considered by checking with the progression rate and dynamical response of the systems. In vibroimpact systems, the excitation force acting on the internal mass was usually in the sinusoidal form. In previous studies, the excitation force was treated as a dimensionless number, counted as the ratio between the real amplitude of the excitation force and the Coulomb friction value. The progression rate and/or moving direction of the system was checking as a function of such force ratio (See for example in [5, 12, 14–16]). In experimental studies, the effect of excitation force was taken into account with certain dry friction with given levels of frictional force (See [14, 19–22]). The effects of various friction levels on the system response have rarely been experimentally considered [3, 23] but not in interaction with the excitation force. Beside, some interesting observations have been reported [14, 24]. For example, when the elastic force acting on the capsule is larger than the threshold of the friction, backward motion of the capsule is observed [24]. At some situations, the average speed of forward progression of the capsule using small amplitude of excitation is much larger than the one with backward progression using large amplitude of excitation. This observation somehow reveals the fact that a large amplitude of excitation cannot improve the performance of the capsule system [14]. However, such interesting observations were found at certain conditions of experiments only and need more practical considerations and experimental validations. Consequently, this study was made to give deeper insights of the effect of different levels of friction force on the system response. Three levels of friction forces, representing for small, middle and large resistances were examined combined with four different values of the force ratios. The results revealed that with the same force ratio, the system have various behaviors with different levels of frictions. In addition, under the same friction force, varying the force ratio provides different trends of the moving direction of the vibration driven locomotion system. The paper is organized as below. Basic principle of the vibration driven locomotion system is briefly presented in Sect. 2. The experimental setup is then described in detail
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in Sect. 3. The results and discussions are given in Sect. 4. Some remarkable conclusions and recommendations are made in the last section.
2 Basic of the Study 2.1
Working Principle of the System
The physical model of a typical vibrationdriven locomotion system with impacts is depicted in Fig. 1(a). The system is consisted of a body mass, m2 and an inertial mass, m1. An elastic spring with stiffness k couples the two masses. The two masses are connected using an elastic spring and a viscous damper, c. The system body m2 can move along a straight line on a resistive horizontal plane. The internal mass oscillates inside the body along the line parallel to the motion line of the body. A harmonic force Fm with amplitude A and frequency X exerts on both masses. The friction force, Fr, occurred at the contact surface between the body and the resistive plane is assumed to obey the Coulomb dry friction law, as shown in Fig. 1(b). In this study, the friction force is assumed to be isotropic, i.e. R+=R−=R.
Fig. 1. A vibrationdriven locomotion model (a) and Coulomb isotropic friction model (b)
Impact characteristics between the two masses were modeled as stiffness k0. In Fig. 1, X1 and X2 represent the absolute displacements of the internal mass and the frame body, respectively. The motion Xi (i = 1, 2) is considered as forward motion if the value of Xi is positive and vice versa. The two masses are initially positioned with a gap G. When the relative displacement X1X2 is greater than or equal to the gap G, impact occurs and the system can move forward. The friction force plays a very important role in the working principle of the vibration locomotion systems [4, 5]. The system may either not progress or move in unexpected direction if the friction force is too small or too large. Naturally, in order to increase the average velocity of motion, the coefﬁcient of friction of the body mass should be increased and that between the internal mass and the body mass decreased [4]. The results of this study would be a practical recommendation not only on the
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upper limitation when increasing friction force but also the smallest friction, with respect to the excitation force, that can help the system move in desired direction. 2.2
Objectives of the Study
Generally, studies on the vibrationdriven locomotion systems have usually been done by following steps: – Describe the physical and mathematical models of the system; – Implement experiments to validate the mathematical model; – Analysis the system using the validated mathematical model, usually focus on the progression rate and the dynamical response of the system. In order to expand the results to different scales of the prototypes, the mathematical model was usually transformed into dimensionless form. The equations describing the motion of the system shown in Fig. 1 can be written as: 8 d2 X dX2 1 < m1 dt21 ¼ Fm Fspr c dX dt dt H ½k0 ðX1 X2 GÞ dX d 2 X2 dX1 dX2 2 : m2 dt2 ¼ Fm þ Fspr þ c dt dt þ H ½k0 ðX1 X2 GÞ Rsgn dt Fm ¼ A cosðXtÞ
ð1Þ
where H is the Heaviside step function deﬁned as: ( H¼
1 ; if ðX1 X2 GÞ [ 0 0 ; if ðX1 X2 GÞ 0
ð2Þ
Assuming the spring is linear with the stiffness k, the spring force can be described as: Fspr ¼ kðX1 X2 Þ
ð3Þ
Using the following nondimensional variables and parameters: rﬃﬃﬃﬃﬃﬃ k k k X s ¼ X0 t; x1 ¼ X1 ; x2 ¼ X2 ; X0 ¼ ;x¼ ; R R m1 X0 c A k0 k m2 ; a ¼ ; r ¼ ; c ¼ G; l ¼ f¼ 2m1 X0 R R k m1
ð4Þ
the dimensionless form of the model (1) can be expressed as:
x001 ¼ a cosðxsÞ ðx1 x2 Þ 2f x01 x02 hrðx1 x2 cÞ x002 ¼ a cosðxsÞ þ ðx1 x2 Þ þ 2f x01 x02 þ hrðx1 x2 cÞ sgnðx02 Þ l1
ð5Þ
where ()’ and ()’’ denotes the ﬁrst and the second time derivative d()/ds and d2()/ds2, respectively; and h is a Heavisde function deﬁned as:
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h¼
1 ; if ðx1 x2 cÞ [ 0 0 ; if ðx1 x2 cÞ 0
185
ð6Þ
Generally, the effect of the excitation force (Fm) on the system behavior have been taken into account by varying the value of the force ratio, a (See [11, 13, 24, 25] for example). In the mentioned studies, a given value of a provided one value of the dimensionless x2. From Eq. (4), since both k and R are positive, x2 must have the same sign with X2. It means that, for a given value of a, different values of friction level R will result in different values of the progression X2, but not different in the moving direction. Our experimental results revealed that with the same force ratio a, the system can either move forward or backward, i.e. with different signs (either negative or positive) of the progression X2. In other words, the force ratio, a may not be totally correct to represent the effect of the excitation force, as usually suggested in many previous studies. Consequently, this study was made to experimentally examine of the effect of different levels of friction force on the system response. The results would provide a basic knowledge to give deeper insight into the locomotion systems working under different levels of the environmental resistance. Further work of modelling and analyzing the system can be made based on these ﬁndings. The objectives of this study thus are as following: – To develop an experimental apparatus which can vary the friction force when keeping the system weight; – To carry out how different levels of the friction magnitude can effect on the progression rate of the system body under the same force ratio; – To verify if the force ratio is not able to fully represent the excitation magnitude as previously suggested. In this experimental study, three levels of friction forces, representing for small, middle and large resistances were examined combined with four different values of the force ratios. The experimental setup and implementing method are presented in the next section.
3 Experimental Implementation The above model was realized as shown in Fig. 2, as developing from the apparatus built at TNUT’s laboratory by Nguyen et al. [3, 13, 20]. A mini electrodynamical shaker (1) is placed on a slider of a commercial linear bearing guide (4), providing a tiny rolling friction force. An additional mass (3) was clamped on the shaker shaft with the support of sheet springs (2). Generally, applying a sinusoidal current to the shaker leads to relative linear oscillation of the shaker shaft with the mass added on. Hereafter, the moveable mass, combined by the addition mass and the shaker shaft, is assigned as inertial mass, m1, playing the role of the internal mass of the system. The relative motion of the inertial mass was measured by a noncontact position sensor (9). Motion of the shaker body was recognized by a linear variable displacement transformer (8). The body shaker, including the sensors and the
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Fig. 2. A photograph of the experimental apparatus
carbon tube, is referred as the mass m2. A force sensor (7) was used as the obstacle block to measure the impact force. In order to vary the friction force when remaining the body mass, a carbon tube (5) is connected with the shaker body by means of a flexible joint, avoiding any misalignment when moving. The tube can slide between two aluminium pieces in the form of a Vblock (6). The two Vblocks are ﬁxed on two electromagnets, as depicted in Fig. 3(a). Supplying a certain value of electrical current to the coupled electromagnets provides a desired clamping force on the tube and thus a corresponding value of sliding friction. The friction force was measured by pulling the body to move at a steady speed. By adjusting the voltage supplied to the coupled electromagnets, the corresponding friction force can be obtained (See [3] for more detail). The shaker is powered by a sinusoidal current generated by a laboratory function generator and then ampliﬁed by a commercial ampliﬁer. The application of a sinusoidal current to the shaker leads to oscillation of the shaker shaft and the mass attached on it. As provided by the shaker supplier, the magnetic force, Fm is solely depended on the current supplied. Adjusting the sinusoidal supplying to the ampliﬁer can provide a desired excitation force. A supplementary experiment was implemented to verify the relation of the magnetic force and the current supplied. A load cell was used as an obstacle resisting the shaker movement and thus to measure the magnetic force induced. A DC voltage was supplied to the shaker to generate the magnetic force. Varying the voltage, several pairs of the current passing the shaker and the force were collected. Experimental data conﬁrmed that the excitation force is proportional to the current supplied to the shaker (see [13] for detailed information of how to determine this relation). For operational parameters, three levels of friction force were selected at ﬁrst. With respect to the total weight of the two masses as 2.336 kg, the friction force levels were set as 2.4, 6.8 and 13.6 N, corresponding to three levels of friction coefﬁcient as approximately 0.1, 0.3 and 0.6. These levels can be respectively considered as low, middle and high friction coefﬁcients. The minimum magnitude of the excitation force chosen needs to be strong enough to make the mass oscillate and collide to the obstacle block. Consequently, three higher levels of the excitation force were selected as strong
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(a) α=0.59
(b) α=0.79
(c) α=0.99
(d) α=1.19
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Fig. 3. Progression velocity of the system for (a) a = 0.59; (b) a = 0.79; (c) a = 0.99 and (d) a = 1.19. For all subplots, Ff1 < Ff2 < Ff3.
enough to make signiﬁcant differences between the system responses. The overall experimental parameters are given in Table 1. Table 1. Parameters of experiments. Parameter Internal mass Body mass Impact gap Friction force Force ratio Excitation frequency
Notation m1 m2 G Ff a = A/R fexc
Value Unit 0.518 Kg 1.818 Kg 0.5 mm 2.4; 6.8; and 13.6 N 0.59; 0.79; 0.99; and 1.19 – [5–20], 1 Hz step Hz
Twelve sets of experiments were implemented for four levels of the force ratio, a and three levels of the friction magnitude. Each experimental set, including 16 runs, was implemented at 16 values of the excitation frequency ranged from 5 Hz to 20 Hz with an incremental step of 1 Hz. Overall, excitation frequencies less than 5 Hz or higher than 20 Hz seemed as not efﬁcient to operate the system. For each run, the data
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of supply current passing the shaker, the displacements of the two masses and the impact force were collected and then analyzed. The results are shown in the next section.
4 Results and Discussion Firstly, the progression rate of the whole system is considered. Figure 3 presents the average velocity of the system body at four levels of the force ratio, a. As can be seen, for a less than 1 (Fig. 3(a, b)), with the same a, higher friction forces provided higher forward velocities. Increasing a resulted in faster moving velocity of the forward motion of the system. However, forward velocities with a1 (Fig. 3c) provided a lower velocity, compared to that with a = 0.79. The backward motion of the system appeared at then higher range of excitation frequency, fexc= [12..18] Hz with the two highest levels of friction force (Ff2 and Ff3), as shown in Fig. 3d. From such observations, the following interesting issues can be remarked: – It seems to be agreed with previous studies that, increasing the relative magnitude of the excitation with respects to friction force (i.e. increasing the force ratio a) may increase the forward velocity of the system; – When the force ratio is higher than 1 (i.e. the excitation magnitude is larger than the friction force), backward motion would appear, even though the impact force is in forward direction; – A new remarkable ﬁnding is that, with the same force ratio, the motion of the system would be either forward or backward, depending on the friction level. This would not agree with several previous suggestions that with a certain set of parameters, a given value of the force ratio provides only one direction of the system progression (As mentioned in the end of Sect. 2). Another view of the effects of friction levels is depicted in Fig. 4 to support the abovementioned ideas. At each of the two investigated levels of friction, the progression rate of the system with for levels of the force ratio are presented.
(a) Ff = 2.3 N
(b) Ff =13.6 N
Fig. 4. Progression velocity of the system for (a) Ff = 2.3 N; (b) Ff = 13.6 N. For all subplots, a1 < a2 < a3 < a4.
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As can be seen in Fig. 4, with the same friction level, higher force ratio would not provide higher progression rate. With low friction (Fig. 4a), the highest forward velocity was obtained at a3 = 0.99, whereas the highest force ratio (a4) mostly provided lower velocity. With high level of the friction (Fig. 4b), highest forward velocity appeared with a2, whereas highest backward velocity performed at the highest level of force ratio, a4. There has still been an open question about the reason of the backward motion of the system even though the impact force acting in the forward direction. Figure 5 illustrate the time history of the system motion. The two subplots have the same force ratio (a = 1.19) and other parameters, except that one for small friction (Fig. 5a, Ff = 2.3 N) and one for large friction (Fig. 5b, Ff = 13.6 N). Working with a small friction, the system appeared to move forward (Fig. 5a), whereas it moved backward with the larger friction (Fig. 5b). On each subplot, two redthin vertical lines limit one oscillation period of the mass m1. Another blackdash line divided each period into two areas: Area 1 where the body m2 moved forward, and area 2 where the body m2 moved backward.
Fig. 5. Time histories of motions of the internal mass X1 (dash curve) and of the body X2 (blue solid curve): (a) Ff = 2.3 N, and (b) Ff = 13.6 N. A force ratio a = 1.19 was applied; fexc = 17 Hz.
At presented on the ﬁgure, the impacts occurred at the instant of the displacement peaks of m1. Under small friction (Fig. 5a), the body moved forward not by impact, but for reaction between the two masses. The forward motion of the body occurred at somewhere when the mass m1 was going backward. Under higher friction (Fig. 5b), forward motion of the body seemed to be the result of impact force – it happened just at the impact instant. Different from that of the former case, in each period, the backward displacement was larger than the forward motion. Consequently, the overall motion of the system in this case was backward, although the impact already acted as a source for the forward moving of the system. Noted that negative values of the displacements, X1 and X2, mean that the motions of the two masses were in backward direction, as relative to the origin coordinate (X1 = X2 = 0). From this practical observation, it can be concluded that forward motion of the system may not only depend on how large the impact force is, but also how the
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interaction of the relative motion of the two masses. It is worth noting that, the mechanism behind of moving forward or backward motion of the whole system is till an open dynamic problem. The data obtained would be a good resource for further studies in the ﬁeld.
5 Conclusions This paper presented experimental observations and several initial analyses on a vibration driven locomotion apparatus. The experimental setup provided a capacity of varying friction force when keeping the weight of the whole system unchanged. Based on that, a series of experimental tests were implemented, providing a deep insight of the system behavior, both in progression rate and the relative motions of the masses. The following remarks would be useful for further studies: – The force ratio between the excitation totally correct to present the excitation – The level of friction force would be a system move, but also which direction
magnitude and friction level would not be effects in modeling the system; signiﬁcant effect on not only how fast the of the progression.
Acknowledgement. This research was funded by Vietnam Ministry of Education and Training, under the grant number B2019TNA04. The authors would like to express their thank to Thai Nguyen University of Technology, Thai Nguyen University, and Vinh University of Technology Education for their support for the study.
References 1. Liu, P., Yu, H., Cang, S.: Modelling and analysis of dynamic frictional interactions of vibrodriven capsule systems with viscoelastic property. Eur. J. Mech. A. Solids 74, 16–25 (2019) 2. Yan, Y., et al.: Proofofconcept prototype development of the selfpropelled capsule system for pipeline inspection. Meccanica 53(8), 1997–2012 (2017) 3. Nguyen, V.D., La, N.T.: An improvement of vibrationdriven locomotion module for capsule robots. Mech. Based Des. Struct. Mach. 1–15 (2020) 4. Chernous’ko, F.L.: The optimum rectilinear motion of a twomass system. J. Appl. Math. Mech. 66(1), 1–7 (2002) 5. Pavlovskaia, E., Wiercigroch, M., Grebogi, C.: Modeling of an impact system with a drift. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 64(5 Pt 2), 056224 (2001) 6. Chernous’ko, F.L.: Analysis and optimization of the motion of a body controlled by means of a movable internal mass. J. Appl. Math. Mech. 70(6), 819–842 (2006) 7. Nunuparov, A., et al.: Dynamics and motion control of a capsule robot with an opposing spring. Arch. Appl. Mech. 89(10), 2193–2208 (2019) 8. Huda, M.N., Yu, H.: Trajectory tracking control of an underactuated capsubot. Auton. Robots 39(2), 183–198 (2015) 9. Su, G., et al.: A design of the electromagnetic driver for the internal forcestatic friction capsubot. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (2009)
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10. Li, H., Furuta, K., Chernousko, F.L.: Motion generation of the capsubot using internal force and static friction. In: Proceedings of the 45th IEEE Conference on Decision and Control (2006) 11. Gu, X.D., Deng, Z.C.H.: Dynamical analysis of vibroimpact capsule system with Hertzian contact model and random perturbation excitations. Nonlinear Dyn. 92(4), 1781–1789 (2018). https://doi.org/10.1007/s110710184161x 12. Duong, T.H., et al.: A new design for bidirectional autogenous mobile systems with twoside drifting impact oscillator. Int. J. Mech. Sci. 140, 325–338 (2018) 13. Nguyen, V.D., et al.: The effect of inertial mass and excitation frequency on a Dufﬁng vibroimpact drifting system. Int. J. Mech. Sci. 124–125, 9–21 (2017) 14. Liu, Y., Pavlovskaia, E., Wiercigroch, M.: Experimental veriﬁcation of the vibroimpact capsule model. Nonlinear Dyn. 83(1–2), 1029–1041 (2015) 15. Liu, P., et al.: A selfpropelled robotic system with a viscoelastic joint: dynamics and motion analysis. Eng. Comput. 36(2), 655–669 (2019) 16. Yan, Y., Liu, Y., Liao, M.: A comparative study of the vibroimpact capsule systems with onesided and twosided constraints. Nonlinear Dyn. 89(2), 1063–1087 (2017) 17. Liu, P., Yu, H., Cang, S.: Optimized adaptive tracking control for an underactuated vibrodriven capsule system. Nonlinear Dyn. 94(3), 1803–1817 (2018) 18. Xu, J., Fang, H.: Improving performance: recent progress on vibrationdriven locomotion systems. Nonlinear Dyn. 98(4), 2651–2669 (2019) 19. Nguyen, V.D., et al.: A new design of horizontal electrovibroimpact devices. J. Comput. Nonlinear Dyn. 12(6), 061002 (2017) 20. Nguyen, V.D., et al.: Identiﬁcation of the effective control parameter to enhance the progression rate of vibroimpact devices with drift. J. Vib. Acoust. 140(1), 011001 (2017) 21. Ho, J.H., Nguyen, V.D., Woo, K.C.: Nonlinear dynamics of a new electrovibroimpact system. Nonlinear Dyn. 63(1–2), 35–49 (2010) 22. Su, G., et al.: A design of the electromagnetic driver for the “internal forcestatic friction” capsubot. In: The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2009) 23. Nguyen, V.D., Woo, K.C., Pavlovskaia, E.: Experimental study and mathematical modelling of a new of vibroimpact moling device. Int. J. NonLinear Mech. 43(6), 542–550 (2008) 24. Liu, Y., et al.: Forward and backward motion control of a vibroimpact capsule system. Int. J. NonLinear Mech. 70, 30–46 (2015) 25. Liu, Y., et al.: Modelling of a vibroimpact capsule system. Int. J. Mech. Sci. 66, 2–11 (2013)
Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain MinhQuang Tran1,2,3(&), MengKun Liu3,4, and QuocViet Tran5 1
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Industry Implementation 4.0 Center, National Taiwan University of Science and Technology, Taipei 10607, Taiwan [email protected] Center of CyberPhysical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan 3 Department of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam 4 Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Abstract. Chatter is problematic during metal cutting process. It destroys the surface ﬁnish and reduce productivity and quality of productions. Therefore, prediction and identiﬁcation of chatter vibration are needed to determine the range of stable cutting conditions. In this paper, the force signal in time domain is used to analyze the behaviors of the milling chatter vibration. The largest Lyapunov exponent index which can describe the different nonlinear characteristics of dynamical system behaviors is used as an effective indicator to distinguish the stable and unstable cutting conditions. The proposed chatter detection approach has been successfully validated by experiment. Keywords: Milling stability
Dynamic force model Chatter analysis
1 Introduction The milling operation is one of the most common forms of machining by which the surface of workpiece is generated progressively as it is fed to a rotating cutter [1]. During the milling process, the engagement of each tooth with the workpiece is discontinuous. The cut of each tooth is less than half of the tooth revolution so the chip thickness varies periodically as the tooth enters and exits the cut [2]. As a result, an impact is produced when the edge touches the workpiece, a phenomenon also known as the forced vibration. Additionally, the tool could vibrate because of the variation of the chip thickness. This selfexcited machining behavior is named chatter vibration [2–4]. The presence of chatter causes machining instability during the cutting process. Undoubtedly, the detection of machinetool chatter is one of the most important topics in the milling process. Chatter has a more frequent occurrence when a fast machine tool is utilized along with a high cutting speed, large engagement angle, and high material removal rate. The combination of the aforementioned may cause © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 192–199, 2021. https://doi.org/10.1007/9783030647193_22
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unexpected cutting outcomes such as noise, poor surface roughness, an increase of tool wear, reduction of tool life, and reduction in productivity rate [2, 4, 5]. Therefore, this phenomenon should be monitored and avoided during the cutting process to avoid machining instability and to improve the productivity. Regenerative chatter in milling is generated because the previous and current cuts are out of phase. The instantaneous chip thickness is variable, and it governs the cutting force which, in turn, affects subsequent tool vibrations [6, 7]. Therefore, the dynamic cutting force model plays an important role in analyzing and predicting chatter vibration. Furthermore, the accurate model could be used to analyze the machining process and optimize the cutting parameter. The cutting force was a function of instantaneous chip thickness, which is the engagement between cutter and workpiece. The uncut chip thickness was reportedly proportional to the cutting force [8]. Tsai [9] proposed a predictive force model in the endmilling process based on the geometrical analysis. A generalized geometric model of milling cutters was reported by Altintas [10]. The basics of chatter vibration in cutting operations described by an accurate model were proposed by Merritt [4]. An analysis of milling chatter vibration based on force signal in time domain is presented in the following sections.
2 Dynamic Cutting Force Model This section presents a dynamic cutting force model in which the cutting edges are decomposed into a set of elements by discretizing along the tool axis. The geometry of a general end mill with its schematic representation and a cutting force model with two orthogonal degrees of freedom in x and y directions is shown in Fig. 1 [11].
Fig. 1. The dynamic cutting force model.
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where the vibrations normal to the cutting surface are used to determine the instantaneous chip thickness. If the trajectory of the cutter is assumed to be a circular path, the instantaneous chip thickness for ith disk element, jth flute, and kth angular position is then represented by Eq. (1). hði; j; kÞ ¼ ft sinðhi;j;k Þ þ ½nðt sÞ nðtÞ þ q0
ð1Þ
where the ﬁrst term represents the effect of feed per tooth. The second term represents the tool vibration in the normal direction, n, related to the dynamic displacement in x and y directions is determined by [2]. The last term represents the vector that contains the toothtotooth radii error which was introduced in [3]. Assuming that the tooth radii error is not varied along the tool axis. The tool immersion angle and lag angle due to the helix angle of cutting edge was described in [12]. The different cutting forces in the normal and tangential directions are written as the following. The cutting force in each direction includes both the forces due to shearing (Kn, Kt) and the rubbing term (Kne, Kte) at the flank of the cutting edge [2]:
dFn ði; j; kÞ ¼ Kn hði; j; kÞdb þ Kne db dFt ði; j; kÞ ¼ Kt hði; j; kÞdb þ Kte db
ð2Þ
where h is the instantaneous chip thickness and db is the discretized axial depth of cut. The differential cutting force with respect to the workpiece coordinate frame is then represented by [2]:
cosð/j Þ dFx ði; j; kÞ ¼ sinð/j Þ dFy ði; j; kÞ
sinð/j Þ cosð/j Þ
dFt ði; j; kÞ dFn ði; j; kÞ
ð3Þ
3 Experimental Setup A series of end milling experiments was conducted on a 3axis CNC milling machine (with Heidenhain TNC620 controller). The workpiece was a block of Al6061T6 which is commonly used in automobile and aerospace industries owing to its high strength to weight. An end mill cutter with a diameter of 12 mm, helix angle of 26°, and two flutes was utilized. The toothtotooth radii error is 6 lm. The cutting force signals were measured by a Kistler dynamometer mounted between the workpiece and workbench, as shown in Fig. 2. The dynamics of tool tip was investigated using impact testing [13]. The cutting force coefﬁcients yielded from experiments are shown in Table 1.
4 Simulated and Experimental Results The cutting force under certain cutting conditions is shown in Fig. 3 which illustrates a very good agreement between the simulated and experimental results.
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Fig. 2. Experimental setup. Table 1. Cutting force coefﬁcients. Ks N/mm b (o) Kn N/mm Kt N/mm Kne N/mm Kte N/mm 813 65.9 331.8 742.2 12 23
Fig. 3. The cutting force at the spindle speed of 1500 rpm, depth of 1.6 mm, and feed rate of 150 mm/min.
The numerical simulations of cutting force under different cutting conditions are shown in Fig. 4. In which the displacement is determined by numerical integration [3]. Figure 4(a) and (c) represent the onceperrevolution sampled cutting force data of two cutting conditions (1.2 mm & 3750 rpm and 1.6 mm & 1500 rpm), respectively. Both of them show a repetitive behavior from one to the next revolution. It also represents that the single clutter of onceperrevolution sampled data shown in the plot of x and ydirection vibrations of two cutting conditions in Fig. 4(a) and (c). Evidently, this behavior indicates maintained stable operation. On the other hand, sampled force data in Fig. 4(b) and (d) show different behaviors with the quasiperiodic, the repetitive behavior from one to the next revolution does not occur in both cases. In addition, the sampled x and y vibrations show the elliptical distribution known as the Hopf instability [3]. This demonstrates unstable behaviors in both two cutting conditions (1.6 mm & 3750 rpm and 1.2 mm & 5250 rpm) in Fig. 4(b) and (d).
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Fig. 4. Numerical simulations of cutting force, historical displacements and vibratory plot in x and y directions (onceperrevolution sampled data ‘+’).
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5 Estimation of Lyapunov Exponent Lyapunov exponent from a single time series is known as a method for exploring the divergence or convergence of signal exponents in phase space, it could present nonlinear characteristics of dynamical system behavior [14, 15]. In this study, the average Lyapunov exponent is used to estimate the exponent of the phase plane from the experimental vibration signals. The algorithm used to calculate of largest Lyapunov exponents using phase space are follows. Assume Npoint time series represented the vibration signal such that x = {x1, x2 … xN}. The time series is reconstructed into attractor dynamics based on delay coordinates. The reconstructed trajectory can be represented as: X ¼ ½X1 ; X2 ; . . .; XM T , where each row of X is phase space vector Xi ¼ ½xi ; xi þ s ; . . .; xi þ ðn1Þs Þ, s is reconstruction delay, n is embedding dimension, M = N − (n − 1)s. The reference point in the space is set by Xj, the nearest neighbor, Xj’, could be found by searching the point which has Euclidean norm as Lj ð0Þ ¼ minXj Xj; . The distance between the jth pair of nearest neighbors steps (a period of time is equal to k.Δt) is after k discretetime Lj ðkÞ ¼ Xj þ k Xj; þ k . The largest Lyapunov exponent is determined in Eq. (4) [16]: k1 ¼
k X 1 1 M Lj ðkÞ ln kDt M i j¼1 Lj ð0Þ
ð4Þ
According to the trend of the largest Lyapunov exponent, it can be used to distinguish whether the cutting is stable in the time domain. In this study, for each signal point index of the Lyapunov exponent of the signal, the average of thirty largest values are calculated in order to achieve a small amount of singularity of the values of the stable conditions. The results of Lyapunov exponents of different cutting conditions are shown in Fig. 5. It can see that the Lyapunov exponents are very sensitive to the chaos of the signal, the largest Lyapunov exponents proportional to the amount of chaos of the system are increasing with high depth of cut and high cutting speed. Particularly, when the system becomes unstable this value increases rapidly. Therefore, the milling stability can be predicted using Lyapunov exponent. In this system, the value of threshold plane is set with Lyapunov exponent of 0.96, which can successfully deﬁne the cutting stability, as shown in Fig. 5. In the stable cutting condition, the largest Lyapunov exponent is kept within small values. On the contrary, when increasing the spindle speed and depth of cut, the cutting became unstable cutting and the indicator grew rapidly. This implied that more nonlinear behaviors obviously emerged in the cutting process so that the measured cutting force deviated from the output of the cutting force model. While the original RMS of cutting force signal in time domain shown in Fig. 6 does not indicate these behaviors and it is not able to distinguish whether chatter occurs. The largest Lyapunov exponent index shows as an effective indicator to detect and predict the occurrence of chatter.
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Fig. 5. The largest Lyapunov exponent under different cutting conditions.
Fig. 6. RMS values of the cutting force in time domain.
6 Conclusions Machining stability monitoring has an important role in increasing productivity and tool life in modern manufacturing. In this study, a dynamic cutting force model based chatter stability analysis was presented. Once the chatter occurred, the nonlinear and chaotic behaviors were found from the cutting force signal. The stable and unstable behaviors were fully investigated within the force signal, followed by the experimental veriﬁcation. Furthermore, it was found that the largest Lyapunov exponent which is proportional to the amount of chaos of the system was increased rapidly when the chatter happens. While the value of Lyapunov exponent will keep smaller if the stable cutting is maintained. Thus, it can be effectively used to distinguish the stable and unstable machining in the time domain.
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Acknowledgment. This work was ﬁnancially supported by the “Center for Cyberphysical System Innovation” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
References 1. Stephenson, D.A., Agapiou, J.S.: Metal Cutting Theory and Practice. Marcel Dekker, New York (1996) 2. Altintas, Y.: Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, 2nd edn. Cambridge University Press, Cambridge (2012) 3. Schmitz, T., Smith, K.S.: Machining Dynamics: Frequency Response for Improved Productivity, pp. 1–303 (2009) 4. Merritt, H.E.: Theory of selfexcited machinetool chatter: contribution to machinetool chatter research  1. J. Manuf. Sci. Eng. 87(4), 447–454 (1965) 5. Quintana, G., Campa, F., Ciurana, J., Lacalle, L.: Productivity improvement through chatterfree milling in workshops. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 225, 1163–1174 (2011) 6. Cheng, K.: Machining Dynamics: Fundamentals, Applications and Practices (2009) 7. Tlusty, J., Polacek, M.: The stability of the machine tool against self  excited vibration in machining. International Research in Production Engineering. ASME (1963) 8. Kline, W.A., DeVor, R.E., Lindberg, J.R.: The prediction of cutting forces in end milling with application to cornering cuts. Int. J. Mach. Tool Des. Res. 22(1), 7–22 (1982) 9. Tssi, C.L.: Analysis and prediction of cutting forces in end milling by means of a geometrical model. Int. J. Adv. Manuf. Technol. 31, 888–896 (2007) 10. Altintas, Y., Engin, S.: Generalized modeling of mechanics and dynamics of milling cutters. CIRP Ann. 50(1), 25–30 (2001) 11. Chung, C., Tran, M.Q., Liu, M.K.: Estimation of process damping coefﬁcient using dynamic cutting force model. Int. J. Precision Eng. Manuf. 21, 623–632 (2020) 12. Liu, M.K., Tran, M.Q., Chung, C., Qui, Y.W.: Hybrid model and signalbased chatter detection in the milling process. J. Mech. Sci. Technol. 34(1), 1–10 (2020) 13. Liu, M.K., Tran, M.Q., Qui, Y.W., Chung, C.: Chatter detection in milling process based on timefrequency analysis. In: ASME 2017 12th International Manufacturing Science and Engineering Conference, Los Angeles, U.S.A., vol. 1 (2017) 14. Skokos, C., Gottwald, G., Laskar, J.: Chaos Detection and Predictability. Lecture Notes in Physics, vol. 915, pp. 1–280 (2015) 15. López, A., Camacho, C., García, A.: Effect of parameter calculation in direct estimation of the lyapunov exponent in short time series. Discrete Dyn. Nat. Soc. 7(1), 41–52 (2002) 16. Yao, T., Liu, H., Xu, J., Li, W.: Estimating the largest Lyapunov exponent and noise level from chaotic time series. Chaos: Interdisc. J. Nonlinear Sci. 22(3), 033102 (2012)
Analytical Study of the Power Parameters of Electric Traction Drive for Modern Vehicles Aleksey Kolbasov1, Kirill Karpukhin1,2, Dmitry Sheptunov1, Povalyaev Andrey1, Nguyen Khac Tuan3, and Nguyen Khac Minh2,3(&) 1
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NAMI Russian State Scientiﬁc Research Center, 2 Avtomotornaya Str., Moscow 125438, Russia Moscow Automobile and Road Construction State Technical University (MADI), 64 Leningradsky Ave., Moscow 125319, Russia [email protected] 3 Faculty of Automotive and Power Machinery Engineering, Thai Nguyen University of Technology, No. 666 Tich Luong Ward, Thai Nguyen 24131, Vietnam
Abstract. The development of electric freight transport is becoming increasingly important against the backdrop of growing environmental problems, especially in megacities. The selection of the actual parameters of the power plant for electric freight vehicle is important here. This article analyzes the implemented projects in the world. The information gains particular relevance against the background of the absence of industrial production of power plants in the Russian Federation. The paper presents data on freight vehicles with a traction electric drive and deduces patterns in the value of the power of the used electric motors relative to the mass of the car in order to clarify the most popular standardsize series. This study was carried out with the aim of a feasibility study of models of electric motors for commercial vehicles proposed for launch into serial production. Keywords: Electric vehicle Hybrid vehicle Ecology Electric drive Electric machines Electric truck
Energy efﬁciency
1 Introduction The prospect of electricpowered commercial vehicles in Russia has often been discussed, despite the slow growth, demand for each year. The level of demand for the growth curve in the world [1, 2]. Despite the fact that the prices for hydrocarbons in Russia are relatively low, the development of commercial vehicles on electric traction is developing rapidly [3, 4]. Currently, there are about 450 electric buses in use on public roads in Moscow, and trends are outlined for an increase in the fleet by 300 units annually. A further increase in the dynamics of development is impossible without the local production of electric motors. The electric vehicle market is constantly evolving. For example, sales of passenger electric vehicles increased from 450 thousand units in 2015 to 2.1 million in 2019. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 200–209, 2021. https://doi.org/10.1007/9783030647193_23
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According to forecasts of the research service Bloomberg NEF, their sales will decrease to 1.7 million electric vehicles in 2020 due to the coronavirus but will increase to 8.5 million by 2025, 36 million by 2030, and 54 million by 2040 [5]. Currently, there are a large number of trucks with electric transmissions of various carrying capacities [6] and the electric drive manufacturer needs to determine which characteristics will be most in demand. This requires an analysis of existing vehicles and their characteristics. In Russia, despite the clearly insufﬁcient number of electric stations, the lack of service for quickly replacing discharged batteries with charged ones by analogy with the Gogoro Network [7, 8], and other restrictions, the electric vehicle market is also gradually developing. If we assume that even an electric vehicle for commercial cargo transportation will not be so popular, then the operation of buses with hydrogen fuel cells looks quite promising since hydrogen can signiﬁcantly increase the range, thus, the electric transmission remains relevant and does not depend on the scenario of generating electricity [9]. All of the above facts once again emphasize the relevance of the topic raised by the author of the article. The main purpose of the study was to determine the power parameters of the power plant of an electric freight vehicle, depending on the class. The data of the analysis of the parameters of the power plant can be used in the design of electric trucks, for the selection of primary power values for the purpose of further calculations on the mathematical model, as well as for the development of technical speciﬁcations for the creation of a power plant.
2 Methods During the research, the method of systematic analysis of the data on the parameters of the power plants of electric freight transport was used. Each selected component was analyzed as part of the problem, to identify positive and negative features. An empirical scientiﬁc approach is used, consisting of data collection, scientiﬁc analysis, hypothesis formulation, and theory development.
3 Research and Discussion 3.1
Data Collection
Collection of statistical data on the parameters of trucks of world manufacturers. The main parameters of electric trucks produced, as well as ready for production and denominated since 2017 [5–11] are summarized in Table 1.
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Name, features
Manufacturer
Full weight, tons
Characteristics of the electric motor, kW
Model 520, Transpower ElecTruck, 8 load class BYD 8R LDV EV80 ZRD BYD BYD5071XXYBEV BYD BYD5030XXYBEV1 BYD BYD5070XXYBEV BYD BYD5031XXYBEV BYD BYD5070XXYBEV1 BYD BYD5110XXYBEV BYD BYD1070A7BBEV BYD BYD5070XLCBEV BYD1180D8DBEVD (T8C) BYD BYD3250EEFBEVD BYD BYD5160GSSBEVD BYD BYD1030K77BEVD BYD1071A7BBEVD BYD1070A7BBEVD1 Jiefang J6F electric GINAF Dura Truck E2121, Renault Trucks D Emoss,
Peterbilt
36,00
280
BYD SAIC ZRD Auto BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD BYD FAW GINAF Renault Trucks Emoss Mobile Systems B.V VDL Bus & Coach EFORCE ONE AG Cummins Thor Trucks Toyota Motor North America Inc RENAULT RENAULT RENAULT
26,00 3,50 2,00 7,49 2,78 7,32 3,50 7,32 10,70 7,32 7,32 18,00 25,00 16,00 3,50 7,50 7,32 4,50 12,00 13,00
2 148 92 18 125 80 150 160 125 150 110 150 270 180 270 80 125 90 50/100 280 103 350
37,00 44,00 34,00 36,00 36,00
240 350/550 276 224/522 2 250
3,10 16,00 26,00
57 130/185 260/370
27,00
400/330 4 560
VDL Iveco Eforce E44 Cummins AEOS, 4 2, Thor ETOne Toyota Fuel Cell Truck Semi, RENAULT MASTER ZE RENAULT TRUCKS D ZE RENAULT TRUCKS D WIDE ZE Volvo FL Electric, Futuricum 26E (Volvo FM), Freightliner eCascadia, Freightliner eM2 106 Tesla Semi. Nikola One MercedesBenz Sprinter, Orten ET35M
Volvo Designwerk Products AG Daimler Trucks Daimler Trucks Tesla Nikola ORTEN ElectricTrucks
36,00 5,5 4,2
545 358 750 746 81
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The Type of Power Plants Used
By the type of electric machines used, electric trucks are no exception and, like allelectric vehicles, they mainly use threephase brushless DC motors with neodymium magnets, i.e. BLDC or PMSM motors (valve motor according to the Soviet abbreviation), which have signiﬁcant advantages: The advantages of BLDC motors: 1) Lack of a brush assembly (as well as the lack of regular replacement of brushes, cleaning). 2) Simplicity of design and no excitation losses. 3) Higher speciﬁc power (kW/kg) in comparison with asynchronous motors (asynchronous motors of comparable power are noticeably heavier). 4) Low inertia with signiﬁcant torque. 5) Maintaining the torque on the shaft, regardless of the rotor speed. 6) Higher starting torque than induction motors or brush motors of the same power. For electric vehicles, the moment of starting at the start is of great importance in order to move the vehicle from a state of rest. 7) High efﬁciency in the entire range of rotor speeds, including at reduced speeds. 8) Small dimensions. For example, an asynchronous machine of the same power and energy efﬁciency class is 2 times larger than an asynchronous motor. 9) Less cost of the controller in comparison with induction motors of comparable power. 10) More technological and cheaper in serial production. 3.3
The Choice of the Power Plant
The use of one or another electric vehicle on trucks is primarily associated with the basic requirements for a truck: A) B) C) D)
Gross vehicle mass. Maximum speed of movement. Dynamics of movement. The cost of a pair of motor + controller
When choosing a power plant, they are guided by the following criteria: A) B) C) D)
Engine power, nominal  maximum  peak Moment on the shaft, nominal  maximum  peak Motor weight Cooling type.
The type of transmission used and the gear ratio of the gearbox, which affect the torque and power characteristics of the selected power unit, are important when choosing. Trucks use various transmissions from the direct drive (through a gearbox) to a 6speed automatic. The operation of the selected power plant is checked on a mathematical model, depending on the operating conditions.
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The power characteristics of the power plant are associated primarily with the total weight of the electric truck. Having analyzed this dependence, we build a power diagram versus the total mass of implemented electric truck projects (see Fig. 1), based on research data (see Table 1).
Fig. 1. Power of electric motor versus the total mass of implemented electric truck
It is not difﬁcult to notice points with overestimated power characteristics, falling out of the general picture. Since products with overestimated characteristics are prototypes, we delete these points and build an approximating line showing us the average dependence of power on the total mass of an electric truck (see Fig. 2).
Fig. 2. The dependence of average power of electric motor on the total mass of electric truck
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This line can serve as a starting point for choosing the initial power of the power machine, with a known total mass, with further miscalculation and correction on a mathematical model. 3.4
The Range of Powers of the Applied Power Plants by Classes
The most effective classiﬁcation of electric trucks should be considered the classiﬁcation by gross weight, due to the fact that the weight of the supply battery assembly is quite signiﬁcant and has a strong influence on the choice of the power plant [10]. The classiﬁcation of trucks by gross weight is regulated by the standard UN 025 27066. It is convenient to represent the designation system of automobile rolling stock in the form of a table (Table 2). Table 2. Vehicle types by purpose (operation). Weight, t to 1,2 1,2–2,0 2,0–8,0 8,0–14,0 14,0–20,0 20,0–40,0 up 40,0
Flatbed 13 23 33 43 53 63 73
Tractor 14 24 34 44 54 64 74
Dump Truck Tanker 15 16 25 26 35 36 45 46 55 56 65 66 75 76
Van 17 27 37 47 57 67 77
Special 19 29 39 49 59 69 79
Some classes from 18 to 78 are missing from indexing, this is a reserve. The designations are as follows: digit “1”  truck class (gross weight); digit “2”  type of automatic telephone exchange: 3  A flatbed truck or pickup; 4  Truck tractor; 5  Dump truck; 6  Tanks; 7  Van; 8  Reserve digit; 9  Special vehicle. numbers “3” and “4”  serial number of the model; digit “5”  car modiﬁcation; digit “6”  type of execution: 1  Cold climate; 6  Moderate; 7  Tropical. We sort the data in Table 1 for compliance with the total mass and power, taking into account the points out of the sequence (clause 3) From the sample of the digital
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sequence, we obtain the range of powers of the power plants used in the corresponding weight class and enter it in Table 3. A similar classiﬁcation of trucks is used in the United States [11]. In our case, it is necessary to obtain a range of applied capacities, therefore, the initial data is clearly insufﬁcient for an assessment in this way. More objective data can be obtained from the graph of the correspondence of the total mass and power by means of mathematical operations. Create the upper and lower border of the averaged values (Fig. 3). According to the classiﬁcation, we ﬁnd the values of the power spreads in the corresponding range from the lower to the upper approximation line and the average value. Result in Table 4.
Table 3. Correspondence of total weight and power. Correspondence of total weight (tons) and power (kW) 2 18 2,78 80 3,5 80 4,5 50 7,32 150 7,32 125 7,32 110 7,32 150 7,32 90 7,5 125 7,5 125 10,7 150 13 103 16 150 18 150 25 180 34 276 36 280 36 300 37 240 44 350
Type
Power (kW)
1,2–2,0 2,0–8,0
18 50–150
8,0–14,0
103150
14,0–20,0
150
20,0–40,0
180–300
up 40
350
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Fig. 3.
Table 4. Vehicle types by purpose (operation). 1 2 3 4 5 6 7
Type to 1,2 1,2–2,0 2,0–8,0 8,0–14,0 14,0–20,0 20,0–40,0 Up 40
Power range, kW Average value, kW to 50 25 10–70 40 18–145 82 65–195 130 110–235 173 150–340 245 Up 255 310
A signiﬁcant spread in capacities is due to: 1. The use of transmissions with different gear ratios. 2. Different requirements for speed modes and dynamics. Example: If you set the task of determining the optimal characteristics of the power plant for electric trucks of category 5 (gross weight 14–20 tons), then 3 trucks can be safely included in this category based on Table 1: – RENAULT TRUCKS D ZE (van 16t) rated power 130 kW – BYD1180D8DBEVD (chassis 18t) rated power 150 kW – BYD BYD5160GSSBEVD (chassis 16t) rated power 150 kW
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Thus, for the 5th category of electric trucks (gross weight 14–20 tons), the initial choice of the power plant with the following parameters will be optimal: – type: permanent magnet synchronous motor. – rated power 150 kW – peak power 200 kW.
4 Conclusion After analyzing existing vehicles and their electric motors, it is possible to identify the most optimal power ranges for each speciﬁc type of truck. Knowing the market capacity of certain types of trucks, you can calculate the number of electric motors that will be in demand. This information can be used for technical and economic calculations when preparing an electric motor for production. The authors of the work are aware that for each vehicle it is necessary to do a power calculation, but most of this calculation depends on the transmission. Therefore, the initial choice of the engine can be carried out based on the classiﬁcation carried out and the ratio of truck sizes with the power of the electric motor.
References 1. Tuan, N.K., Karpukhin, K.E., Terenchenko, A.S., Kolbasov, A.F.: World trends in the development of vehicles with alternative energy sources. ARPN J. Eng. Appl. Sci. 13(7), 2535–2542 (2018) 2. Kamenev, V.F., Terenchenko, A.S., Karpukhin, K.E., Kolbasov, A.F.: The strategy of creating urban electric vehicle: challenges and solutions. Int. J. Civil Eng. Technol. 8(12), 895–905 (2017) 3. Kozlov, V.N., Kolbasov, A.F., Karpukhin, K.E., Katanaev, N.T.: Mathematical model of an electric vehicle with a nonflat battery of photovoltaic converters. In: IOP Conference Series: Materials Science and Engineering, vol. 819, p. 012014 (2020). https://doi.org/10.1088/ 1757899X/819/1/012014 4. Kolbasov, A.F., Kurmaev, RKh, Karpukhin, K.E.: Implementation of dualcircuit system for additional power supply based on photovoltaic converters for electric vehicles. Energies 12 (20), 4010 (2019) 5. Electric Vehicle Outlook 2020. Bloomberg Research Service. https://about.bnef.com/ electricvehicleoutlook. Accessed 25 Aug 2020 6. The Gogoro Network Battery Swapping Platform. gogoro.com. https://www.gogoro.com/ gogoronetwork 7. Lebkowski, A..: Electric vehicles trucks  overview of technology and research selected vehicle. Zeszyty Naukowe Akademii Morskiej w Gdyni, pp. 2451–2486 (2017)
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8. Truck News, Electric trucks to become more prominent: ACT Research 2018. https://www. trucknews.com/equipment/electrictruckstobecomemoreprominentactresearch/ 1003087096/ 9. International Council on Clean Transportation, Transitioning to ZeroEmission HeavyDuty Freight Vehicles (2017). https://www.theicct.org/sites/default/ﬁles/publications/Zeroemissionfreighttrucks_ICCTwhitepaper_26092017_vF.pdf. Accessed 25 Aug 2020 10. IEA 2016, Global EV Outlook 2016: Beyond One Million Electric Cars, IEA, Paris. https:// www.iea.org/publications/freepublications/publication/globalevoutlook2016.html 11. Dygalo, V., Keller, A.: Realtime diagnostic complex of automated car active safety system unit. In: IOP Conference Series: Materials Science and Engineering, vol. 819, p. 012039 (2020). https://doi.org/10.1088/1757899X/819/1/012039
Automatic Extraction and Welding Feature Recognition from STEP Data Lan Phung Xuan(&) and Linh Tao Ngoc Hanoi University of Science and Technology, No. 1 Dai Co Viet Street, Hanoi, Vietnam [email protected]
Abstract. Feature recognition has been considered as an important bridge between CAD and CAPP in process planning for welding. These recognition data can be used as input data not only for generating welding tasks in process planning but also for programming work of welding industrial robot without using teach pendant. This paper introduces an effective method to extract and recognize welding features from STEP ﬁle which can be provided by any 3D CAD system. The computer program quickly extracts data from various strings in the STEP ﬁle, saves them in the database and recognizes features for welding by the developed algorithm. The welding paths with detailed geometry characteristics are recognized in a short time. All recognition results are stored in the database for further processes. A case study is used to verify the validity of the recognition method. Keywords: STEP ﬁle
Welding feature Feature recognition
1 Introduction Automatic feature recognition plays a crucial role in computeraided process planning such as machining, welding, assembling, etc. A 3D model is composed of many design features, but they are not direct form features of the manufacturing process. Thus, it is necessary to develop a module for recognizing the form feature from design features. Form features include machining features, welding features, assembling features, etc. Due to increasing demands for automation, studies on feature recognition from 3D models have been developed strongly [1, 2]. Welding work is very common in many manufacturing areas including automobile or airplane production. However, most of the studies focus on machining features, there are very few studies focusing on welding [3]. Moreover, welding is done primarily by industrial robots in modern production lines. Programming for the industrial robots uses by teaching methods using teach pendant. These works are timeconsuming with low accuracy. Thus, automation in welding process planning and robot programming is developed to improve quality, reduce time and labor costs. There are two popular input format types of recognition systems including the speciﬁc format of CAD/CAM commercial software and neutral ﬁle. The main limitation of the ﬁrst type is mandatory for speciﬁc software. Thus, the computer program should be integrated with CAD/CAM software and it is necessary to install the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 210–215, 2021. https://doi.org/10.1007/9783030647193_24
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commercial CAD/CAM software on the computer and only this software can use this module. However, it has the main advantage of using a huge library of geometric objects in the CAD/CAM software which is helpful for programming. Meanwhile, most of the neutral ﬁle formats can be imported or exported from any CAD/CAM software but the extraction algorithm will be more complex. In our module, the neutral ﬁle is chosen as an input data format that helps create an independent recognition system, regardless of any commercial CAD/CAM software. Among many neutral ﬁles (such as IGES, STEP, DXF, STL), STEP ﬁle is the most widely used neutral CAD format. It is a 3D model ﬁle formatted in Standard for the Exchange of Product Data, an ISO standard exchange format [2]. It includes boundary representation data that can be recognized by most CAD/CAM software such as SolidWorks, CATIA, ProE, NX, etc. Boundary representation is the principal solid modelling method in which the geometry of the model is described by its bounding surfaces. Moreover, it is interoperability between different design and manufacturing systems (e.g. STEP AP203 for the part and assembly design, STEP AP214 for automotive mechanical design processes, STEP AP242 for managed modelbased 3D engineering, STEP AP224 for feature based process planning, STEP AP238 for manufacturing) [5].
2 Methodology The proposed methodology for the extraction and recognition process is shown in Fig. 1. After constructing a 3D model from any commercial software, it should be exported or saved as into STEP (AP203) ﬁle. The ﬁrst step in the developed system is to extract geometry data from STEP ﬁle. The extraction information includes the entity’s information and dimension data. The speciﬁc database for each model is created for storing all extraction information. Based on extraction data, an algorithm for recognizing welding features is applied and the result is stored in the database. 3D CAD Model from any commercial software
Export into STEP file
Extract Entities such as Closed_Shell, Faces, Loops, Edges, Vertices v.v.
Extract dimension data of design feature
Create database and Save Geometry Information in SQL Server
Recognize welding feature by recognition algorithms
Computer aided process planning for welding Save feature recogntion result in database and file
Fig. 1. Flow chart of the proposed methodology
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Geometric Data Extraction from STEP File
STEP ﬁle is a text ﬁle that contains strings of characters that start with the hashtag (#). Its following number is entity identiﬁcation. The hierarchical structure starts from CLOSED_SHELL which is a collection of one or more faces bounding a region. The following elements include ADVANCED_FACE, FACE_OUTER_BOUND, FACE_BOUND, Face Type, END_LOOP, ORIENTED_EDGE, EDGE_CURVE, Edge Type, AXIS_PLACEMENT_3D, CARTESIAN_POINT, DIRECTION and Dimension (Radius, Angle etc.) [4]. The extraction process is important to extract the object’s lines, circles, direction, vertex, Cartesian point and boundaries of the 3D model. The extraction data from STEP neutral ﬁle is stored in a structured database which is speciﬁcally designed for this purpose. The information of each entity is saved in one data table. Based on the hierarchical structure of the basic STEP ﬁle, the database for storing information is created and the relationship diagram among data tables is described in Fig. 2. A large amount of data is retrieved quickly and efﬁciently using the TSQL command which is a standard language for accessing, manipulating, and communicating with the database. The developed program reads the STEP ﬁle line by line and the following steps are applied to extract information for each line. tbl_ClosedShell PK
PK
tbl_ClosedShelFace
ClosedShellID
ClosedShellID
ClosedShellName
AdvancedFaceID
tbl_Line
tbl_Circle
EdgeTypeID
PK
EdgeTypeName FK
PointID
FK
VectorID
EdgeTypeID
tbl_AdvancedFace AdvancedFaceID FK
FaceID
FK
FaceTypeID
FaceTypeID FaceTypeName
FK
AxisPlaceID MainDim1
tbl_FaceLoop PK
EdgeTypeName FK
tbl_FaceType PK
MainDim2
FaceID EdgeLoopID
AxisPlaceID
InOut
MainDim
tbl_EdgeLoop PK
EdgeLoopID
FK OrientedEdgeID tbl_Direction
tbl_Vector PK
VectorID
FK
DirectionID
PK
DirectionID DirX DirY DirZ
tbl_Point PK
DirectionType
PointID X Y Z PointType
tbl_AxisPlace PK
AxisPlaceID
FK
PointID
FK
DirectionID
FK
XDirectionID
tbl_OrientedEdge PK OrientedEdgeID FK
EdgeCurID
tbl_EdgeCur
AxisType PK
tbl_VertexPoint
EdgeCurID EdgeCurName
PK
VertexPointID
FK
SPointID
FK
PointID
FK
EPointID
FK
EdgeTypeID
Fig. 2. Entityrelationship diagram of extraction data from STEP ﬁles
Step 1. Split the line by semicolon and save in a list Step 2. Find the entity name in the ﬁrst item of the list for example CLOSED_SHELL, PLANE, EDGE_CURVE, VERTEX_POINT, etc. Step 3. Get the ﬁrst hashtag number and save as an entity identiﬁcation like ClosedShellID, EdgeCurID, VertexPointID, etc.
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Step 4. For each entity, speciﬁc extraction process is applied depending on the components of each entity as shown in Fig. 3. For example, information in line “#20 = EDGE_CURVE (‘NONE’, #1268, #460, #3382,.T.)” is saved into tbl_EdgeCur where EdgeCurID = 20, EdgeCurName = ‘NONE’, SPointID = 1268 (Start Vertext Point), EPointID = 460 (End Vertex Point), EdgeTypeID = 3382. Step 5. Store all information in the database. After storing all extraction data in the database, it is easy to obtain speciﬁc information from many data tables based on many conditions using TSQL language. The extraction data is used as input of the feature recognition process.
SP: (60, 0, 55) EP: (60, 0, 55)
#2907 (Shell) #1063(Edge) #1492 (Edge)
SP: (60, 0, 55) EP: (60, 0, 55)
SP: (0, 2.5, 140) EP: (0, 2.5, 20)
#1329 (Edge) #369 (Face)
SP: (20, 2.5, 0) EP: (140, 2.5, 0)
#290 (Edge)
#516 (Face) #326 (Shell)
#1472 (Face) Z Y X
#1568 (Face) #1382 (Shell)
Fig. 3. Assembly model for case study
2.2
Feature Recognition
Based on the extracted information in the previous step, the developed algorithm is applied to recognize the welding feature as follows: Step 1. Find all Closed_Shells represented for welding parts. Step 2. Consider each pair of different Closed_Shells. Step 3. Find each Face in each Closed_Shell and put it into a pair of Faces together. Step 4. Check and choose only two Faces that meet the contact condition on the domain together. If both Faces are Plane, the contact condition includes coplanner and direct contact. If both Faces are Cylindrical_Surface, the contact condition is coaxial and direct contact. Step 5. Determine base face, base part and weld face based on bounding condition according to the rule “If the Closed_Shells has bigger bounding box volume, it is a base part, the contact face is base face and weld face belongs to another Closed_Shell” Step 6. Determine common edges (coedges) between two faces. The number of coedges is chosen depending on the length of coedges and area of contact face. Step 7. Determine the basic characteristics of the weld edge from extraction information. For a line, the extraction information includes ID, Cartesian coordinates of start point, endpoint and direction which is deﬁned as perpendicular to the
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coedge and lies on the bisector plane of the two faces creating the coedge. For an arc or circle, besides the above information, radius and direction are determined by the axis of the arc or circle. Repeat steps 3 to 7 until all faces of each pair of Closed_Shells are considered. Repeat steps 2 to 7 until all Closed_Shells of parts are checked. 2.3
Development of Module
The module has been designed and developed using Visual C# language with the STEP ﬁle as input. Microsoft SQL Server is used as a database management system. When starting the program, the main window opens as shown in Fig. 4. In this module, the input ﬁle is showed under a list box in the ﬁrst region of the main window. Another region presents the extraction data including many view types. The 3D model is also viewed in the module and the last region is for showing feature recognition results.
Fig. 4. Result of extraction and recognition for the case study in developed module
2.4
Case Study
An assembly model shown in Fig. 3 is used to evaluate the efﬁciency of the method. This consists of three design parts corresponding to three CLOSED_SHELL (#326, #2907, #1382). Each shell composes of faces, edges and vertex points. The task of the module is to extract necessary data and recognize welding features along with the basic geometric parameters of welding features. There are two popular types of welding path in this case study including lines and circles.
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3 Result and Discussions The result of extraction and recognition is shown in Fig. 4. It takes 4 s for extracting and saving data in the database and about 5 s for recognizing welding features. In this case study, four weld edges in line form and two circle compound weld edges are recognized. Based on this preliminary result, welding experts may establish rules to select only a few or all these recognized welding paths. Each welding feature is automatically recognized with basic information such as weld edge type, position, radius, and direction in 3D space. For example, weld edge #290 is recognized as line with the start vertex point (20, −2.5, 0), end vertex point (140, −2.5, 0) and direction (0, −0.707, 0.707). It agrees with the characteristics of its design feature on the 3D model as shown in Fig. 3. The result showed that the proposed method for welding feature recognition can automatically identify the welding features in both line and circle type. This result can be used as input data for further process planning for welding or dynamic kinematic calculations for welding industrial robots.
4 Conclusions The main research contribution is the development of a module to automatically extract and recognize the welding feature directly from STEP neutral ﬁle. The module takes a short time to recognize and generate welding features with basic geometry characteristics. The process is an important step towards the development of an automatic welding system including computeraided process planning for welding and weld path calculation and programming for industrial welding robots. Other welding feature recognition for Bcurves or ellipse etc. and dynamic kinematic calculations for welding robot based on these recognition data will be focused on future work.
References 1. Bhandarkar, M.P., Nagi, R.: STEPbased feature extraction from STEP geometry for agile manufacturing. Comput. Ind. 41(1), 3–24 (2000) 2. Venu, B., Komma, V.R., Srivastava, D.: STEPbased feature recognition system for Bspline surface features. Int. J. Autom. Comput. 15(4), 500–512 (2018) 3. Kiani, M.A., Sa, H.A.: Automatic spot welding feature recognition from STEP data. In: 2019 International Symposium on Recent Advances in Electrical Engineering (RAEE), Pakistan, pp. 1–6. IEEE (2019) 4. Oussama, J., Abdelilah, E., Ahmed, R.: Manufacturing computer aided process planning for rotational parts. Part 1: automatic feature recognition from STEP AP203 Ed2. Int. J. Eng. Res. Appl. 1–13 (2014) 5. Rampur, V.V., Reur, S.: Compilation of step AP214 data to generate computeraided process planning for automotive parts. Int. J. Innov. Technol. Exploring Eng. (IJITEE) 9(1), 645–653 (2019)
Characterization of Gelatin and PVA Nanoﬁbers Fabricated Using Electrospinning Process Cuong Nguyen Nhu1, Nhung Vu Thi2, Nam Nguyen Hoang3, Thao Pham Ngoc1, Trinh Chu Duc1, Van Thanh Dau4, and Tung Bui Thanh1(&) 1
University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam [email protected] 2 Vietnam Japan University, Vietnam National University, Hanoi, Vietnam 3 Hanoi University of Science, Vietnam National University, Hanoi, Vietnam 4 School of Engineering and Built Environment, Grifﬁth University, Brisbane, QLD, Australia
Abstract. Nanoﬁbers has been recently received tremendous attention from researchers due to its advantages. Among the fabrication methods for nanoﬁbers, electrospinning appears to be an intriguing technique, allowing the fabrication of nanoﬁbers in a simple and inexpensive way. In this paper, we prepared and fabricated nanoﬁbers using the electrospinning process with two polymers: gelatin and PVA. The process and the fabricated ﬁbers were examined under scanning electron microscope. The effect of the polymeric concentration was also studied. The experimental results indicate that nanoﬁbers were successfully fabricated, and higher polymer concentration leads to the uniformity of the ﬁbers. In addition, the nanoﬁber mat with silver nanoparticles was successfully fabricated, opening the potential for medical applications of the fabricated ﬁbers. Keywords: Electrospinning
Nanoﬁber Polymer ﬁber
1 Introduction Nanoﬁbers have been recently received tremendous attention from researchers due to its advantages. Compared to conventional ﬁbrous structures, nanoﬁbers are lightweight and have smaller pores and high surfacetovolume ratio. Such ﬁbers hold great potentials and have been successfully applied to various ﬁelds such as tissue engineering scaffolds [1], ﬁltration [2], flexible electronics [3] or biomedical [4, 5]. Among the fabrication methods for nanoﬁbers, electrospinning appears to be an intriguing technique. Electrospinning employs electrostatic force to produce long ﬁbers of nano to micrometer diameter. This method offers the ability to consistently produce ﬁbers in the submicron range and a versatility in spinning a wide variety of polymeric ﬁbers such as collagen, cellulose, chitosan or polylactic acid. Electrospinning process allows the fabrication of nanoﬁbers in a simple and inexpensive way. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 216–222, 2021. https://doi.org/10.1007/9783030647193_25
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In this work, we prepared and fabricated nanoﬁbers using the electrospinning process with two polymers, namely gelatin and PVA. The principle of electrospinning is to be discussed indepth. The process and the fabricated ﬁbers were also examined by simulation and scanning electron microscope images. The effect of the polymeric concentration is also studied. The experimental results lay the foundation for further application of the electrospinning in general and nanoﬁbers in speciﬁc.
2 Nanospinning Process Electrospining is an intriguing approach that allows the creation of very thin ﬁbers with the diameters ranging from a few to hundreds of nanometers. A typical electrospinning system, as shown in Fig. 1, is comprised of three major components: an extremely high voltage source, a syringe and a grounded collecting plate. The syringe is introduced with the polymer solution and controlled by a micropump. The collecting electrode can be in several shapes such as a rotating cylinder or a flat metal surface. The spinning process is started by injecting a liquid or melt polymer solution into the nozzle at a constant flow rate. A high voltage from the DC power source of several tens of kVs is placed at the tip of the syringe, creating a high electric ﬁeld between the tip and the collecting plate. When an electric charge is induced on the liquid surface, there exists an extra electrostatic force pulling the polymer solution towards the collector of opposite polarity. As the supply voltage surpasses the threshold, the repulsive electric force overcomes the surface tension force, creating jet of polymer solution. As the jets travel to the grounded collector, they create a mat of ﬁne and thin ﬁbers.
Fig. 1. Setup of a typical electrospinning system
3 Material Preparation and Methods 3.1
Polymer Solution Preparation
Two polymers, namely PVA and gelatin, were chosen in this study. These polymers have a good biocompatibility and are nontoxic. The polymer powder is mixed with the appropriate solvent. PVA solution was dissolved in deionized water while gelatin was dissolved in acetic acid. Acetic acid solution was diluted in deionized water with the
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ratio of 1:1 in volume. The solution was stirred at the speed of 50 rpm/min while being baked on a hot plate at 80 degree Celsius for 45 min until the polymer was completely dissolved in the solvent, creating a homogeneous solution. The polymer solution then rested for another 30 min to cool down and rid of the undesired air bubbles suspended in the solution during the stirring process. 3.2
Nanospinning Process
The prepared polymer solution was added into a 60ml BD syringe. A rotating drum was used as the collector and was located 10 cm apart from the spinnerets. The micropump was used to apply a pressure to the polymer solution through the syringe. As soon as the polymer solution reached the capillary tip, the flow rate was kept constant at 15 ml/min, and the high voltage supply, which can generate a high voltage up to 45 kV, was turned on. The high voltage was set to 35 kV. The tip of the syringe was wired to the anode of the voltage power supply while the cathode was connected to the collector. The collectors in the form of a rotating drum were covered with aluminum foil on which electrospun ﬁbers are supposed to deposit on. The rotating drum was set to rotate at the angular velocity of 250 rpm/min. The spinning of the polymer solution was carried out for 45 min. The nanoﬁber mat rested for 30 min before being taken out in order for the solvent to evaporate completely even though most of the solvent had already evaporated during the spinning process.
4 Results and Discussion 4.1
Nanospun Fibers Fabrication
A numerical simulation was conducted to study the high voltage involved in electrospinning process. 3D simulation model was performed by COMSOL Multiphysics software to solve for the electric ﬁeld with the meshing model shown in Fig. 2 (a). The electric potential contour is shown in Fig. 2 (b). It is can be observed from the results that the electric potential was particularly high at the region near the needle, the contour lines are dense. The electric potential proﬁle as well as the electric ﬁeld streamline is presented in Fig. 2 (c).
Fig. 2. (a) Meshing model (b) Electric potential contour (c) Electric potential of the system
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The formation of jets at the tip of the needle as the applied high voltage is captured and shown in Fig. 3 (a). It can be clearly seen that the electrospinning process occurred as there are jets of polymer solution developed at the region between the needle and the rotating drum. As comparing the simulation results of the electric potential line, and the jets, we could conclude that the direction of the jets follows the electric ﬁeld line. This matches with the proposed theory of electrospinning process because the polymer solution induced charged as being place under high voltage thus force that is responsible for the repulsion obey Coulomb’s law. It is, however, should be noted that these jets are only stable in the vicinity of the spinnerets, as they are apart from the spinnerets, it becomes unstable and travels in whipping motion. In addition, instead of only one, there exists several jets developed at the tip of the needle.
Fig. 3. (a) Needles under electrospinning. Nanoﬁbers on (b) aluminum foil (c) plastic food wrap
Figure 3(b) shows the nanoﬁbers successfully deposited on the aluminum foil. In addition to the aluminum foil, we also tried to let the nanoﬁbers deposit on other surfaces such as microscope glass slide, plastic food wrap as in Fig. 3 (c). This demonstrates the advantages of the electrospinning process as we can create nanoﬁbers deposition on several surface, in other words, electrospinning process is not depositingsurface dependent. The fabricated samples were examined under scanning electron microscope (SEM). The SEM images of PVA 10% and gelatin 25% are shown in Fig. 4. The nanoﬁbers for two polymer solutions were quite homogeneous. The diameter of PVA ﬁbers was approximately 900 nm while that of gelatin was about 1 µm.
Fig. 4. SEM images of electrospinning (a) gelatin 25% (b) PVA 10% ﬁbers with the scale bar of 500 nm and (c) gelatin 25% (d) PVA 10% ﬁbers with the scale bar of 250 nm
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Dependence on Polymer Concentration
Motivated by the initial results with the fabrication of PVA and gelatin nanoﬁbers, we continue to study the impact of polymer solution concentration to the formation of nanoﬁbers. We conducted the experiments with four different concentrations of gelatin: 10%, 15%, 20% and 25% with the SEM images shown in Fig. 5. From the captured results, we can infer that there are signiﬁcant differences among the nanoﬁbers fabricated from various polymer concentrations. At the low concentration of polymer, i.e. 10% of gelatin, the mat of nanoﬁbers is spares; the ﬁbers are not uniform in diameter as in Fig. 5 (a). Interestingly, the nanoﬁbers mat appears to have myriad beads. In contrast, there are almost no beads appear in nanoﬁbers of higher gelatin concentration. For example, with the concentration of 15% as in Fig. 5 (b), no undesirable ﬁbers are created. It is also noteworthy that at 15% gelatin, there are ﬁbers of small and large size. The large ﬁber is 180 nm in diameters while small ﬁber has the diameter of 40 nm. It can be inferred that small ﬁbers are notfully developed ﬁbers. Thus, they create the disconnection between the ﬁbers. The nanoﬁber mats fabricated from 10% and 15% gelatin concentration do not possess good elasticity and durability. From the concentration of 20%, there ﬁbers become more uniform as shown in Fig. 5 (c). At the concentration of 25% (Fig. 5 (d)), for example, there are almost no beads or undeveloped ﬁbers. This could lead to the nanoﬁbers with higher durability. In addition, the ﬁbers created from higherpolymerconcentration solution also have larger average diameters. The diameter of nanoﬁbers created from 15%, 20% and 25% are 120, 180 and 220 µm respectively. It can be concluded that highly concentrated polymer solutions create more uniform ﬁbers. However, we should also consider the fact that highly concentrated polymer solution has signiﬁcantly high viscosity, and thus, causing trouble working with the syringe and micropump.
Fig. 5. Scanning electron micrographs of electrospun ﬁbers at different acid concentration (wt%): (a) 10% (b) 15% (c) 20% (d) 25%
4.3
Nanoﬁbers with Silver Nanoparticles
We continue to fabricate with silver nanoparticles (AgNPs) 30 ppm mixed with the PVA solution of 5% concentration. The results are shown in Fig. 6. Some beads were undesirably formed, and the ﬁbers are not quite uniform. However, with the knowledge from the previous study on the impact of concentration, we can infer that the issue is due to the low concentration of polymer. The heterogeneity of ﬁbers and bead formation could have been mitigated by increasing the concentration of PVA. AgNPs have been demonstrated to possess effective antibacterial effect. Two widely accepted antibacterial mechanisms of AgNPs on bacteria are contact killing and ionmediated
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killing [6, 7]. The corporation of AgNPs into the nanoﬁbers open the potential for the application of the fabricated nanoﬁbers in wound healing and medical tissue.
Fig. 6. SEM images of PVA + AgNPs ﬁbers at the scale of (a) 2 lm (b) 300 nm
5 Conclusion The principle of electrospinning process has been discussed. The electric ﬁeld distribution was analyzed by simulation. The nanoﬁber mats of two polymers, namely PVA and gelatin, were successfully fabricated using electrospinning and examined under SEM. Study on the impact of polymer concentration was also performed. From the SEM images, we concluded that lowly concentrated polymer solution leads to thinner ﬁbers with unwanted beads, while those fabricated from solution with higher concentration tend to be more uniform and do not have beads or clumps. The same experiment setup was also employed to electrospin solution of PVA and AgNPs. The fabrication of nanoﬁber mat with AgNPs provides the foundation for the application of mats in medicals such as wound healing or medical tissue. Acknowledgements. This work has been supported by VNU University of Engineering and Technology under project number CN20.18.
References 1. O’Connor, R.A., McGuinness, G.B.: Electrospun nanoﬁbre bundles and yarns for tissue engineering applications: a review. In: Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 230, no. 11, pp. 987–998. SAGE Publications Ltd, 1 November 2016. https://doi.org/10.1177/0954411916656664 2. Lv, D., et al.: Green Electrospun Nanoﬁbers and Their Application in Air Filtration. Macromol. Mater. Eng. 303(12), 1800336 (2018). https://doi.org/10.1002/mame.201800336 3. Dinh, T., et al.: Polyacrylonitrilecarbon nanotubepolyacrylonitrile: a versatile robust platform for flexible multifunctional electronic devices in medical applications. Macromol. Mater. Eng. 304(6), 1900014 (2019). https://doi.org/10.1002/mame.201900014 4. MohitiAsli, M., Loboa, E.G.: Nanoﬁbrous smart bandages for wound care. In: Wound Healing Biomaterials, vol. 2, pp. 483–499. Elsevier Inc. (2016) 5. Abbasi, N., Hamlet, S., Dau, V.T., Nguyen, N.T.: Calcium phosphate stability on melt electrowritten PCL scaffolds. J. Sci. Adv. Mater. Devices (2020). https://doi.org/10.1016/j. jsamd.2020.01.001
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6. Le Ouay, B., Stellacci, F.: Antibacterial activity of silver nanoparticles: a surface science insight. Nano Today 10(3), 339–354 (2015). https://doi.org/10.1016/j.nantod.2015.04.002 7. B. Khalandi et al: A review on potential role of silver nanoparticles and possible mechanisms of their actions on bacteria. Drug Res. 67(2), 70–76 (2017). https://doi.org/10.1055/s0042113383
Choice of Selection Methods in Genetic Algorithms for Power System State Estimation ThanhSon Tran(&) and ThiThanhHoa Kieu Faculty of Electrical Engineering, Electric Power University, Hanoi, Vietnam {sontt,hoaktt}@epu.edu.vn
Abstract. The state estimator plays an important role in power system operation. It is used to monitor state parameters, thereby it helps the operators make control decisions when the parameters exceed the permissible limits to ensure the system operate in a normal and secure state. To solve this problem, we can use artiﬁcial intelligence methods such as genetic algorithms. The genetic algorithms consist of three main operators: selection, crossover, and mutation. Among them, the selection of individual parents plays an essential role as it affects the performance of the algorithm. This paper studies the effect of selection methods on results of power system state estimation. The results depend signiﬁcantly on the choice of selection methods and show that the roulette wheel selection is the best choice. Keywords: Power System State Estimation Genetic Algorithm Roulette wheel selection Boltzmann selection Rank weighting selection Cost weighting selection Tournament selection Pairing from top to bottom Random pairing
1 Introduction Power System State Estimation (PSSE) problems estimate voltage magnitudes and angles at all buses of the system based on measurement data and mathematical formulations [1, 2]. Many methods have been developed to solve the problem, one of which is to apply weighted least square method (WLS). This method transforms the problem into an optimal one. To ﬁnd the optimal solution, one can use traditional methods such as Normal Equations Method, Orthogonal Methods, linear programming,… [3–7] or artiﬁcial intelligence (AI) algorithms which including Artiﬁcial Neural Network (ANN), Particle Swarm Optimisation (PSO), Genetic Algorithm (GA),… [8, 9]. The application of genetic algorithms has been developed but not much and not yet speciﬁc as well. Actually, the GA is mainly studied for optimal placement of phasor measurement unit [10–12]. In [13], the GA is applied for PSSE. It is shown that GA can estimate the state of 6bus system and cannot estimate the state of the IEEE 14bus system. The selection operator is one of the main processes in the genetic algorithm. Choosing chromosomes to the matting pool plays an important role and has a significant impact on the result. There are different types of selection operators in GA, such as roulette wheel selection, Boltzmann selection, rank weighting selection, cost © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 223–231, 2021. https://doi.org/10.1007/9783030647193_26
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weighting selection, tournament selection, pairing from top to bottom, and random pairing [14–18]. In this work, seven different types of selection operators of GA are implemented and then applied for power system state estimation. Through simulation of the 5bus and the IEEE 14bus systems, it is shown that the roulette wheel selection is the most suitable for PSSE by GA.
2 Power System State Estimation by GA 2.1
Formulation [1, 2, 19]
Consider a system which includes a set of measurements zi, i = 1 … m: 2
3 2 3 2 3 h1 ðx1 ; x2 ; . . .; xn Þ e1 z1 6 z2 7 6 h2 ðx1 ; x2 ; . . .; xn Þ 7 6 e2 7 6 7 6 7 6 7 z ¼ 6 .. 7 ¼ 6 7 þ 6 .. 7 ¼ hð xÞ þ e .. 4 . 5 4 5 4 . 5 . zm
hm ðx1 ; x2 ; . . .; xn Þ
ð1Þ
em
where: zi is the measurement value of the measurement i, hi(x) is the nonlinear function relating the measurement ith to the state vector x, ei is the measurement error of the measurement ith. Assume that the measurement errors are independent and each has the same Gaussian probability density function; ri is the standard deviation of measurement i. The weighted least square estimator is preferred to use for estimating the state of power system due to its good accuracy and its ability to process bad data [5]. This model minimizes the following objective function: J ð xÞ ¼ w2i ðzi hi ð xÞÞ2 ¼
m X 1 2 e 2 i r i¼1 i
ð2Þ
In power system state estimation, the measurement values include active and reactive power injected into a bus (denoted by Pi, Qi), active and reactive power flows (denoted by Pij, Qij), voltage magnitudes and angles (denoted by Vi, hi), branch currents flowing through the transmission lines (denoted by Iij). The state vector x is the voltage magnitudes and angles of all the buses excluding the reference bus voltage angle. The formula of the function hi(x) depends on the measurement types. Measuring the active and reactive power injected into the bus i, the hi(x) is given by the following formula: Pi ¼ Vi
N X
Vj Gij :coshij þ Bij :sinhij
j¼1
Qi ¼ Vi
N X j¼1
Vj Gij :sinhij Bij :coshij
ð3Þ
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The formula of hi(x) for the active and reactive power flows from bus i to bus j is: Pij ¼ Vi2 gsi þ gij Vi Vj gij :coshij þ bij :sinhij Qij ¼ Vi2 bsi þ bij Vi Vj gij :sinhij bij :coshij
ð4Þ
where: N is a set of bus numbers that are directly connected to bus i, hij = hi  hj, Gij + jBij is the ijth element in bus admittance matrix, gij + jbij is the admittance of the series branch connecting bus i and bus j, gsi + jbsi is the shunt admittance of the bus i. 2.2
Application of GA for Power System State Estimation
The Genetic Algorithm (GA) is an algorithm which is adapted to the evolutionary process of biological populations based on Darwin’s theory. GA was ﬁrst introduced by Holland and was developed by Godlberg. In GA, the optimal individual search is initiated with a population. The chromosomes of the present populations are the source for the next generation population through three main operators: selection, crossover, and mutation. At each step, the individual is evaluated through its ﬁtness value. Chromosomes which have good ﬁtness value will survive and the others will be discarded. The classic GA works with the binary chromosomes. However, in many problems, to perform decimal variables which require high accuracy in binary form, the increase of chromosome length may happen. Besides, the binary algorithm requires encoding – decoding process. To prevent these disadvantages of binary GA, we can use GA algorithms with variables that are performed in floatingpoint number (The Continuous Genetic Algorithm  CGA). Compared to binary GA, the CGA algorithm requires less memory, allows the variable value to be performed according to the accuracy of the computer and has shorter calculation time because the encrypt and decrypt process are removed [14]. This paper uses the CGA for estimating the power system state. Initial Population. To begin the CGA, an initial population of N chromosomes is generated. Each chromosome composes of n continuous variables of which their values are generated in a random way between their boundaries. Fitness Function. The advantage of GA over the other classic algorithm is that the searching path is lead by the ﬁtness value. The minimum objective function with constraining which illustrated in Sect. 2.1 can be transformed into a nonconstrain problem maximize objective. Maximize the ﬁtness function below: F ð xÞ ¼
1 J ð xÞ þ P ð xÞ
ð5Þ
where F(x) is the ﬁtness function; J(x) is the objective function as in (2); P(x) is the penalty term which check the condition of state variable mentioned in (3)
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Pð xÞ ¼ k
n X
n X 2 2 max 0; xi xmax þ k max 0; xmin xi i i
i¼1
ð6Þ
i¼1
k is the penalty factor. Selection. To select a chromosome to the matting pool, the selection operator is applied. There are several selection methods such as roulette wheel selection, Boltzmann selection, rank weighting selection, cost weighting selection, tournament selection, pairing from top to bottom, random pairing [14–18]. Roulette Wheel Selection. According to this method, the probability of a selected individual i is proportional to its ﬁtness value F(i): FðiÞ Pi ¼ Pn ; i ¼ 1; 2; . . .; N j¼1 F ð jÞ
ð7Þ
where Pi is the probability of chromosome i; F(i) is the ﬁtness value of chromosome i; N is the number of chromosomes. The cumulative probability of chromosome i is evaluated as below CPi ¼
Xi j¼1
Pj
ð8Þ
where CPi is the cumulative probability of chromosome i. A random number r between zero and one is generated and compared with the cumulative probabilities. If r < CP1, chromosome number one is chosen to the matting pool. If r > CP1 and CPi1 < r < CPi, chromosome ith will be selected. Boltzmann Selection. Assume that the population follows the Boltzmann distribution. Firstly, transform the ﬁtness of individuals through the following formulation F ðiÞ ¼ ef ðiÞ=T
ð9Þ
where f(i) is the ﬁtness of individual i; F(i) is the Boltzmann ﬁtness of individual i. Secondly, select the individuals using roulette wheel selection according to Boltzmann ﬁtness of individuals. Rank Weighting Selection. The advantage of this method is the probability of chromosome i is calculated once from its rank. The rank depends on its ﬁtness value. The best chromosome is ranked number one, and the worst is ranked number N. N iþ1 Pi ¼ PN ; i ¼ 1; 2; . . .; N j¼1 j
ð10Þ
where Pi is the probability of chromosome rank ith; N is the number of chromosomes. Cost Weighting Selection. The probability of selection calculates from the cost of the chromosome. In this process, the individuals are also sorted in a sequence depending
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on the objective function, the best one rank 1th and the worst rank Nth. The parents will be selected from n best individuals in a set of N individuals. Individuals in the order of (n + 1) to N will be discarded. For each chromosome, to form a normalized ﬁtness, we subtract the highest ﬁtness of the discarded chromosomes from the ﬁtness value of all the chromosomes in the mating pool. C'ðiÞ ¼ CðiÞ Cðn þ 1Þ
ð11Þ
where C’(i) is the normalized ﬁtness of chromosome ith; C(i) and C(i + 1) are the ﬁtness value of chromosome ith and chromosome (n + 1)th sequently. The probability of a selected individual i: C0 ðiÞ Pi ¼ Pn 0 j¼1 C ð jÞ
ð12Þ
Tournament Selection. In this process, a small subset of chromosomes (two or three) from the population is randomly picked and the chromosome with the best ﬁtness value in this subset becomes a parent. The tournament repeats for every needed parent. Pairing from Top to Bottom. This method starts at the top of the list and pairs the chromosomes two at a time until the needed parent pairs are selected. More speciﬁcally, the algorithm pairs odd particles with even ones. Random Pairing. The selected chromosome can take from the whole population or from n best individuals in a set of N individuals. This operator generates an integer random number between one and N or n. The chromosome refers to that number becomes parent. Crossover. In crossover process, two parents will be selected to create offspring. In this paper, the CGA uses heuristic crossover [20]. Suppose that a parental pair is denoted as Da and Mo. Heuristic crossover uses values of the ﬁtness function in determining the direction of the search. Consider that Fit(Da) and Fit(Mo) are the ﬁtness values of chromosome Da and chromosomes Mo respectively. In this operator, only one offspring is generated. C ði Þ ¼
bðiÞ:ðDaðiÞ MoðiÞÞ þ DaðiÞ; if FitðDaÞ [ FitðMoÞ bðiÞ:ðMoðiÞ DaðiÞÞ þ MoðiÞ; if FitðMoÞ [ FitðDaÞ
ð13Þ
where Da(i), Mo(i) and C(i) are the ith gene of chromosome Da, chromosome Mo and offspring C respectively; b(i) is the coefﬁcient regenerated for each gene in chromosome. Mutation. Amputation operation is performed to a state variable value to increase the search space and avoid the problem of premature convergence [14]. In this operator, the mutation position is randomly selected and its value is replaced with a random value within the limit of the state variable.
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The Population Regeneration. After a number of iterations, the individual set of the CGA algorithm tends to become homogeneous [21–23]. To overcome this situation, the new population will be created when 30 consecutive maximum error values between results of kth and (k1)th iteration are smaller than the allowed values. The newly created population will retain several chromosomes from the old generation and the others are regenerated.
3 Results In this paper, the CGA is applied to estimate the state variables of two systems, which are the 5bus system [24] and the IEEE 14bus power system [25], as shown in Fig. 1. The measurement data are real and reactive power injected into all buses and the magnitude voltage at the slack bus as shown in Table 1.
(a)
(b)
Fig. 1. a) Diagram of 5 bus system; (b) Diagram of 14 bus system
The CGA parameters are set as: • • • • •
Maximum iteration: 15000 Population size: 40 Mutation rate: 0.05 Selection rate: 0.3 Reproduction rate: 0.2
The GAs is a randomized search algorithm. To eliminate the effect of random factors, and ensure the results of the algorithm, the program runs 70 times for each system. In each case study, the simulation accounts for ten times per one selection method and gets the best results for comparison. The results presented in Table 2 and Table 3 correspond to seven different cases of the selection method, as described in Sect. 2.
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Table 1. Supposed measured value for simulations (in per unit). Bus 5 buses P 1 0.8305 2 0.2 3 0.1 4 −0.5 5 −0.6 6 7 8 9 10 11 12 13 14
network Q V 0.0727 1.06 0.3181 0.0918 −0.3 −0.4
IEEE 14 bus P Q V 2.336 −0.0578 1.06 0.183 0.5951 −0.942 0.2046 −0.478 0.039 −0.076 −0.016 −0.112 −0.075 0 0 0 0 −0.295 −0.166 −0.09 −0.058 −0.035 −0.018 −0.061 −0.016 −0.138 −0.058 −0.149 −0.05
Table 2. Maximum errors of bus voltage magnitude estimation (%). Network Selection method Roulette Boltzmann wheel selection selection
Rank weighting selection
Cost weighting selection
Tournament selection
5 buses 0.536 14 buses 0.509
0.238 0.859
0.356 4.947
0.014 5.583
2.534 6.693
Pairing from top to bottom 0.165 4.158
Random pairing
0.495 5.591
Table 3. Maximum errors of bus voltage angle estimation (%). Network Selection method Roulette Boltzmann selection wheel selection
Rank weighting selection
Cost weighting selection
Tournament selection
5 buses 0.988 14 buses 1.843
2.149 8.199
0.657 14.300
0.027 13.177
20.529 39.606
Pairing Random from top pairing to bottom 0.255 8.475 30.185 42.343
In Table 2 and Table 3, the estimated results of voltage magnitude and voltage phase angle are shown, respectively. These tables show the differences of the estimated error percentage in seven categories of selection methods.
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For the 5bus test system, the bus magnitude simulated results are all less than 0.6%, except for the case of Boltzmann selection, which has an error of about 2.5%. For voltage angle estimation, there are four selection processes which are roulette wheel, cost weighting, tournament, and paring from top to bottom, perform well with the errors less than 1%. The other functions (Boltzmann, rank weighting, and random pairing) have higher errors. The highest one is 20.529% when using Boltzmann selection. These tables illustrate that, in case of the 5bus system, the CGA with tournament selection results in a better estimate than others. Moreover, within the error range, we can use CGA with roulette wheel selection, cost weighting selection, or pairing from top to bottom. The estimation result of the voltage magnitude of the IEEE 14bus system has less than 0.9% error when using the roulette wheel selection and rank weighting selection. The remaining operators give the errors of greater than 4%. For the voltage angle estimation results, only the CGA with roulette wheel selection performs well with a result of about 1.84%. The CGA with other selection methods has higher errors with values up to 42.3% when using random pairing.
4 Conclusions In this paper, the effect of selection methods on the power system state estimation by the CGA is presented. The selection operators have a signiﬁcant influence on the performance of the CGA. For the problem with a 5bus grid, the estimated results of most case studies have acceptable errors. However, with the IEEE 14 bus system, it is noticeable that CGA with roulette wheel selection gives the best results. The comparison of the simulation results in both case studies shows that the roulette wheel selection in CGA is the most suitable for power system estimation.
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8. Rahman, Md.A., Venayagamoorthy, G.K.: A hybrid method for power system state estimation using cellular computational network. Eng. Appl. Artif. Intell. 64, 140–151 (2017) 9. Tungadio, D.H., Jordaan, J.A., Siti, M.W.: Power system state estimation solution using modiﬁed models of PSO algorithm: comparative study. Measurement 92, 508–523 (2016) 10. Kumar, A., Das, B., Sharma, J.: Genetic algorithmbased meter placement for static estimation of harmonic sources. IEEE Trans. Power Deliv. 20, 1088–1096 (2005) 11. Aminifar, F., Lucas, C., Khodaei, A., FotuhiFiruzabad, M.: Optimal placement of phasor measurement units using immunity genetic algorithm. IEEE Trans. Power Deliv. 24, 1014– 1020 (2009) 12. Müller, H.H., Castro, C.A.: Genetic algorithmbased phasor measurement unit placement method considering observability and security criteria. IET Gener. Transm. Distrib. 10, 270– 280 (2016) 13. HossamEldin, A.A., Abdallah, E.N., ElNozahy, M.S.: A modiﬁed genetic based technique for solving the power system state estimation. Int. J. Electr. Comput. Eng. 3, 1438–1447 (2009) 14. Haupt, R.L., Haupt, S.E.: Pratical Genetic Algorithm, 2nd edn. Wiley, Hoboken (2004) 15. Pencheva, T., Atanassov, K., Shannon, A.: Modelling of a roulette wheel selection operator in genetic algorithms using generalized nets. Int. J. Bioautom. 13, 257–264 (2009) 16. Saini, N.: Review of selection methods in genetic algorithms. Int. J. Eng. Comput. Sci. 6 (12), 22261–22263 (2017) 17. Jebari, Khalid, Madiaﬁ, Mohammed: Selection methods for genetic algorithms. Int. J. Emerg. Sci. 3(4), 333–344 (2013) 18. Jun, L.I., et al.: A realcoded genetic algorithm applied to optimum design of low solidity vaned diffuser for diffuser pump. J. Thermal Sci. 10, 301–308 (2001) 19. Abur, A., Expósito, A.G.: Power System State Estimation  Theory and Implementation. Marcel Dekker, New York (2004) 20. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd revised and extended edn. Springer, Heidelberg (1996) 21. Chelouah, R., Siarry, P.: A continuous genetic algorithm designed for the global optimization of multimodal functions. J. Heuristic 6, 191–213 (2000) 22. Bessaou, M., Siarry, P.: A genetic algorithm with realvalue coding to optimize multimodal continuous functions. Struct. Multidiscip. Optim. 23, 63–74 (2001) 23. Arqub, O.A., AboHammour, Z.: Numerical solution of systems of secondorder boundary value problems using continuous genetic algorithm. Inf. Sci. 279, 396–415 (2014) 24. Saadat, H.: Power System Analysis. McGrawHill, New York (1999) 25. http://labs.ece.uw.edu/pstca/pf14/ieee14cdf.txt. Accessed 28 Feb 2020
CollisionFree Path Following of an Autonomous Vehicle Using NMPC NgoQuocHuy Tran1(&), Ionela Prodan2, and NguyenDuyMinh Phan1 1
The University of DanangUniversity of Technology and Education, 48 Cao Thang, Danang 550000, Vietnam [email protected] 2 Univ. Grenoble Alpes, Grenoble INP, LCIS, F26000 Valence, France [email protected]
Abstract. This paper deals with the obstacle avoidance problem for an autonomous vehicle using NMPC (Nonlinear Model Predictive Control) while following an a priori given path. The repulsive potential of the operating space is constructed from the bounded convex regions describing the static obstacles for collisionfree navigation. The contribution lies in using the Hausdorff distances among the obstacles and the agent in order to activate/inactivate the repulsive potential ﬁeld. This potential ﬁeld component is introduced in a NMPC framework to penalize collision. This proposal shows good results in simulations and comparisons with our previous work. Keywords: Obstacle avoidance
Path following NMPC
1 Introduction Over the past two decades, the autonomy technologies for vehicles have had great advances and there have been so many types of autonomous vehicles (AVs) such as Unmanned ground vehicles (UGVs), Unmanned aerial vehicles (UAVs), Unmanned surface vehicles (USVs) and so on. Much of the literature has, in recent years, concentrated on the path following problem using various control techniques, for example, backstepping approach for 6DOF quadrotor UAV [1]; sliding mode control was applied for a UGV [2]; or barrier Lyapunov function to handle state constraints for USV [3]. These methods are either difﬁcult to deal with the constraints of the dynamical systems or not trivial to deal with nonconvex constraints coming from the collision avoidance conditions. As also stated in the literature, we believe that, NMPC is an appropriate method which can handle constraints and references following. Usually, the nonconvex constraints which arise from collision avoidance conditions are handled as explicit constraints, within a MixedInteger Programming
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 232–241, 2021. https://doi.org/10.1007/9783030647193_27
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(MIP) framework [4]; or implicit constraints, within an Artiﬁcial Potential Field (APF) framework [5]. However, each approach has its own shortcomings: MIP is NPhard1 [6] and APF does not handle well the local minima issue [7]. This paper extends our previous work on NMPC using APFbased implicit constraints which was able to handle collision avoidance constraints [8]. The contributions reside in using Hausdorff distances instead of Chebyshev circles, which are considered in the inverse activation function in order to activate the repulsive potential ﬁelds of the bounded convex regions representing static obstacles. As a result, the exact distances between agents and obstacles can be obtained to guarantee a safe distance for obstacle avoidance. Moreover, this problem is also integrated in path following framework. For convenience in comparison with the previous work, the AV in this paper is speciﬁed as a USV. This paper is organized as follows: Sect. 2 presents the repulsive potential working space, inverse activation function and Hausdorff distance. Section 3 introduces an associated function which is integrated into a NMPC framework to activate the obstacle constraints. Section 4 shows the simulation results and comparison with another approach. Section 5 presents the conclusions and future work. The following notation will be used throughout the paper. For a vector x 2 Rn and pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ a positive (semi)deﬁnite matrix P 2 Rnn , jjxjjP denotes the weighted norm xT Px. We denote by dH ðO‘ ; PÞ, the Hausdorff distance among the ‘th obstacle and the agent. A polytope is a bounded polyhedron and has a dual representation in terms of intersection of halfspaces or convex hull of extreme points: O ¼ fx 2 Rn : Sx Kg ¼ fx 2 Rn : x ¼
P
ui vi ;
P
ui ¼ 1; ui 0g.
2 Preliminaries 2.1
Bounded Convex Regions
Let us ﬁrst introduce the bounded convex regions representing the ﬁxed obstacles and the autonomous agent. Indeed, from the geometrical point of view of the obstacles or agent, we can take them into account as the polytopic convex regions deﬁned by [9]. Therefore, the workspace of an autonomous agent, W Rn , which comprises a union of forbidden polytopic convex regions, characterizing multiple ﬁxed obstacles as follows: W O;
ð1Þ
where O is deﬁned as follows: O¼
N obs [
O‘ ;
ð2Þ
‘¼1
1
I.e., the computational complexity increases exponentially with the number of binary variables used in the problem formulation.
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where O‘ is considered as the ‘th bounded polyhedral O‘ ¼ fx 2 Rn j a‘k x b‘k ; k ¼ 1; . . .; n‘h g, with a‘k 2 R1n , b‘k 2 R, n‘h is the number of halfspaces describing O‘ , ‘ ¼ 1; . . .; Nobs , and Nobs is the number of forbidden polytopic regions accounting for ﬁxed obstacles. Similar to the approach of the constructing polytopes for the ﬁxed obstacles, a polytopic region standing for the safety region for an autonomous agent can be parameterized in relative coordinates: Pðxa Þ ¼ fx 2 Rn : am ðx xa Þ bm ; m ¼ 1; :::; nah g;
ð3Þ
where xa is the center of the bounded region deﬁned by Pðxa Þ; am 2 R1n , bm 2 R, nah is the number of halfspaces describing Pðxa Þ. 2.2
Repulsive Potential Working Space
For dealing with the obstacle avoidance, the repulsive potential working space will be constructed from the set of bounded polyhedron of the static obstacles deﬁned in (2). Let us consider ﬁrst the sum function [10] deﬁned by: ‘
c‘ ðxÞ ¼
nh X
a‘k x b‘k þ a‘k x b‘k :
ð4Þ
k¼1
By analyzing Eq. (4), it is obvious that this piecewise afﬁne function is zero whenever x 2 O‘ and strictly positive whenever x 62 O‘ . In other words, the value of the piecewise linear function will thus grow piecewise linearly with the distance from the polytopic region. Using (4), the union of repulsive potentials of the ﬁxed obstacles are deﬁned as: F¼
N obs [ ‘¼1
F‘ ðc‘ ðxÞÞ;
ð5Þ
with F‘ ðc‘ ðxÞÞ deﬁned as: F‘ ðc‘ ðxÞÞ ¼
c1‘ ðc2‘ þ c‘ ðxÞÞ2
;
ð6Þ
where c1‘ and c2‘ are positive parameters representing the strength and effect ranges of repulsive potential. Figure 1b illustrates repulsive potential working space based on the bounded convex regions Fig. 1a representing two ﬁxed obstacles.
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(a) Set of the bounded convex regions (2). (b) Repulsive potential fields using sum function (4).
Fig. 1. The operating space.
2.3
Hausdorff Distance
In order to activate the repulsive potential for obstacle avoidance, Hausdorff distance will be used and its main advantage is the fact that it gives the exact distance between the static obstacles and the agent in the bounded polyhedron framework.
Fig. 2. Hausdorff distance between the two polytopes (O1 ; O2 ) and agent (P).
Assuming that o‘ and p are points of sets O‘ and P respectively, and dH ðo‘ ; pÞ is any metric between these points; for simplicity, dH ðo‘ ; pÞ is considered as the Euclidian distance between o and p. As a result, the Hausdorff distance can be understood as the maximum distance of a set to the nearest point in the other [11] and described by: dH ðO‘ ; PÞ ¼ max min dH ðo‘ ; pÞ o2O‘ p2P
ð7Þ
Hence, from (7), the Hausdorff distance between the two polytopes (O1 ; O2 ) and agent (P) can be computed and illustrated as can be seen in Fig. 2.
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Inverse Activation Function
Activation function is used to determine the output in Artiﬁcial Neural Networks [12] or Deep Neural Networks [13], and a useful tool in the formulation of the forthcoming path following which activates the repulsive potential ﬁelds representing static obstacles based on the Hausdorff distances. We propose the socalled “inverse activation function”: I act ¼
1 ; 1 þ eaðxxo Þ
ð8Þ
where a [ 0, is considered as an activated coefﬁcient. This function has the property shown in the Fig. 3, i.e., if x [ x0 then I act ¼ 0 (inactivation); or if x\x0 then I act ¼ 1 (activation). Figure 3 illustrates the inverse activation function for xo ¼ 0 and varying a.
Fig. 3. Inverse activation function with difference a.
3 Safe Navigation and Path Following in NMPC Framework All the elements presented above will be implemented in the NMPC framework for following a given path in the presence of ﬁxed obstacles for autonomous surface vehicle. 3.1
Autonomous Surface Vessel Dynamic
Let me ﬁrst describe a general form of continuoustime autonomous surface vessel model [14]: x_ ¼ f ðxðtÞ; u(t)) ¼
g_ ¼ RðwÞv : M v_ ¼ CðvÞv Dv þ u;
ð9Þ
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>
where the state vector, x ¼ ½ g m > 2 R6 includes the vector g ¼ ½ p w 2 R3 with p ¼ ½ x y > , the system position and w, the heading angle in the NorthEast Down frame. It also includes vector m ¼ ½ u v r > 2 R3 describing the surge, sway and yaw rates. The input vector, u 2 R3 , with u ¼ ½ T u T v T r > contains the surge, sway forces and yaw moment produced by the vessel actuators (propellers, thrusters, and rudders). Also, in (9), RðwÞ, M, CðmÞ and D 2 R33 are the rotation, mass, Coriolis and damping matrices, respectively. 3.2
Safe Navigation Through Repulsive Potential Activation
Safe navigation is guaranteed by activating the repulsive potential ﬁelds, the combination between repulsive potential and inverse activation function is necessary. This is trivial since both of them are scalar functions. Therefore, an associated function will be proposed through Hausdorff distance (7) which was deﬁned between agent and static obstacle. To construct an associated function, we ﬁrst redeﬁne the inverse activation function as presented in (8) based on Hausdorff distance and associated safe distance w.r.t. ‘th ﬁxed obstacle. It can be detailed as follows: I ‘act ðdH ðO‘ ; PÞÞ ¼
1 ; a [ 0; 1 þ eaðdH ðO‘ ;PÞDs Þ
ð10Þ
where dH ðO‘ ; PÞ and Ds ð [ 0Þ are Hausdorff and safe distance between agent and ‘th ﬁxed obstacle respectively. As a consequence, an associated function, socalled repulsive potential activation which is used for collisionfree is described as follows: X¼
Nobs X ‘¼1
I ‘act ðdH ðO‘ ; PÞÞF‘ ðc‘ ðxÞÞ;
ð11Þ
with F‘ ðc‘ ðxÞÞ was given in (6). 3.3
NMPC Implementation
Collisionfree path following of autonomous vessel (9) is taken into account NMPC framework by solving a ﬁnite horizon openloop OCP (optimal control problem) at time t, using the measured state xðtÞ over the prediction horizon Tpred : tþ ZTpred
½LðpðsÞ; uðsÞÞds þ Eðpðt þ Tpred ÞÞ;
min
uð:Þ;pð:Þ t
ð12Þ
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subject to: :
:
x ¼ f ðx; uÞ; xðtÞ ¼ xðtÞ; uðsÞ 2 U; uðsÞ 2 DU; xðsÞ 2 X; 8s 2 ½t; t þ Tp :
ð13Þ
The stage cost LðÞ has the following expression: 2 _ 2 þ XðsÞ; LðÞ ¼ pðsÞ pref ðsÞQ þ kuðsÞk2R þ uðsÞ DR
ð14Þ
and the terminal cost is deﬁned as: 2 Eðpi ðt þ Tpred ÞÞ ¼ pðt þ Tpred Þ pref ðt þ Tpred ÞP :
ð15Þ
In (13) f ð; Þ is presented in (9), xðsÞ; uðsÞ are the predicted states and inputs while uðÞ stands for the predicted input trajectory along the prediction horizon Tpred . :
In the cost per stage (14), uðsÞ denotes the predicted input variations, pref ðÞ is the reference path, XðsÞ is the associated function for collisionfree path given in (11). Q, R, and P are (semi)positive deﬁnite weighting matrices of appropriate dimensions. It is worth noting that pðsÞ is prediction output vector with dimension is 1 0 0 0 0 0 pðsÞ ¼ xðsÞ, and pref ðÞ in (14), (15) is a desired path. 0 1 0 0 0 0
4 Simulation Results The speciﬁcations of Cybership II2 [15] are used in the simulations and comparisons with another approach3. – Weighting matrices4: 2
3 2 cos w sin w 0 25:8 RðwÞ ¼ 4 sin w cos w 0 5; M ¼ 4 0 0 0 1 0 2 3 0:72 0 0 D¼4 0 0:89 0:03 5 0 0:03 1:9
0 33:8 1:0115
3 0 1:0115 5; 2:76
– The surge, sway forces and yaw moment are T u 2 ½2; 2 [N], T v 2 ½2; 2 [N] and T r 2 ½1:5; 1:5 [N.m], respectively.
2
3 4
This vessel was identiﬁed and developed in the Marine Cybernetics Laboratory at Norwegian University of Science and Technology. I.e., our previous work which uses Chebyshev center instead of Hausdorff distance. For simplicity, the Coriolis matrix is neglected.
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The desired path of the vessel is a eightshaped curve, pref ¼ ½ xref yref > , is described: xref ¼ 18 sinð0:01tÞ; yref ¼ 12 sinð0:02tÞ: The number of static obstacles, Nobs ¼ 2. The parameters of repulsive potential activation function (11) are given by a ¼ 2, c11 ¼ c12 ¼ 300; c21 ¼ c22 ¼ 0:5; Ds ¼ 0:65 [m]5. Other parameters of the NMPC optimization problem in (12): the prediction horizon, Tpred 6 is 8 and the number of control discretization, Nc is 4; the number of 10 0 1 0 , R¼ , simulation Nsim ¼ 320; the weighting matrices are Q ¼ 0 10 0 1 0:01 0 10 0 DR ¼ ,P¼ . 0 0:01 0 10 The simulations are done by using interiorpoint solver IPOPT [16] in the Casadi framework [17]. In here, the proposed method (i.e., using Hausdorff distance to activate the repulsive potential ﬁelds) and the previous method [8] are simulated and compared related to tracking error and obstacle avoidance. It is worth noting that the Integral of Absolute magnitude of the Error (IAE) over the position is used: TZ pred ¼8
IAE ¼
p pref dt
t¼0
The IAE values and the percentage errors over the trajectory are provided in Table 1. The percentage errors represent the average errors over a length unit, which are deﬁned as Dl% ¼ IAE l 100% using the total length of the desired path l ¼ 131:3 [m]. Figure 4 illustrates the collisionfree path following of USV using NMPC in two approaches, the solid blue line7 is actual motion of the proposed method, and the solid red line is actual motion of the previous method. Both trajectories are tracking the desired path (plotted in dashed blue line. The shapes of ship (of the proposed method) are shown at 8 different time instances (i.e., the eight light blue convex regions). At each time instance, the two Haudorff distances (i.e., dH ðO1 ; PÞ and dH ðO2 ; PÞ) are depicted by dotted red and black line respectively. As a consequence, the proposed approach gives us a better result than the method using Chebyshev circle [8] with an actual length of the trajectory is shorter. This is also detailed in Table 1 via tracking errors (IAE) and percentage errors (Dl%).
5 6
7
Safe distance, Ds is equal to the maximum length of a vessel’s hull. The prediction horizon is chosen enough large to guarantee obstacle avoidance but not too large because of the computational burden of the solver. It’s worth noting that the ship is very close the ﬁxed obstacle 1 but does not collide due to the repulsive ﬁeld.
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Fig. 4. Actual motion of the Cybership II under comparison with previous approach [8]. Table 1. Comparison of tracking errors. Tracking error Previous method [8] Proposed method IAE 5.92 5.70 Dl% 4.51% 4.34%
5 Conclusions This paper introduced an approach to activate the repulsive potential ﬁelds through Hausdorff distance for obstacle avoidance problem. In this approach, the real distances between agent and obstacles were computed exactly and directly instead of using an approximate method as Chebyshev circle. This improvement does not only provide a better percentage error for the path following but also guarantees obstacle avoidance. Future work is concentrated on the multiautonomous vehicles and stability issues under uncertainties.
References 1. Wang, R., Liu, J.: Trajectory tracking control of a 6DOF quadrotor UAV with input saturation via backstepping. J. Franklin Inst. 355(7), 3288–3309 (2018) 2. Matraji, I., AlDurra, A., Haryono, A., AlWahedi, K., AbouKhousa, M.: Trajectory tracking control of skidsteered mobile robot based on adaptive second order sliding mode control. Control Eng. Pract. 72, 167–176 (2018)
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3. Li, L., Dong, K., Guo, G.: Trajectory tracking control of underactuated surface vessel with full state constraints. Asian J. Control (2020) 4. Stoican, F., Prodan, I., Grøtli, E.I., Nguyen, N.T.: Inspection trajectory planning for 3D structures under a mixedinteger framework. In: 2019 IEEE 15th International Conference on Control and Automation (ICCA). pp. 1349–1354. IEEE (2019) 5. Wang, H., Huang, Y., Khajepour, A., Zhang, Y., Rasekhipour, Y., Cao, D.: Crash mitigation in motion planning for autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 20(9), 3313– 3323 (2019) 6. Garey, M.R., Johnson, D.S.: Computers and Intractability, vol. 29. W.H Freeman, New York (2002) 7. Goerzen, C., Kong, Z., Mettler, B.: A survey of motion planning algorithms from the perspective of autonomous UAV guidance. J. Intell. Robot. Syst. 57(1–4), 65 (2010) 8. Tran, N., Prodan, I., Grøtli, E., Lefevre, L.: Potentialﬁeld constructions in an MPC framework: application for safe navigation in a variable coastal environment. IFACPapersOnLine 51(20), 307–312 (2018) 9. Boyd, S., Boyd, S.P., Vandenberghe, L.: Convex Optimization. Cambridge Univ. Press, Cambridge (2004) 10. Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, New York (2013) 11. Kraft, D.: Computing the Hausdorff distance of two sets from their signed distance functions. arXiv preprint arXiv:1812.06740 (2018) 12. Feng, J., Lu, S.: Performance analysis of various activation functions in artiﬁcial neural networks. J. Phys. Conf. Ser. vol. 1237, p. 022030. IOP Publishing (2019) 13. Srinivasan, P., Guastoni, L., Azizpour, H., Schlatter, P., Vinuesa, R.: Predictions of turbulent shear flows using deep neural networks. Phys. Rev. Fluids 4(5), 054603 (2019) 14. Fossen, T.I.: Marine control systems–guidance. navigation, and control of ships, rigs and underwater vehicles. Marine Cybernetics, Trondheim, Norway, Org. Number NO 985 195 005 MVA (2002). www.marinecybernetics.com, ISBN: 82 92356 00 2 15. Zheng, H., Negenborn, R.R., Lodewijks, G.: Trajectory tracking of autonomous vessels using model predictive control. IFAC Proceedings, vol. 47(3), 8812–8818 (2014) 16. Wachter, A., Biegler, L.T.: On the implementation of an interiorpoint ﬁlter line search algorithm for largescale nonlinear programming. Math. Program. 106(1), 25–57 (2006) 17. Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation (2018, in press)
Compare the Efﬁciency of the Active Filter and Active Rectiﬁer to Reduce Harmonics and Compensate the Reactive Power in Frequency Controlled Electric Drive Systems Le Van Tung1,2(&), Pham Thanh Long3, Ngo Van An3(&), and Bogdan Vasilev1 1
St. Petersburg Mining University, St. Petersburg 199106, Russian Federation 2 Quang Ninh University of Industry, 18 street, Quang Ninh City 208830, Viet Nam 3 Thai Nguyen University of Technology, 666, 3/2 street, Tich Luong Ward, Thai Nguyen City 251750, Viet Nam [email protected]
Abstract. Currently, frequency converters using diode rectiﬁers generate a lot of harmonics, which adversely affect the quality of the grid and the durability of electrical equipment. On the other hand, asynchronous motors that consume a large amount of reactive power of the source cause losses on the grid and reduce the power factor at the input of the converter. Therefore, researching the method of using active ﬁlters at the input of frequency converters with diode rectiﬁers will reduce current harmonics and compensate reactive power for the grid. The active ﬁlter will produce harmonics with a phase angle inversely to the phase angle of the harmonics generated by the load, which will suppress most highorder harmonics. The frequency converters use active rectiﬁers with a direct power control method so that the reactive power q = 0 ensures the standard sine current at the input, the power factor equals 1, there is the stability of a DC voltage and the power is exchanged in two directions between the motor and the grid. The paper compares the effectiveness of the two methods when considering the load as three asynchronous motors that work in different modes such as motor mode and generator mode. The research results are veriﬁed by Matlab & Simulink software. Keywords: Active ﬁlter Active rectiﬁer Direct power control Threephase inverter Torque control Uncontrolled rectiﬁer
1 Introduction Currently, frequency converters using diode rectiﬁers are commonly used in industry and this is the cause of most harmonics on the grid. Current and voltage harmonics have a great influence on the quality of electricity, the transmission process, the working status, and the life expectancy of electrical equipment [1]. The diode rectiﬁer © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 242–253, 2021. https://doi.org/10.1007/9783030647193_28
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will generate current harmonics at the 5, 7, 11, 13, 17, 19, and so on. On the other hand, semiconductor devices in converters with nonlinear load characteristics will cause grid current distortions [2]. In electric drive systems, asynchronous motors are the devices that consume a lot of reactive power in the grid, about 60–65%. This results in losses on the line and reduces the power factor at the converter input [3]. A number of solutions to reduce harmonic and reactive power compensation in the motor frequency control drive system have been studied and applied widely. For instance, the use of reactors, capacitors, and passive ﬁlters [3]. However, the method of using an active ﬁlter (AF) has many technical advantages (Figs. 1 and 2).
Fig. 1. The waveform of voltage and current at the input of the frequency converter.
Fig. 2. The waveform of the voltage and current at the output of the converter.
Active ﬁlters use power electronic circuits, controlled according to the source voltage and the measured load current, to produce harmonics with an amplitude equal to but opposite the phase angle of the harmonics generated by the load. As a result, harmonic components are removed, and based on the measurement of load current and grid voltage, the reactive power consumed by the load is calculated, from which the ﬁlter producing reactive power needs to be compensated [4]. There is currently research and manufacturing of converters that require high quality of the power factor and harmonic reduction. Frequency converters with active rectiﬁers are gradually replacing diode rectiﬁers. The direct power control (DPC) method for an active rectiﬁer will estimate the grid voltage and then control the active and reactive power. The advantage of this method is to compensate the reactive power, that is, to control the power q = 0 at the input. This ensures that the factor cosu = 1, reduces the grid current harmonics, controls the charging current for the capacitor, and causes energy to exchange in two different directions [5]. The working mode of the asynchronous motors also greatly affects the quality of supply at the frequency converter input. The paper uses the DTCSVM method to control the speed and torque of three motors with different working modes at different times. Direct torque control (DTC) is being widely used in industry with advantages such as very low torque response time, high reliability, and very good dynamic characteristics of electromagnetic torque [6, 7]. Through the working mode of the motors, the advantages and disadvantages of active ﬁlters and active rectiﬁers in the asynchronous motor frequency control system will be evaluated.
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2 Active Filter Design Method 2.1
Working Principle of Active Filter
The active ﬁlter consists of a capacitor combined with an active rectiﬁer (IGBT) and sensors measuring voltage and current. By measuring the load current and the grid voltage, the control system for the rectiﬁer will be designed to generate compensating currents with harmonics generated by the load to ensure that the grid current only has the basic wave component. The principle is described in Fig. 3.
Fig. 3. The working principle of active ﬁlter.
According to Akagi’s instantaneous power theory, in the electrical system, the instantaneous power p and instantaneous reactive power q of the load can be divided into two components: the component p, q corresponding to the basic wave component of the load current, and the oscillator component ~p, ~ p corresponding to the higher harmonic component. This theory is very flexible, allowing the selection of signals of the desired frequency, which is the basis for designing active ﬁlters [8].
Fig. 4. Active ﬁlter control structure by power method pq.
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Where HPF – high pass ﬁlter; HCC – hysteresis current controller; PWM – threephase active rectiﬁer which generates compensated current on the grid; PI – DC voltage regulator which compensates for power loss p0, FCIM – frequency converter and induction motor. Assuming the current at the input of the frequency converter is distorted by harmonics, this is the load current iL. AF will measure iL current and calculate the compensation current iC so that the current of the source iS = iL + iC is always standard sine. As such, iC will compensate for most harmonics generated by the load. The AF control method consists of two loops: the outer circuit which calculates the compensating current icref based on the load current iL (the compensated current icref is the value set for the inner loop or the current that the active rectiﬁer will generate) and the inner circuit which is responsible for generating a compensation current iC that follows the current to be compensated icref by the active rectiﬁer control. When AF is working, the DC voltage change across the capacitor at the output of the active rectiﬁer representing the active power loss p0. The auxiliary loop uses the PI regulator to maintain the DC voltage constant. The outer loop is responsible for measuring the current of the load and the source voltage is used to calculate the instantaneous power and the reactive power to be compensated, thereby calculating the currents to be compensated. 2.2
The Required Compensation Current Determination Method
The control structure of AF is shown in Fig. 4, with a threephase electrical system without neutral wires, the current component i0 does not exist, satisfying ia + ib + ic = 0. Load current measured iL ¼ ½ iLa iLb iLc T and source voltage us ¼ ½ ua ub uc T , converted to a ﬁxed coordinate system 0ab by Clarke transformation as follows [5, 8–10]:
ua ub ia ib
rﬃﬃﬃ" 2 1 ¼ 3 0
12 pﬃﬃ
#2 3 ua 12 pﬃﬃ 4 u 5 b 23 uc
ð1Þ
rﬃﬃﬃ" 2 1 ¼ 3 0
12 pﬃﬃ
#2 3 iLa 12 pﬃﬃ 4 i 5 Lb 23 iLc
ð2Þ
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From Eqs. (1) and (2) calculate the total apparent power of the load on the coordinate system 0ab: S ¼ u:i ¼ ua þ jub : ia j:ib S ¼ ua i a þ ub i b þ j ub i a ua i b
ð3Þ
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Thus, the instantaneous active power and reactive power are determined: p ¼ ua ia þ ub ib ¼ þ p p
ð4Þ
q ¼ ub i a ua i b ¼ þ q q
ð5Þ
Solve the system of equations to calculate ia, ib as follows: ia ¼
1 ðp:ua þ q:ub Þ u2a þ u2b
ð6Þ
ib ¼
1 ðp:ub q:ua Þ u2a þ u2b
ð7Þ
The power will supply the DC power component to the load, the motors, and the power loss p0 of the active rectiﬁer is seen at the ﬁlter circuit. The active ﬁlter is responsible for providing the AC power component ~p of p and the reactive power q including q and ~q. Because asynchronous motors consume the reactive power of the grid, the active ﬁlter needs to compensate this reactive power including q and ~ p. Also, the capacitor voltage is usually unstable. Hence so to ensure that the capacitor voltage is constant, the power supply needs to provide a power p0 to maintain the capacitor voltage constant. The AF ﬁlter will provide power: pAF ¼ p p ~p þ p0
ð8Þ
qAF ¼ q ~q
ð9Þ
From Eqs. (6), (7), (8) and (9), calculate the current to be compensated: ica ¼
1 ðp :ua þ qAF :ub Þ u2a þ u2b AF
ð10Þ
icb ¼
1 ðp :ub qAF :ua Þ u2a þ u2b AF
ð11Þ
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From Eqs. (6) to (11), the grid current will be retested according to the coordinate system 0ab when compensation has been made and the result is only the basic harmonic wave component (iS = iL + iC): isa ¼ ica þ ia ¼
1 ð p þ p0 Þ:ub u2a þ u2b
ð12Þ
isb ¼ icb þ ib ¼
1 ð p þ p0 Þ:ub u2a þ u2b
ð13Þ
Equations (10) and (11) calculate the necessary compensation current on the 0ab coordinate system with two functions of harmonic ﬁltering and reactive power compensation. From the compensation current in the system 0ab. We will calculate the current to compensate in the real coordinate system abc according to the formulas (1), (2), (10) and (11): 2
3 3 rﬃﬃﬃ2 1 0ﬃﬃ ica p 2 3 1 4 icb 5 ¼ icref ¼ 42 5 ica 2pﬃﬃ icb 3 icc 12 23
ð14Þ
Thus, the AF control algorithm according to the instantaneous power theory pq is to ﬁnd the required compensation current to be the value set for the inner loop. The inner loop will generate current following the preset compensation current.
3 Active Rectiﬁer Control Method The DPC control structure (Fig. 5) is based on the active and reactive power control circuits. In the DPC structure, the switching states of the converter are chosen by the switch table based on the difference between the estimated value and the control value of active power p and reactive power q. Therefore, the DPC method requires a quick and accurate estimation of power p and q, [5, 8, 11]. In Fig. 5, the load is the inverter circuit and the asynchronous motor, PI  DC voltage regulator, PWM  active rectiﬁer circuit (IGBT), L – reactor and cUL  phase shift angle between voltage vector and a axis. The PI regulator in the voltage loop works to keep the Udc voltage constant on capacitor C according to the set value. That is, control the flow of active power flowing to the capacitor C. Always ensure the output voltage of the rectiﬁer is equal to the Udcref value set when the load changes. Estimating power is an important part of the system. The purpose is to determine the calculated power and then compare it with the set power to give a reasonable
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Fig. 5. The DPC control structure.
control signal. The power p and q of the active rectiﬁer are calculated on the coordinate system 0ab as follows: p ¼ ua ia þ ub ib
ð15Þ
q ¼ ub i a ua i b
ð16Þ
Set value of reactive power qref = 0. The corresponding power factor cosu = 1. The active power pref, at the output of the PI regulator, is compared with the active power at the output of the unit estimated power. The deviation between comparisons is the input of the power controller [12]. Two power regulators are designed with a hysteresis controller. The output signal of the active power regulator is determined as follows: ð17Þ ð18Þ The same, output signal of the reactive power regulator is as follows: ð19Þ ð20Þ Where Hp, Hq  delay range. The voltage vector is divided into 6 or 12 sectors on the coordinate system 0ab. However, through the research works, the switching table uses 12 sectors of higher quality [5, 10]. Based on the change of instantaneous power, it is possible to determine the control voltage vector on the plane [10] (Table 1).
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Table 1. The working mode of the motors corresponds to the load torque and the present speed. Motor Power (Kw) Speed x (rpm) and time t(s) Load torque Mc (N.m) and time t(s) 1 110 x = [1200 0] Mc = [0 550 −550] t = [ 0 4] t = [ 0 1.3 4.5] 2 110 x = [1300 0] Mc = [0 590 −590)] t = [ 0 4] t = [ 0 1.3 4.5] 3 150 x = [1400 0] Mc = [0 800 −800] t = [ 0 3] t = [ 0 1.5 4]
4 Results and Analysis Using Matlab & Simulink software to build functional blocks and simulate electric drive systems DTCSVM (Fig. 6).
Fig. 6. DTCSVM control diagram with an active rectiﬁer.
Where: 1 – threephase source; 2, 6, 12, 15 – current and voltage sensors; 3 – inductance; 4 – active rectiﬁer (IGBT); 5, 11, 14 – voltage inverting circuit (IGBT); 7, 13, 16 – asynchronous motor; 8 – active rectiﬁer control unit by DPC method; 9 – direct torque control (DTC); 10 – DC voltage sensor; Mc1, Mc2, Mc3 – torque load. The simulation results when using reactors at the input of the frequency converter with a diode rectiﬁer are as follows (Figs. 7 and 8):
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Fig. 7. Threephase current of the power supply (t < 1.3 s).
Fig. 8. Total harmonic distortion of the power supply current (THD = 17.7%).
The simulation results when using an active ﬁlter at the input of the frequency converter with a diode rectiﬁer are as follows:
Fig. 9. Threephase current of the supply source.
Fig. 10. The type of current to be compensated by the active ﬁlter.
Figure 9 shows that without an AF ﬁlter, the load current is equal to the supply current, iS = iL. When using an AF ﬁlter, the harmonic current generated by the load will be suppressed by the current of the AF ﬁlter (Fig. 10) and the iS current shape as shown in Fig. 9 and Fig. 11. Figure 12 shows that when the motors work in different modes, there are harmonics orders 3, 5, 7, 9 but there is a very small amplitude and the total harmonic distortion of
Fig. 11. The current iS of the source at the input of the frequency converter.
Fig. 12. Total harmonic distortion of the source current (THD = 4.25%)
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the grid current with THD is 4.25%. Compared with the IEEE STD 519 standard, THD value meets the above standard (THD < 5%).
Fig. 13. Active power and instantaneous reactive power of the source.
Fig. 14. Characteristics of the converter input power factor (cosu).
Figure 13 shows that the electric drive system consumes the reactive power of the grid to be compensated by the active ﬁlter (AF), which shows the instantaneous reactive power of the grid q 0 and the power factor cosu 1, as shown in Fig. 14. Simulation results when the frequency converter uses direct power control method for active rectiﬁer:
Fig. 15. The phase current of the source is at the converter input.
Fig. 16. Total harmonic distortion of the source current (THD = 0,46%).
Figure 15 shows that at the time t 1.3s the motors work stably with load torque. The phase currents at the input of the converter are of standard sine form and the amplitude is smaller than the grid current amplitude in the active ﬁltering method (Fig. 11). The results shown in Fig. 16 show that when the motors work in different modes, there are harmonics at the input source with very small amplitude (Imax 0.2A) and the total harmonic distortion of source THD is 0.46%. Compared with the IEEE STD 519 standard, the THD coefﬁcient satisﬁes the standard (THD < 5%).
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Fig. 17. Active power and instantaneous reactive power of the source.
Fig. 18. Characteristics of the converter input power factor (cosu).
Figure 17 Shows that the reactive power of the grid consumed by the load will be compensated by the active rectiﬁer, which shows the reactive power of the source q = 0. Figure 18 shows the increased power factor at the input of the converter (cosu = ± 1). Table 2. The comparison result of the total harmonic distortion of grid current. Filter method THDI (%) Not ﬁltered 131.04 Reactor 17.7 Active ﬁlter 4.25 Active rectiﬁer 0.46
Table 2 shows that using an active rectiﬁer method will eliminate highorder harmonics and have advantages over the remaining methods.
Fig. 19. Torque characteristics of motors IM1, IM2, IM3 at different times using the DTCSVM method.
Figure 19 shows that the electromagnetic torque characteristics of motors (IM1, IM2, IM3) are of good quality, always following load torque. At times of changing load torque, the motors do not have a torque overshoot and are stable.
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5 Conclusion Asynchronous motor frequency control electric drive system works in different modes which greatly affect the quality of the grid. Theoretical analysis and the results obtained from the simulation show that active ﬁltering and active rectiﬁer methods will reduce the distortion of the grid current and compensate for the reactive power. The frequency converter using an active rectiﬁer has many advantages over the active ﬁlter, which not only reduces harmonic, compensates reactive power, higher power factor but also controls the charging current for the capacitor. Energy exchange in two directions between the grid and the motors should save energy for the system. The DTCSVM control method in the inverter circuit with the active rectiﬁer is an effective solution in a practical asynchronous motor frequency control system. Acknowledgments. The authors would like to express their thanks to the Thai Nguyen University of Technology for all support and encouragement.
References 1. Santosh, A., Tejulal, A.: Effects of harmonics on electrical equipment and their compensation by using shunt active power ﬁlter. Int. Refer. J. Eng. Sci. 3(11), 126–131 (2014) 2. Khliﬁ, K., Haddouk, A., Hlaili, M., Mechergui, H.: Harmonic pollution caused by nonlinear load. Analysis and identiﬁcation. Int. J. Energy Environ. Eng. 12(7), 510–517 (2018) 3. Zakis, J., Rankis, I., Zhiravetska, A.: Investigation of an active system of reactive power compensation for induction motors. Electron. Electr. Eng. 6(70), 10–15 (2006) 4. Narongrit, T., Areerak, K.L., Areerak, K.N.: The comparison study of current control techniques for active power ﬁlters. Int. J. Electr. Comput. Eng. 5(12), 1–6 (2011) 5. Malinowski, M.: Sensorless control strategies for threephase PWM rectiﬁers. pp. 1–128. Ph. D, Thesis, Warsaw (2011) 6. Kozaruk, A.E.: Energyefﬁcient electromechanical complexes of mining and transport vehicles. Zapiski Mining Inst. J. Mining Inst. 218, 261–269 (2018) 7. Kozyaruk, A.E., Albert, M.K.: Improving the energy efﬁciency of the electromechanical transmission of an openpit dump truck. J. Mining Inst. 239, 576–582 (2019) 8. Akagi, H.: Control strategy and site selection of a shunt active ﬁlter for damping of harmonic propagation in power distribution systems. IEEE Trans. Power Delivery 12(1), 25–32 (1997) 9. Pawar, D.S., Vadirajacharya, K.: Design of shunt active ﬁlter to improve power quality using pq theory. Int. J. Eng. Res. Technol. (IJERT) 5(3), 311–316 (2016) 10. Czarnecki, L.S.: On some misinterpretations of the instantaneous reactive power pq theory. IEEE Trans. Power Electron. 19(3), 828–836 (2004) 11. Gui, Y., Kim, C.: Improved direct power control for gridconnected voltage source converters. IEEE Trans. Ind. Electron. 65(10), 804–8051 (2018) 12. Muangruka, N., Nungam, S.: Direct power control of threephase voltage source converters using feedback linearization technique. Procedia Comput. Sci. 86(1), 365–368 (2016)
Comparing the Application of Gas Sensor Fabrication of Nanomaterials ZnO Fabricated by Hydrothermal and Chemical Vapor Deposition Method Hoang Van Han1(&), Dao Huy Du2(&), and Do Anh Tuan1(&) 1
Hung Yen University of Technology and Education, Hải Dương, Vietnam [email protected], [email protected] 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]
Abstract. In this report, we introduce a method of ZnO nanomaterials fabrication. Particularly, we study the structure of the material, compare the structure of the fabricated materials. The size of the created material is about 30–40 nm in diameter, the length is from 1 to 10 nm. The material responds well to NO2, the resistance of the material changes 25 times with 10 ppm of NO2 at 205 °C. Gassensing characterizations revealed that the ZnO sensors exhibited a relatively high response to subppm NO2 with excellent stability of switching from NO2 to air without signiﬁcant response reduction. Keywords: NO2 Nanosensors ZnO Zinc oxide nanowire High aspect ratio
1 Introduction ZnO is a potential sensing material because of its outstanding properties; it is ntype semiconductor that has a direct and wide band gap of 3.37 eV, interenergy large exciton at room temperature (*60 meV) [1, 2]. ZnO has high thermal and chemical stability, high electronic mobility, ease of synthesis, low cost, high sensitivity to target gases, and large surfacetovolume ratio. It has been demonstrated to have enormous applications in electronic [3], optoelectronic [4], electrochemical, and electromechanical devices [5, 6], ﬁeld emission [7, 8], high performance nanosensors [9, 10], solar cells [11, 12], piezoelectric nanogenerators, and nanopiezotronics. The morphology, crystal quality and surface of the ZnO nanostructures can strongly affect the sensing properties. Therefore, it is expected that ZnO with onedimensional nanostructures can be used to performance gas sensors. Onedimensional (1D) ZnO nanostructures have been synthesized by a wide range of techniques, such as wet chemical methods [12, 13], physical vapor deposition [14–16] metal–organic chemical vapor deposition (MOCVD) [17, 18], molecular beam epitaxy (MBE), pulsed laser deposition, sputtering, flux methods, eletrospinning and even topdown approaches by etching. In the last years, authors have studied vapour phase growth processes for different semiconducting oxide nanostructures [19–23]. Among them, they found that ZnO onedimensional nanostructures have a special feature, i.e. they can be obtained with a very © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 254–261, 2021. https://doi.org/10.1007/9783030647193_29
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large yield even in a laboratory scale reactor. In this study, the application of gas sensor fabrication of nanomaterials ZnO fabricated by hydrothermal and chemical vapor deposition method is compared. Porous ZnO possesses a large speciﬁc surface area for gas adsorption.
2 Experimental 2.1
Hydrothermal Method
In a typical synthesis, we used 1,36 g zinc chloride powder (ZnCl2) and 1,36 g P123 were dissolved in 100 ml distilled water. The solution was stirred and adjusted pH by dropwise addition of ammonium (NH4OH). The ﬁnal solution was then in a Teflon lined autoclave and hydrothermal treated at 150 °C for 20 h. Filtering solid precipitates are collected through centrifugation and washed with distilled water and ethanol. After drying, the resulting powder is oxidized at 500 °C for 8 h to remove surfactant polymers. 2.2
Chemical Vapor Deposition
In a typical synthesis, zinc oxide nanowires are synthesized through thermal evaporation and vapor transport of pure ZnO powder mixture (Merck, 99.99%) and graphite powder (Merck) in proportion. The weight ratio is 1: 1. The mixed material is put into a porcelain boat (size 1–8 cm) and put into the center of the horizontal quartz tube furnace (with a length of 100 cm and a road clear glass 4 cm). The furnace is heated at a rate of 10 °C/minute and the tube reaches a temperature of 1000 °C. One end of the pipe is connected to the flow control and air supply system. The gas mixture was introduced as high purity nitrogen (99.99%) and air at a volume ratio of 1.7: 1. with a constant flow rate of 2880 sccm into the furnace. The flow rate is modulated with digital block flow control sys (Aalborg, USA). The reactions begin within 2–3 min and continue for approximately 13–15 min, depending on the amount of initial material. A highlight here is the use of atmosphere as the source of oxygen for the reaction. The synthetic product observed was white in color and the diameter and length were very uniform. 2.3
Identiﬁcation of Structural Morphology
The morphology, size distribution, crystallinity, and composition of synthesized products were characterized using electron microscope with ﬁeld emission scanning (FE SEM 4800, Hitachi, Japan) operating at 10 kV. Transmission electron microscopy (TEM), selected area electron diffraction (SEAD), and highresolution transmission electron microscopy (HRTEM) examinations were conducted using a JEOL JEM3010 system at an accelerating voltage of 300 kV. The Xray diffraction (XRD) patterns were obtained using a Siemens diffractometer with CuK radiation.
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Investigation of Gas Sensitivity Properties
To prepare for the investigation, the sensors with fabricated materials are used. First Pt microelectrodes were made using standard photolithography with a ﬁnger width of 10 µm and a distance size of 20 µm. After that, the dispersion containing ZnO NWs was dropped on Pt interdigitated microelectrodes. Then, after drying and removing ethanol, the manufactured gas sensors are placed at the sensor furnace and then heat treated at 600 °C for 2 h to remove any remaining organic contaminants. Furthermore, to recrystallize ZnO NWs to enhance the physical adhesion between the ZnO NWs layer and the Pt substrate. Sensor characteristics are characterized at operating temperatures about 150–350 °C. Before sensing measurements, all microelectrodes are digitally preconditioned at operating temperatures for 10 h. Different concentrations of NO2 are obtained through dilution of premixed concentrations of NO2 in air. The sensor response (S) is deﬁned as the resistance of the device exposed to oxidizing gas compared to the resistance in the air.
3 Results and Discussion 3.1
Morphology and Structure of ZnO
To examine the structure of the materials, we use Xray diffraction spectrum, ultraviolet absorption spectroscopy. Materials fabricated by the hydrothermal method Figure 2a shows that the powder XRD pattern of the synthesized material exhibits typical diffraction peaks at 31.801, 34.601, 36.201,47.501, 62.901, and 67.901 which belong to the (100), (002), (101), (102), (103), and (112) reflections of the hexagonal ZnO, respectively (JCPDS, No. 361451). The average crystal size calculated by using the Scherrer formula was found to be about 27 nm. Figure 2b, c indicate that the optical properties of the synthesized materials characterized by using UV adsorption and the UV adsorption spectrum exhibit strong adsorption at a wavelength of about 400 nm, which corresponds to the band ZnO adsorption (Fig. 2b). The plots be derived from the UV adsorption data are shown in the inset of Fig. 2c. The intercept of the tangent to the plot gives a good approximation of the band gap energy of about 3.2 eV. Surface morphology of fabricated ZnO nano samples with pH values of 8, 9, 10 and 11, respectively, is shown by scanning electron microscopy (SEM) as shown in Fig. 3. From the results of this SEM image it is shown that for each ZnO sample at a pH value, the ZnO particle size is relatively uniform and the shape is quite similar. However, when comparing the samples with different pH values, the morphology of ZnO materials created is signiﬁcantly different. With a low pH concentration (pH = 8), granular material and the size about 100 nm, the particles tend to clump together to form clumps of particles (Fig. 3a). As the pH value increased, the particle size of the sample also increased but mainly developed into ricelike form. The ZnO nano tends to grow in length. With pH = 9, the length of the particles about 200 nm (Fig. 3.b), at pH = 10, the length of the particles can be up to more than 500 nm (Fig. 3.c). The size
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Initial material Colected product
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and shape of the particles are homogeneous when pH = 10. When pH = 11, we see that the sample size increases sharply (Fig. 3d) and has a hexagonal cylinder shape. Materials fabricated by the Chemical vapor deposition In the process of making ZnO nanowires by thermal evaporation method, we selected samples manufactured with N2 flow rate from 960 sccm to air flow of 80 sccm, giving the best number of products as indicated in Fig. 4e. The results on these ﬁgures are investigated for two sampling areas of the product; and region I is at the wall of the cup (corresponding to Fig. 4a, b) and zone II is at the center of the cup (corresponding to Fig. 4c, d). These FESEM image results show that the surface morphology of ZnO nanomaterials has uniform morphological structure. The morphological structure of synthetic materials can be divided into two quite distinct forms, the nanowire type and the form with three attached nanowire branches (tetrapod). The diameter of these nanowires is quite uniform with a diameter of about
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Fig. 3. SEM image of ZnO material fabricated by hydrothermal method with different pH pH = 8 (a), pH = 9 (b), pH = 10 (c) and pH = 11 (d).
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Fig. 4. The image of the cup collecting materials of fabricated process. a, b) SEM image of nanomaterials with tetrapods structure obtained on the wall of the cup. c, d) SEM image of the nanowire material in center of the cup.
30 nm. However, we can observe quite clearly that the length of the nanowires formed tetrapod varies with the N2 flow. Similar to znO nanorods fabricated by hydrothermal method, we survey the crystal structure characteristics as well as evaluate the defects and lattice defects of ZnO nanowires fabricated through schematic analysis. Xray diffraction (XRD). Figure 5 shows the Xray diffraction diagram of ZnO nanowires, in which we can see a sample with a hexagonal (hexagonal) crystal structure typical of ZnO oxide. In which the diffraction peaks corresponding to the Miller index (hkl) are (100), (002), (101), (102),
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Fig. 5. Diagram of Xray diffraction ZnO nanowires fabricated by thermal evaporation method.
Based on the above results, it is realized that: ZnO nanomaterials fabricated by the two methods above for structural forms are rods and wires. The dimensions of the material and structure are quite similar. From here we can predict the gassensitive properties of similar materials. 3.2
Gas Sensitive Characteristics of ZnO Nanomaterials
After fabricating ZnO nanomaterials are covered by micro electrodes (Pt on SiO2/Si substrates) to form sensors, the sensor system is annealed to 600 °C. The heating process is slowly increased from room temperature to 600 °C for a period of 6 h then held for 6 h and naturally cooled to room temperature to obtain the sensor component. The gassensitive characteristics of ZnO nanorod sensors were studied with NO2 (concentration from 0,5 to 10 ppm) in the working temperature range 200–350 °C. Figure 6a, b shows the response of the sensor using ZnO nanorod and nanowires fabricated by hydrothermal and chemical vapor deposition method, at a temperature of 250 °C with the NO2 gas flow varying from 0.5 ppm to 10 ppm. As the adsorption of gas has an increased oxidizing resistance of the sensor, this proves that ZnO is a ntype semiconductor. We found that the response uses two materials with similar properties. The response to NO2 concentration of 10 ppm is about 35 times and the response increases with increasing gas concentration. Sensor used nanorod has faster response and recovery time when using nanowires. This can be explained by the fact that ZnO nanowires are made by porous evaporation methods rather than hydrothermal methods. Therefore, the adsorbed air into the more porous material will be harder to release and absorb with the nanorod material.
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From the above results, we realize that the ZnO nanomaterial fabricated by both methods respond well to NO2 gas.
4 Conclusions A simple hydrothermal method for fabricating singlecrystal ZnO nanorods, effective NO2 gas nanosensor applications is demonstrated. The singlecrystal ZnO nanorods has an average 900 nm length and 23 nm width. A simple chemical vapor deposition method is also used to fabricate singlecrystal ZnO nanowires. The singlecrystal ZnO nanorods has an average 10 µm length and 40 nm width. The nanorods and nanowires exhibited a high response to sub ppm NO2 gas concentration and excellent stability and fulﬁlled the practical application of a sensor for monitoring toxic gases.
References 1. Heo, Y.W., et al.: ZnO nanowire growth and devices. Mater. Sci. Eng. R Reports 47(1–2), 1–47 (2004) 2. Rodnyi, P., Khodyuk, I.: Optical and luminescence properties of zinc oxide (Review). Opt. Spectrosc. 111(5), 776–785 (2011) 3. Wang, C., Wang, Y., Zhang, G., Peng, C., Yang, G.: Theoretical investigation of the effects of doping on the electronic structure and thermoelectric properties of ZnO nanowires. Phys. Chem. Chem. Phys. 16(8), 3771–3776 (2014) 4. Samanta, P.K., Chaudhuri, P.R.: Substrate effect on morphology and photoluminescence from ZnO monopods and bipods. Front. Optoelectron. China 4(2), 130–136 (2011) 5. Wan, Q., et al.: Fabrication and ethanol sensing characteristics of ZnO nanowire gas sensors. Appl. Phys. Lett. 84(18), 3654 (2004) 6. Banerjee, D., et al.: Synthesis and photoluminescence studies on ZnO nanowires. Nanotechnology 15(3), 404–409 (2004) 7. Nguyen, T., et al.: Nearinfrared emission from ZnO nanorods grown by thermal evaporation. J. Lumin. 156, 199–204 (2014)
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8. Lee, C.J., Lee, T.J., Lyu, S.C., Zhang, Y., Ruh, H., Lee, H.J.: Field emission from wellaligned zinc oxide nanowires grown at low temperature. Appl. Phys. Lett. 81(19), 3648 (2002) 9. Kumar, R., AlDossary, O., Kumar, G., Umar, A.: Zinc oxide nanostructures for no2 gas– sensor applications: a review. NanoMicro Lett. 7(2), 1–24 (2014) 10. Wang, C., Yin, L., Zhang, L., Xiang, D., Gao, R.: Metal oxide gas sensors: sensitivity and influencing factors. Sensors 10(3), 2088–2106 (2010) 11. Choopun, S., Tubtimtae, A., Santhaveesuk, T., Nilphai, S., Wongrat, E., Hongsith, N.: Zinc oxide nanostructures for applications as ethanol sensors and dyesensitized solar cells. Appl. Surf. Sci. 256(4), 998–1002 (2009) 12. Liu, H., Kameoka, J., Czaplewski, D.A., Craighead, H.G.: Polymeric nanowire chemical sensor. Nano Lett. 4(4), 671–675 (2004) 13. Hulanicki, A., Glab, S., Ingman, F.: Chemical sensors: deﬁnitions and classiﬁcation. Pure Appl. Chem., 63(9), January 1991 14. Choi, M.Y., Park, H.K., Jin, M.J., Ho Yoon, D., Kim, S.W.: Mass production and characterization of freestanding ZnO nanotripods by thermal chemical vapor deposition. J. Cryst. Growth 311(3), 504–507 (2009) 15. Kim, J., Sohn, D., Sung, Y., Kim, E.R.: Fabrication and characterization of conductive polypyrrole thin ﬁlm prepared by in situ vaporphase polymerization. Synth. Met. 132(3), 309–313 (2003) 16. Tigli, O., Juhala, J.: ZnO nanowire growth by physical vapor deposition. In: 2011 11th IEEE International Conference on Nanotechnology, pp. 608–611 (2011) 17. Menzel, A., Subannajui, K., Bakhda, R., Wang, Y., Thomann, R., Zacharias, M.: Tuning the growth mechanism of ZnO nanowires by controlled carrier and reaction gas modulation in thermal CVD. J. Phys. Chem. Lett. 3(19), 2815–2821 (2012) 18. van Deelen, J., Illiberi, A., Kniknie, B., Steijvers, H., Lankhorst, A., Simons, P.: APCVD of ZnO:Al, insight and control by modeling. Surf. Coat. Technol. 230, 239–244 (2013) 19. Hsu, N.F., Chung, T.K.: A rapid synthesis/growth process producing massive ZnO nanowires for humidity and gas sensing. Appl. Phys. A Mater. Sci. Process. 116(3), 1261– 1269 (2014) 20. Kundu, S., Sain, S., Satpati, B., Bhattacharyya, S.R., Pradhan, S.K.: Structural interpretation, growth mechanism and optical properties of ZnO nanorods synthesized by a simple wet chemical route. RSC Adv. 5(29), 23101–23113 (2015) 21. Han, L., Wang, D., Cui, J., Chen, L., Jiang, T., Lin, Y.: Study on formaldehyde gassensing of In2O3sensitized ZnO nanoflowers under visible light irradiation at room temperature. J. Mater. Chem. 22(25), 12915 (2012) 22. Mute, A., Peres, M., Peiris, T.C., Lourenço, A.C., Jensen, L.R., Monteiro, T.: Structural and optical characterization of ZnO nanowires grown on alumina by thermal evaporation method. J. Nanosci. Nanotechnol. 10(4), 2669–2673 (2010) 23. Bahruji, H., et al.: Pd/ZnO catalysts for direct CO2 hydrogenation to methanol. J. Catal, April 2016
Convergence Parameters for DType Learning Function Cao Thanh Trung1(&), Nguyen Thu Ha1, Tran Kim Quyen2, and Nguyen Doan Phuoc1 1
School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected] 2 College of Industry and Trade TuyHoa, PhuYen, Vietnam
Abstract. Two sufﬁcient convergence conditions of Dtype iterative learning function for practical applications are proposed in this paper. In these two convergence conditions, it is not required absolutely, that the relative degree of controlled repetitive plans has to be equal one, which is happened consistently in other convergence conditions. Hence, their opportunity for practical application range becomes wider. Some illustrative simulations afterward have conﬁrmed this afﬁrmation. Keywords: Dtype learning functions Iterative learning control Model free control Intelligent control
1 Introduction Iterative learning control (ILC) is a ﬁeld of intelligent control concept, which can be used to overcome some of the inherent difﬁculties of conventional control methods related to system model inaccuracy. Based on ILC we can design an output tracking controller for repetitive processes, without using their mathematical model [1–4]. Within ILC conception the system inputs will be reﬁned from previous trial to the next trial and so on, based on the measured system information of past executing performance, until the desired tracking error is reached. The essential of successful input reﬁnements in ILC is an appropriate choice of learning function, also called update law algorithm, which will be used consistently from trial to trial to update the system inputs. Therefore, in the past decades the number of works of literature associated with choosing appropriately the learning functions is growing regularly [2–11]. Two overall surveys of them are founded in [9, 10]. The most used simple linear learning functions in ILC, under which also the D, PD, and PIDType, are introduced in [2–4, 7, 8]. The condition for the asymptotic convergence of learning processes by using the learning functions proposed in these references requires consistently that the relative degree of LTI repetitive plans has to be an equal one, i.e. CB of the plan ðA; B; CÞ has full range, or at least there exists an integer j so that CA j B has a full range. Additionally, the repetitive plans are assumed to be invertible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 262–269, 2021. https://doi.org/10.1007/9783030647193_30
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It has been shown furthermore in [4, 6, 8] that by applying these learning functions the ILC will bring the robust behavior to controlled systems. To applying these linear learning functions for nonlinear plans, these requirements about the relative degree of LTI systems will be replaced with the requisite that nonlinear plans have to be smooth and satisfy the Lipschitz condition [2, 7]. In the circumstance that no parameters of the chosen learning function satisfy available convergence conditions, an optimization outline will be used [2, 11]. However, in our opinion, the splendor of ILC concept, as already seen from its original point in [1], is that ILC is a very simple control technique and not difﬁcult to implement, but provides an extremely effective improving the tracking performance for some repetitive production processes. It means consequentially, that any effort to complicate it academically will be not necessary, even worthless for praxis. Therefore, in this paper, we will focus mainly on the simplest learning functions, which is used commonly in ILC, the Dtype learning function. More precisely, our aim here in this paper is to enlarge the application range of the linear Dtype learning function in the sense that it could be applied also for repetitive processes of any relative degree, just only through determining appropriately its parameters.
2 Main Results Our object to study in this section is a periodically working plan, also often called the repetitive plan, which is described by the inputoutput mapping: yðsÞ ¼ f p ðuðsÞÞ for all 0 s T:
ð1Þ
It is emphasized here that a model is always required for the convergence analysis of an applied learning algorithm, although for designing iterative learning controllers, the having of any mathematical model is not mandatory. It means that the mathematical model (1) may not be precise enough for designing any conventional controller, but it could be quite sufﬁcient for a guideline to choose convergent parameters for a learning function. Next, we assume that the model (1) is linear, i.e. it satisﬁes: f p ðauðsÞ þ bvðsÞÞ ¼ af p ðuðsÞÞ þ bf p ðvðsÞÞ
ð2Þ
for any real numbers a; b. We consider here that the ILC controller will be carried out by using a general DType learning function: uk þ 1 ðsÞ ¼ uk ðsÞ þ f l ðek ðsÞÞ
ð3Þ
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where ek ðsÞ ¼ rðsÞ yk ðsÞ
ð4Þ
is the output tracking error and the operator f l ðÞ is linear, i.e. the equation: f l aek ðsÞ þ bzk ðsÞ ¼ af l ðek ðsÞÞ þ bf l zk ðsÞ
ð5Þ
is satisﬁed for all real number a; b. The index k in (3) above designates the iterative learning step, i.e. the trial number, and T in (1) indicates the periodically ﬁnite working time of repetitive processes (1). Such processes always execute their operation in a ﬁxed working time interval and repeat it afterward over and over. During an operation we have: 0 s ¼ iTs \T or 0 i\N ¼ T=Ts ; with the sampling time Ts . As already shown by us in [12], the required convergence condition for learning process in the sense of: e k þ 1 ðsÞ \kek ðsÞk for all 0 s T
ð6Þ
will be accomplished, if the following inequality holds: 1e ðf p f l Þ\1;
ð7Þ
where 1e denotes the identity operator. Note that besides the condition (7), an appropriate learning function is furthermore the one, which should fulﬁll additionally the asymptotic requirement [2, 3, 7]: limk!1 kek ðsÞk ¼ 0
ð8Þ
which will be leaved out momentary in this paper. However, even the condition (7) above of monotonous tracking error decreasing presented in [12] was still not realized there in detail for practical purposes. Hence, we will complete it in this paper. 2.1
Realization in Time Sequence
We consider here a repetitive plan (1) described approximately by the discrete time state model ðA; B; CÞ as follows: (
xðs þ 1Þ ¼ AxðsÞ þ BuðsÞ yðsÞ ¼ CxðsÞ
where x; u; y are the vectors of system states, inputs and outputs, respectively.
ð9Þ
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As mentioned before, the model (9) here may not be precise enough for designing any conventional controllers, but could be sufﬁcient as a starting point for choosing convergence matrix K of Dtype learning function: uk þ 1 ðsÞ ¼ uk ðsÞ þ Kek ðs þ 1Þ:
ð10Þ
Based on the approximated model (9) above we attain the plan output at a time instant 0 s N, belonging to kth trial, as follows: yk ðsÞ ¼ CAs xk ð0Þ þ
s1 X
CAsi1 Buk ðiÞ;
ð11Þ
i¼0
from which we have also the output tracking error (4) at the same time instant s: ek þ 1 ðsÞ ¼ ðI CBK Þek ðsÞ
s2 X
CAsi1 BKek ði þ 1Þ
i¼0
1 ek ð1Þ C B .. C B . s1 C B ¼ CA BK ; . . . ; CABK ; 1 CBK B C @ ek ðs 1Þ A 0
ð12Þ
ek ðsÞ The index i in (11) denotes the updated time instant during periodically working time ½0; T of the repetitive plan (1). Hence, by rewriting (12) for all s ¼ 1; 2; . . . ; N we have: 0
1 0 ek þ 1 ð1Þ I CBK B ek þ 1 ð2Þ C B CABK B C B .. B C¼@ .. @ A . . CAN1 BK ek þ 1 ðNÞ
0 I CBK .. .
CAN2 BK
.. .
10 e ð1Þ 1 k ek ð2Þ C CB B CB . C A@ . C . A I CBK ek ðNÞ 0 0 .. .
ð13Þ
which is equivalent to: ek þ 1 ¼ ðI UK Þek ) ek þ 1 kI UK k kek k;
ð14Þ
where: 0
1 0 ek ð1Þ CB B ek ð2Þ C B CAB B C e k ¼ B . C; U ¼ B .. @ @ .. A . CAN1 B ek ðNÞ
0 CB .. . CAN2 B
.. .
1 0 0 C : .. C . A
ð15Þ
CB
Therefore, the required convergence of the learning process in the sense of (7), i.e. of monotonous decreasing of Euclid norm of all tracking errors ek :
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ek þ 1 \kek k
ð16Þ
will be satisﬁed, if the following inequality of induced matrix norm holds: kI UK k\1:
ð17Þ
Finally, to summarize, we have: “In time sequence approach and under assumption that the repetitive plan (1) is described approximately by state model (9), the Dtype learning function (10) will satisfy the convergence requirement (16), if its parameter K has been chosen accordingly to (17)”. 2.2
Optimal Realization
Again, we suppose here that the repetitive plan (1) could be approximated by the state model (9), which may not be precise enough for designing any conventional controllers, but could be quite sufﬁcient for choosing the convergent parameter K of Dtype learning function (10). The previous subsection had accommodated that with the parameter K, which is determined accordingly to the requirement (17), will satisfy the required the convergence of learning process by using Dtype learning function in the sense of (16). But a problem occurs here that whether such a parameter K exists and what we have to do in the insolvable situation. Since the requirement (17) is only a sufﬁcient condition, a convergence parameter K may be existed, even it does not satisfy the requirement (17). Therefore, in this situation, in place of (16), we will try to ﬁnd it accordingly to the following constrained optimization problem: Kk ¼ arg mina K b ek þ 1 ¼ arg mina K b kðI UK Þek k; ð18Þ with two bounded matrices a; b, where the comparison a K b is understood for each element of them. In this circumstance, the solution Kk is valid for Dtype learning function only during the next trial, i.e. for the ðk þ 1Þth trial. Since the right side of (18) can be expressed as follows: kðI UK Þek k ¼ ½ðI UK Þek T ðI UK Þek ¼ eTk ek 2eTk UKek þ eTk ðUK ÞT UKek we obtain: Kk ¼ arg mina K b eTk ðUK ÞT UKek 2eTk UKek :
ð19Þ
It is emphasized again at this point that this obtained parameter matrix from the reduced optimization problem (19) will be used to reﬁne the input only during ðk þ 1Þth trial. It means that:
Convergence Parameters for DType Learning Function
uk þ 1 ðsÞ ¼ uk ðsÞ þ Kk ek ðs þ 1Þ
267
ð20Þ
and so on. For the next trial, the learning parameter will be updated again based on measured system information from the previous trial, via solving the optimization problem (19). So, to summarize, we obtain: “Under the assumption that the learning process with the Dtype learning function (10) is convergent, the optimal parameter Kk of it, using for the next trial k þ 1, could be determined by solving the constrained quadratic optimization problem (19)”.
3 Illustrative Simulations 3.1
Simulation 1
To illustrate the convergent condition in time sequence (17) proposed above, we consider the linear repetitive plan (9) with the following system parameters: A¼
0:2 0:2
0:3 0:3 0:2 ;B¼ and C ¼ 0:3 0:5 0
ð21Þ
This plan will be operated periodically in a ﬁnite number of steps N ¼ 120 from initial states x0 ¼ 0. So in each trial afterward, the plan initial states have to be reset to the same x0 . By choosing the parameter K ¼ 0:1 for Dtype learning function (10) the required convergence condition (17) will be satisﬁed with: kI UK k ¼ 0:9976\1: Figure 1 demonstrates the convergence behavior of applied iterative learning process, while the reference is a trapeze signal: 8 < 2:5 for 25s t 95s rðtÞ ¼ 0:1t for 0 t 25s : 0:1ðt 95Þ þ 2:5 otherwise As exhibited there, the learning process with the chosen parameter K ¼ 0:1, which is satisﬁed with the requirement (17), had met the expectative convergence. It means that this Dtype learning function had produced the monotonous reduction of tracking error accordingly to the increasing of trials number k. Note that K ¼ 0:1 is only one of many parameters for Dtype learning function (10) which satisﬁes the required convergence condition (17). However, since the fact that belonging to theses parameters, the bigger K is chosen, the faster the convergence will be, we should choose the biggest one of them to improve the learning process, what is related obviously to an optimization problem.
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2.5
2.5
a)
2
2
1.5
1.5
1
b)
1 reference output error
0.5 0 0
20
40
60
80
reference output error
0.5 0
100
120
0
20
40
60
80
100
120
Fig. 1. The simulation results about output tracking behavior of repetitive plan after a) 70 trials (left) and b) 700 trials (right) by using the convergence condition in time sequence.
3.2
Simulation 2
Now we consider the linear repetitive plan (9) with the following system parameters: A¼
0:2 0:2
0:3 1 1 ;B¼ and C ¼ 0:3 1 1
ð22Þ
In comparison with the plan (21) in the previous simulation, the system model (22) here has CB ¼ 0, which deduces: kI UK k 1 for all K: Hence, the condition in time sequence (17) is not applicable to this system. In this situation, we have to use the optimal realization (19). By choosing both bounded values a; b for determining the parameter Kk accordingly to solving constrained optimization problem (19), and the reference signal rðtÞ for system output y, as follows: a ¼ 0:01; b ¼ 10; rðtÞ ¼ sinð2p s=N Þ where N ¼ 120 we obtain the simulation results exhibited in Fig. 2. 1
1 reference output error
a) 0.5
b) 0.5
0
0
0.5
0.5
1
0
20
40
60
80
100
120
reference output error
1
0
20
40
60
80
100
120
Fig. 2. The simulation results about output tracking behavior of repetitive plan after a) 20 trials (left) and b) 70 trials (right) by using the optimal convergence condition.
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As expected, these simulation results have conﬁrmed the applicability of optimal realization (19) for situations, when the condition in time sequence (17) is insolvable. Moreover, through picking up from Fig. 2, it produces a convergence rate even faster (just after 70 trials in comparison with 700 trials in previous simulation already exhibited in Fig. 1).
4 Conclusions In this paper, we have presented two conditions for choosing convergence parameters for Dtype learning function, which is often used in iterative learning control. The ﬁrst one, called the “condition in time sequence”, does not require that the controlled plan, with an approximated state model (9), has to be satisﬁed CAi B ¼ 0 for all i ¼ 1; 2; . . . ; n. The second one, the “optimal condition”, does not require CB 6¼ 0. Two numerical simulations afterward have conﬁrmed illustratively their applicability in the practice. After all, the remaining deﬁciency, that they satisfy additionally the asymptotic ability (8), will be our work in the future.
References 1. Uchiyama, M.: Formation of highspeed motion pattern of a mechanical arm by trial. Trans. Soc. Instrum. Control Eng. 14(6), 706–712 (1978) 2. Moore, K.L.: Iterative Learning Control for Deterministic Systems. Springer, Berlin (2012) 3. Arimoto, S.: Iterative learning control for robot systems, pp. 393–398. Proc. IECON, Tokyo (1984) 4. Cheng, Y., Wen, C.: Iterative Learning Control: Convergence Robustness and Application. Springer, Berlin (1999) 5. Barton, A.D., Lewin, P.L., Brown, D.J.: Practical implementation of a realtime iterative learning position controller. Int. J. Control 73(10), 992–999 (2000) 6. Shen, D., Li, X.: Iterative Learning Control for Systems with IterationVarying Trial Lengths: Synthesis and Analysis. Springer, Berlin (2019) 7. Xu, J.X., Tan, Y.: Linear and Nonlinear Iterative Learning Control. Springer, Berlin (2003) 8. Tian, S., et al.: A PDtype iterative learning control algorithm for singular discrete systems. Adv. Differ. Equ. 1, 321 (2016) 9. Owens, D.H., Amann, N., Rogers, E.: Iterative learning controlan overview of recent algorithms. Appl. Math. Comput. Sci. 5(3), 425–438 (1995) 10. Tharayil, D.A.B.M., Alleyne, A.G.: A survey of iterative learning control  a learningbased method for high performance tracking control. IEEE Control Syst. Mag. 26(3), 96–114 (2006) 11. Owen, D.H.: Iterative learning control. An optimization paradigm. Springer, Berlin (2016) 12. Nguyen, T.D., Pham, H.P., Nguyen, Q.D., Nguyen, D.P.: Iterative learning control for vshaped electrothermal microactuator. Electronics 8(12), 1410 (2019)
Current Harmonic Eliminations for SevenPhase Nonsinusoidal PMSM Drives applying Artiﬁcial Neurons Duc Tan Vu1,2(&) , Ngac Ky Nguyen1 , Eric Semail1 and Thi Thanh Nga Nguyen2
,
1
Univ. Lille, Arts et Metiers Institute of Technology, Centrale Lille, Yncrea HautsdeFrance, ULR 2697L2EP, HESAM, F59000 Lille, France [email protected] 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam
Abstract. This study is to deal with unwanted current harmonics in rotating (dq) frames of a 7phase nonsinusoidal permanent magnet synchronous machine (PMSM) in a wyeconnected winding topology. The machine is supplied by a 7leg voltage source inverter (VSI) fed by a DCbus voltage. In control, current responses are expected to properly track their references. However, several unwanted harmonics of the nonsinusoidal back electromotive force (backEMF) and the inverter nonlinearity generate unwanted harmonic components in dq currents. These current harmonics cannot be nulliﬁed by controllers such as conventional proportionalintegral (PI) controllers. Consequently, the current responses cannot track their references. In this study, a combination of conventional PI controllers and simple adaptive linear neurons (ADALINEs) is proposed to eliminate these current harmonics, improving current control quality of the drive. The effectiveness of the proposed control structure is veriﬁed by experimental results. Keywords: Multiphase machine Sevenphase PMSM Nonsinusoidal backEMF Current harmonics ADALINE Artiﬁcial intelligence
1 Introduction Electric multiphase drives have become more interesting due to their advantages over the conventional threephase drives such as higher reliability and lower power per phase rating [1]. In an electric drive, machine design and inverter nonlinearity can affect current control quality [2, 3]. Owing to the multireference frame theory [4], only one harmonic of backEMF should be associated with each dq frame. As a result, all currents and backEMFs in dq frames are constant, creating constant torques. It means that there are no current harmonics in dq frames. In general, a nphase machine should contain only (n − 1)/2 harmonics in its backEMF. For example, a 7phase machine has three dq and one zerosequence frames. Therefore, three is the maximum number of harmonics that should exist in the backEMF to create constant torques with constant dq currents. Nevertheless, because of machine design, more than one backEMF © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 270–279, 2021. https://doi.org/10.1007/9783030647193_31
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harmonic can be associated with each dq frame. These unwanted backEMF harmonics cause not only current harmonics in dq frames but also torque ripples. In addition, extra current harmonics in dq frames are generated by the inverter nonlinearity with deadtime voltages. These deadtime voltages are analyzed in [5] for inverters with different numbers of legs. To eliminate current harmonics, study [6] uses lowpass ﬁlters (LPFs) to obtain harmonics from winding currents. However, the computation is complicated. Hence, study [7] applies simple ADALINEs to compensate the unwanted backEMFs and the deadtime voltages for 3phase machines. For multiphase machines, there have been some studies [8, 9] applying either an improved model predictive control strategy or an inverse modelbased disturbance observer to reduce the voltage and current harmonics for 5phase or dual 3phase machines. However, existing highfrequency components in [8] and calculating complications in [9] are several drawbacks. In this study, ADALINEs, with selflearning ability, fast convergence and simplicity as discussed in [7, 10, 11], are added to conventional PI controllers for current harmonic eliminations in dq frames of a 7phase nonsinusoidal PMSM drive. Current control improvements are veriﬁed with experimental results under the presence of the unwanted harmonics of backEMF and deadtime voltages from the inverter nonlinearity. This paper is organized as follows. The modeling of a 7phase drive is presented in Sect. 2. Origins of current harmonics are analyzed in Sect. 3. Solutions are discussed in Sect. 4. A veriﬁcation with experimental results is presented in Sect. 5.
2 Modeling of a SevenPhase PMSM A sevenphase drive with a 7leg VSI fed by a DCbus voltage VDC is shown in Fig. 1. Several assumptions of a 7phase PMSM in the considered drive are described as follows: the 1st, 3rd, and 9th harmonics of its backEMF account for highest proportions; unwanted harmonics such as 11th, 13th, and 19th have small but unneglected proportions in the backEMF; 7 phase windings of the machine are equally shifted and wyeconnected. The 7dimensional phase voltage vector v of the machine can be expressed in (1).
VSI + VDC
D C B
E F
A
G
Fig. 1. Schematic diagram of a sevenphase drive in a wyeconnected winding topology.
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v ¼ Rs i þ ½L
di þe dt
ð1Þ
where i and e are the 7dimensional vectors of phase currents and backEMFs, respectively; Rs is the resistance of one phase of stator; [L] is a 7 by 7 stator inductance matrix. To control the machine drive using the ﬁeldoriented control (FOC), Clarke and Park matrices are applied to convert the machine parameters from natural frame into dq frames. For example, the transformation for currents is presented in (2). ½ id1
iq1
id9
2 6 6 6 rﬃﬃﬃ6 26 6 ½C ¼ 6 76 6 6 6 4 2
iq9
id3
iz T ¼ ½P½C½ iA
iq3
1
cosðdÞ
cosð2dÞ
cosð3dÞ
0 1
sinðdÞ sinð2dÞ cosð2dÞ cosð4dÞ
sinð3dÞ cosð6dÞ
0 1
sinð2dÞ sinð4dÞ cosð3dÞ cosð6dÞ
sinð6dÞ cosð9dÞ
sinð3dÞ pﬃﬃﬃ 1 2
sinð9dÞ pﬃﬃﬃ 1 2
0 pﬃﬃﬃ 1 2
sinð6dÞ pﬃﬃﬃ 1 2
cosðhÞ sinðhÞ 6 sinðhÞ cosðhÞ 6 6 6 0 0 6 6 ½P ¼ 6 0 0 6 6 0 0 6 6 0 0 4
0
0
cosð9hÞ sinð9hÞ
sinð9hÞ cosð9hÞ
0 0
0 0
0
0
0
0
0
0
iB
iC
cosð4dÞ
iD
iE
iG T
iF
cosð5dÞ
cosð6dÞ
ð2Þ 3
sinð5dÞ sinð6dÞ 7 7 7 cosð10dÞ cosð12dÞ 7 7 sinð8dÞ sinð10dÞ sinð12dÞ 7 7; 7 cosð12dÞ cosð15dÞ cosð18dÞ 7 7 7 sinð12dÞ sinð15dÞ sinð18dÞ 5 pﬃﬃﬃ pﬃﬃﬃ pﬃﬃﬃ 1 2 1 2 1 2 3 0 0 0 0 0 07 7 7 0 0 07 7 7 0 0 07 7 cosð3hÞ sinð3hÞ 0 7 7 7 sinð3hÞ cosð3hÞ 0 5 sinð4dÞ cosð8dÞ
0
0
1
where [C] is the Clarke matrix; d is the spatial phase shift angle 2p/7; [P] is the Park matrix associated with the 1st, 9th, and 3rd harmonics due to the assumptions of the considered backEMF; h is the electrical position of the machine. In dq frames, the 7phase machine is decomposed into three ﬁctitious 2phase machines (FM1, FM2, FM3) with decoupled frames (d1q1), (d9q9), and (d3q3), respectively, and one zerosequence machine (ZM) with reference frame z [4]. Due to wyeconnected stator windings, impacts of the zerosequence component on current
Table 1. Fictitious machines and associated harmonics of a 7phase machine (odd harmonics). Fictitious machine 1st machine (FM1) 2nd machine (FM2) 3rd machine (FM3) Zerosequence machine (ZM)
Frame d1q1 d9q9 d3q3 z
Associated harmonic ðm 2 N0 Þ 1, 13, 15, …7 m ± 1 5, 9, 19, …7 m ± 2 3, 11, 17, …7 m ± 3 7, 21, …7 m
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control and torque performances are eliminated. A ﬁctitious machine with its corresponding dq frame is associated with a group of harmonics as presented in Table 1.
3 Current Harmonic Analyses According to the multireference frame theory [4], the backEMF of a 7phase machine should have only 3 harmonics (1st, 9th, and 3rd, for example) associated with 3 dq frames. However, in Table 1, two harmonics (1st, 13th) are associated with (d1q1) while two harmonics (9th, 19th) harmonics are associated with (d9q9). Then, two harmonics (3rd, 11th) are associated with (d3q3). The backEMF of a phase is described in (3). ( ej ¼
2p 2p E 1 sin h ðj 1Þ 2p 7 þ E 3 sin 3 h ðj 1Þ 7 þ u3 þ E 9 sin 9 h ðj 1Þ 7 þ u9 þ 2p 2p E 11 sin 11 h ðj 1Þ 7 þ u11 þ E 13 sin 13 h ðj 1Þ 7 þ u13 þ E19 sin 19 h ðj 1Þ 2p 7 þ u19
ð3Þ where ej is the backEMF of phase j (from 1 to 7, representing phases A to G); (E1, E3, E9, E11, E13, E19) and (u3, u9, u11, u13, u19) are the amplitudes and phase shift angles of the harmonic components of the backEMF. By applying the classical Clarke and Park transformations, backEMFs in dq frames can be expressed as follows: 8 pﬃﬃﬃﬃﬃﬃﬃﬃ > ed1 ¼ 7=2 E sinð14h þ u Þ > > pﬃﬃﬃﬃﬃﬃﬃﬃ 13 pﬃﬃﬃﬃﬃﬃﬃﬃ 13 > > E þ e ¼ 7=2 7=2 E13 cosð14h þ u13 Þ > q1 > pﬃﬃﬃﬃﬃﬃﬃﬃ 1 > < ed9 ¼ 7=2 E 19 sinð28h þ u19 Þ pﬃﬃﬃﬃﬃﬃﬃﬃ pﬃﬃﬃﬃﬃﬃﬃﬃ > eq9 ¼ 7=2 E 9 þ 7=2 E19 cosð28h þ u19 Þ > > p ﬃﬃﬃﬃﬃﬃﬃ ﬃ > > > E e ¼ 7=2 sin ð 14h þu Þ d3 > pﬃﬃﬃﬃﬃﬃﬃﬃ 11 pﬃﬃﬃﬃﬃﬃﬃﬃ 11 > : eq3 ¼ 7=2 E 3 þ 7=2 E11 cosð14h þ u11 Þ
ð4Þ
All dq backEMFs in (4) are not constant because of the existing unwanted backEMF harmonics. The backEMFs have frequencies 14h in (d1q1), 28h in (d9q9), and 14h in (d3q3). Especially, these backEMF harmonics generate corresponding current harmonics in dq frames. The current harmonic amplitudes in dq frames depend on the harmonic distribution in the backEMFs, and especially on the rotating speed. The 7leg VSI supplying the 7phase machine creates extra current harmonics in dq frames due to its nonlinearity. Indeed, the dead time, a time interval in which both switches of one inverter leg are off, mainly causes the VSI nonlinearity. According to [5], the deadtime voltage in a phase of the VSI can be generally expressed in (5).
vj
dead
8 1 1 2p þ 5 sin 5 h ðj 1Þ 2p > sin h ðj 1Þ 2p 7 þ 3 sin 3 h ðj 1Þ 7 1 1 7 4< 1 2p 2p ¼ Vdead þ 9 sin 9 h ðj 1Þ 7 þ 11 sin 11 h ðj 1Þ 7 þ 13 sin 13 h ðj 1Þ 2p 7 p> : 1 1 1 þ 17 þ 19 sin 15 h ðj 1Þ 2p sin 17 h ðj 1Þ 2p sin 19 h ðj 1Þ 2p þ 15 7 7 7
ð5Þ
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dead with V dead ¼ TTPWM VDC where vj_dead is the deadtime voltage of phase j; Vdead is a constant voltage; Tdead is the dead time; TPWM is the switching period of the inverter; VDC is the DCbus voltage. The harmonic amplitudes are inversely proportional to their orders so the considered harmonics can be up to 19h. The deadtime voltages in dq frames are presented in (6).
8 > vd1 > > > > > vq1 > > < vd9 > v q9 > > > > > vd3 > > : vq3
dead dead dead dead dead dead
pﬃﬃﬃﬃﬃﬃﬃﬃ ¼ 7=2 ½4Vdead =p½1=13 þ 1=15 sinð14hÞ pﬃﬃﬃﬃﬃﬃﬃﬃ ¼ 7=2 ½4Vdead =pf1 þ ½1=13 þ 1=15 cosð14hÞg pﬃﬃﬃﬃﬃﬃﬃﬃ ¼ 7=2 ½4Vdead =p½1=5 sinð14hÞ þ 1=19 sinð28hÞ pﬃﬃﬃﬃﬃﬃﬃﬃ ¼ 7=2 ½4Vdead =pf1=9 þ ½1=5 cosð14hÞ þ 1=19 cosð28hÞg pﬃﬃﬃﬃﬃﬃﬃﬃ ¼ 7=2 ½4Vdead =p½1=11 þ 1=17 sinð14hÞ pﬃﬃﬃﬃﬃﬃﬃﬃ ¼ 7=2 ½4Vdead =pf1=3 þ ½1=11 þ 1=17 cosð14hÞg
ð6Þ
In (6), the deadtime voltages in (d1q1) and (d3q3) frames have a frequency of 14h while the voltages in (d9q9) have two frequencies of 14h and 28h. Therefore, current harmonics in dq frames are also caused by the inverter nonlinearity with the deadtime voltages. The amplitudes of these current harmonics do not depend on the rotating speed but Vdead. Voltage Vdead is related to Tdead, TPWM, and VDC as described in (5). Table 2. Current harmonics in dq frames caused by the unwanted backEMF harmonics and the inverter nonlinearity with deadtime voltages in a 7phase PMSM drive. Frame d1q1 d9q9 d3q3
Current harmonics caused by unwanted backEMF harmonics 14h 28h 14h
Current harmonics caused by deadtime voltages 14h 14h, 28h 14h
Finally, unwanted current harmonics in dq frames caused by the unwanted backEMF harmonics in (4) and the deadtime voltages in (6) are summarized in Table 2.
4 Eliminations of Current Harmonics with ADALINEs As described in Table 2, current harmonics in dq frames have frequencies of 14h and 28h. Due to the complexity of the experimental drive, these values need to be automatically learned in real time to correctly eliminate these current harmonics. A current control structure is proposed in Fig. 2 in which current ix (can be id1, iq1, id9, iq9, id3, or iq3) is controlled by a PI controller with an adaptive compensating voltage (vx com ). A ﬁctitious machine is represented by a transfer function with inductance Lx, resistance Rs and Laplace operator s. By using vx com , all current harmonics in dq frames described in Table 2 can be eliminated. Voltage vx com for current ix is generated by an ADALINE, hence, there are 6 ADALINEs for 6 dq currents (id1, iq1, id9, iq9, id3, or iq3)
Current Harmonic Eliminations for SevenPhase NonSinusoidal PMSM Drives
w1_x
275
PI
w2_x
∑
w3_x
w 1_x w 2_x w 3_x w 4_x
w4_x
+ Updating laws = w 1_x + η ierror cos(14θ ) = w 2_x + η ierror sin(14θ ) = w 3_x + η ierror cos(28θ ) = w 4_x + η ierror sin(28θ )
+
+ +
+ Transfer funcƟon
Fig. 2. The proposed control structure for current ix with the ADALINE compensation.
in the considered 7phase drive. The general structure of an ADALINE in Fig. 2 is based on current harmonics in Table 2. The ADALINE output (vx com ) is generally calculated from ADALINE inputs with harmonics 14h and 28h, as described in (7). vx
com
¼ ½w1
x
cosð14hÞ þ w2
x
sinð14hÞ þ ½w3
x
cosð28hÞ þ w4
x
sinð28hÞ
ð7Þ
where harmonic weights w1_x and w2_x are associated with harmonic 14h; w3_x and w4_x are used for harmonic 28h. In Fig. 2, weights of a harmonic are updated by Least Mean Square rule with learning rate η, current error ierror, and the corresponding harmonic. An increase in η can shorten the learning time but η should be between 0 and 1 to guarantee the weight convergence and the stability of the drive. According to Table 2, if x is d1, q1, d3 or q3, the ADALINE structure only has two weights for harmonic 14h. Meanwhile, if x is d9 or q9, the ADALINE uses four weights for both harmonics 14h and 28h. Therefore, the number of weights is optimized to avoid the calculation burden.
5 Experimental Results The proposed control structure in Fig. 2 is veriﬁed by an experimental drive as described in Fig. 3a and Table 3. The experimental 7phase PMSM is mechanically connected to a load drive that is an industrial threephase synchronous machine. The load drive is controlled to tune the rotating speed of the 7phase PMSM. A 7leg VSI with insulated gate bipolar transistors (IGBTs) supplies the 7phase PMSM. Its switching frequency is set to 10 kHz. A dSPACE 1005 board with I/O interface is used to transfer PWM signals to the IGBT driver of the inverter and collect the measured data of speed and currents. The experimental 7phase PMSM is introduced in [12]. This machine has an axial flux with double rotors. When the two rotors have different numbers of poles and spatially shifted an angle of 7°, the backEMF waveform and its speciﬁc harmonics are presented in Fig. 3b. Proportions of the backEMF harmonics over the 1st harmonic are 32.3% for the 3rd, 12.5% for the 9th, 10.3% for the 11th, 5.02% for the 13th, 1.98% for the 19th. The current harmonics in dq frames of the considered experimental drive have been discussed in [3]. In this study, current harmonic eliminations of the proposed control structure with ADALINE compensation are
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100%
FM1
Percentage (%)
FM2
80
FM3 ZM
60 40
32.3% 12.5% 10.3% 9.4% 0.29% 1.26% 3.2% 5.02% 1.98% 1.7% 1.74% 1% 0.25%
20 0
1
3
5
7
9
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13
15
17
19
21
23
25
27
Harmonic components
(a)
(b)
Fig. 3. The experimental drive (a), the speednormalized backEMF and its harmonic spectrum of the experimental 7phase PMSM (b). Table 3. Electrical parameters of the experimental 7phase PMSM drive. Parameter Unit X Stator resistance Rs Selfinductance L mH Inductance Ld1 = Lq1 mH mH Inductance Ld9 = Lq9 Inductance Ld3 = Lq3 mH Number of pole pairs p Rated RMS current A DCbus voltage VDC V
No comp.
ADALINE compensaƟon
(a)
No comp.
ADALINE compensaƟon
(b)
Value 1.4 14.7 30.5 7.1 10 3 5.1 200
No comp.
ADALINE compensaƟon
(c)
Fig. 4. (Experimental result) Currents in (d1q1) frame (a), currents in (d9q9) frame (b), currents in (d3q3) frame (c) without (No. comp.) and with the ADALINE compensation at 20 rad/s.
shown in Fig. 4. Current control quality is signiﬁcantly improved, especially in (d9q9) and (d3q3) frames. Figures 5a and 6a show the weight updates with learning rate η = 0.0003 for (d9q9) and η = 0.0001 for (d3q3). The convergence time is about 20 s for (d9q9) and 25 s for (d3q3). Meanwhile, Figs. 5c and 6c describe the improvements of dq currents in one period at 20 rad/s with the ADALINE compensation compared to Figs. 5b and 6b, respectively.
Current Harmonic Eliminations for SevenPhase NonSinusoidal PMSM Drives No comp.
277
With ADALINE compensaƟon
w1_d9
w4_q9
w4_d9
w3_q9
w3_d9
w2_q9
w2_d9
w1_q9
(a)
(b)
(c)
Fig. 5. (Experimental result) Weight learning within 20 s of the ADALINE for currents in (d9q9) (a), currents in (d9q9) frame without (b) and with (c) the ADALINE compensation at 20 rad/s. No comp.
With ADALINE compensaƟon
w1_d3
w2_q3 w2_d3
w1_q3
(a)
(b)
(c)
Fig. 6. (Experimental result) Weight learning within 25 s of the ADALINE for currents in (d3q3) (a), currents in (d3q3) without (b), and with (c) the ADALINE compensation at 20 rad/s.
8
8 100%
7
6
Current (A)
Current (A)
7
5 4 3
32.4%
5 4 3
32.3%
2
2 1
5%
0 1
(a)
100%
6
3
5
13.4% 5.8%
1.2%
7
1%
0.9%
2.6% 1.2%
9 11 13 15 17 19
Harmonic components
(b)
13%
1
1.3% 0.8%
0 1
3
5
7
0.9% 0.6% 0.8% 0.9% 1.5%
9 11 13 15 17 19
Harmonic components
(c)
Fig. 7. (Experimental result) PhaseA current without (iA_no_com) and with (iA_com) the ADALINE compensation (a), harmonic spectrums of iA_no_com (b) and iA_com (c) at 20 rad/s.
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10 rad/s
(a)
30 rad/s
(b)
Fig. 8. (Experimental result) Current control with variable speeds (a), and variable current references (b) in (d9q9) frame using the ADALINE compensation.
The current of phase A without and with the ADALINE compensation is shown in Fig. 7a. Current waveforms in the two cases are slightly different but the root mean square current is almost unchanged at 5.1 A. The current harmonics in natural frame generated by the unwanted backEMFs and deadtime voltages are mostly eliminated as shown in Fig. 7c compared to Fig. 7b, especially the 11th harmonic from 5.8% to 0.9%. Finally, almost only the 1st, 3rd, and 9th harmonics remain in the phase current. Variations of the rotating speed (from 20 to 10 and 30 rad/s) in Fig. 8a and changes in current references in Fig. 8b have modest effects on current control quality in (d9q9) frame. The current control performance in (d1q1) and (d3q3) frames is similar. The performance validates the dynamic responses of the proposed control structure.
6 Conclusion This paper has introduced an option to improve control quality of a nonsinusoidal multiphase machine drive. Simple adaptive linear neurons combined with PI controllers have eliminated most current harmonics in dq frames caused by the unwanted backEMF harmonics and the inverter nonlinearity. Dynamic performances have been validated with sudden variations in the rotating speed or current references. The proposed control structure can be further applied to an industrial drive thanks to its adaptivity and easy implementation. Acknowledgment. We would like to thank Thai Nguyen University of Technology, Thai Nguyen city, Vietnam, and the CE2I project of European Union for the ﬁnancial support.
References 1. Barrero, F., Duran, M.J.: Recent advances in the design, modeling, and control of multiphase machines part I. IEEE Trans. Industr. Electron. 63(1), 449–458 (2016) 2. Hwang, J., Wei, H.: The current harmonics elimination control strategy for sixleg threephase permanent magnet synchronous motor drives. IEEE Trans. Power Electron. 29(6), 3032–3040 (2014)
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3. Vu, D.T., Nguyen, N.K., Semail, E., Moraes, T.J.D.S.: Control strategies for nonsinusoidal multiphase PMSM drives in faulty modes under constraints on copper losses and peak phase voltage. IET Electr. Power Appl. 13(11), 743–1752 (2019) 4. Semail, E., Kestelyn, X., Bouscayrol, A.: Right harmonic spectrum for the backelectromotive force of an nphase synchronous motor. In: 39th IEEE Industry Applications Conference, Seattle, WA, USA, vol. 1, pp. 71–78 (2004) 5. Grandi, G., Loncarski, J.: Analysis of deadtime effects in multiphase voltage source inverters. In: 6th IET International Conference on Power Electronics, Machines and Drives (PEMD 2012), pp. 1–6 (2012) 6. Liu, G., Chen, B., Wang, K., Song, X.: Selective current harmonic suppression for highspeed PMSM based on highprecision harmonic detection method. IEEE Trans. Industr. Inf. 15(6), 3457–3468 (2019) 7. Wang, L., Zhu, Z.Q., Bin, H., Gong, L.M.: Current harmonics suppression strategy for PMSM with nonsinusoidal backEMF Based on adaptive linear neuron method. IEEE Trans. Ind. Electron, p. 1 (2019) 8. Li, G., Hu, J., Li, Y., Zhu, J.: An improved model predictive direct torque control strategy for reducing harmonic currents and torque ripples of ﬁvephase permanent magnet synchronous motors. IEEE Trans. Industr. Electron. 66(8), 5820–5829 (2019) 9. Karttunen, J., Kallio, S., Peltoniemi, P., Silventoinen, P.: Current harmonic compensation in dual threePhase PMSMs using a disturbance observer. IEEE Trans. Industr. Electron. 63(1), 583–594 (2016) 10. Sediki, H., Bechouche, A., Abdeslam, D.O., Haddad, S.: ADALINE approach for induction motor mechanical parameters identiﬁcation. Math. Comput. Simul. 90, 86–97 (2013) 11. Nguyen, N.K., Semail, E., Belie, F.D., Kestelyn, X.: Adaline neural networksbased sensorless control of ﬁvephase PMSM drives. In: IECON 2016  42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, pp. 5741–5746 (2017) 12. Locment, F., Semail, E., Piriou, F.: Design and study of a multiphase axialflux machine. IEEE Trans. Magn. 42(4), 1427–1430 (2006)
Design and Some Experimental Results of the Robust Current Controller of DoublyFed Induction Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer C. X. Tuyen1(&) and N. T. Huong2 1
Faculty of Electrical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Electronics Faculty, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]
Abstract. With the increasing requirements of quality of electrical network, now grid codes determine that wind turbine connected to the network, especially for wind farm, must be able to connect to the grid during three phase voltage dips, and must be able to support the voltage of the grid during and immediately following the grid fault by supplying reactive power to the network, that guarantees the stability of the network. The robust current controller of doubly fed induction generator in wind power plant with the Backstepping technique based disturbance observer has solved the above problem when the voltage sags to the 15% of its normal value. The results have been veriﬁed on experiment system based on the dSpace Multi Processors ALPHACOMBO system. Keywords: Doubly fed induction generator Backstepping technique Disturbance observer Robust control Wind power plant dSpace Multi Processors ALPHACOMBO system
1 Introduction Nowadays, the demand for electricity in the world is increasing and the trend of the use of clean energy sources in the world, especially the wind electricity power source, is developing strongly [1]. Among the wind electricity power systems used today, the wind electricity power system using doublyfed induction generator (DFIG), which is called with the name as the wound – rotor induction machine, is the most commonly used in largecapacity wind electricity turbine systems, because of its flexibility and ability to control active and reactive power [2]. In this system, the power of the inverter is reduced and thus costs are reduced, because the inverter is connected to the rotor circuit of the generator, its capacity is usually only one third of the total system power, and the attached devices such as ﬁlters are also cheaper [1, 3, 4]. So, in this paper, we consider the wind electricity power system using doublyfed induction generator. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 280–290, 2021. https://doi.org/10.1007/9783030647193_32
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Figure 1 gives an overview of the structure of a wind power generation system using the doubly fed induction generator (DFIG).
Fig. 1. Overview of the structure of a wind power generation system using the doubly fed induction generator
In Fig. 1, NIVT is grid inverter, GIVT is generator inverter, GB is gear box. The system in Fig. 1 consists of a DFIG with a stator coil directly connected to the threephase grid. The rotor side coil is connected to an inverter system (using semiconductor valves) which can control the flow of energy in two directions. The semiconductor valves are controlled to open and close according to the principle of spatial vector modulation [15] by the digital signal processor (DSP). The main drawback of wind electricity power turbines using the DFIG is sensitive to any grid disturbance, especially in faulty grid [1]. Faulty grid in the power system, even far from the turbine placement, will cause a drop in the grid voltage, result in an oscillating transient flux, which induces a high electromotive force in the rotor circuit and if it is greater than the maximum power that the inverter can produce, it will cause loss of rotor current control and cause a large rotor current [1], which may damage the inverter. In order to meet the high requirements of the present grid codes [5], now there are three applied fault ridethrough (FRT) strategies for DFIG based wind turbines. The ﬁrst is the protective devices/circuits only during transient state, the second is the reactive power injectingdevices during transient state and the third is the proper control structures during both steady state and transient state. In this paper, the third strategy is emphasized combining with the remaining two strategies. To now, some of the advanced FRT control strategies include: (a) robust control [6], (b) slidingmode control [7], (c) adaptive control [8], (d) model predictive control [9], (e) FuzzyLogic control [10], and (f) InputOutput Feedback Control [11]. However, the above advanced FRT control strategies do not consider all the nonlinear disturbances that happen when faulty grid occurs. In order to meet the high requirements of the present grid codes [5], we design the robust current controller of doubly fed generator in wind power plant with the Backstepping technique based disturbance
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observer that considers all disturbances in detail when faulty grid occurs, that are nonlinear disturbances of the rotor circuit angle frequency, the stator flux, the stator voltage and the generator speed [12, 13]. The results have been veriﬁed on experiment system based on the dSpace Multi Processors ALPHACOMBO system [14], that the control system has the ability to connect to the grid during three phase voltage dips and support the voltage of the grid during and immediately following the grid fault by supplying reactive power to the network, that guarantees the stability of the network.
2 Design the Robust Controller of Doubly Fed Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer According to [1, 12, 13], when the grid is collapsed, the rotor current and the stator flux fluctuate, the electric torque of the generator also fluctuates, the rotor speed and thus the rotor circuit angular frequency also fluctuate. This leads to the fluctuation of the slip factor and the induced voltage in the rotor circuit. If the amplitude of the rotor voltage is too large beyond the capacity of the inverter, the rotor current will be out of control for a short time and a large overcurrent occurs. From the above analyses and comments, in order to improve the control quality, the controller needs to consider the fluctuating factors as mentioned above. Where, the variables of the system are expressed in the synchronous dq coordinate ﬁxed to the space stator voltage vector [15]. The oscillation of the rotor circuit angle frequency, the stator flux, the stator voltage and the generator speed are taken into account by the corresponding nonlinear oscile0 ; w e0 ; e e r; x e; w lating noise components x u sq , which are added to the their corsd sq u sd ; e responding values in the normal mode as follow: b 0 ¼ w0 þ w e0 ; w b 0 ¼ w0 þ w e0 ; b er ; x b ¼ xþx e ;w b r ¼ xr þ x u sd ; b u sq x sd sq sd sd sq sq u sd ¼ usd þ e ¼ usq þ e u sq ð1Þ e0 ; w e0 ; e e r; x e; w Where: x u sq are attenuation nonlinear fluctuation disturbances of sd sq u sd ; e the rotor circuit angle frequency, rotor speed, direct and quadrature stator flux, direct and quadrature stator voltage; x; xr ; w0sd ; w0sq ; usd ; usq are their normal values. In order to meet the increasing requirements of quality of electrical network that the wind electrical power system must be able to connect to the grid during three phase voltage dips, and must be able to support the voltage of the grid during and immediately following the grid fault by supplying reactive power to the network, that remains the stability of the network, the control system of doubly fed induction generator in wind power plant needs to consider the fluctuating factors as mentioned above. To achieve that goal, ﬁrstly we must identify the above nonlinear fluctuation disturbances by using the disturbance observer, then we choose the appropriate control technique to design the controller, which has abilities to cancel the above nonlinear fluctuation
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disturbances and is suitable for nonlinearity of DFIG. In this paper, we use the Lyapunov stability theory based Nonlinear Backstepping technique to design both controller and disturbance observer. There are two control loops in the control system of DFIG in wind power plant. The outer control loop is the active and reactive power control loop, the inner control loop is the current control loop. Among them, the current control loop is the most important, which determines the quality of the control system. Therefore, in this paper, we focus on the design of the current control loop. According to [15], the rotor current model of DFIG written in the synchronous dq coordinate ﬁxed to the space stator voltage vector is given by following equations: (
dird dt dirq dt
¼ aird þ xr irq þ ew0sd bxw0sq þ curd dusd ¼ airq xr ird þ ew0sq þ bxw0sd þ curq dusq
ð2Þ
Where: ird ; irq ; urd ; urq are the direct, quadrature rotor current and voltage components in dq coordinate; usd ; usq are the stator direct and quadrature votage components in =
dq coordinate; wsd ¼ wsd =Lm ; wsd is the direct stator flux; Lm – mutual inductance of DFIG; w=sq ¼ wsd =Lm ; wsq is the quadrature stator flux; r ¼ 1 L2m =Lr Ls ; Lr, Ls are rotor and stator inductances of DFIG; x is the rotor speed of DFIG; xr is the rotor angle frequency of DFIG; Tr = Lr/Rr; Ts = Ls/Rs; Rr, Rs are the rotor and stator resistances per phase of DFIG; a ¼ 1=rTr ð1 rÞ=rTs ; b ¼ ð1 rÞ=r,c ¼ 1=rLr , d ¼ ð1 rÞ=rLm ,e ¼ ð1 rÞ=rTs : From (1) and (2), we will design the proposed control system in the next parts by using the Lyapunov stability theory based Nonlinear Backstepping technique [15]. According to Backstepping technique, to make a close control loop with a feedback state variable, the error of the desired and the real values of that state variable is deﬁned, then to make a Lyapunov control function corresponding with the state variable error. From condition of negative Lyapunov control function, we choose an appropriate value for the next state variable and equations for disturbances if they are present in the model of the object. The process will stop if the ﬁnal state variable is the real control variable. Where, the real control variable is the rotor voltage. 2.1
Design the Robust Direct Current Controller of Doubly Fed Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer
Select ird as the control variable, its desired value ird* is taken from the active power controller. Deﬁne the difference between ird and its desired value ird* as: z1 ¼ ird ird
ð3Þ
z_ 1 ¼ ðdird =dtÞ dird =dt
ð4Þ
Derivate z1, we have:
Choose the Lyapunov control function as:
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V1 ¼ ð1=2Þz21
ð5Þ
V_ 1 ¼ z1 z_ 1
ð6Þ
Derivate V1, we have:
From (2), (4) and (6), we have: V_ 1 ¼ z1 aird þ xr irq þ ew0sd bxw0sq þ curd dusd dird =dt
ð7Þ
We choose curd as the real control variable, from (7), to make the derivation of V1 negative, we choose the real control variable urd as curd ¼ aird xr irq ew0sd þ bxw0sq þ dusd þ dird =dt k1 z1
ð8Þ
Where k1 is the positive constant. When grid fault is occurred, there are nonlinear fluctuation disturbances, Eq. (8) becomes ^ r irq ew^0 sd þ bx ^ w^0 sq þ d^ c^urd ¼ aird x usd þ dird =dt k1 z1
ð9Þ
From (1) and (9), we have: 0 0 e0 c^urd ¼ aird xr irq ewsd þ bxwsq þ dusd þ bx w sq 0 e0 x e 0 þ de e wsq þ b x ew e r irq e w þ bx u sd k1 z1 þ dird =dt sq sd
ð10Þ
Replace curd in (7) with c^urd in (10), we have 0 e 0 þ bx e0 x e 0 þ de e e e w þ b x w i e w u V_ 1 ¼ z1 k1 z1 þ bx w r rq sd sq sq sq sd
ð11Þ
From (11), we see that, the derivation of V1 is not negative, so the controller is not stable. To have the stable controller, we choose the Lyapunove control function, which considers the nonlinear fluctuation disturbances, as follows: ^1 ¼ V1 þ V
e xw sq 0
2
2c01
þ
e w0sq x
2
2c11
þ
e ew x sq 0
2c21
2
e r irq x þ 2c31
2 þ
e0 w sd
2c41
þ
ðe u sd Þ2 ð12Þ 2c51
^1 in (12), we have Where, c01 ; c11 ; c21 ; c31 ; c41 ; c51 are positive constants. Derivate V ^_ 1 ¼ V_ 1 þ V
0 0 e e xw sq d x w sq c01
e w0sq d x e w0sq x
e0 d x e0 ew ew x sq sq
þ þ dt c11 dt c dt 0 0 21 e e w sd d w sd e r irq e r irq d x x ðe u sd Þ u sd Þ d ðe þ þ þ c51 dt c41 dt c31 dt
ð13Þ
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From (11) and (13), we have 0 d 0 1 0 d 0 e e ^_ 1 ¼ k1 z21 þ 1 x w e e x w V bz bz x w þ c þ c þ x w 01 1 11 1 sq sq sq sq c01 dt c11 dt d 1 e0 d e0 1 e w sq þ c21 bz1 þ e r irq c31 z1 e w sq e r irq þ x x x x c21 dt c dt 31 1 e0 d e0 1 d w sd c41 ez1 þ u sd Þ þ c51 dz1 þ w sd ðe u sd Þ ð e c41 dt c51 dt ð14Þ ^1 negative, we choose From (14), to make the derivation of V 8 0 ~ 0 þ c bz1 ¼ xw ~ 0 ; d xw < d xw ~ 0sq ; dtd ð~ ~ usd usd Þ þ c51 dz1 ¼ ~ 01 sq dt sq þ c11 bz1 ¼ xw dt sq : d x ~ 0 c ez1 ¼ w ~0 ; d x ~0 ~ 0 þ c bz1 ¼ x ~w ~ r irq ; dtd w ~w 21 41 sq sq dt ~ r irq c31 z1 ¼ x sd sd dt ð15Þ The Eqs. (15) describe the model of the Backstepping technique based disturbance observer of the direct current controller (10). In (15), the nonlinear disturbances of 0 ~ 0 ; xw ~0 ; x ~0 ~w xw usd ; x sq ~ sq ; ~ sq ~ r irq ; wsd are observed for their corresponding values in the direct current controller (10). The Eqs. (15) and (10) are combined to make the robust direct current controller of doubly fed generator in wind power plant with the Backstepping technique based disturbance observer. We call this controller with that name, because we have designed the controller by Backstepping technique as mentioned above. 2.2
Design the Robust Quadrature Current Controller of Doubly Fed Generator in Wind Power Plant with the Backstepping Technique Based Disturbance Observer
Do the same as the design of the robust direct current controller of doubly fed generator in wind power plant with the Backstepping technique based disturbance observer we have the robust quadrature current controller described in the Eq. (16) with the Backstepping technique based disturbance observer described in the Eq. (17) as below: 0 0 e0 c^urq ¼ airq þ xr ird ewsq bxwsd þ dusq bx w sd
e þx e þ de e wsd b x ew e r ird e w b x u sq k2 z2 þ dirq =dt sd sq 0
0
0
ð16Þ
8 0 ~ 0 c bz2 ¼ xw ~ 0 ; d xw < d xw ~ 0sd ; dtd ~ usq usq þ c52 dz2 ¼ ~ 02 sd sd dt ~ sd c12 bz2 ¼ xw dt 0 0 0 0 : d x ~ ~ ; d ðx ~ ~ ~w ~ r ird ; dtd w ~w sd c22 bz2 ¼ x sd dt ~ r ird Þ c42 z2 ¼ x sq c32 ez2 ¼ wsd dt
ð17Þ
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Where, k2 ; c02 ; c12 ; c22 ; c32 ; c42 ; c52 are the positive constants. 0 ~0 ; x ~0 ~ 0 ; xw ~w usq ; x In (17), the nonlinear disturbances of xw sd ~ sd ; ~ sd ~ r ird ; wsd are observed for their corresponding values in the quadrature current controller (16). The construction of the proposed generator side controller is described in Fig. 2. There are main blocks in Fig. 2 as follow: RVC is the block calculating the values of current references [15], PCTL is the bock controlling the active power of the system [15], QCTL is the block controlling the reactive power of the system [15], REALVC is the block calculating the real values [15], PA&FM is the block measuring phase angle and angle frequency of grid voltage [15], CCTL is the block controlling currents expressed by Eqs. (10) and (16). DOB is the disturbance observer expressed by Eqs. (15) and (17).
Fig. 2. The construction of the proposed generator side controller
3 Experimental Results 3.1
Experimental System
The experimental system [14] is shown in Fig. 3, it consists of the following main blocks: Power electronics block (Power module) is the Electronic power block BUS623 manufactured by Baumueller (Germany); a doubly fed induction generator; threephase asynchronous machines (acting as a source of wind); Control algorithm is installed on multimicroprocessor system of dSpace, ALPHA COMBO.
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287
DFIG
Power module
Induction motor acts as wind source
Alpha Combo
Fig. 3. The experimental system
ALPHA COMBO multimicroprocessor system includes: a DS1003 master DSP board, which performs digital input/output tasks; an extra processor (SLAVE) DS1004, which is primarily used for computing tasks; Peripheral cards: I/O card, ADC, DAC, Encoder interface. The values of the experimental parameters as follow:  The parameters of DFIG: number of pole pair zp = 2; rated power: 4 kW; the normal voltage of stator: 230/400 V(D/Y); the normal voltage of rotor: 366 V; the normal stator frequency: 50 Hz; the normal stator current: 15.2/8.8A(D/Y); power factor: 0.78; the normal rotor speed: 1950 rpm; Rs = 1.07Ώ; Rr = 1.32Ώ; Lm = 0.1601H; Ls = 0.2261 Ώ; Lr = 0.1699 Ώ; the inertial constant: J = 0.032 kg.m2.  The parameters of grid side: the inductance of ﬁlter Ld = 0.0002H; the resistance of ﬁlter Rd = 0.01 Ώ; the capacitance of RC ﬁlter Cf = 400 lF; the resistance of RC ﬁlter Rf = 0.2 Ώ; the capacitance of intermediate DC circuit capacitor: Cdc = 1470lF. 3.2
Experiment with the Robust Current Controller When Grid Voltage Is Collapsed with Remaining Voltage of 15% of Its Normal Value and the Time Lasts 8 Cycles, Ird = 1.5A, Irq = −2.7A
The results of the experiment in this case are shown in Fig. 4.
Current (A)
ird
6
ird*
4
2
irq
0
2
irq*
4
6
0.2
0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
time (s) Fig. 4. Responses of the rotor current when grid fault occurs with the remaining grid voltage of 15% of its normal value
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Through the results of the experiment in Fig. 4, it is clear that when the grid voltage is 15% of its normal values, with the disturbance observer based robust current controller, the real values of the rotor current components remain well according to their set values. That means, the goal of control of active and reactive power and connection of DFIG to grid in the case of grid voltage collapse is achieved. 3.3
Experiment with the Current Controller Without Disturbance Observer When Grid Voltage Is Collapsed with Remaining Voltage of 15% of Its Normal Value and the Time Lasts 8 Cycles, Ird = 0A, Irq = − 2.7A
In this case, the experimental results are shown in Fig. 5.
Current (A)
Current (A)
10
20
ird
irq
5
10
0
0
5
10
10 20
15 30
20
40
25 30 0.5
0
0.5
a)
time (s)
1
1.5
50 0.5
0
0.5
b)
1
time (s)
1.5
Fig. 5. a) Response of the direct rotor current and b) response of the quadrature rotor current when grid fault occurs with the remaining grid voltage of 15% of its normal value with the current controller without disturbance observer
In Fig. 5, when we do experiments with the backstepping current controller without disturbance observer in the case of the grid voltage collapsed with remaining grid voltage of 15% of its normal value and the grid collapse lasts 8 cycles, the value of direct rotor current component is set to zero, the value of the quadrature rotor current component is set to −2.7A. That means, the active power of DFIG is set to zero and the reactive power is supplied to the network to hope that, the stability of the network is still achieved and the DFIG is still controlled to connect to the grid during grid fault. However, the experimental results show that, the control system is not stable due to the large current amplitude (the rotor component of ird has a maximum amplitude of −25A, it is much more different from its desired value of 0A, the rotor component of irq has a maximum amplitude of −40A, it is approximately 15 times greater than its rated value of −2.7A), so the protector of the rotor inverter has disconnected the system from the grid and the goal of control and connection of the system to the grid is failed. The cause of this phenomenon is that the current controller do not consider the strong nonlinear fluctuation disturbances when the grid voltage collapse occurs.
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4 Conclusion The experimental results have shown that, with the nonlinear backstepping disturbance observer based robust current controller, the control system of the doublyfed induction generator in wind power plant has been able to connect to the grid during three phase voltage dips, and supports the voltage of the grid during and immediately following the grid fault with remaining grid voltage of 15% of its normal value by supplying reactive power to the network, that remains the stability of the system. In addition, the experimental results of the control system based on the backstepping current controller without disturbance observer in the same situation have shown that, the control system without disturbance observer cannot meet the high requirements of quality of electrical network. Acknowledgment. This research was supported by a grant for the university research from Thai Nguyen University of Technology (TNUT). We thank our colleagues from TNUT who provided insight and expertise that greatly assisted the research.
References 1. Jackson, J.J., Francis, M., Jin, W.J.: Doublyfed induction generator based wind turbines: a comprehensive review of fault ridethrough strategies. Renew. Sustain. Energ. Rev. 2(6), 255–265 (2015) 2. Mahdi, J.H., Sahand, G.L., Mohammad, T.: Compensating stator transient flux during symmetric and asymmetric faults using virtual flux based on demagnetizing current in DFIG wind turbines. Int. Power Syst. Conf. (PSC) 3(8), 357–364 (2019) 3. Tazil, M., Kumar, V., Bansal, R.C., Kong, S., Dong, Z.Y., Freitas, W.: Threephase doubly fed induction generators: an overview. IET Electric Power Appl. 4(2), 75–89 (2010) 4. Mwasilu, F., Justo, J.J., Ro, K.S., Jung, J.W.: Improvement of dynamic performance of doubly fed induction generatorbased wind turbine power system under an unbalanced grid voltage condition. IET Renew Power Gen. 6(6), 424–434 (2012) 5. Naderi, S.B., Negnevitsky, M., Jalilian, A., Hagh, M.T., Muttaqi, K.M.: Low voltage ridethrough enhancement of DFIGbased wind turbine using DC link switchable resistive type fault current limiter. Int. J. Electr. Power Energ. Syst. 6(86), 104–119 (2017) 6. Costa, D., Patric, J., Pinheiro, H., Degner, T., Arnold, G.: Robust controller for DFIGs of gridconnected wind turbines. IEEE Trans. Ind. Electron. 58(9), 4023–4038 (2011) 7. Benbouzid, M., Beltran, B., Amirat, Y., Yao, G., Han, J., Mangel, H.: Highorder sliding mode control for DFIGbased wind turbine fault ridethrough. In: Industrial Electronics Society, IECON 39th Annual Conference of the IEEE, pp. 7670–7674 (2013) 8. Beheshtaein, S.: Optimal hysteresis based DPC strategy for STATCOM to augment LVRT capability of a DFIG using a new dynamic references method. In: Industrial Electronics (ISIE), IEEE 23rd International Symposium on, pp. 612–619 (2014) 9. Hou, G., Wang, Z., Jiang, P., Zhang, J.: Multivariable predictive functional control applied to doubly fed induction generator under unbalanced grid voltage conditions. In: Industrial Electronics and Applications, vol. 8(9), pp. 2644–2650 (2009) 10. Riouch, T., Rachid, E.B.: Improvement lowvoltage ridethrough control of dﬁg during grid faults. In: Multimedia Computing and Systems (ICMCS), International Conference, pp. 1596–1601 (2014)
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11. Liu, J.H., Chu, C.C., Lin, Y.Z.: Applications of nonlinear control for fault ridethrough enhancement of doubly fed induction generators. IEEE J. Emerg. Select. Top. Power Electron. 2(4), 749–763 (2014) 12. Lopez, J., Sanchis, P., Roboam, X., Marroyo, L.: Dynamic behavior of the doubly fed induction generator during threephase voltage dips. IEEE Trans. Energ. Conver. 200–209 (2006) 13. Lima, F.K.A., Luna, A., Watanabe, E.H.: Rotor voltage dynamics in the doubly fed induction generator during grid faults. IEEE Trans. Power Electron. 25(1), 255–262 (2010) 14. Lan, P.N.: Linear and nonlinear control approach of doubly – fed induction generator in wind power generation. Doctoral thesis, TUDresden (2006) 15. Tuyen, C.X.: Synthesis of nonlinear algorithms on the basis of Backstepping method to control doubly fed induction machines in wind power generation system. Doctoral thesis, HaNoi University of science and technology (2008)
Design and Some Experimental Results of the UType Permanent Magnet ThreePhase Linear Motor Based Position Control System with the Backstepping Technique Based Disturbance Observer C. X. Tuyen1(&) and N. T. Huong2 1
Faculty of Electrical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 2 Electronics Faculty, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]
Abstract. Nowadays, permanent magnet linear motors are widely used in high accuracy CNCs, because they make the linear moving without the mechanical system, that transfers rotating moving to linear moving, therefore the accuracy in position control of the system is improved. Among them, Utype permanent magnet threephase linear motors are more widely used because there are not attractive forces in these motors and the thrust force is high. However, the nonlinearity of the motor and the force disturbances that are caused by the end effect, load, slot tooth, high order harmonic waves still reduce the accuracy in position control of the system. In order to solve this problem, this paper presents the design of the UType permanent magnet threephase linear motor based position control system with the Backstepping technique based disturbance observer. The proposed position control system is experimented on the DSP based experimental system, which uses DSP TMS 320F2812. The experimental results show that the performance of the proposed position control system has high accuracy, that meets the commands of the high precision CNC machines in the industry. Keywords: Position control system UType permanent magnet threephase linear motor Backstepping technique based disturbance observer DSP TMS320F2812 High precision CNC machines
1 Introduction Nowadays, the trend of using the permanent magnet threephase linear motors in the industry becomes more and more popular in developed countries, the main advantage of this type of motors is that they make the linear moving without the mechanical system, that transfers rotating moving to linear moving, therefore the accuracy in position control of the system is improved [1]. Among them, Utype permanent magnet © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 291–302, 2021. https://doi.org/10.1007/9783030647193_33
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threephase linear motors (UPMLM) are more widely used because there are not attractive forces in these motors and the thrust force of the Utype permanent magnet linear motor is high [2]. However, the Utype permanent magnet threephase linear motor is deﬁned as a high nonlinear dynamic, MIMO electromechanical actuator including adverse impacts of the end effect and the adverse effect of the force of highorder harmonic waves [3, 4] and the adverse effect of variation of the viscous friction coefﬁcient, these features of UPMLM have reduced the accuracy of the position control system of UPMLM [2, 3]. To deal with the end effect, there are some ways in literature, that include: measure the impact of the end effect by experiment and compensate it [4], including the end effect in the model of the motor [5]. The solution in [4] requires additional experiments that are expensive. The solution in [5] requires a big calculation and it is too complicated to implement in reality. In this paper, the end effect is considered as a force disturbance for simplicity, also it is necessary to select the method of designing the position control system in accordance with the nonlinear properties of the motor including the load change, the variation of the viscous friction coefﬁcient, the adverse impacts of the end effect and the adverse effect of the force of highorder harmonic waves in the system. In this paper, the Backstepping technique [6] is used to design the disturbance observerbased position control system of UPMLM based on ﬁeldoriented control [3]. The proposed control system is experimented on the DSP based experimental system, which uses DSP TMS320F2812. The experimental results show that the error of position is quite small, about 1.5 10−8 m, these results prove that the performance of the proposed position control system has high accuracy and meets the commands of the high precision CNC machines in the industry
2 Construction, the Working Principle and Mathematical Model of UPMLM 2.1
Construction of UPMLM
According to [2], the structure of the Utype linear motor is described as shown in Fig. 1, the Ushaped static part is attached with permanent magnets on the sides, in the middle of UPMLM there is a double steel core, on which two identical threephase windings are placed each one on each side and independently of each other.
Fig. 1. The structure of UPMLM
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2.2
293
Working Principle of UPMLM
The working principle of UPMLM is based on the principle of every single permanent magnet threephase linear motor (SPMLM). In terms of structure, SPMLM is a flat shape of a permanent magnet synchronous threephase rotary motor, in other words, SPMLM is a permanent magnet synchronous threephase rotary motor when the rotor radius tends to inﬁnity, with the permanent magnets are ﬁxed to the stator part and the moving magnetic circuit carries the threephase windings, so the working principle of each SPMLM is similar to the permanent magnet synchronous threephase rotary motor, except that the torque making the rotor to rotate in permanent magnet synchronous threephase rotary motor is replaced by electromagnetism thrust force to make the mover part of SPMLM to move. From the construction of UPMLM that is presented above, the electromagnetism thrust force of UPMLM is the sum of electromagnetism thrust forces of two SPMLMs. 2.3
Mathematical Model of UPMLM
From the structural characteristics of UPMLM, we see that the motor consists of two identical single permanent magnet threephase linear motors with a rigidly connected moving part, so UPMLM’s mathematical model consists of two mathematical models of an SPMLM combined with a common load of 2FL, that contains load force and multinoises of force. According to [3], we have the system of mathematical equations which describes for each single permanent magnet threephase linear motor in dq coordinate [3] as follows: 2 dird 3
2
2pvLrq 1 sLrd irq þ Lrd urd 2pvwp 1 1 Trq irq þ Lrq urq sLrq
T1rd ird þ
dt 6 6 dirq 7 6 2pvLrd ird 6 dt 7 ¼ 6 sLrq 4 dv 5 6 3ppwp Bv FL 4 dt sm irq m v m ds dt v
3 7 7 7 7 5
ð1Þ
Where, ird and irq are the direct current (A) and the quadrature current (A) respectively; urd is the direct voltage (V); urq is the quadrature voltage (V); R is the resistance in each phase of the motor (X); Lrd is the direct selfinductance in each phase of a motor (H); Lrq is the quadrature selfinductance in each phase of the motor (H); p is the number of pole pairs of motor. Wp is flux linkage amplitude of permanent magnet (Vs); v is the velocity of the motor (m/s); s is mover position (m); FL is the load force (N); Bv is the viscous friction coefﬁcient (Ns/m); Trd = R/Lrd; Trq = R/Lrq; m is the mass of moving part (kg); s is the pole pitch (m).
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3 Design the UType Permanent Magnet ThreePhase Linear Motor Based Position Control System with the Backstepping Technique Based Disturbance Observer 3.1
The Overall Structure of the UPMLM’s Position Control System
The overall control system structure for UPMLM is depicted in Fig. 2, where each SPMLM is controlled by a separate position control system. In Fig. 2, the PCTR1 block controls the ﬁrst single linear motor SPMLM1, the PCTR2 block controls the second SPMLM2, LD block is the load for UPMLM.
S (feedback position)
PCTR1
SPMLM1 LD
S*(Reference position)
PCTR2
SPMLM2
Fig. 2. The overall structure of the UPMLM’s position control system
3.2
The Overall Structure of the SPMLM’s Position Control System
The overall structure of the SPMLM’s position control system is depicted in Fig. 3. In the Fig. 3, there are following basic blocks: (1)  the direct current controller; (2)  the position controller; (3)  the inverse Park transformation block, which transforms motor’s voltages (urd, urq) in dq coordinate to motor’s voltages (ura, urb) in ab coordinate; (4)  the Park transformation block, which transforms motor’s currents (ira, irb) in ab coordinate to motor’s currents (ird, irq) in dq coordinate; (5)  the Clark transformation block, which transforms motor’s phase currents (ia, ib, ic) to motor’s currents (ira, irb) in ab coordinate; (6) the space vector pulse width modulation; (7) the voltage source inverter; (8)  the current sensor block, which is used to measures phase currents ia, ib, ic; (9) – the single permanent magnet three phase linear motor (SPMLM); (10)  the velocity sensor, which measures the velocity of the mover; (11) – the block, which calculates the electrical position of the SPMLM; (12) – the block, which calculates the position of the SPMLM; (13)  the velocity control block; (14) the quadrature current control block; (15)  the Backstepping technique based disturbance observer block.
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Fig. 3. Structure of the single PMLM’s position control system
3.3
Design the Disturbance ObserverBased Position Control System for SPMLM
The disturbance observerbased position control system of SPMLM is designed based on the Backstepping technique. According to the Backstepping technique [6], ﬁrstly, the outer closed control loop, which is the closed position control loop, is designed. Secondly, the middle closed control loop, which is the closed velocity control loop, is designed. Finally, the interest closed control loop, which is the closed current control loop, is designed. In order to design the position control system, the errors of position, velocity, and currents are deﬁned as the following equations: e1 ¼ s s
ð2Þ
e2 ¼ v vdes
ð3Þ
e3 ¼ irq irqdes
ð4Þ
e4 ¼ ird ird
ð5Þ
Where: s* is the position reference; ird is the direct current reference; vdes is the desired velocity, which is the output of the closed position control loop; irqdes is the desired quadrature current, which is the output of the closed velocity control loop. Step1: Design the closed position control loop From (2), we derivate the position tracking error and have e_ 1 ¼ s_ s_ ¼ v s_ Where v is the velocity of the mover. Choose v as a control variable, choose the Lyapunov Function [6] as
ð6Þ
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V1 ¼ ð1=2Þe21
ð7Þ
Derivate V1 and combine with (6), we have V_ 1 ¼ e1 e_ 1 ¼ e1 ðv s_ Þ
ð8Þ
According to Lyapunov stability theorybased Backstepping technique [6], to make the position tracking error converge to zero, the derivative of V1 has to be negative, to achieve that, we choose the desired control variable v as below: v ¼ k1 e1 þ s_ ¼ vdes
ð9Þ
Where k1 is a positive constant. From (9), we have the position controller, which is depicted in Fig. 4.
+ s
e1 
k1
+ +
s*
vdes
d/dt Fig. 4. The structure of the position controller
However, v is just a state variable and not the real control variable, we implement step two. Step two: Design the velocity control loop Do the same as step 1 with an extended Lyapunov Function [6] as V2 ¼ ð1=2Þe21 þ ð1=2Þe22
ð10Þ
To combine with (1) and to make the derivation of V2 negative, we choose the desired value of irq as ! bv B 2 k1 k1 e1 m ! bv b v s_ bL B sB sF sm€s k1 þ þ e2 þ m 3pwp 3pwp 3pwp
bi rqref ¼ sme1 sm 3pwp 3pwp
sm 3pwp
ð11Þ
The parameters Bv, FL are changed in the working of SPMLM because of end effect, load disturbance, and the force of highorder harmonic waves, so we do not have their b L and (11) becomes b v; F exact values. Their estimated values are B
Design and Some Experimental Results of the UType
bi rqref
! bv B 2 k1 k1 e1 m ! bv b v s_ bL B sB sF sm€s k1 þ þ e2 þ m 3pwp 3pwp 3pwp
297
sme1 sm ¼ 3pwp 3pwp
sm 3pwp
ð12Þ
However bi eqref is just a state variable and not the real control variable, we continue to implement step three. Step three: Design the quadrature current control loop with disturbance observer based on the Backstepping technique With the estimated values of Bv and FL, the Eq. (4) becomes be 3 ¼ irq bi rqref
ð13Þ
b t and the real values Bv, Ft as b v; F Deﬁne the difference between the observed values B the disturbances, which are expressed as below: b v Bv ; F eL ¼ F b L Ft ¼ DFL þ DFend ev ¼ B B
effect
þ DFhigh
hm
ð14Þ
Where: DL is the load disturbance, Dend effect is the disturbance caused by endeffect, Dhigh hm is the disturbance caused by high order harmonic waves. The Eq. (14) means that we consider adverse impacts of the load disturbance, of the end effect, of the force of highorder harmonic waves, and of variation of the viscous friction coefﬁcient in the proposed control system. We also do the same as step 1 with an extended Lyapunov Function that considers the above disturbances in (14) as below: 1 2 1 ~2 1 ~ 2 2 Ve ¼ e þ e2 þ ^e3 þ Bv þ FL 2 1 c1 c2
ð15Þ
Where c1 ; c2 are positive constants? To combine with (1) and to make the derivation of Ve negative, we choose the disturbance observer that is expressed in Eqs. (16), (17), and the desired value of the control variable urq as (18). ^_ v ¼ c1 B
sk1 s s sk12 sk1 s€s e2 s_ e3 þ e1 e3 þ e2 e3 þ e1 e3 e2 e3 þ e3 þ 3 3pwp Trq 3pwp Trq 3pwp Trq 3pwp 3pwp 3pwp m
!
ð16Þ ^_ t ¼ c2 F
s e3 3pwp Trq
ð17Þ
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urq ¼
3pwp Lrq 2pLrd Ird 2pLrd Ird 2pLrd Ird s_ 1 2 sm smk12 e2 e3 þ e1 e2 e3 e2 k1 e1 e3 þ e3 þ e e1 e3 þ e1 e3 Trq 3 3pwp Trq e3 sm sLrq sLrq sLrq 3pwp Trq
2pwp 2pwp k1 2pwp s_ smk1 smk1 sm smk1 smk13 €s e3 þ e2 e3 þ e2 e3 e1 e3 þ e3 e2 e3 þ þ e1 e3 e1 e3 3pwp 3pwp Trq 3pwp Trq sLrq sLrq sLrq 3pwp 3pwp ^_ v k1 ^_ v ^_ v s_ ^_ L smk12 sB sB sB sF smsv sBv k1 sBv þ e2 e3 e1 e3 þ e2 e3 þ e3 þ e3 þ e3 k1 e23 e1 e3 þ e2 e3 3pwp 3pwp 3pwp 3pwp 3pwp 3pwp 3pwp Trq 3pwp Trq # sBv s_ sk 2 Bv sBv k1 sBv€s Bv sFL þ e3 þ 1 e1 e3 e2 e3 þ e3 þ e23 þ e3 3pwp Trq 3pwp 3pwp 3pwp m 3pwp Trq
ð18Þ
Step four: Design the direct current control loop We do the same as step1 with a Lyapunov Function as V4 ¼ ð1=2Þe24
ð19Þ
To combine (19) with (1) and (5), in order to make the derivation of V4 negative, we choose the desired value of urd as urd ¼ k4 Lrd e4 þ ðLrd =Trd Þird 2pvLrq =s irq þ dird =dt
ð20Þ
Where k4 is a positive constant.
4 Experiments and Discussion 4.1
DSP Based Experimental System for UPMLM
In order to evaluate the performances of the proposed position control system, we do experiments for UPMLM. The principle diagram of DSP based UPMLM driving system is described in Fig. 5 and the DSP based experimental system for UPMLM driver is shown in Fig. 6. The TMS320F2812 DSP kit, which is connected to the computer, also is used to measure the position and the position error of the UPMLM. In this experimental system for UPMLM, we use one DSPTMS320F2812 to control two SPMLMs of UPMLM. The UPMLM has following values of parameters: p = 4, s = 20 mm, m = 2 kg, R = 3. Ώ, Lrd = 2.182 mH, Lrq = 2.012 mH, wp = 9.35 Wb, FN = 90 N, Fmax = 380 N, normal velocity vN = 1.5 m/s, normal current IN = 1.1 A, Bv = 0.002 Ns/m. In Fig. 5, the onephase AC voltage source with a frequency of 50 Hz is connected to the rectiﬁer consisting of four diodes. The output DC voltage of the rectiﬁer is ﬁltrated by a ﬁlter capacitor. The two threephase inverter modules are supplied with the DC voltage source, each threephase inverter module has six MOSFETs connected to threephase windings of each SPMLM of UPMLM. The six PWM outputs (from
Design and Some Experimental Results of the UType
299
Fig. 5. The DSP based UPMLM driving system
Current sensors DSPTMS320 kit
Rectifier and Inverter modules
Spring load
Cable connected to Computer
encoder
UPMLM
Fig. 6. DSP based experimental system for UPMLM driver
PWM1 to PWM6) of the DSP supply six MOSFETs of the ﬁrst threephase inverter module of the ﬁrst SPMLM of UPMLM with pulses through the driver circuit. The other six PWM outputs (from PWM7 to PWM12) of the DSP supply six MOSFETs of the second threephase inverter module of the second SPMLM of UPMLM with pulses through the driver circuit. The Encoder is used to measure the mover’s velocity of the motor. Two outputs of the encoder are connected to speciﬁcally applied inputs of QEP0
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and QEP1 of DSP TMS320F2812. Four hall effect current sensors are used to measure the line currents of UPMLM, their outputs are connected to analog inputs of ADCA0, ADCA1, ADCA2, ADCA3 of the DSP through the low pass ﬁlters (LPFs). The load of UPMLM is the spring load that contributes to making disturbances. The proposed control system is experimented by using TMS320F2812 DSP kit of Texas Instruments. We use Matlab/Simulink to build the control system, then connect with Code Composer Studio software of Texas instruments to make and load the code of controllers to the TMS320F2812 DSP kit. 4.2
Results of Experiments
The desired reference trajectory of the motor mover is depicted in Fig. 7. In Fig. 7, the position trajectory of the motor mover is commanded to track the linear line in the forward direction from 0 m at the time of 0 s to 0.4 m at the time of 0.75 s, then the motor stops at this position. After that, at the time of 0.25 s, the motor changes the direction and rotates according to the linear line in the reverse direction to the original position at 0.35 s and stops at this position. The real trajectory of the motor’s mover is shown in Fig. 8, and the position error is shown in Fig. 9.
Fig. 7. The desired reference trajectory of the motor’s mover
The results of experiments show that the UType permanent magnet threephase linear motor based position control system with the Backstepping technique based disturbance observer makes the real trajectory of the motor’s mover to reach the desired reference trajectory with the quite small error of a position, it is about 1.5 10−8 m. These results prove that the performance of the proposed position control system has high accuracy and meets the commands of the high precision CNC machines in the industry.
Design and Some Experimental Results of the UType
Fig. 8. The real trajectory of the motor’s mover
Fig. 9. The position error
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5 Conclusions This paper presents the design of the UType permanent magnet threephase linear motor based position control system with the Backstepping technique based disturbance observer and experiments of the proposed position control system, which is based on DSP TMS320F2812. The experiments are implemented with different velocities and different directions. The experimental results show that the performance of the proposed position control system is quite good, the position error of the proposed position control system is quite small and meets the increasing requirements of high accuracy of CNC machines in the industry. The experiments also show the feasibility of the proposed position control system in the industry, especially, in high accuracy CNC machines. Acknowledgment. This research was supported by a grant for the university research from Thai Nguyen University of Technology (TNUT). We thank our colleagues from TNUT who provided insight and expertise that greatly assisted the research.
References 1. Dailin Zhang, D., Youping Chen, Y., Ai, W., Zude Zhou, Z.: Precision motion control of permanent magnet linear motors. Int. J. Adv. Manuf. Technol. 6(35), 301–308 (2007) 2. Team, A.: U Channel Linear Motors Hardware Manual. Aerotech, Inc, EC Declaration of Conformity (2012) 3. Nam, D.P.: Improve the quality of linear motion systems by using linear motor drives. The thesis of engineering doctor, Hanoi university of technology (2012) 4. Cupertino, F., Giangrande, P., Pellegrino, G., Salvatore, L.: End effects in linear tubular motors and compensated position sensorless control based on pulsating voltage injection. IEEE Trans. Ind. Electron. 58(2), 494–502 (2011) 5. Quang, N.H.: Research and control design for Electric drive systems using a Polysolenoid linear motor when the end effects are considered. The thesis of engineering doctor, Thai Nguyen university of technology (2019) 6. Tuyen, C.X.: Synthesis of nonlinear algorithms on the basis of Backstepping method to control doublyfed induction machines in wind power generation system. The thesis of engineering doctor, Hanoi university of technology (2008)
Detail Design of IPM Motor for Electric Power Traction Application Bui Minh Dinh(&) and Dang Quoc Vuong Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. This paper deals with the electromagnetic and thermal analysis of an interior permanentmagnet (IPM) synchronous motor with V shape used as traction drive in a medium commercial electric vehicle (EV) according to the traction requirements of the electric vehicle under normal operating conditions and overload conditions or peak power. The key dimensions were calculated on an analytical program by Matlab. The ﬁnite element method (FEM) simulation model of the IPM motor was built by using SPEED software. The influenced geometric structures of the IPM motor including the PM dimensions and skewed PMs on electromagnetic torque were investigated, and the temperature distribution of the motor under rated operating condition and the condition of maximum speed were calculated. Finally, the thermal simulation results of the IPM motor running in various operating modes were investigated and the medium commercial electric vehicle driving applications were required. The contributions of this paper are optimal V angle of magnetic and skew angle slot to minimize torque ripples for a 60 kW IPM 12P/72 slots. Keywords: Electric vehicle Traction motor
IPM motor Electromagnetic design FEM
1 Introduction Electric vehicles (EV) have been identiﬁed as the most viable solution to reduce emissions in the ﬁeld of transportation and have received increasing attention in automobile industry and transportation applications such as passenger car, commercial bus and trolley bus, etc. [1–3]. There are some key issues for a typical EV motor: torque density in terms of weight and volume, torquespeed capability, costs in manufacturing and maintenance [4, 5]. Permanentmagnet (PM) motors are good candidates for EV traction system because they have higher torque density and efﬁciency compared with IMs. There are several types of IPM motors, i.e. surfacemounted PM (SPM), interior PM (IPM), flux switching PM (FSPM) motor and so on for EV drive [5–7]. The rotor of an IPM motor with the Vshaped PMs has a robust structure, highspeed operational capabilities, and magnetic saliency effect [8, 9]. Magnetic saliency effect can lead to the reluctant torque generation; thereby highspeed operation of a motor through flux weakening control is enabled [10, 11–13]. Therefore, the IPM motor with the Vshaped PMs has a much larger overload torque over the entire speed © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 303–310, 2021. https://doi.org/10.1007/9783030647193_34
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range [14], a safer back electromotive force (EMF) in uncontrolled generator operation [15], and little sensitivity to PM temperature. The IPM motor has higher joule loss at low speed, but a properly stator slots number and rotor segments can keep the harmonic loss under control. This paper focused on an IPM motor for medium commercial EV traction application. A 2D FEM simulation model was built according to the theory of PM motor. In order to improve the electromagnetic torque, the geometric structure of the Vshaped PM and the influence of PMs skewed along the axial direction on airgap flux density were investigated. In addition, the temperature distribution of the motor under two typical operating conditions was calculated. Finally, the experimental results and simulation results of the prototype motor running in various driving modes were compared. This paper focused on an IPM motor for medium commercial EV traction application. A FEM simulation model was built according to the theory of PM motor. In order to improve the electromagnetic torque, the geometric structure of the Vshaped PM and the influence of PMs skewed along the axial direction on airgap flux density were investigated. In addition, the temperature distribution of the motor under two typical operating conditions was calculated. Finally, the experimental results and simulation results of the prototype motor running in various driving modes were compared.
2 Speciﬁcation of Electric Power Traction Speciﬁc motor designed for EV application should consider torquespeed curves under peak (dashed line) and continuous (solid line) conditions. Figure 1 shows the standard characteristics of a motor used in the medium commercial EV. The EV requires constant torque at low speed and constant power at high speed in both rated (continuous) and overload conditions.
Fig. 1. Speciﬁcation of the EV power train
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Basic parameters design of IPM V shape is requested for medium commercial vehicle as in Table 1. In order to ensure thermal safety when the designed motor operates continuously at rated load and at least for a couple of minutes at overload, the stator liquid cooling was adopted. During the calculation, the temperature of stator winding and PM was set to be 120 °C. Table 1. Target parameter design No 1 2 3 4 5 6 7 8 9 10 11 12
Parameters AC voltage (V) 380 Power rating P1 (kW) Torque rating T1 (Nm) Rating efﬁciency (%) Current rating (A) Rating speed (rpm) Max speed (rpm) Max Current@1000 rpm (A) Max Torque T0@1000 rpm (Nm) Max Power@1000 rpm (kW) Max Power@4500 rpm (kW) Effective motor size (mm)
Value 380 60 385 95.5 150 1500 4500 380 1150 120 120 U340 L200
3 Electromagnetic Performance Evaluation In order to determine operation points of permanent magnet circuit, some basic parameters of magnetic circuit have calculated in an analytical model. This point depends on remanent flux density and silicon steel material. In this paper, the magnet of LSPMSM has been carried out by the analytical model in Fig. 2 and NdFeB35 is magnet material used due to its good thermal stability and remanent flux density (*1.3T) allowing its use in applications exposed to high temperature about 180 °C. The flux density is estimated about 0.88 T. Based on the analytical method, some geometry parameters of stator and rotor can be calculated as the following chart in Fig. 3. An analytical model was undergone many calculating steps to deﬁne basic parameters. Based on torque volume density TVR from 20 to 30 kNm/m3 [5], if we assume rotor diameter equal to rotor length, the rotor diameter D and length L sizes of LSPMSM is determined as follows: sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ T 47:75 3 3 D¼L¼ p ¼ 3:14 TVR 4 4 25
ð1Þ
The main parameters (such as outer diameter, rotor diameter, motor length, stator slot and air gap length) are deﬁned by taking some practical factors into account with desired input requirements [3]. The main part of the process is to design the rotor
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Fig. 2. Magnetic properties of NdFeB35
Fig. 3. Calculation process
conﬁguration which is embedded permanent magnet. The PM conﬁguration needs to create sufﬁciently magnetic voltage for magnetic circuit. After investigation I, U and V permanent magnetic shape, the V shape PM conﬁguration has a great influence on motor efﬁciency and material cost. After sizing the main dimension and choosing the winding parameters of the proposed IPM motor in Fig. 4, the next stage for the design should be based on the 2D FEM which models saturation, PM material characteristics and nonlinearity of core materials to provide the rated power at base speed and to simulate all performance of the motor running at full speed range.
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Table 2. Motor parameters Parameter Air gap length Stator inner diameter Stator outer diameter Rotor outer diameter Rotor shaft diameter Rotor/stator length PM poles Number of PM piece Stator slot
Value 0.5 240 340 239.5 180 180 12 24 72
Unit mm mm mm mm mm mm mm – –
Fig. 4. Stator and rotor layout (a) and skew angle (b) of PM.
The rotor PM skewed distance is one teeth width, 11 mm, namely, the skew mechanical angle is 5°. Figure 5 shows the airgap flux density waveform and its harmonics amplitude considering the PM skew and no skew when the IPM motor running at noload and rated load respectively. Where the airgap flux density waveform of PM skewed is the average value of the superposition of flux density on the axial direction. It can be seen that the total harmonics TDH of Back EMF is weakened obviously when the slot skew distance is equal to one teeth pitch. In order to evaluate overload 1150 Nm and maximum speed 4500 rpm. The parameters such as total losses and efﬁciency are also listed in Table 2 calculated by FEM (Fig. 6).
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Fig. 5. Back EMF waveform no skew TDH 11% (a) with skew 1 slot THD 4.8%
Fig. 6. Torque waveform under overload (a) and efﬁciency at maximum speed 4500 rpm (b)
The thermal simulation of the IPM motor was calculated in the whole speed range and overload condition. It is noted that this IPM motor can run safely at peak torque, 1150 Nm, the efﬁciencies of the motor in most of the operation areas are more than 90%, and the maximal efﬁciency can achieve 95.7% which has met the requirements of the manufacturer. The temperature distribution of IPM was shown in Fig. 7. This paper simply presents the temperature distribution under special operating conditions by simulation, and the thermal analysis and the temperature rise test of the IPM motor will be discussed in detail in another paper.
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Fig. 7. Temperature distribution of IPM
4 Conclusion An IPM motor with a Vshaped PM rotor was designed on the basis of the traction requirement characteristics of a medium commercial electric vehicle, and its performance was analyzed and tested. Firstly, the key dimensions of the designed motor were computed by empirical formulas of the permanent magnet motor theory. Then, the FEM simulation model, which considered the model saturation, skin effect, slot skew and nonlinearity of core materials for the IPM motor, was built; and the performance of the rated operating point were calculated and checked with the traction characteristics of the EV. The influence geometric structures of the IPM motor including the PM dimensions and skewed PMs on electromagnetic torque were investigated, and the temperature distribution of the motor under rated working condition and maximum speed mode were calculated. The performance of three typical operating modes, i.e. rated speed, overload torque and max speed were investigated respectively. The performance of the IPM control system, such as the fluxweakening rate, overload ability, peak torque, speed range and so on, which are influenced by these parameters determined by the Vshaped PM, will be researched. Acknowledgment. This paper has received supports from the Viettel High TechnologyVHT, Viettel Group for simulating and calculating Software and PCs.
References 1. Kim, K., Kim, S.J., Kim, W.H., Im, J.B., Cho, S., Lee, J.: The optimal design of the rotor bar for LSPMSM considering the starting torque and magnetic saturation. In: 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC) (2010) 2. Sorgdrager, A.J., Wang, RJ., Grobler, A.J.: Transient performance investigation and Taguchi optimization of a linestart PMSM. In: 2015 IEEE International on Electric Machines & Drives Conference (IEMDC), pp. 590–595 (2015)
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3. Isfahan, A.H., VaezZadeh, S.: Effects of magnetizing inductance on startup and synchronization of linestart permanentmagnet synchronous motors. IEEE Trans. Magn. 47(4), 823–829 (2011) 4. Hendershot, J.R., Miller, T.J.E.: Design of Brushless Permanentmagnet Motors. Magna Physics publishing and Clarendon press, Oxford (1994) 5. Zeraoulia, M., Benbouzid, M., Diallo, D.: Electric motor drive selection issues for HEV propulsion systems: a comparative study. IEEE Trans. Veh. Technol. 55(6), 1756–1764 (2006) 6. Zhu, Z.Q., Chan, Q.Q.: Electrical machine topologies and technologies for electric, hybrid, and fuel cell vehicles. In: Proceedings of the IEEE VPPC, Harbin, China, pp. 1–6, September 2008 7. Boldea, I., Tutelea, L.N., Parsa, L., Dorrell, D.: Automotive electric propulsion systems with reduced or no permanent magnets: an overview. IEEE Trans. Ind. Electron. 61(10), 5696– 5711 (2014) 8. Zhu, Z.Q., Howe, D.: Electrical machines and drives for electric, hybrid, and fuel cell vehicles. Proc. IEEE 95(4), 746–765 (2007) 9. Pellegrino, G., Vagati, A., Guglielmi, P., Boazzo, B.: Performance comparison between surfacemounted and interior PM motor drives for electric vehicle application. IEEE Trans. Ind. Electron. 59(2), 803–811 (2012) 10. Cao, R.W., Mi, C., Cheng, M.: Quantitative comparison of flux switching permanentmagnet motors with interior permanent magnet motor for EV, HEV, and PHEV applications. IEEE Trans. Magn. 48(8), 2374–2384 (2012)
Detecting Common Web Attacks Based on Machine Learning Using Web Log Xuan Dau Hoang(&) Posts and Telecommunications Institute of Technology, Hanoi 10000, Vietnam [email protected]
Abstract. SQL injection (SQLi), Crosssite Scripting (XSS) attacks have long been considered major threats to webbased applications and their users. These types of web attacks can cause serious damage to web applications and web users, ranging from bypassing authentication systems, stealing information from databases and users, to even taking control of server systems. To cope with web attacks, many measures have been researched and applied to protect web applications and users. Among them, the detection of web attacks is a promising approach in the defensive layers for web applications. However, some measures can only detect a single type of web attacks, while others require frequent updates to the detection rule sets, or require extensive computational power because of using complex detection methods. This paper proposes a web attack detection model based on machine learning using web log. The detection model is built using the inexpensive decision tree algorithm and it does not require frequent update. Our experiments on a labelled dataset and real web logs show that the proposed model is capable of detecting several types of web attacks effectively with the overall detection accuracy rate of 98.56%. Keywords: Web attack detection SQLi detection XSS detection traversal detection Machine learningbased attack detection
Path
1 Introduction Web attacks, such as SQLi, XSS, CMDi (operating system Command injection) and Path traversal have been considered constant and dangerous threats to websites, web applications and web users [1, 2]. These types of attacks are very common because of the popularity of websites and web applications and the availability of the web attack tools on the Internet [3]. We name the web attack group of SQLi, XSS, CMDi and Path traversal as “common web attacks”. Common web attacks can cause serious consequences to websites and web applications and their users. They can help the attackers to bypass the web systems’ authentication mechanisms, to carry out unauthorized modiﬁcations to web content, databases, to extract data from web application databases, to steal sensitive information of web servers and web users, and even to take control of the web servers and/or database servers [1, 2]. Figure 1 shows an example of SQLi attack to a website, in which the Attacker inserts the malicious code into the search keyword to delete a table of the website’s database.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 311–318, 2021. https://doi.org/10.1007/9783030647193_35
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Fig. 1. An example of SQLi attack to a website
Due to the danger of common web attacks, a number of measures have been researched and applied into practice to detect and prevent these attacks to protect websites, web applications and web users. Generally, there are 3 defensive approaches for these attacks, including (1) validate all data inputs, (2) reduce the attacking surfaces and (3) use “defense in depth” strategy [1, 2]. Speciﬁcally, approach (1) requires all input data to web applications must be checked thoroughly and only legitimate inputs are passed to next steps for processing. On the other hand, approach (2) requires dividing a web application into several parts and applies suitable access controls to limit user accesses. For approach (3), several defensive measures are applied in layers to protect the web applications and web users. This paper proposes a model to detect common web attacks based on machine learning using web log, which belongs to approach (3). We attempt to use a machine learning technique to construct the detection model in order to eliminate the manual construction and update of input data ﬁlters and to increase the detection rate. On the other hand, web logs generated by the web server for each hosted website by default are used as the input to the detection model. The rest of this paper is organized as following: Sect. 2 presents some related works; Sect. 3 is our proposed method, experiments and results, and Sect. 4 is the conclusion of the paper.
2 Related Works As mentioned in Sect. 1, a number of measures of the three approaches have been proposed and applied to defend again common web attacks. Because of the paper length limit, we only review some closely related proposals to our paper, including OWASP Core Rule Set (CRS) [4], SQLID [5], XSSGUARD [6], Liang et al. [7] and Pan et al. [8]. CRS is a set of rules developed by OWASP for the detection of various types of web attacks in top 10 OWASP [1] with low false alarm rate. It can be used in ModSecurity that is a web application ﬁrewall (WAF) module attached to Apache web server. CRS is wellsupported by OWASP and the web security community. However, it may be a difﬁculty to use CRS in some other WAFs or to integrate with other web servers, such as Microsoft Internet Information Services. SQLID [5] is SQLi attack detection system based on speciﬁcations. SQLID ﬁrst built a set of speciﬁcation rules described structures of legitimate SQL queries generated by the web application to be sent to database server for execution. Then, it monitors, preprocesses and classiﬁes incoming SQL queries based on the built rule set. Only SQL queries classiﬁed as ‘legitimate’ are sent to the database server for
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execution. Otherwise, illegitimate queries will be blocked and logged. SQLID is reported to have 0% false alarm rate and it can be used to protect multiple websites because it is implemented as a proxy server between the web server and the database server. However, the manual construction of the speciﬁcation rule set is sitespeciﬁc and time consuming. XSSGUARD [6] is a framework that monitors and prevents XSS attacks by generating a ‘shadow page’ and comparing it with the real page before sending the real page to client. The shadow web page is created in parallel with the real web page from the response of the web server, but with the clean input (without scripts) generated automatically, which has the same length as the real input. Experiments show that XSSGUARD is able to prevent various types of XSS attacks listed by OWASP. In addition, it does not require bulky and frequentupdated rule sets. However, XSSGUARD adds considerable loads to web servers because of the generation and comparison of shadow pages with real pages. Using other approach, Liang et al. [7] proposes to use deep learning of RNNs to build the web attack detection models. Experiments on the CSIC 2010 dataset [9] show that the proposed method achieves the overall detection accuracy of over 98%. In addition, the proposed method can eliminate the manual and timeconsuming task of the feature selection and extraction. On the other hand, Pan et al. [8] proposes the Robust Software Modelling Tool (RSMT) to monitor and extract runtime information of an application and then use collected information to train the stacked denoising autoencoder to build the detection model. Experiments show that the proposed approach can detect various types of web attacks and achieves the F1score of over 91% on average. From the above reviews, we can draw some comments as the following: OWASP Core Rule Set [4] can detect web attacks effectively, however it requires frequent update and it faces the compatible problem in the practical deployment; SQLID [5] or XSSGUARD [6] can only detect one type of web attacks. Moreover, they either face the problem of the manual construction of rule sets, or the performance degradation of servers; Liang et al. [7] and Pan et al. [8] both use deep learning for web attack detection. This is a new approach in the ﬁeld, however deep learning is generally expensive and it may not be suitable for realtime web attack detection. In addition, their detection performance is lower (Pan et al. [8]), or slightly higher than that of the traditional machine learning (Liang et al. [7]). Furthermore, using RSMT to monitor server execution may cause serious performance degradation to the server. In our approach, we propose a model to detect common web attacks based on machine learning using web log with the following advantages over the previous works: (1) our model can detect 4 major types of web attacks, including SQLi, XSS, CMDi and Path traversal; (2) the decision tree  an inexpensive machine learning technique is used to achieve good detection performance; (3) the process of feature selection, extraction and model construction can be done automatically; (4) our detection model does not require frequent update; and (5) the web logs are used as the input of the detection model, which are available by default on most web servers. This means the data collection is fairly simple and this makes it easier for practical deployment.
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3 Proposed Detection Model for Common Web Attacks 3.1
Proposed Web Attack Detection Model
The proposed detection model consists of two stages: (1) the training stage and (2) the detection stage. The training stage as shown in Fig. 2 includes the following steps: 1. Collection of the training data set, including normal URIs (Uniform Resource Identiﬁer) and attacked URIs; 2. Training data set is preprocessed to extract features. After the preprocessing, each URI is converted into a vector; 3. Training data set in the form of vectors is put into Training process to construct the Classiﬁer or the Model that is used for the detection stage.
Fig. 2. Proposed web attack detection model: the training stage
Fig. 3. Proposed attack detection model: the detection stage
The detection stage as described in Fig. 3 consists of the following steps: 1. URIs are extracted from web logs and each URI is the detection process’s input; 2. The URI is preprocessed using the same method done for training data. The output is a vector for the URI, which is used in the next step; 3. The URI’s vector is classiﬁed using the Classiﬁer built in the training stage. The result of this step is either Normal or Attacked.
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Experiments on HTTP Param Dataset
The HTTP Param Dataset [10]. This data set consists of 31,067 URI payloads of web requests, including the payload length and labels of payloads. There are 2 payload labels: Norm (no attack) and Anom (attack). Anom payloads in turn have 4 types: SQLi, XSS, CMDi and Pathtraversal. The data set is divided into 2 parts: • Training set consists of 20,000 payloads for training to construct the detection model; • Testing set consists of 11,067 payloads for checking the model’s performance. Preprocessing. This step is responsible for extracting URI features and vectorizing these features. The preprocessing includes two tasks as following: • Extracting URI features using ngram method. The ngram method is selected because it is simple and fast. We select 3gram to extract URI features. • Vectorizing URI features using the TFIDF (Term FrequencyInverse Document Frequency) method. For each 3gram, a tfidf value is calculated as following:
tf ðt; d Þ ¼
f ðt; d Þ maxff ðw; d Þ : w 2 d g
ð1Þ
N jfd 2 D : t 2 d gj
ð2Þ
idf ðt; DÞ ¼ log
tfidf ðt; d; DÞ ¼ tf ðt, dÞ idf ðt; DÞ
ð3Þ
where tf(t, d) is term frequency of 3gram t in URI d; f(t, d) is the number of occurrences of 3gram t in URI d; max{f(w, d):w 2 d} is the maximum number of occurrences of any 3gram in URI d; D is the set of all URIs and N is the total number of URIs. Because the number of URI features (3gram) is large, the PCA (Principle Component Analysis) method is used to reduce the number of features to 256, which is selected by based on empirical. Training. The training step uses CART decision tree algorithm supported by sklearn library in Python. The reason that the decision tree algorithm is selected is it is fast and therefore suitable for online/realtime detection systems. The result of this step is the Classiﬁer or the Model for use in the Detection stage. Experimental Measurement. The experimental measurement is ACC (Accuracy) that is calculated as the following: ACC ¼ ðNumber of records detected correctlyÞ=ðTotal number of recordsÞ 100% ð4Þ
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The ACC is calculated for each type of web attacks, for the classiﬁcation of normal URIs and all URIs in the testing set. Detection and Results. In this stage the Testing set is used to validate the detection performance of the Classiﬁer built in the training stage. Each URI of the Testing set is preprocessed using the same procedure done for URIs of the Training set. Then, each URI vector is classiﬁed using the Classiﬁer and the result is either Normal or Attacked (together with type of attacks). Table 1 shows the detection accuracy on Testing set. Table 1. Detection accuracy on Testing set Detection label Normal payload SQLi attack XSS attack CMDi attack Path traversal attack Overall detection rate
Accuracy (%) 98.60 99.34 85.88 73.33 97.94 98.56
Discussion. The experimental results shown on Table 1 conﬁrm that: • The proposed web attack detection model achieves high overall detection rate of 98.56% on the testing data set. This is slightly better than the highest accuracy of 98.42% of Liang et al. [7] and much better than that of 91.40% of Pan et al. [8], which use expensive deep learning techniques for web attack detection. • The detection rate for SQLi attacks is very high at 99.34%, followed by the rates for Normal payloads and Path traversal attacks. The detection rates of XSS and CMDi attacks are not high because the amount of training data for these types of attacks is not sufﬁcient to build a good detection model. 3.3
Experiments on Real Web Logs
In this section, we provide experimental results on real web logs collected from our web servers. Generally, common web servers, such as Mozilla Apache, Microsoft IIS and Ng ngix can create logs for each website they host on the web requests of users. Most web logs are in the form of plain text and a text line is a log record. Although there have been many log formats, the W3C Extended log ﬁle format [11] is most widely used and supported by most modern web servers. Figure 4 shows a part of web log ﬁle created by Microsoft IIS using the W3C Extended log ﬁle format. From the web log ﬁle, we extract each access URI that is a combination of csuristem and csuriquery ﬁelds of a log record. Then the URI is put into the detection stage as described on Fig. 3. Experiments on real web logs conﬁrm that our proposed model is able to detect common web attacks correctly and efﬁciently. Table 2 shows typical attack payloads detected using real web logs.
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Fig. 4. A part of web log ﬁle using W3C Extended log ﬁle format Table 2. Typical attack payloads detected using real web logs Detection label SQLi attack XSS attack CMDi attack Path traversal attack
Attack payload used fpw = (select%20convert(int%2cCHAR(65))) type = vh01i’>ooq5g fpw = WEBINF/web.xml%3f type = ../../../../../../../../../../../etc/passwd%00
4 Conclusion This paper proposes a web attack detection model that is based on supervised machine learning using web logs. The proposed model is capable of detecting 4 types of most dangerous web attacks, including SQLi, XSS, CMDi and path traversal. Experiments on the labelled data set and real web logs conﬁrm that our model achieves the overall high detection rate of 98.56% and it is able to detect common web attacks effectively. The proposed model is trained using the inexpensive decision tree algorithm to gain good detection performance. The training process to construct the model can be done automatically. For future work, we will extend the proposed model so that it can detect more types of web attacks as well as have higher detection rates for some special types of web attacks, including XSS and CMDi.
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References 1. OWASP Project. https://owasp.org. Accessed 20 June 2020 2. Baranwal, A.K.: Approaches to detect SQL injection and XSS in web applications, EECE 571B, Term Survey Paper. University of British Columbia, Canada, April 2012 3. Website Attack Tools. https://sourcedefense.com/glossary/websiteattacktools/. Accessed 20 June 2020 4. OWASP ModSecurity Core Rule Set. https://www.owasp.org/index.php/Category:OWASP_ ModSecurity_Core_Rule_Set_Project. Accessed 20 June 2020 5. Kemalis, K., Tzouramanis, T.: SQLIDS: a speciﬁcationbased approach for SQL injection detection. In: SAC 2008, Fortaleza, Ceará, Brazil, 16–20 March 2008, pp. 2153–2158. ACM (2008) 6. Bisht, P., Venkatakrishnan, V.N.: XSSGUARD: precise dynamic prevention of crosssite scripting attacks. In: Proceeding of 5th Conference on Detection of Intrusions and Malware & Vulnerability Assessment. LNCS, vol. 5137, pp. 23–43 (2008) 7. Liang, J., Zhao, W., Ye, W.: Anomalybased web attack detection: a deep learning approach. In: ICNCC 2017, Kunming, China, 8–10 December 2017, pp. 80–85 (2017) 8. Pan, Y., Sun, F., Teng, Z., White, J., Schmidt, D.C., Staples, J., Krause, L.: Detecting web attacks with endtoend deep learning. J. Internet Serv. Appl. 10(16) (2019) 9. HTTP Dataset CSIC 2010. https://www.isi.csic.es/dataset/. Accessed 20 June 2020 10. HTTP Param Dataset. https://github.com/Morzeux/HttpParamsDataset. Accessed 20 June 2020 11. Extended Log File Format. https://www.w3.org/TR/WDlogﬁle.html. Accessed 20 June 2020
Determination of Kinematic Control Parameters of Omnidirectional AGV Robot with Mecanum Wheels Track the Reference Trajectory and Velocity Trinh Thi Khanh Ly1(&), Nguyen Hong Thai2, Le Quoc Dzung1, and Nguyen Thi Thanh3 1
Faculty of Automation Technology, Electric Power University, Hanoi, Vietnam [email protected] 2 Department of Mechanical Design and Robotics, School of Mechanical Engineering, HanoiUniversity of Science and Technology, Hanoi, Vietnam 3 Department of Control and Automation, Faculty Electrical Engineering, Economic and Technical University of Industry, Hanoi, Vietnam
Abstract. Nowadays, in order to save the floor space of the production workshops, the mecanum wheels have been applied to the design of Automated Guided Vehicles (AGV) robot to create highly flexible and omnidirectional AGV robot. Omnidirectional robotic platforms have vast advantages over a conventional design in terms of mobility in congested environments. These environments are commonly found in factory workshops ofﬁces, warehouses, hospitals and elderly care facilities. To be able to control these robots in any given trajectory, it is necessary to set up the kinematic control parameters. The paper presents the method of establishing the kinematic equation of omnidirectional robots by the centre of instantaneous velocity, thereby determining the control parameters of AGV robot following a reference trajectory. In addition, the authors also designed the robot’s motion trajectories using the NURBS curve to solve the robot’s trajectory planning problem according to different application scenarios of modern industrial production. Keywords: AGV robot Kinematic of AGV wheel Trajectory design NURBS curve
Omnidirectional mecanum
1 Introduction Mecanum omnidirectional wheels described in Fig. 1 were invented by a Swedish engineer Ilon in 1973 at Mecanum Company [1] and applied to the design of the ﬁrst AGV in 1997. When torque is transmitted to the wheels, the rollers on the wheel that come into contact with the floor form two velocity components: in the direction of the wheel’s movement and is perpendicular to the roller axis and depends on the direction of applied torque. Therefore, when controlling the whole motor, the wheels will form a resultant pushing AGV robot in different directions, increasing the flexibility of the robot. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 319–328, 2021. https://doi.org/10.1007/9783030647193_36
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Therefore, in recent years, this wheel has also been used in the design of AGV robots for logistics, transportation in tight spaces, which is not enough to design the turning path for AGV. such as the omnidirectional AGV model of L. Schulze et al. [2] using mecanum wheels with the conveying and towing functions described in Fig. 2, or that of Michael Göller et al. [3, 4] with mecanum wheels for supermarket customers. In addition, there are a number of other studies [5, 6] that show different AGV robots for different industrial production application scenarios.
ω
ωL
Fig. 1. Mecanum wheels
Fig. 2. Omnidirection AGV robots using mecanum wheels [2]
To kinematically control the omnidirection AGV robot in the desired orbit, the ﬁrst thing is to set up the kinetic model of the AGV robot. In terms of this, Gfrerrer [7] and Tatsuro [8] determined the effect of roller size, wheels and number of rollers on the speed of robots, or another research of Hamid [9] and Lin [10] using the matrix method and the nonslip condition of the wheel on the floor to establish forward and inverse kinematic model, thereby designing adaptive controller. That has led to too many parameters in the kinematic equation and such a complex problem, to overcome the above disadvantage, in this article the authors set up the inverse kinematic equations directly with instantaneous center of velocities. On that basis, the parameters of dynamically controlling ominidirection AGV robots by the reference trajectories and still achieve the desired speed of the robot. In addition, the article also presents the direction design method for robots with Nonuniform rational Bspline (NURBS) in the general case.
2 Determination of Kinematic Control Parameters of Omnidirection AGV Robot with Mecanum Wheels 2.1
Establishment of Kinematic Equations of Omnidirectional AGV Robot with Mecanum Wheels
In order to establish the kinematic equation of AGV robot, we let #f fOf xf yf g as a ﬁxed reference frame attached to the floor of the robot; #R fGR xR yR g is a reference system mounted on AGV robot (see Fig. 3);
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Fig. 3. Diagram of AGV robots with mecanum wheels
qf ¼ ½ xf ðtÞ yf ðtÞ /ðtÞ T is the robot positioning parameters in the frame #f , and qR ¼ ½ xR ðtÞ
/ðtÞ T
yR ðtÞ
is
the
parameter
in
the
frame
#R ,
if
qf ¼
T
½ xf ðtÞ yf ðtÞ /ðtÞ is in the frame of #f fOf ; xf ; yf g, we can determine the control parameter q_ R of Robot with: q_ R ¼ Qð/ÞT q_ f Where: 2
cos / sin / Qð/Þ ¼ 4 sin / cos / 0 0 #R to the reference frame #f . For the reference frame #R
ð1Þ
3 0 0 5 is a directing cosine matrix of the reference frame 1 of robot: To the wheel No. 1 (Fig. 3), we can see:
V1 ðtÞ þ VL1 cos c ¼ VGyR ðtÞ L2 XðtÞ VL1 sin c ¼ VGxR ðtÞ d2 XðtÞ
ð2Þ
Where: L, d is the distance between the two wheels, the distance between the front and rear wheels; X (t) is the angular velocity of AGV robot; V1 ðtÞ is the speed of wheel 1 such as V1 ðtÞ ¼ rx1 ðtÞ where x1 ðtÞ is the angular velocity of wheel No.1 and r is the radius of the wheels (see Fig. 4)
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Fig. 4. The velocity components on the mecanum wheels
Develocity of roller VL1 in Eq. (2), we get the velocity of wheel No. 1 as follows: L d 1 V1 ðtÞ ¼ VGyR ðtÞ XðtÞ þ VGxR ðtÞ XðtÞ 2 2 tgc
ð3Þ
We apply the same method to wheels No. 2, 3, 4, after all we have: 1 8 L d V ðtÞ ¼ V ðtÞ XðtÞ þ V ðtÞ XðtÞ 1 Gy Gx > R R 2 2 tgc > > < V ðtÞ ¼ V ðtÞ þ L XðtÞ V ðtÞ d XðtÞ 1 2 GyR Gx R 2 2 tgc 1 > V3 ðtÞ ¼ VGyR ðtÞ þ L2 XðtÞ þ VGxR ðtÞ þ d2 XðtÞ tgc > > 1 : L d V4 ðtÞ ¼ VGyR ðtÞ 2 XðtÞ VGxR ðtÞ þ 2 XðtÞ tgc
ð4Þ
To change the direction of the omnidirection AGV in any direction. Set c ¼ 45 in (4) and change to the algebraic form, we have: 2
3 2 1 x1 ðtÞ r 6 x2 ðtÞ 7 6 1 6 7 ¼ 6 1r 4 x3 ðtÞ 5 4 r 1r x4 ðtÞ
1 r 1 r 1 r 1 r
3 3 2r1 ðL þ dÞ 2 1 7 VGxR ðtÞ ðL þ dÞ 2r 74 VGyR ðtÞ 5 1 5 2r ðL þ dÞ XðtÞ 2r1 ðL þ dÞ 2
Let: x ¼ ½ x1 ðtÞ x2 ðtÞ x3 ðtÞ
1 r
61 r x4 ðtÞ T and J ¼ 6 4 1 r
1r
1 r 1 r 1 r 1 r
ð5Þ
3 2r1 ðL þ dÞ 1 7 2r ðL þ dÞ 7 1 5 ðL þ dÞ 2r 2r1 ðL þ dÞ
Equation 5 is rewritten as follows: x ¼ J q_ R
ð6Þ
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Determination of Kinematic Control Parameters by the Given Trajectories and Velocity T _ VG , we determine q_ f ¼ x_ f ðtÞ y_ f ðtÞ /ðtÞ in the With orbit nðxf ðtÞ; yf ðtÞÞ and ~ reference frame #f , then substitute into Eq. 1 and combine with Eq. 6 we have: x ¼ JQT ð/Þq_ f
ð7Þ
From (7) we determine the control parameters of the matching variables on the mecanum wheels:
8 _ > x1 ðtÞ ¼ 1r aðtÞ_xf ðtÞ þ bðtÞ_yf ðtÞ 12 ðL þ dÞ/ðtÞ > >
> > > _ < x2 ðtÞ ¼ 1 aðtÞ_yf ðtÞ bðtÞ_xf ðtÞ þ 1 ðL þ dÞ/ðtÞ r 2
1 1 > _ > _ x ðtÞ ¼ ðtÞaðtÞ þ bðtÞ_ y ðtÞ þ ðL þ dÞ /ðtÞ x 3 f f > r 2 >
> > : x ðtÞ ¼ 1 bðtÞ_x ðtÞ þ aðtÞ_y ðtÞ 1 ðL þ dÞ/ðtÞ _ 4 f f r 2
ð8Þ
aðtÞ ¼ cos /ðtÞ sin /ðtÞ bðtÞ ¼ cos /ðtÞ þ sin /ðtÞ Equation 8 determines the control parameters of the angular velocity of the driven motors on the Mecanum wheel axle so that the omnidirection AGV robot follows the trajectory and achieves the given velocity. Where:
3 Design of Moving Trajectory of AGV Robots The trajectory of AGV robots is usually designed as straight lines along the corridors used as the paths of the production line [11, 12] and at the intersections softened by curves by robot’s breakdown velocity and safety corridor. This will modelize the trajectory into a ﬁxed map in the memory of the robot. To make it easier to design and encode the robot’s route map, we use the coordinate matrix as a database, and the interpolation will be done by the NURBS to guide the robot well. According to the work [13] the nonuniform B  Spline rational curve (NURBS) is deﬁned by: PðtÞ ¼
nX þ1
Bi Ri;k ðtÞ
ð9Þ
i¼1
Where Bi is the vertices of the interpolated polygons in 2D space, and Ri;k ðtÞ is the basis function of the BSpline rational curve and is given by: hi Ni;k ðtÞ Ri;k ðtÞ ¼ n þ 1 P hi Ni;k ðtÞ i¼1
ð10Þ
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As: hi 0 for all values of i, Ni;k ðtÞ given by the recursive formula: 8 > xi t xi þ 1 < Ni;1 ðtÞ ¼ 1 0 > : Ni;k ðtÞ ¼ ðtxi ÞNi;k1 ðtÞ þ ðxi þ k tÞNi þ 1;k1 ðtÞ xi þ k1 xi xi þ k xi þ 1
ð11Þ
xi is the value of the node vector and satisﬁes the condition xi xi þ 1 . The tangent to the curve is given by: 2
3 _ hi Ni;k ðtÞ hi Ni;k ðtÞ7 nX þ1 6 h N_ ðtÞ 6 i i;k 7 i¼1 _ PðtÞ ¼ Bi 6n þ 1 2 7 4 5 nP þ1 P i¼1 hi Ni;k ðtÞ hi Ni;k ðtÞ nP þ1
i¼1
ð12Þ
i¼1
Thus, at each coordinate point K(xf, yf) on the interpolation curve {n} can we completely determine the tangent vector ~ s.
4 Simulation Examples Applied to omnidirectional AGV robots with dimension parameters: radius of mecanum r = 5 cm; The distance between the two wheels L = 50 cm; Distance between front and rear wheel d = 80 cm; With the assumption: (1) Considering at the time the omnidirection AGV robot moves at a steady velocity VG = 0.3 m/s; (2) there is no horizontal and vertical sliding of robots on the road surface. The coordinates of the grid node on the trajectory are given in Table 1 below.
Table 1. Coordinates of polygon nodes designing trajectory of the vehicle. B1 0 0
B2 B3 B4 6:5 6:5 3 0 5 5
B9 B8 6:5 6:5 13 8
B10 3 8
B11 3 5
B5 B6 B7 3 6:5 6:5 8 8 13 B12 B13 6:5 6:5 5 0
Figure 5 is the roadmap of the AGV robot and Fig. 6 is the trajectory of the robot after interpolation with NURBS curve.
Determination of Kinematic Control Parameters yf
B7
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y [m]
B8 13
3
12 10
B6
B3
B10
B5
B9
B12
B4
B11
8 4
2
6
{ξ }
4 2
B2
B1 Of
1
xf B13
Fig. 5. Roadmap of nodes on the trajectory of AGV robot
8
6
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2
x 0
2
4
6
8
Fig. 6. Robot’s trajectory is interpolated with NURBS curves
Corresponding to each point K(xf, yf) on the trajectory {n} in the reference frame #f we determine the tangent vector ~ s through Eq. 12. Attach the center coordinate GR(xG, yG) of the AGV robot to the point K(xf, yf) and the yR axis coincide with ~ s then the angle /ðtÞ is determined: /ðtÞ ¼ \ð~ sðtÞ; yf Þ
ð13Þ
VG _ /ðtÞ ¼ XðtÞ ¼ qðtÞ
ð14Þ
_ And /ðtÞ is found by
Where q(t) is the curvature radius of {n} corresponding to the point K(xf, yf) at time t and depicted in Fig. 7, and the graph depicting the angular velocity X(t) of the robot (The speed changes by the direction in the trajectory) is shown in Fig. 8.
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0.3 35
1
3
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ρ (t) (m)
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5 0 0
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0 0.3
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Fig. 7. Turning center radius on the moving path of AGV robot
t (s)
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200
Fig. 8. Velocity changing robot change direction
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ω3 [rad/s]
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ω4 [rad/s]
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5
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Fig. 9. The angular velocity of the wheels as the AGV robot moves along the trajectory
Insert Eqs. 13, 14 and 15 into Eq. 8 to determine the angular velocity x1 ðtÞ; x2 ðtÞ; x4 ðtÞ; x4 ðtÞ, so that the AGV robot can follow the given trajectory and achieve the velocity VG = 0.3 m/s. Figure 9 is a graph of angular velocity of four wheels. On the other hand, from the previous VG velocity we have:
Determination of Kinematic Control Parameters
x_ f ðtÞ ¼ VG sin /ðtÞ y_ f ðtÞ ¼ VG cos /ðtÞ
327
ð15Þ
From Figs. 5, 6, 7, 8 and 9, we ﬁnd that: i) When making the trajectory map of AGV robot with NURBS interpolation method through grid nodes. As a result, the database that stores the robot’s path in memory is greatly reduced. Instead of having to remember all the data for the path, the route designer just needs to enter the grid nodes. ii) When the AGV Robot travels from its initial position “1” to “2”, the robot’s rotation in the trajectory changes from clockwise to counterclockwise (see Fig. 8) and this cycle repeats 4 times according to the symmetry of the moving trajectory. Especially when passing through position “2” and “3”, it suddenly changes direction, the angular velocity of the robot has a greater variation at the position of “2” and “4”and much greater than the positions “1” and “3”. The reason is that the instantaneous radial radius at positions “2” and “4” is the smallest (see Fig. 7). Therefore, in order not to have a sudden change of direction when designing the trajectory, it is necessary to avoid small turning radius and at these points the wheels get sliding phenomenon on the road surface causing errors of position and direction.
5 Conclusion This study has established four inverse kinematic control parameters for omnidirection AGV robots with mecanum wheel to ensure that the robot follows the given trajectory and achieves the desired velocity. Using the grid of coordinates as a database to store the robot’s road map will reduce the memory capacity of the robot and the interpolation with the NURBS curve will be softer and more flexible than the studies previously ﬁnding equivalent arcs through the problem of breakdown velocity and the safety corridor of the workshop. In addition, the study also showed that at the locations where the robot changes direction, it is necessary to determine the minimum radius of curvature so that sliding does not occur on the road surface, causing the position errors of robots. This is an issue that authors will continue to publish in the near future. Acknowledgement. This research was funded by the Ministry of Industry and Trade in a ministeriallevel scientiﬁc and technological research project, conducted in 2020, code: DTKHCN.076/20.
References 1. Doroftei, I., Grosu, V., Spinu, V.: Omni Directional Mobile Robot – Design and Implementation; Bioinspiration and Robotics Walking and Climbing Robots, pp. 511–528 (2017)
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2. Schulze, L., Behling, S., Buhrs, S.: Development of a micro drive  under tractor research and application. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. II, HongKong, 16–18 March 2011 (2011) 3. Göller, M., Kerscher, T., Zöllner, J.M., Dillmann, R., Devy, M., Germa, T., Lerasle, F.: Setup and control architecture for an interactive shopping cart in human all day environments. In: Proceeding of the International Conference on Advanced Robotics, pp. 1–6 (2009) 4. Göller, M., Kerscher, T., Ziegenmeyer, M., Ronnau, A., Zöllner, J.M., Dillmann, R.: Haptic control for the interactive behavior operated shopping trolley InBOT. In: International Conference on Advanced Robotics (2009) 5. Puppim de Oliveira, D., Pereira Neves dos Reis, W., Morandin Junior, O.: A qualitative analysis of a USB camera for AGV control. Sensors (2019).https://doi.org/10.3390/ s19194111 6. Gfrerrer, A.: Geometry and kinematics of the mecanum wheel. Comput. Aided Geom. Des. 25, 784–791 (2008) 7. Peng, T., Qian, J., Zi, B., Liu, J., Wang, X.: Mechanical design and control system of an omnidirectional mobile robot for material conveying. Procedia CIRP 56, 412–415 (2016) 8. Terakawa, T., Komori, M., Yamaguchi, Y., Nishida, Y.: Active omni wheel possessing seamless periphery and omnidirectional vehicle using it. Precis. Eng. 56, 466–475 (2019) 9. Taheri, H., Qiao, B., Ghaeminezhad, N.: Kinematic model of a four mecanum wheeled mobile robot. Int. J. Comput. Appl. 113(3) (2015). 09758887 10. Lin, L.C., Shih, H.Y.: Modeling and adaptive control of an omnimecanumwheeled robot. Intell. Control Autom. 4, 166–179 (2013) 11. Lee, J.H., Lee, B.H., Choi, M.H.: A realtime trafﬁc control scheme of multiple AGV systems for collision free minimum time motion: a routing table approach. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 28(3), 347–358 (1998) 12. Cook, G.: Mobile Robots Navigation Control and Remote Sensing. Wiley (2011) 13. David, F.R.: An Introduction to NURBS. Morgan Kaufmann Publishers (2001)
Development of New Method for Choosing Standard Components Subject to Minimal Cycle Time and Minimal Sum of Purchasing Cost Tan Nguyen Dang1 and Manh Cuong Nguyen2(&) 1
2
Hanoi University of Mining and Geology, Hanoi, Vietnam Thai Nguyen University of Technology, Thái Nguyên, Vietnam [email protected]
Abstract. An assembly line is made from different machines and a machine can contain different stations and function carriers. Each function carrier even is performed by different standard function carrier variants. The designer can calculate and select the available function carrier variants to construct into a new machine. The advantages of using the available function carriers are the low cost, reduction of design and manufacture time and improvement of machine working life. If a machine has n function carriers, each function carrier contains m variants. Hence, there are nm combinations to make the machine. The number of these combinations increase rapidly according to a large number of function carriers. To select the suitable function carrier variants subject to optimal cycle time and total purchasing cost, the designer cannot manually select the best solution from so many options. Nowadays, there is no effective method to solve this problem. To solve this problem, this paper will set up the operating cycles for an assembly machine and establish linear optimization in standard form for choosing the best function carrier variants from the given database. To minimize both the cycle time and total purchasing cost of an assembly machine, the linear programming must contain two stages and run in sequence. The large linear optimization is programed and solved by using the IBM ILOG CPLEX Optimizer. The optimal results will be exported to tables in a database. This makes the designer easy evaluate and select the best solution. In addition, designer can expand the scope of study to design other machines. Keywords: Assembly machine Cycle time Function carrier optimization Standard component Total purchasing cost
Linear
1 Introduction 1.1
Compensation of Cycle Time
The product portfolio of numerous companies, such as Schunk, Zimmer Group, Festo or Bosch etc., includes standardized components with technical data, which are stored in catalogues or databases. These data mainly include dimensions, load capacities, repeat accuracy, speeds and accelerations as well as possible cycle times [1, 2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 329–340, 2021. https://doi.org/10.1007/9783030647193_37
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To perform the assembly and handling, function carriers are often used that implement assembly and handling task. A strong trend in designing machine is the use of standardized component units to reduce design time of complexity assembly lines [3, 4]. With a focus on selecting suitable components, criteria of function carriers must be deﬁned, such as types, capacity, price, effectiveness and service [5]. The priority for a desired system is not only the shortest cycle times, but also an optimum price/performance ratio [6]. Minimizing the cycle time T is an important step in theplanning and design of an assembly system. By minimizing the cycle time, different target values can be achieved [7]. The assembly and handling operations are assigned to the various stations. Due to the different assembled products with the different assembly and handling operations, there are different cycle times between the stations [8]. To compare the cycle times of individual stations, the cycle time diagram should be created. A cycle time diagram shows the station with the maximum cycle time Tmax , which is the socalled bottleneck station and reduces the efﬁcient utilization in the assembly line. This assembly and handling processes in the stations with shorter cycle times than Tmax must wait for the bottleneck station [9]. The following methods can be used to reduce the different cycle times and eliminate the bottleneck station. To balance the cycle times between the stations in assembly line, the assembly and handling operations are renewed to allocate to the different stations [7]. 1.2
Removal of the Bottleneck Station
To select elements from sets of function carrier variants according to the objective function of the cycle time and purchasing cost, nowadays there is no efﬁcient method. Many companies in the Germany, designing assembly machines such as SIM Automation GmbH, USK GmbH etc., manually select these elements from the supplier, and then calculate the raw cycle time and total purchasing cost. If a machine has n function carriers, each function carriers contains m variants, there are nm different options for selecting the components of the assembly machine. Therefore, if the selecting process is manual, the number of tests will be extremely large, and it will not be able to ﬁnd an optimal solution. The goal of this research is to build an algorithm to ﬁnd an optimal solution according to the given conditions in term of minimal cycle time and total purchasing cost [10]. An assembly system comprises different stations, a station consists of the different function carriers.
2 Methods of Study 2.1
Describe the Operations of Assembly and Handling Function Carriers
The operations of all function carriers are determined based on the assembly and handling task. These individual operations of a function carrier in the station decide a cycle time of a station. To determine the cycle time of the station, all operations must be indicated in pathstep diagrams and divided into different steps. A change of
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operation, e.g. switching a rotary indexing table, starting or stopping a slide unit, is referred to a step. A cycle begins with step 1 and ends with step n (see Fig. 1). After the last step n has completed, a new cycle starts. With a focus on minimizing cycle time, a diagram should be set up so that carrier functions have the least waiting time as possible.
Fig. 1. Pathstepdiagram of a station
As a means to deﬁne the time variables of the function carriers and the cycle time of a station, the change of operation is labelled in the pathstep diagrams. For example, eight changes from 1 to 8 are deﬁned for the oscillating movement. Thus, the events of this function carrier differ from other function carriers. The optimization program selects the function carrier variants (FCV) from the function carrier list (FCL) according to the deﬁned restrictions. Therefore, the FCL serve as the input parameters of the optimization problem. The execution time and purchasing cost of an FCV are the criteria for selection. Thus, the times, e.g. test or measurement time, movement time or response time of FCV as well as the purchasing cost, must be given in the databases (see Fig. 2).
Fig. 2. Structure of database of an assembly system in MS access
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Establish the Optimization Problem by Mathematical Formulas
The optimization problem will be described by following standard form: Stage 1: minimization of cycle time In this section, the optimal cycle time T of the entire assembly system is assigned by the biggest cycle time Tmax of all stations, or in other word, the cycle time of the assembly system T is determined by the slowest station. ð1Þ
T ¼ maxr2S Tr where: S  Set of all stations of the entire assembly system Tr  Cycle time of a station r 2 S
The cycle time of the station r is deﬁned by all points of time of the function carriers of the pathstep diagram: r Tr ¼ max j 2 F tj;d ; 8r 2 S
d2
ð2Þ
r Drk
where: Fr  Set of all function carries of the station r 2 S Drj  Set of points of time in the pathstep diagram of the function carrier j 2 Fr in the station r r  Points of time according to a change of operation d 2 Drj of a function carrier tj;d j in the station r Therefore, the minimization of the cycle time T applies to a min_max optimization problem. That means, the objective function is to minimize the maximum cycle times of all stations: 1
0
C B C B B r C min T , minðmaxr2S Tr Þ , minBmax r 2 S tj;d C C B A @ j2F
ð3Þ
r
d 2 Drj
Currently, the objective function () has nonlinearity, but can easily be reformulated and linearized by a series of constraints:
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objective function min T under the constraints 8r 2 S T Tr ; r Tr tj;d ; 8r 2 S; j 2 Fr ; d 2 Drj
ð4Þ
To shrink the many constraints above, the end times of the cycles of each station must be determined. In addition, the times of all function carriers in all stations must also be deﬁned. These times are determined by the following conditions: – Relationships between the times of the different function carriers inside a station and outside other stations: In the pathstepdiagram, there is one or more changes of operations in a step. To calculate the points of time, the relationships between these changes must be considered. For example, in a step i (pathstepdiagram) of a station r, a function r ~ begins a carrier j ends a movement at the point of time tj;d , and a function carrier j movement at the point of time tj~r;~d . Thus, such relationships must be represented by the following constraint. To determine a starttime movement of a function carrier in step 1 (cycle start), its time is assigned 0. r tj~r;~d tj;d
ð5Þ
– Relationships between the start and end times of a function carrier: For an operation p (for example, opening or closing of a gripper) of a function r r carrier j of a station r, there are a start time tj;p as well as an end time tj;p , its START END difference represents the actual operating time of the operation p. Therefore, the following constraint must be deﬁned: r r r tj;p tj;p tj;p END START
ð6Þ
r on the right side represents the minimum actual where: the positive variable tj;p operating time of the execution p and depends on the function carrier variants (FCVs), which is selected for the function carrier j.
In most cases, the right side of the constraint (8) must be considered not only for a single FCV, but also for the FCL. Therefore, a binary variable krj;v is introduced for an FCV v of the FCL. The value of the binary variable krj;v shows, whether an FCV v is selected in the list of a function carrier j. krj;v ¼
1; chosen 0; otherwise
ð7Þ
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For a function carrier j, only one FCV v is selected. Therefore, the sum of the values of binary variables is written: X kr ¼ 1; 8r 2 S; j 2 Fr ð8Þ v2C j;v j
where: Cj  Set of all FCV of the function carrier j 2 [ r2S Fr r Thus, the variable tj;p is redeﬁned: r ¼ tj;p
X v2Cj
spv krj;v ; 8r 2 S; j 2 Fr ; p 2 Prj
ð9Þ
where: Prj  Set of all executions/processes of the function carrier j of the station r. It should be noted that 8p 2 Prj : fpSTART ; pEND g Drj . spv  Catalogue parameter represents the nominal execution time of the execution p of the FCV v of the function carrier j 2 Fr in the station r. From the Eq. (11), the secondary condition (8) is rewritten: X r r tj;p t sp krj;v ; 8r 2 S; j 2 Fr ; p 2 Prj j;p END START v2C v j
ð10Þ
Stage 2: minimum total purchasing cost according to the condition of cycle time Stage 1 minimizes the cycle times of all stations. This means, that only the FCV with the smallest execution times from all FCL are selected. Therefore, all stations contain the FCV with the shortest execution times. There are usually different cycle times between these stations. The stations, which have cycle times less than the cycle time of the system T ¼ Tmax , can choose another FCV, can select FCV to have a longer execution times and cheaper purchasing costs. For this reason, the optimization problem will be run in stage 2. For the second stage, the cheaper FCV than FCV in stage 1 are searched, so that the new cycle times of the stations do not exceed the cycle time T. The different results between the ﬁrst and second step are shown in Fig. 3.
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Fig. 3. Difference between stage 1 and stage 2
It is essential in stage 2 to integrate total purchasing cost and the cycle time T into one optimization model. To optimize the total purchasing cost, the total cost of all selected FCV must be minimized. The purchasing cost of an FCV v of the function carrier j is called cv . Thus, the objective function of stage 2 is deﬁned: X X X min CSUM , min c krj;v ð11Þ r2S j2F v2C v r
j
Cycle Time and Total Cost of Diagram After solving the optimization problem in stage 1 and 2, only one solution is found. The FCV according to minimal cycle time and total purchasing cost are issued. The minimum cycle time is often not a primary goal to select an assembly machine. A designer wants to know, how much a machine costs with a given cycle time or how long the cycle time takes with a given total purchasing cost. By manually changing the FCV in a database, the different values of the cycle time T and total cost CSUM can be determined. However, it requires different tests and is not suitable for such an assembly system with numerous FCV. To solve this problem, the given total cost CSUM will be deﬁned in stage 1. X
X r2S
j2Fr
X v2Cj
cv krj;v CSUM
ð12Þ
3 Application Example An example for assembly product consists of three individual parts: a socket, a chip and a chip holder. Since both the chip and the socket are inserted into the chip holder, the chip holder is a base part. The shape and dimensions of the individual parts are shown from Fig. 4.
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Fig. 4. Shapes and dimensions of socket, chip and chip holder
To assemble a chip, socket in chip holder, a handling process will be arranged in ﬁve stations: • • • • •
Station Station Station Station Station
1: 2: 3: 4: 5:
Handover a chip holder Insert a chip into chip holder Insert a socket into chip holder Attendant test a chip in chip holder Ejection a chip holder
The new method for choosing standard components subject to minimal cycle time and minimal sum of purchasing cost can be applied in the different assembly and handling machines. In this paper, the new method is used in ﬁnding the best function carrier variants (handling module) of an automatic rotary indexing machine. This machine contains ﬁve stations (see Fig. 5). To optimize the cycle and purchase cost of components, it is necessary to point out the structure of each station, component as well as the work cycle. This example will present the conﬁguration of station 1 as well as the pathstepdiagrams of station 1 to station 5. The remaining stations are conducted similarly. To optimize the cycle and purchase cost of components, it is necessary to point out the structure of each station, component as well as the work cycle. This example will present the conﬁguration of station 1 as well as the pathstepdiagrams of station 1 to station 5. The remaining stations are conducted similarly.
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Fig. 5. Automatic rotary indexing machine
1 divider; 2 gripper; 3 vertical slide; 4 horizontal slide; 5 rotary indexing table Fig. 6. Construction of station 1
Figure 6 shows the function carriers of station 1 and their working principles. A Chip holder is separated and handed over to the chip holder feeder. A transfer of the chip holder to the workpiece carriers on the rotary indexing table is generated by a pick & place module, consisting of a horizontal and a vertical axis. In this example, the automatic rotary indexing machine contains ﬁve stations, so it requires to create ﬁve pathstep diagrams. The cycle begins with the transfer of a chip holder in the gripper to the workpiece holder on the rotary indexing table. The pathstep diagram of station 1 consists of nine steps and is described in Fig. 7.
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Fig. 7. Pathstep diagram of station 1
where: ZTCT1 to ZTCT4—change points of the divider No. 1 GFCT1 to GFCT4—change points of the gripper No. 2 VSCT1 to VSCT8—change points of the vertical slide No. 3 HSCT1 to HSCT4—change points of the horizonal slide No. 4 RST1, RST2—change points of the rotary indexing table No. 5 The results after stage 1 and 2 in Fig. 8 represent only the list of selected FCV. To control the points of time of each function carrier in the pathstep diagram, the selected FCV, points of time, and costs are written to the other tables in database 2. Based on the points of time of the selected FCV, the timestep diagrams will be created. The differences between the selected variants in stage 1 and 2 are the moving times and purchasing costs. These differences are shown by the pathtime diagram in Fig. 16, example for station 1. The cycle time of station 1 was determined by the end times of the rotary indexing table No. 5, horizontal slide No. 4 and divider No. 1. In stage 1, these times are: 1 1 tRST;2 ¼ 0;76 s; tHSCT;4 ¼ 0;89 s;
and
1 tZTCT;4 ¼ 0;89 s
In stage 2, the other variants of the rotary indexing table with the bigger step times and lower costs is selected. The step time of the rotary indexing table has a value of 0.44 s instead of a value of 0.32 s. The end time of the rotary indexing table is 1 tRST;2 ¼ 0;89 s and does not exceed the cycle time T ¼ 0;89 s.
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Fig. 8. Pathtime diagram of station 1
4 Conclusions The most important parameters of an assembly line are capacity and assembly cost. To reduce the assembly cost and increase the quality of machine, the designer should use the standard components. A large amount of function carrier variants can be chosen. It makes the designer difﬁcult decide, which variants are better. For each solution, he must calculate the cycle time, total purchasing cost. This manual method spends a lot of time and cannot ﬁnd the best solution. This study has shown the new method to optimize the cycle time and total purchasing cost by selecting the suitable variants from a given database. The main contents of this new method can be described as follows: • The pathstep diagrams of all station of a machine and assembly line must be established. These diagrams will determine the constraints of linear optimization. • Based on the function carriers, the function carrier variants are listed and added to the tables of Microsoft Access. • The standard form of linear optimization contains two stages. The ﬁrst stage minimizes the cycle time of an assembly line. The second stage minimizes the total purchasing cost. The constraints of linear optimization are execution times of function carrier variants and pathstep diagrams. To ﬁnd out the other minimal cycle time and total purchasing cost, the constraint of total cost is deﬁned in the stage 1.
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• This study used the computer software IBM ILOG CPLEX to solve the linear optimization. All of cycle time, total purchasing cost and selected function carrier variants are exported in the tables of Microsoft Access. Based on these results, the designer can easily choose the suitable solution. Acknowledgement. The work described in this paper is funded by Thai Nguyen University of Technology.
References 1. Krahn, H., Nörthemann, K.H.: Konstruktionselement 3 – Beispielsammlung für die Montage und Zuführtechnik, pp. 57–60. Vogel Buchverlag und Druck GmbH & Co. KG, Würzburg (1999) 2. Loferer, M.: Rechnergestützte Gestaltung von Montagesystem. Dissertation Technische Universität München, München, pp. 98–121 (2001) 3. Roth, K.: Konstruieren mit Konstruktionskatalogen  Band 2 Kataloge, pp. 11–16. Springer, Heidelberg (2001) 4. VDI 2221: Gesellschaft Entwicklung Konstruktion Vertrieb – Methodik zum Entwickeln und Konstruieren technischer Systeme und Produkte. Verein Deutscher Ingenieure, Beuth Verlag GmbH, pp. 5–12 (1993) 5. Römisch, P., Weiß, M.: Projektierungspraxis Verarbeitungsanlagen, pp. 33–41. Springer, Wiesbaden (2014) 6. Sauer, H.P.: Auswahlkriterien von Siebmaschinen in der Aufbereitungstechnik, pp. 367– 372. Aufbereitungstechn, Berlin (2015) 7. Pröpster, M.H.: Methodik zur kurzfristigen Austaktung variantenreicher Montagelinien am Beispiel des Nutzungsfahrzeugbaus. Dissertation Technische Universität München, München, pp. 81–92 (2015) 8. Halubek, P.: Simulationsbasierte Planungsunterstützung für Variantenfließfertigung, pp. 16– 32. VulkanVerlag, Essen (2012) 9. Grundig, C.G.: Fabrikplanung Planungssystematik  Methoden – Anwendungen, pp. 47–62. Carl Hanser Verlag München, München (2013) 10. Berger, M., Nguyen Dang, T.: Funktionsbasierte Methode zur Optimierung der Taktzeit von Montageanlage, pp. 81–101. Fachtagung FüMoTeC, Germany (2017)
Dust Emission During Machining of CFRP Composite: A Calculation of the Number and Mass of the Thoracic Particles Dinh Nguyen Ngoc1(&), Thi Nguyen Hue2, Bui Van Hung3, and Vu Duy Duc3 1
2
Faculty of International Training, Thai Nguyen University of Technology, Thái Nguyên, Vietnam [email protected] Faculty of Fundamental Science, Thai Nguyen University of Technology, Thái Nguyên, Vietnam 3 Faculty of Mechanical Engineering, University of Communications and Transport, Hanoi, Vietnam
Abstract. Machining of CFRP composite creates many kinds of induced damage like delamination, ﬁber pullout, etc. these kinds of damage strongly are associated with appearance of dust generated during machining. The small dust particles can be inhaled by operators in machining areas, which can make them get some diseases. However, there have been few studies dealing with this problem. This study aims to investigate the influences of cutting parameters (cutting speed, feed speed and depth of cut) on the number and mass of thoracic which possess aerodynamic diameter particles lower than 32 µm. Dust particles were collected using a dust monitor. The results show that when cutting speed increases the number of thoracic particles increases. Moreover, an increase of feed speed or depth of cutting leads to reduce the number and mass of thoracic particles. Keywords: Thoracic
Trimming CFRPs Dust analysis Composite
1 Introduction Machining of composite materials is accompanied by a series of brittle fractures because of shearing and cracking of matrix materials under applying cutting forces due to the interaction between cutting tool and workpiece [1]. As a result, several kinds of damage such as delamination, uncut ﬁbers, thermal and/or mechanical degradation of the matrix, interlaminar cracks are created in the machined surfaces [2–4]. In industry, dry machining (without using lubricant) is absolutely recommended by companies. Hence, the generation of above mentioned defects is also strongly accompanied with the emission of ﬁne dust particles with extremely small diameter and sharp edges [1]. These particles are dispersed/suspended in the air and can be inhaled as well as exposed by operators. Therefore, the potential abilities of damaging breathing system and toxic irritations because of exposing can possibly happen. The increasing usage of composite materials in industry leads to more frequently expose with ﬁne dust particles. For this © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 341–349, 2021. https://doi.org/10.1007/9783030647193_38
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reason, further studies of particles generated during machining composite materials are imperative and should be carefully considered by research communities. Surprisingly, there have been few studies of these issues so far [5–7]. Haddad M et al. [7] analyze the influence of tool geometries, cutting parameters (cutting speed and feed speed) on dust particles generated during trimming laminated composite materials (CFRP) at two ranges of cutting speed, e.g. standard and high cutting speed. For standard cutting speed, three kinds of cutting tools including burr tools coated and uncoated and four flute end mills were utilized, while only burr tool uncoated was used at high cutting speed. The results showed that at standard cutting speed, tool geometries influence on a number of harmful particles when dust particles created by four flutes end mills were superior to those generated by using both coated and uncoated burr tools. Moreover, coating has a little impact on the generation of dust particles. Moreover, the number of harmful particles increases with decreasing feed speed and increasing cutting speed in both case of cutting speed ranges. NguyenDinh et al. [6] investigate the influence of cutting parameters such as cutting speed, feed speed, radial depth of cut and tool geometries on the number of harmful particles. The results showed that machining with high cutting speed, low feed speed and low depth of cut strongly reduces the emission of dust. This result is similar to that documented by Haddad [7]. Furthermore, four serrated straight flute tools exhibit the ability of minimizing harmful particles compared to other (2 straight flute tools, 2 helix flute tools).
Fig. 1. The inhalable, thoracic and respirable conventions as percentages of total airborne particles [8].
According to [9], the emitted dusts in the air can be divided in some categories depending on the aerodynamics diameters. Figure 1 presents the evolution between total particles in suspension and aerodynamics diameters. It is observed that the particles having size lower than 10 lm denoted by “1” harmful particles reach the deepest to the respiratory system. The previous studies have dealt with this category. However, it is observed that dust particles which is larger than 10 lm assigned by “2” – thoracic particles are also dangerous when they reach inside the operator’s body. However, there have been few studies dealing with this category of emitted dust particles.
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The main objective of this work is to analyze the impact of cutting parameters (cutting speed, feed rate, and radial depth of cut) on the number and mass of thoracic fraction of dust particles which have the aerodynamic diameters lower than 30 lm. This means that the investigated fraction includes the harmful fraction which has been analyzed in [6]. CFRP specimens are trimmed using PCD tool with a full experimental design including three levels of feed rate, and two levels of cutting speed. The number of particles generated during trimming is counted using a GRIMM dust monitor.
2 Experimental Procedure The CFRP laminates (T700M21) used in this study were twenty layers of prepregs corresponding to the thickness of 5.2 mm and have stacking sequence of [90°/90°/ −45°/0°/45°/90°/−45°/90°/45°/90°]s. The mechanical properties of machined specimens are listed in Table 1. A full factorial design of cutting condition including three levels of feed rates (500 mm/min, 1000 mm/min, 1500 mm/min) and two levels of cutting speeds (150 m/min and 250 m/min) was utilized. A radial depth of cut of 2 mm was constantly kept for all cutting conditions. New polycrystalline diamond cutters (PCD) with two straight flutes and diameter of 6 mm were used for all cutting conditions (Ref. Fig. 2). Three 280 mm long specimens corresponding with six faces were trimmed for each cutting condition. No coolant was utilized during machining process. Trimming process is carried out on a CNC center 5axis milling machine. The acquisition of cutting forces is recorded using a dynamometer (Kistler 9272). Machining temperatures are registered by an infrared camera ‘Thermo Vision’ A40. In order to measure the number and the average size of the dust particles generated during trimming, a GRIMM dust monitor (model 1.109) is utilized. This device can count the number of particles which have sizes ranging from 0.25 µm to 32 µm present one liter of air. The interval for each measurement was set up for 6 s. The average number of particles is calculated to get the representative for each machining condition. The details of experimental devices used trimming test are schematically shown in Fig. 3.
Fig. 2. PCD tool used with two straight flutes.
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Composite materials (T700/M21) Ply thickness: 0.26 mm Fiber content: Vﬁber = 59% Stacking sequence with respect to the feed direction: [90°/90°/−45°/0°/45°/90°/−45°/90°/45°/90°]s Young modulus: E1 = 142 Gpa, Et = 8.4 Gpa Shear modulus: G12 = 3.8 Gpa Glass transition temperature: Tg = 187 °C Energy release rate: GIC = 0.35 N.mm, GIIC = 1.21 N.mm
Fig. 3. Schematic view showing the machining conﬁguration: axes, ﬁber orientation and the machining directions.
3 Results and Discussion 3.1
Influence of Cutting Parameter on the Number of Thoracic Particles
The thoracic fraction of particles emitted during machining is measured using dust monitor. The authors [8] documented that the thoracic particles can reach deeper breathing system at the position of trachea, bronchus, pharynx, larynx. Hence, dust particles of this fraction can destroy the component of the respiratory system. Determination for this fraction is crucially necessary for both industry and research community. According to [9] this fraction can be determined from the fraction of total particles. The protocol to calculate the thoracic particles can be found by the Eq. (1). EI ¼ 50ð1 þ exp½0:06DÞ ET ¼ 0:01EI ER ¼ 0:01ET
ð1Þ
Where: D is the aerodynamic diameter (µm), EI, ET, ER are the Inhalable fraction, Thoracic fraction, and respirable fraction respectively.
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Figure 4 shows the evolution between the number of thoracic particles and the size of measured particles (aerodynamics diameter). It is observed that an increase in feed speed leads to reduce the number of thoracic particles. Moreover, when cutting speed reduces, the number of thoracic particles seems to reduce, too. For example, when feed speed varies from 500 mm/min to 1500 mm/min the thoracic particles number reduces by 32% and 7% corresponding to cutting speed of 150 m/min and 250 m/min. Regarding the influence of cutting speed, the thoracic particle number increases by 33%, 36%, 48% when cutting speed augments from 150 m/min to 250 m/min at feed speed of 500 mm/min, 1000 mm/min, and 1500 mm/min respectively.
Fig. 4. Evolution of the thoracic particle number and cutting parameters.
The influences of cutting speed and feed speed can be explained by the chip formation process of machining composite materials. Indeed, Janardhan et al. [10] proposed a concept, theoretical chip thickness which is described by the Eq. (2) and graphically presented in Fig. 5. It is visualized that theoretical chip thickness is strongly dependent on cutting parameters. hq ¼ fz
rﬃﬃﬃﬃﬃ ae D
ð2Þ
Vf fz ¼ 1000:Vc Z pD Where: fz feed per tooth; Vf feed speed; Vc cutting speed; D – diameter of cutting tool, and ae – radial depth of cut.
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Fig. 5. The relations between theoretical chip thickness and cutting condition.
Trimming is carried out with high theoretical chip thickness corresponding to high feed speed or low cutting speed, the surface integrity is typically rougher. For this reason, majority of broken chips or dust particles are therefore larger and quickly drop because of their heavier weight. In opposite way, just remaining particles having smaller sizes can emit in the air and measured by dust sensor. As a result, the number particles received by trimming with high theoretical chip thickness (increase of feed speed or decrease of cutting speed) are smaller than those gotten when trimming with low theoretical chip thickness (decrease of feed speed or increase of cutting speed). Based on the previous analysis, it can be concluded that the variation of thoracic particles vs cutting parameter for thoracic fraction is identical to those for inhalable fraction which were detailed in literature [6, 7]. 3.2
Influence of Cutting Parameter on the Mass of Thoracic Particles
Figure 6 presents the evolution of total number of particles presenting in 1 L of the air with the particle size. We can see that the number of particles and percentage of dangerous particles are high, but their sizes are too small. Haddad [7] showed that the number of harmful particles is about 98% on the total particles. However, in term of mass, the weight of this particle category is minor. Hence, the information of the particle number is not enough to conduct quantitatively the effect of particles on the human health.
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Fig. 6. The total number of particles present in 1 L of air vs. particle sizes for different cutting conditions.
Fig. 7. The mass of thoracic particles in supposed cubic form presents in 1 L of air vs. particle sizes for different cutting conditions.
The cubic form which was proposed by [7] is utilized to calculate the mass of thoracic fraction particles in this study. The protocol of mass calculation can be consulted in [7]. Figure 7 shows the influences of cutting parameters (cutting speed and feed speed) on the mass of thoracic fraction. It can be seen that an increase in cutting
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speed and/or a decrease in feed speed leads to augment the mass of thoracic particles. For example, when cutting speed of 150 m/min is utilized, the mass increases by 19% if feed speed reduces from 1500 mm/min to 500 mm/min. Corresponding variation with cutting speed of 250 m/min is 41%. This influential tendency of cutting parameters on the thoracic mass is identical to that on the number of thoracic particles. The explanation can be followed by that for the influence of cutting parameters on the thoracic number as previously mentioned in which the theoretical chip thickness has a strong impact.
4 Conclusions This work presents an experimental study of dust emission during trimming CFRP laminates by using PCD tools. The thoracic fraction of dust particles is measured by a dust monitor. The influences of cutting parameter on the number and mass concentration of thoracic particles are investigated. Based on an experimental analysis, the following conclusions are made. The number of particles generated during trimming present in one liter of the air is influenced by cutting conditions (cutting speed and feed speed). Trimming process carried by higher theoretical chip thickness (high feed speed and low cutting speed) creates less particles in the air than that carried by low theoretical chip thickness (low feed speed and high cutting speed). The effect of cutting parameters on thoracic fraction particles exhibits the same tendency to the total number of particles presenting in the air. Cutting conditions clearly influence the mass of thoracic particles estimated by cubic forms. When feed speed varies from 500 mm/min to 1500 mm/min, the thoracic particle mass reduces by 19% and 41% with cutting speed of 150 m/min and 250 m/min respecitvely. Acknowledgments. The authors wish to thank Thai Nguyen University of Technology for supporting this work.
References 1. Ramulu, M., Kramlich, J.: Machining of ﬁber reinforced compositereview of enviromental and health effects (2004) 2. Hintze, W., Hartmann, D., Schütte, C.: Occurrence and propagation of delamination during the machining of carbon ﬁbre reinforced plastics (CFRPs) – an experimental study. Composit. Sci. Technol. 71(15), 1719–1726 (2011) 3. Azmi, A.I.: Chip formation studies in machining ﬁbre reinforced polymer composites (2013) 4. SheikhAhmad, J., Urban, N., Cheraghi, H.: Machining damage in edge trimming of CFRP. Mater. Manuf. Process. 27(7), 802–808 (2012) 5. Klocke, F.: Environmental effects and safety in machining ﬁbrous composites (1996) 6. NguyenDinh, N., et al.: New tool for reduction of harmful particulate dispersion and to improve machining quality when trimming carbon/epoxy composites. Composit. Part A Appl. Sci. Manuf. 131 (2020)
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7. Haddad, M., et al.: Study of the surface defects and dust generated during trimming of CFRP: Influence of tool geometry, machining parameters and cutting speed range. Composit. Part A Appl. Sci. Manuf. 66, 142–154 (2014) 8. Environnement, E.S. Élimination des particules (2006) 9. STANDARD, B. European Standard Norm EN 481. Workplace atmospheres – size fraction deﬁnitions for measurement of airborne particles (1993) 10. Janardhan, P., SheikhAhmad, J., Cheraghi, H.: Edge trimming of CFRP with diamond interlocking tools (2006)
Dynamic Surface Control of the AxialFlux Permanent Magnet Motor with Speed Sensorless Algorithm Manh Tung Ngo1,2, Quang Dang Pham1, Huy Phuong Nguyen1, and Tung Lam Nguyen1(&) 1
2
Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi, Viet Nam {lam.nguyentung,lam.nguyentung}@hust.edu.vn Hanoi University of Industry, No.298, Cau Dien Street, Bac Tu Liem District, Hanoi, Viet Nam
Abstract. The permanent synchronous motor drive system incorporates magnetic bearings to perform speed control and balance rotor control between the two stators. The paper designed a system to perform adjusting motor speed sensorless based on measured current and voltage components. The electromotive force (backEMF) generated in the stator is estimated by a HighGain observer. The angular position and velocity rotor are calculated through the ab components of the backEMF. The motor drive is built in a vector control structure based on the rotor flux. In which based on the Lyapunov stabilization function, the dynamic surface control method is used to calculate the axial position and speed controller of the motor. Simulation results show that the proposed controller and observer are fully capable of meeting the system control requirements. Keywords: Magnetic selfbearing motor Dynamic surface control
BackEMF Highgain observer
1 Introduction In recent years, the motor with integrated magnetic bearing has been increasingly studied and applied due to its advantages compared to traditional ball bearing [1]. The motor which has the magnitude of the air gap between the stator and the rotor is constantly varying, affecting the motor parameters and increasing the nonlinearity of the target. To deal with this problem, the control process needs to separate the channel between the torque generating the rotation and the force acting the axial position. Hence, based on the principle of control based on the rotor flux, the current id controls the axial force, while the radial current iq controls the torque [2]. Dynamic surface control method is used to design the axial position controller and the motor speed control. This is a technique applied to nonlinear system objects, applied to grip control and stability for the system [3–5]. Dynamic Surface Control (DSC) technique features
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 350–358, 2021. https://doi.org/10.1007/9783030647193_39
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backstepping and sliding multisurface control (MSS) [6]. However, it has added a ﬁrst order ﬁlter to overcome the problem of explosion of term. During the control process, the control loops need to perform coordinate transformation [7]. This leads to a need for accurate angular position information of the rotor by sensors that measure the rotational angle. This increases costs, size, and affects the mechanical strength and requires extensive maintenance. Due to this, methods of replacing speed sensors with calculation/observer techniques are studied extensively. In the published works on nonmeasuring rotation speed control for synchronous motors, two common approaches for estimating the angular position and rotor speed can be given in [8, 9]. This article presents a HighGain observation based on the backEMF electrodynamic reactivity estimation, it has the advantage that the rule of observation is simple, easy to design, and there is no project using HG observers for magnetic motors. Although [10] uses a similar observational structure, the hydraulic system applies. The paper proposes a drive system using a vector control structure, in which the HG observer estimates the backEMF value from the stator current and the reference voltage value, thereby calculating the Rotor angle position and rotation speed. The feasibility of the proposed methods is demonstrated through simulation of the control system without speed sensor on the software. Simulation results show that both position response and speed response follow the set trajectory in fast time. The system is also stable to the coupling effects of the two control loops.
2 Mathematical Model The permanent magnet synchronous motor incorporating the axial magnetic bearing has the structure shown in Fig. 1. The motor structure consists of a rotor and two stators [1]. The torque T that rotates the rotor is the sum of the two component moments and the F axial force that stops the rotor from moving in the z direction is calculated by the difference of the two axial forces. To design the control system, the mathematical model of the motor is shown on the dq rotating coordinate or ab static coordinate system. For L’sd0 and L’sq0: the d axis and q axis inductance per gap unit; Lsl: leakage inductance; g = g0 ± z: clearance between stator and rotor; g0: clearance at equilibrium position; z: displacement. Voltage vector equation of a stator on the ab coordinate system is given as: Us ¼ is Rs þ Ls dis =dt þ Es
ð1Þ
The inductance is presented as [1, 11]: Lsd ¼ 3L0sd0 =2g þ Lsl ; Lsq ¼ 3L0sq0 =2g þ Lsl
ð2Þ
Where is the stator current vector, Us is the stator voltage vector. Es is a sine function and is expressed as components on the ab coordinate system as follows:
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Fig. 1. The structure of the motor
Esa ¼ jkm jxe sin he ; Esb ¼ jkm jxe cos he
ð3Þ
With xe is the rotor speed, he is the position of the rotor flux vector. From (1) we have: (
disa =dt ¼ Rs isa =Lsd þ usa =Lsd Esa =Lsd disb =dt ¼ Rs isb =Lsq þ usb =Lsq Esb =Lsq
ð4Þ
We have the equation for calculating the total torque and total axial force as follows [11, 12]: F ¼ 4KFd if id ; T ¼ 2KT iq
ð5Þ
3 Dynamic Surface Controls The motor’s rotation equation can be written as follows: T TL ¼ J dx=dt or can be written as x_ ¼ ðT TL Þ=J
ð6Þ
x_ ¼ iq 2KT =J TL =J ¼ M iq N
ð7Þ
According to (6) we have:
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The deﬁnition of a sliding surface is: z1 ¼ x x d
ð8Þ
z_ 1 ¼ x_ x_ d ¼ Miq N x_ d
ð9Þ
The derivative of z1 is:
According to DSC, virtual control signal: iq ¼ cx z1 =M þ ðN þ xd Þ=M
ð10Þ
Choose cx > 0, the signal controls iq through the ﬁlter which is the ﬁrstorder ﬁlter to obtain the reference signal iqref: _iqref ¼ ðiq iqref Þ=sx
ð11Þ
Choose time constant sx [ 0. Set: ~iq ¼ iqref iq
ð12Þ
Deﬁne a Lyapunov candidate function as follows: V ¼ z21 =2 þ ~i2q =2
ð13Þ
In order for the speed loop to be controlled asymptotically, then the derivative of V: V_ ¼ z1 z_ 1 þ ~iq~_iq 0
ð14Þ
According to (14), the time constant satisﬁes the following inequality: sx ex =B2 where B >0 is a maximum constant such that _iq B. According to (5): €z ¼ ðF FL Þ=m ¼ 4KFd if id =m F=m ¼ P:id Q
ð15Þ
ð16Þ
Deﬁnition of sliding surface: S ¼ z_ þ kzðwith k [ 0Þ
ð17Þ
Using similar approach to the speed controller design, virtual control signal are deﬁned as [10]:
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id ¼ Q=P k_z=P cz :satðSÞ; _idref ¼ ðid idref Þ=sz
ð18Þ
where cz > 0, sz > 0 and the time constant should be satisﬁed: sz ez =A2
ð19Þ
Where A is a maximum constant that _id A. The position control loop will stabilize after a ﬁnite time with z approaching sliding surface S.
4 HighGain Observer From (3) and (4) we have: disa : Rs isa þ usa ¼ha dt disb : Rs isb þ usb ¼hb esb ¼ jkm jxe cos he ¼ Lsb dt
esa ¼ jkm jxe sin he ¼ Lsa
ð20Þ
The update law for ^esa and ^esb according to the HGO set is written as follows [10, 13], with 1=ea and 1=eb are the coefﬁcients of HGO: ^e_ sa ¼ ðLsa _isa Rs isa þ usa ^esa Þ=ea ¼ ^esa =ea þ ha =ea ^e_ sb ¼ ðLsb _isb Rs isb þ usb ^esb Þ=eb ¼ ^esb =eb þ hb =eb
ð21Þ
The deﬁnition of the error between the actual and the estimated value is as follows: ~esa ¼ esa ^esa ; ~esb ¼ esb ^esb
ð22Þ
According to [13], we have the following values satisfying the inequality: j~esa j eð1=ea Þt j~esa ð0Þj þ ea :qa ðtÞ; ~esb eð1=eb Þt ~esb ð0Þ þ eb :qb ðtÞ
ð23Þ
From (20), the values esa and esb are harmonic functions, so the coefﬁcients h_ amax and h_ bmax exist such that: h_ a h_ a
max
and h_ b h_ bmax
ð24Þ
Thus, the smaller the coefﬁcients ɛa and ɛb, the smaller j~esa j and ~esb are bounded by the smaller bounding regions j~esa ð1Þj and ~esb ð1Þ. Then, the smaller the value ɛa and ɛb, the faster the rate of convergence of the estimated value follows the real values esa and esb of the backEMF. From the output signal of Highgain observer, the values of the angular position and rotor speed are calculated as follows:
Dynamic Surface Control of the AxialFlux Permanent Magnet Motor
^he ¼ arctanðE ^ sa =E ^ sb Þ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2 þE ^ sa ^ 2 =km ðaÞ or: x ^ e ¼ d^ ^e ¼ E h=dt ðbÞ x sb
355
ð25Þ ð26Þ
Note that if the speed is calculated according to (26a), the value of the speed depends on the linkage flux km, which is influenced by the ambient temperature. According to (26b), the speed is more reliable, especially in the medium and highspeed range, but is affected by process noise.
5 Simulation and Result The parameters of the motor include: phase resistance is 2.6 X; the air gap between the stator and the rotor is 1 mm; the rotor mass is 0.28 kg; the moment of inertia is 10.6 10−6 kgm2; the amplitude of the flux generated by the permanent magnet is km = 0.022 Wb. When the set speed is 4500 rpm, at the moment of 0.65 s the load torque affects the system. Figure 2 shows that the estimation speed is very fast with real speed value and the error is negligible.
Fig. 2. Speed response when set value is 4500 rpm under the influence of load torque
Figure 3 shows the response of the two current id and iq, where the iq current at the beginning has a large value to accelerate the motor to quickly reach the set value, then the value drops very small at the end of the transition mode. Figure 4 shows that when
Fig. 3. Current response id and iq
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Fig. 4. esa and esb component of the motor and esa and esb set of the observation
Fig. 5. Speed response
the load is applied, the backEMF component on of the observer adheres to the backEMF component of the motor very fast and few errors. Thus, in this case the HG observer estimates that the components esa and esb have a sinusoidal form, with the same amplitude and following the observed value. When changing the speed setting value from 2500 rpm–3500 rpm–1500 rpm in Fig. 5 and Fig. 6. Estimated speed is still capable of adhering to the real speed. The components es at the output of the HGO continue to be the same sinusoidal form and follow the motor BackEMF. Changing the speed setting value does not change the estimation time of the monitor.
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Fig. 6. BackEMF components on ab trục axis when speed setting value is changed
6 Conclusion The paper presented the control system without measuring the rotation speed for synchronous motor with integrated magnetic bearing, calculating the dynamic surface controllers and designing the HGO unit in the system. This proposed system is simpler and easier to design than the ones in [8] and [9]. By taking the input signal to the observer, the reference voltage and stator current perform an estimate of the backEMF electromotive force as the basis for calculating the position and rotor speed. The system works stably at medium speed range upwards, in which the interaction between axial position control and speed control has also been limited. However, the several spikes caused by the disturbances affects the quality of the output speed.
References 1. Nguyen, Q., Ueno, S.: Salient pole permanent magnet axialgap selfbearing motor no. Im (2009). https://doi.org/10.5772/intechopen.83966 2. He, R., Han, Q.: Dynamics and stability of permanentmagnet synchronous motor. Math. Probl. Eng. vol. 2017 (2017). https://doi.org/10.1155/2017/4923987 3. Hedrick, B.S.J.K.: Dynamic Surface Control of Uncertain Nonlinear Systems. Springer (2011). https://doi.org/10.1007/9780857296320 4. Lan, Y.H., LeiZhou.: Backstepping control with disturbance observer for permanent magnet synchronous motor. J. Control Sci. Eng. 2018 (2018). https://doi.org/10.1155/2018/4938389 5. Yang, Z.J., Nagai, T., Kanae, S., Wada, K.: DYnamic surface control approach to adaptive robust control of nonlinear systems in semistrict feedback form, vol. 16, no. 1. IFAC (2005)
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6. Nguyen, T.D., Tseng, K.J., Zhang, S., Nguyen, H.T.: A novel axial flux permanentmagnet machine for flywheel energy storage system: design and analysis. IEEE Trans. Ind. Electron. 58(9), 3784–3794 (2011). https://doi.org/10.1109/TIE.2010.2089939 7. Quang, N.P., Dittrich, J.A.: Power Systems Vector Control of Threephase AC Machines. Springer (2015). https://doi.org/10.1007/9783662469156 8. Nguyen, Q.D., Ueno, S.: Sensorless speed control of inset type axial gap selfbearing motor using extended EMF. In: 2010 International Power Electronics Conference  ECCE Asia , IPEC 2010, pp. 2260–2264 (2010). https://doi.org/10.1109/ipec.2010.5542012 9. Nguyen, T.D., Foo, G.: Sensorless control of a dualairgap axial flux permanent magnet machine for flywheel energy storage system. IET Electr. Power Appl. 7(2), 140–149 (2013). https://doi.org/10.1049/ietepa.2012.0048 10. Won, D., Kim, W., Shin, D., Chung, C.C.: Highgain disturbance observerbased backstepping control with output tracking error constraint for electrohydraulic systems, no. m, pp. 1–9 (2014). https://doi.org/10.1109/TCST.2014.2325895 11. Nguyen, Q.D., Ueno, S.: Axial position and speed vector control of the inset permanent magnet axial gap type self bearing motor. In: IEEE/ASME International Conference Advance Intelligence Mechatronics, AIM, pp. 130–135 (2009). https://doi.org/10.1109/aim. 2009.5230025 12. Nguyen, Q.D., Ueno, S.: Analysis and control of nonsalient permanent magnet axial gap selfbearing motor. IEEE Trans. Ind. Electron. 58(7), 2644–2652 (2011). https://doi.org/10. 1109/TIE.2010.2076309 13. Khalil, H., Praly, L.: Highgain observers in nonlinear feedback control. Int. J. Robust Nonlinear Control 24(6), 993–1015 (2014). https://doi.org/10.1002/rnc.v24.6
EdgeBased Object Pose Estimation Using Differential Evolution Algorithm Ngoc Linh Tao(&) and Lan Phung Xuan School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. The paper presents an objecttracking method to estimate threedimensional position of objects based on an edge image and the object’s 3D model. The system uses differences between a chamfer matching map and 2D edge gained from a pose hypothesis as the ﬁtness function. Differential Evolution algorithm uses the ﬁtness function in order to ﬁnd the most suitable position of objects. The experiments with the initialization task were carried out. The results of initialization problem proved the effectiveness of our method in solving the most difﬁcult problem of object tracking. The correct poses were found in reasonable runtime. After correct initialization, the searching space is signiﬁcantly reduced, as the more effective of the method could track the object in continuous frames. Keywords: Edgebased tracking
3D pose estimation Differential evolution
1 Introduction Until recently, researchers have archived signiﬁcant improvement in solving object detection and tracking problem by using keypoint features [1]. As coordinate transformations and lighting condition changing has little effect on keypoints searching methods, they seem to be a perfect choice for solving image matching problems from slightly different viewpoints [2]. However, keypointbased approaches work well in textured objects but insufﬁcient textured objects. Textured objects have various keypoints, those have high potential appearing on both images. After ﬁnding keypoints, sample consensus such as RANSAC [3] calculates the most suitable transformation of the object from reference position to current position. The more matched keypoints, the more accurate the transformation is. On objects with insufﬁcient texture, lacking of keypoint repeatability and stability neither reduces the accuracy of sample consensus method nor leads to wrong tracking results. Like keypoints, edges are also invariant to general geometric transformations and illumination changes [4]. Using edges is more suitable as a general approach even with textureless objects in object pose estimation problem. In early computer vision research, to ﬁnd the best alignment between two edge maps, a given prior set of edge templates compare their suitability to the current edge maps to draw the most suitable transformation. The currently proposed method of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 359–365, 2021. https://doi.org/10.1007/9783030647193_40
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chamfer distance matching [5] enhances the cost functions enable for applying global searching algorithm into object tracking problems. Harris [6] and various proposed edgebased tracking system such as [7] used edges and contours for visual tracking task. One drawback of using edges is that they are not distinctive enough to provide effective discrimination in complex background or occlusions, there have been efforts to enhance the previous one by unifying interest points or considering multiple but limited hypotheses on edge correspondences. For consideration of multiple hypotheses in a more general sense global searching algorithm should come into consideration. We propose an approach of using Differential Evolution (DE) [8] as the global searching method to calculate initial position and continuously track 3D positions of an object in camera coordinate.
2 Methodology Initialization is the most important step of the tracking algorithm and is an attractive topic of the paper. The following steps presents the implementation pipeline of the initialization: – Edge image is employed to archive edge images from query images. – Distance maps or chamfer matching maps are calculated from edge images. – Differential Evolution is employed to calculate the best ﬁt pose of the object which create a 2D edge images ﬁtted into the chamfer matching maps for initialization. After initialization, a narrower searching boundary is used to get the accurate results at online speed. If the cost function goes large, initialization is required. 2.1
Chamfer Matching Maps from Query Images
Edge Detection The white lines in Fig. 1 are output of Canny [9] edge detection method. Canny method includes ﬁve different steps: Step 1: Noise removal step by using Gaussian ﬁlter. Step 2: Calculate the intensity gradients of the image Step 3: Apply nonmaximum suppression to get rid of spurious response to edge detection Step 4: Apply double threshold to detect potential edges. Step 5: Track edge by hysteresis: ﬁnalizing the detection of edges by suppressing all the other edges that are weak and not connected to strong edges. Chamfer Matching Maps In the comparison step, points get higher score when they come closer to the edge point. By indexing the value of pixel with its distance to the closest edge point, we get the distance map as shown in Fig. 2.
EdgeBased Object Pose
2.2
361
Camera Model and Edges from CAD Model
From a calibrated RGB camera and object CAD model, we are able to archive ideal visible edges of objects by using a camera model matrix.
Fig. 1. Edge detection result
Fig. 2. Chamfer matching map
This matrix converts a point with coordinate of (x, y, z) in real coordinate to an image point (u, v) as Eq. 1. 2 3 2 u fx 4v5 ¼ 4 0 1 0
0 fy 0
32 x 3 cx =z cy 54 y=z 5 1 1
ð1Þ
To determine visibility of object edges we use edge featuresbased method from [12]. Figure 3 shows an edge between adjacent faces A ¼ hv0 ; v1 ; v2 i and B ¼ hv0 ; v1 ; v3 i with unit face normal nA and nB calculated as in Eq. 2, 3. nA ¼ normalizeð½v1 v0 ½v2 v0 Þ
ð2Þ
nB ¼ normalizeð½v3 v0 ½v1 v0 Þ
ð3Þ
Fig. 3. Edge between two surfaces
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To determine the visibility of edge E ¼ hv0 ; v1 i, we use additional vector ve with direction from v0 to the camera center. E is a visible edge if cross manipulation value of ve with nA or ve with nB positive. 2.3
Initial Pose Searching
Cost Function Calculation The cost function for global searching algorithm is a comparison result between edge maps from image and edge maps from CAD model. To gain an equivalance between ideal edge maps, a resampling step is applied, so the number of edge points in different ideal maps is set equally at N = 200 points. The error cost function is calculated as in Eq. 2, FðR; tÞ ¼ f ðnÞ
n 1X ðE i M i Þ2 n2 1
ð4Þ
where f ðnÞ is function depended on the number of inlier (n) as in Eq. 3. Ei is value of real edge images at inlier i, Mi is value of CAD model edge images at inlier number i. f ð nÞ ¼ 1
n N
ð5Þ
3 Differential Evolution Algorithm Differential evolution (DE), proposed by Storn and Price, is a very popular EA. Like other EAs, DE is a populationbased stochastic search technique. It uses mutation, crossover and selection operators at each generation to move its population toward the global optimum minimum. a) Initialization in DE The initial population was generated uniformly at random in the range lower boundary (LB) and upper boundary (UB). XG¼0 ¼ lbj þ randj ð0; 1Þ ubj lbj i;j
ð6Þ
where randj ð0; 1Þ a random number in [0,1]. b) Mutation operation
G G G ¼ X ; X ; . . .; X In this process, DE creates a mutant vector, XG i i;1 i;2 i;n . For each individual at each generation XG i is a target vector in the current population. There are several variants of DE based on mutation schemes, which are: DE/rand/1, DE/best/1, DE/current to best/1, DE/rand/2, DE/best/2, DE/rand to best/1.
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c) Crossover operation G After mutation process, DE performs a binomial crossover operation on XG i and Vi to G G G generate a trial vector UG i ¼ Ui;1 ; Ui;2 ; . . .; Ui;n for each particle i as shown as Eq. 5. ( UG i
¼
VG i;j XG i;j
if randj CR or j ¼ jrand otherwise
ð7Þ
where i = 1, …, NP. j = 1,…, D is randomly chosen integer in ½1; D, CR 2 ½0; 1 is the crossover control parameter. d) Selection The selection operator is to select the better vector between the target vector XG i;j and the trial vector UG to enter the next generation. i;j þ1 XG ¼ i
UG i XG i
G if f UG i f Xi otherwise
ð8Þ
þ1 is target vector in the next generation where i = 1, …, NP, XG i
4 Experiment and Results 4.1
Experiment Setup
To implement the algorithm, the system hardware included an iBuffalo BSW20KKM11BK camera. All code is implemented on C ++ code on a Macbook Air laptop computer empowered with 1.3 GHz Intel Core i5. In the experiment, we used the box object with size of 145 95 40(mm) in white colour as the tracking object. The object “*.ply” extension use the input model. The searching boundary for the object translation is in [100, 100] and [p=2; p=2] for rotation angles of rollpitchraw. 4.2
Results
Figure 4 shows the results of boundary search of the box with the position as Fig. 1. The box boundary is in blue colour in the left image. The result images showed that, the method was able to ﬁnd the position of the object so the boundary could ﬁt into the edge maps. Depend on the objects, symmetric or nonsymmetric, we need to set different searching boundaries for the searching algorithm (DE). Figure 5 shows the convergence trend of DE. Table 1,2 show consuming time and success rate depending on population number of DE respectively. The success rate stops ỉmpove from population of 250.
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5 Discussion and Conclusions Object tracking has been a challenging task in computer vision. Recently, evolution based global searching methods have proved its potential in solving the tracking problem, ﬁnding robust and accurate global optima solutions. We proposed a novel approach of using differential evolution as a global searching method to ﬁnd the best 3D position of objects. The experimental results showed promising results. In the future, we would like to improve cost function with the method to narrow the searching area for more accuracy.
Table 1. Time consuming on population size Pop size 400 300 200 100 20 Runtime (ms) 1145 801 519 298 60
Table 2. Convergence rate on pupulation size Pop size 300 250 200 150 100 Success rate % 85 88 80 75 70
Fig. 4. Tracking results
Fig. 5. Error convergence trend
References 1. Lowe, D.G.: Distinctive image features from scaleinvariant key points. IJCV 60(2), 91–110 (2004) 2. Grauman, K., Darrell, T.: The pyramid match kernel: Discrimina tive classiﬁcation with sets of image features. In: ICCV, vol. 2, pp. 1458–1465 (2005) 3. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model ﬁtting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)
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4. Luc, V., Caifeng, S., Tommaso, G.: Realtime robust background subtraction under rapidly changing illumination conditions. Image Vis. Comput. 30(12), 1004–1015 (2012) ISSN 0262–8856, http://dx.doi.org/10.1016/j.imavis.2012.08.017 5. Barrow, H., Tenenbaum, J., Bolles, R., Wolf, H.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: IJCAI, pp. 659–663 (1977) 6. Harris, C.: Tracking with rigid objects. MIT Press (1992) 7. Comport, A.I., Marchand, E., Chaumette, F.: Robust modelbased tracking for robot vision. In: IROS, vol. 1, (2004) 8. Storn, R.: Differential evolution – a simple and efﬁcient heuristic for global optimization over continuous spaces. J. Global Opt. 11(4), 341—359 (2007) https://doi.org/10.1023/a: 1008202821328 9. Canny method, http://docs.opencv.org/master/da/d22/tutorial_py_canny.html#gsc.tab=0 10. Bradski, G.: Opencv_library. Dr. Dobb’s Journal of Software Tools (2000) 11. OpenCV Edge Detection Implementation. http://docs.opencv.org/2.4/doc/tutorials/imgproc/ imgtrans/canny_detector/canny_detector.html 12. Morgan, M., John, F.H.: Hardwaredetermined feature edges. In: Proceedings of the 3rd International Symposium on NonPhotorealistic Animation and Rendering (2004) http://doi. acm.org/10.1145/987657.987663
Effect of Changing Grounding Mode to Reduce Power Loss on Lightning Ground Wire by Induced Current  Northern Vietnam Overhead Power Transmission Line Nhat Tung Nguyen1 and Xuan Phuc Nguyen2(&) 1
2
Faculty of Electrical and Electronics Engineering, ThuyLoi University, Hanoi, Vietnam [email protected] Power Network Planning Deptartment, Institute of Energy, Hanoi, Vietnam [email protected]
Abstract. In Vietnam, the overhead power transmission lines are usually equipped with lightning ground wires, include optical ﬁber composite ground wire (OPGW) and common ground wire (CGW). Both lightning wires are grounded at each tower to ensure safety for transmission line but directly affect the power loss, caused by electromagnetic induction. Therefore, the calculation of quantitative power loss shows the urgent necessity to reduce the loss, by changing the grounding mode of lightning ground wire. Both ﬁeld measurement and ATPEMTP simulation of induced current and voltage of lightning ground wires in transmission lines are carried out in this paper. The results allow proposing a solution to reduce the loss on the lightning ground wire of power transmission grid. Keywords: Induced voltage and current
Ground wire Transmission grid
1 Introduction In Vietnam, the lightning ground wires (gw) grounded at each tower [1], make induced voltage and induced current through the electromagnetic and electrostatic coupling by transmission circuit [2–4]. Overall, the induced voltage and current are related to grounding way of gw. Overhead lightning wires that have continuous grounding points at electrical tower produce the large induced currents and large power loss consequently. Another way, insulated overhead gws has one end grounded and another insulated, produce a great induced voltage in it [5]. With the objective of reducing power loss on grid, the study of grounding lightning protection wires on 220 kV and 500 kV lines is necessary. Combined with the parameters of the Northern Vietnam overhead transmission lines, this paper simulates and measures on grid the induced voltage and induced current in lightning gw’s under different grounding mode of those wires. The analyzes of their impact factors are used to propose solutions to reduce losses on these lightning ground wires. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 366–372, 2021. https://doi.org/10.1007/9783030647193_41
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This study is also demanded by the Vietnam Electricity (EVN) to consider separating the grounding of the lightning protection wire on the 220 kV and 500 kV transmission grid.
2 Induced Current and Power Loss in Northern Vietnam Overhead Transmission Lines 2.1
Simulation the Induced Current and Power Loss in Lightning Ground Wires
The study includes 4 different transmission lines of the Northern Vietnam transmission grid, Table 1. The electric wires are mainly ACSR330/43 for 220 kV level and ACSR330/42 for 500 kV level; the lightning gw used type of OPGW 70, 80 and Phlox 116 for CGW. Tower type of 220 kV and 500 kV overhead transmission lines in Northern Vietnam and their parameters are shown in Fig. 1. Especially, line 220 kV T500Pho NoiPho Noi has an area without CGW wire. Table 1. Parameters of overhead transmission lines studied Transmission line
220 kV T500 Pho Noi Pho Noi
Thanh Hoa Nghi Son 2
220 kV West of Hanoi
T500 Pho Noi  Thuong Tin
500 kV
Case study Length (km) Number of circuits
1 15,33 2
2 65,81 2
3 12,7 4
4 34,26 2
Fig. 1. Line tower in Vietnam overhead transmission line 220 kV and 500 kV
Simulation software used in this analysis is the electromagnetic transient program ATPEMTP. The induced voltage and induced current depend on the position of wires on the transmission grid; therefore, the paper uses data line model [6] to simulate the overhead lines. The results in normal operation with different power transmission (%) are showed in Fig. 2 and Fig. 3.
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Fig. 2. Induced current in lightning gw’s of 550 kV T500PhoNoiThuong Tin
With the lightning gws are grounded at each tower, the induced current is stable in the middle of the line, Fig. 2, little affected by earthing resistances. The simulation indicates that, the induced current on lightning gw is up to 150 A at 500 kV transmission line; 50 A (double lines) and 90 A (quadruple lines) at 200 kV transmission line, Fig. 3. This current increases with the increasing of transmission power, number of circuit and voltage lever of the transmission grid. Large induced current in overhead ground wires will bring adverse effects, such as energy loss and operational life reduction. The power loss due to induction on the lightning gws is calculated by the total losses on the lighting wires at each span and the loss on the earth wire of each tower, speciﬁcally according to the following equations: DP = DP1 þ DP2 DP1 ¼
N X
2 ICWG :RCWG þ
1
N X
2 IOPGW :ROPGW
ð1Þ ð2Þ
1
DP2 ¼
N X
2 Ign :Rgn
ð3Þ
1
With RCGW, ICGW; ROPGW, IOPGW; Rgn, Ign are respectively the resistance and current value of CGW, OPGW of each span n, and earth wire of tower n. Results calculation, Fig. 4, indicate that the average values of power loss on the two gw’s is 1,36 kW/mile and 19,3 kW/mile at 220 kV and 500 kV respectively (calculated at 70% transmission power), similarity with value 1,2 kW/mile at transmission grid 345 kV in [7]. However, at 500 kV level, due to the greatly reduced ground resistance at electrical tower, the current induced increases and makes the bigger value of power loss.
Effect of Changing Grounding Mode to Reduce Power Loss on Lightning Ground Wire
0
1000 800 600 400
Transmission Power (%) 20
40
60
80
100
CGW (A) _500 kV_2 circuits
OPGW (A) _ 500 kV_2 circuit
CGW (A)_ 220 kV_2 circuits
OPGW (A)_220 kV_2 circuits
CGW (A)_220 kV_4 circuits
OPGW (A)_220 kV_4 circuits
Power Los (lW)
1200
Induced Current (A)
161 141 121 101 81 61 41 21 1
369
ΔP_220 kV_Phố Nối ΔP_500 kV_2 circuits ΔP_220 kV_2 circuits ΔP_220 kV_4 circuits
200 0 0
20
40
60
80
Transmission Power (%)
100
Fig. 3. Induced current in lightning ground Fig. 4. Power loss with different transmission wires with different transmission power power (%)
The similar calculations are performed for Northern Vietnam transmission lines within 2018 data, in the condition of 50% transmission power, the results indicate that the values of power loss are 0,81 kW/mile and 10 kW/mile for 220 kV and 500 kV respectively. 2.2
Measurement of Induced Current on Lightning Gw’s
The measurements were made with 220 kV and 500 kV Pho Noi  Thuong Tin transmission lines, under 35% transmission power condition, Fig. 5. For 500 kV lines only measurement on the CGW wire are made causes of dangerous for the measuring personnel. The measured and simulated values are showed in Table 1; those values are very close to each other (about 5–7% difference) and specify the exact simulations.
CGW wire 220 kV
OPGW wire 220 kV
CGW wire 500 kV
Fig. 5. Measurement the induced current on grid 220 kV and 500 kV Table 2. Comparation of measurement and simulated current in lightning ground wires Lightning GW Span number CGW of line I 16–17 OPGW of line I 16–17 CGW of line IV 340–341
Measured Current (A) Simulated Current (A) 19,9 21,03 23 25,63 55,8 53
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3 Impact Factors to Reduce the Power Loss on Lightning GW The reduction technique of power loss on lightning ground wires are the transposition, sectionalization and open loop of lightning gw’s, Fig. 6 [7–13]. The open loop of lightning gw’s is difﬁcult and expensive; for this reason, it isn’t considered in this paper. 3.1
Impact of Transposition and Changing the Number of Grounded Points
Table 3. Effect of sparce grounded point on GW loss Distance 0,32 km 1 km 2,5 km 5 km 10 km DP (W) 1624 1622 1624 1592 1589
Table 4. Effect of number of transpositions on GW loss Number of gw transpositions 0
Fig. 6. GW loss reduction Schemes. a) Transposition, b) Sectionalization; c) Open loop
DP (W)
1
2
3
4
1624 1680 1664 1675 1667
Taken transmission line 220 kV double circuit Thanh Hoa  Nghi Son 2, under different segment length from 1 km to 10 km between two grounded point on the lightning gw, Table 3 shows impact of the sparse grounding to the power loss. The reduction of power loss isn’t signiﬁcant effective. The same results happened to the transposition technique, by increasing the number of transpositions from 1 to 4, Table 4. The reason for those results is that, with the double and quadruple circuits of transmission line, the phases wires and the lightning gws are completely symmetrical along the line length, so the transposition makes no signiﬁcant inhibitory effect on the electromagnetic induction. The changing number of grounded points causes the small decrease of power loss, corresponding with a small change in the induction parameters. 3.2
Impact of Sectionalization
This scheme is implemented by dividing the lightning gw’s into several sections [8, 9]. Each section length is between a two navigations tower and grounded at one point, opened at the end, and insulated elsewhere. The point grounded serves to reduce the capacitively induced voltage. According to regulation [1], the CGW and OPGW gw’s should be grounded at each electrical tower within 2 km around the station.
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Fig. 7. Power loss on gw’s when changing grounded mode
The calculations, Fig. 7, show that the power loss is signiﬁcantly reduced when implementing the lightning gw sectionalization solution, reduces 85% at 220 kV level and 91% at 500 kV level. The calculations are performed for Northern Vietnam transmission lines within 2018 data, the results indicate that the values of power loss are reduced at value 0,12 kW/mile and 1,23 kW/mile for 220 kV and 500 kV grids respectively.
4 Conclusion In this study, the simulation of the normal operation mode of doublecircuit and quadruplecircuit of transmission grid is performed. The simulation results are veriﬁed by experiments, show the results with high accuracy. The power loss is not signiﬁcantly reduced when changing the number of points grounded of lightning gw’s or make the transposition. The proposed solution when separating the gw’s and grounding one end has shown a signiﬁcant reduction in loss on lightning gw’s. These initial studies show the feasibility of reducing loss on the transmission grid by reducing loss on the lightning gw’s system. The calculation of the impact factors of the proposed solution on technical speciﬁcations of the grid is a matter of research and supplementation.
References 1. QCVN: 2015/BCT, Quy chuẩn kỹ thuật quốc gia về KTĐ – Phần 1: Hệ thống lưới điện 2. Baba, Y., Rakov, V.A.: Voltages induced on an overhead wire by lightning strikes to a nearby tall grounded object. IEEE Trans. Electromagn. Compat. 48(1), 212–224 (2006) 3. Xuefeng, W., Yanping, L.: Research on reducing the energy loss in lightning shield line. High Voltage Eng. Chin. 31(9), 28–30 (2005) 4. Kai, L., Yi, H.: Analysis and research of grounding modes of optical ﬁber ground composite wire. In: Proceedings IEEE Power Energy Engineering Conference Asia Paciﬁc, pp. 1–4 (2010)
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5. Xiande, H., Hao, Z.: Simulation and analysis of induced voltage and induced current on overhead ground wire of JindongnanNanyang 1000 kV UHV AC transmission line. In: Proceedings 4th International IEEE Electric Utility Deregulation and Restructuring Power Technology Conference, pp. 622–625 (2011) 6. Simões, M.: Transmission line modeling for realtime simulations, Instituto Superior Técnico (2012) 7. Keri, A.J.F., Nourai, A., Schneider, J.M.: The open loop scheme: an effective method of ground wires loss reduction, IEEE Trans. Power App. Syst. PAS103(12), 3615–3624 (1984) 8. Rojas, P.E.M.: The effect of discontinuities in a multiconductor line on lightninginduced voltages. IEEE Trans. Electromagn. Compat. 51(1) 53–66 (2009) 9. Foehmer, B., Thomas, P.: Sectionalizing static wire prevents 500 kV outages. Trans. Distrib. 30, 56–57 (1978) 10. Zhenqiang, L., et al.: Effect of UHV ground wire disposition on its electric energy loss and second arc current. Power Syst. Technol. Chin. 34(2), 24–28 (2010) 11. Militaru, C.: Sectionalizationed OPGW on extra high voltage transmission lines. In: Proceedings of 57th IWCSInternational Wire & Cable Symposium (2008) 12. Benliang, L., et al.: Operation mode of ground wire to reduce ground wire loss of HVAC transmission Lines. PowerSyst. Technol. 35(3), 98–102 (2011) 13. Wang, J., Wang, Y., Peng, X., Li, X., Xu, X., Mao, X.: Induced voltage of overhead ground wires in 500 kV singlecircuit transmission lines. IEEE Trans. Power Del. 29(3), 1054–1062 (2014)
Electromagnetic Design of Synchronous Reluctances Motors for Electric Traction Vehicle Bui Minh Dinh(&), Do Trong Tan, and Dang Quoc Vuong Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. The paper presents comparative performances of different rotor structure synchronous reluctance motors used for electric traction in automotive application. The electrical machine under study in this paper is a synchronous reluctance machine (SynRM) with 8 poles and 48 slots. The study design has implemented two V and U shape flux barriers with 4 layers. Pure Synchronous Reluctance motors potentially operate at high speed due to a costeffective rotor compared to PM and induction motors. In this paper, thermal simulation and mechanical stress was also investigated to evaluated flux bar or sizing of the radial ribs. The approach leads to an original positioning of the radial ribs able to preserve the performances of the motor at high operating speed enhancing the mechanical integrity of the rotor. Some signiﬁcant contributions of this study are new 4U layer barrier rotor and mechanical stress of ribs and bridges at maximum speed. Keywords: Synchronous Reluctance Motor (SynRM) Traction motors Electric vehicle Torque ripple Permanent Magnetic Assistant Synchronous Motor PMASynM
1 Introduction Electric machines have become the primary candidate for mobility [1, 2], improving motor solutions mainly based on high performance permanent magnets (PM) manufactured with rareearth materials [3–6]. The synchronous reluctance machine (SynRM) has similar structure to an induction machine and permanent magnetic assistant synchronous reluctance machine PMASynRM. There is a growing attention in alternative solutions to reduce permanent magnet for electric machines [7]. Many electromagnetic designers also aim to enhance the speciﬁc power of the motordrives often by increasing their maximum operating speed. Many rotor bars or slot layer topologies can be designed to increase the saliency ratio. From the point of view of the lamination orientation, the common two topologies employed are shown in Fig. 1 (a) and (b), axial laminated rotor V and U shape laminated rotor. For electric vehicle application, the most important parameters of this machine is increasing electromagnetic torque in high speed, which is dependent on the inductance difference between d and q axis and saliency ratio, respectively. Those parameters can © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 373–378, 2021. https://doi.org/10.1007/9783030647193_42
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Fig. 1. Rotor V (a) and U (b) shape laminated rotor.
be improved by the geometrical parameters of the rotor and stator of SynRM. One more problem of the ribs and bridges areas of the rotor would fail because of the high centrifugal forces. In order to increase the maximum speed of the SynRM, some small rotor slots can change ribs and bridges or ﬁll the barriers with nonmagnetic materials with high Young’s Module value [8]. This paper will use software SPEED and ﬁnite elements method in order to study the influence of different parameters of the rotor on the machine performances. In order to obtain the best performances (torque ripple, efﬁciency), rotor slot in U and V shapes have been investigated to obtain those performances.
2 Electromagnetic Performance Evaluation The analytical support used to obtain the main parameters of SynRM is dedicated software SPEED developed by CDAdapco’s. Here, using a graphical interface, the user can decide any pole shapes of the machine (Fig. 2).
Fig. 2. Electromagnetic design program of SynRM 3 layers
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For all electrical machines used in automotive applications, the combination between the number of poles and slots is the most important method to getting low torque ripple value. As outlined in the literature the electrical machines used in electrical propulsion in combination with gearbox, are working in the large range of speed (usually between 0 and 20.000 rpm) [5, 6]. In order to obtain the best performances, it is necessary to reduce the iron loss by using a low number of poles to obtain the low value of frequency with direct influences in iron loss. For the SynRM the number of poles recommended in literature is 4, iron losses above this value are very high at considered nominal speed. The design procedure was started according with the performance requirements presented in Fig. 3, and the geometrical constraints: stack length (L = 200 mm), stator outer diameter (Dout= 350 mm).
Fig. 3. SynRM performance requirements
In order to improve the electrical machine performances, several rotor barrier topologies in combination with 8 poles will be analyzed with purpose to reduce the torque ripple. However, the machine was designed initially to develop higher torque and to obtain the shape of electromagnetic torque. The structure obtained in SPEED will be introduced in electromagnetic analysis performed in FEM. The analysis was performed considering several number of barrier layers of U and V rotor shape with number of poles (p = 8) (Fig. 4). Having designed the machine, the following step is to validate torque, efﬁciency at base speed of 4500 rpm by SPEED software in Fig. 5. For the simulation performed SPEED, the type of steel lamination is M27035A. The efﬁciency of the machine over the entire current and speed range of 12.000 rpm is presented. Efﬁciency values lower than 90% are obtained for high currents and low speed because of the high current density required, producing the desired torque, at high speeds. The efﬁciency is higher than 95% because of the low iron losses.
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Fig. 4. Torque ripple of 4 V (a) and 4U (b) barrier layers of SynRM
Fig. 5. Torque, power and efﬁciency vs base speed 4500 rpm and maximum speed 12000 rpm.
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3 Temperature and Mechanical Stress Veriﬁcation Thermal simulations conducted for the analysis of the temperature of the rotor, stator and winding were conducted based on copper and iron losses. The Fig. 6 shows the temperature distribution for a motor RMS current of 300 A.
Fig. 6. Temperature distribution of SynRM
The thermal simulations have been performed using the commercial software package MotorCAD that is a code devoted to electrical motor thermal analysis. The implemented model is based on an analytical lumped circuit. Maximum temperatures of rotor, winding or shaft is 104 °C lower than 160 °C. The mechanical equivalent stress map at max speed (12000 rpm) is reported in Fig. 7, the maximum values is 127 MPa.
Fig. 7. Mechanical stress and displacement of rotor
Figure 7 shows the deformation of the rotor at the airgap at maximum speed operation. In each case, the deformation is less than 10% of the airgap to avoid any risk contact between rotor and stator considering also possible fluctuation of machine due to vibration modes, tolerances and the bearings selection.
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4 Conclusion The paper presents the analytical a numerical results SynRM 48 Slot/8poles concerning torque ripple, temperature and mechanical stress and displacement in electric vehicle application. The influence of rotor designs of 4U and 4 V barrier shape in has been investigated in this paper. The conﬁguration with 48 slots and 4U layers fluxbarrier has the lowest torque ripple. Also this conﬁguration has been numerically evaluated, and the obtained performances has presented in this paper. In continuous power the efﬁciency is 96% and in peak power condition is 89% at rated speed. Acknowledgment. This paper has received supports from the Viettel High TechnologyVHT, Viettel Group for simulating and calculating Software and PCs.
References 1. Credo, A., Fabri, G., Villani, M., Popescu, M.: High speed synchronous reluctance motors for electric vehicles: a focus on rotor mechanical design. In: IEEE International Electric Machines & Drives Conference (IEMDC) (2019) 2. Chan, C.C.: The state of the art of electric and hybrid vehicles. Proc. IEEE 90(2), 247–275 (2002) 3. Zhu, Z.Q., Howe, D.: Electrical machines and drives for electric, hybrid, and fuel cell vehicles. Proc. IEEE 95(4), 746–765 (2007) 4. Hendershot, J.R., Miller, T.J.E.: Design of Brushless PermanentMagnet Machines. Motor Design Books LLC, Tokyo (2010) 5. Williamson, S., Emadi, A., Rajashekara, K.: Comprehensive efﬁciency modeling of electric traction motor drives for hybrid electric vehicle propulsion applications. IEEE Trans. Veh. Technol. 56(4), 1561–1572 (2007). ISSN 00189545 6. Boulanger, A.G., Chu, A.C., Maxx, S., Waltz, D.L.: Vehicle electriﬁcation status and issues. Proc. IEEE 99(6), 1116–1138 (2011) 7. Boldea, I., Tutelea, L., Parsa, L., Dorrell, D.: Automotive electric propulsion systems with reduced or no permanent magnets: an overview. IEEE Trans. Industr. Electron. 61(10), 5696–5711 (2014) 8. Widmer, J.D., Martin, R., Kimiabeigi, M.: Electric vehicle traction motors without rare earth magnets. Sustain. Mater. Technol. 3, 7–13 (2015). Elsevier 9. Online database. www.evspeciﬁcations.com 10. Pellegrino, G., Jahns, T., Bianchi, N., Soong, W., Cupertino, F.: The Rediscovery of Synchronous Reluctance and Ferrite Permanent Magnet Motors. Springer, Cham (2016) 11. Babetto, C., Bacco, G., Bianchi, N.: Synchronous reluctance machine optimization for highspeed applications. IEEE Trans. Energy Convers. 33(3), 1266–1273 (2018) 12. Ferrari, M., Bianchi, N., Doria, A., Fornasiero, E.: Design of synchronous reluctance motor for hybrid electric vehicles. IEEE Trans. Ind. Appl. 51(4), 3030–3040 (2015)
Enhancing Accuracy of Surface Roughness Model Using BoxCox Transformation in Surface Grinding AISI 5120 Alloy Steels Do Duc Trung1, Nguyen Dinh Ngoc2, Tran Thi Hong3, Bui Thanh Danh4, Nguyen Thanh Tu2, Tran Ngoc Giang2, Nguyen Thi Quoc Dung2, and Vu Ngoc Pi2(&) 1
3
Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen City, Vietnam [email protected] Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 4 University of Transport and Communications, Hanoi, Vietnam
Abstract. The aim of this paper is to present a research study on enhancing the model accuracy of surface roughness when surface grinding. The used material is this study is AISI 5120 alloy steel and the grinding wheels are made of CBN. The experimental matrix is prepared according to response surface method in terms of Central composite design. Four cutting parameters, i.e. workpiece speed, feed rate, depth of cut, and dressing depth, are selected to investigate their influences on the surface roughness. Moreover, the interactions between the cutting parameters are also considered. Regression models for predicting surface roughness are suggested. Box – Cox transformation is applied to transform the nonnormal distribution data to the new ones exhibiting normal distribution shapes. The results show that the new model (using Box – Cox transformation) predicts surface roughness better than the old model (without using Box – Cox transformation) does. Keywords: Surface grinding Steel AISI 5120 CBN grinding wheel Surface roughness model Transformation Boxcox
1 Introduction Surface roughness which has been widely utilized to characterize the machining quality of metallic machining has strong influence on the working ability and the lifetime of mechanical components. Hence, improving machining quality corresponding to reduce surface roughness is an important topic of various machining operations such as milling, trimming, turning, grinding. Among these operations, surface grinding process is more frequently adopted to get the ﬁnishing machining quality than others. Studying the influence of machining parameters on the surface roughness and establishing surface roughness predicting model for speciﬁc machining conditions has been attracted by researchers in literature. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 379–390, 2021. https://doi.org/10.1007/9783030647193_43
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Pauwan. K et al. [1] utilized response surface method to analyze surface roughness when grinding process for EN24 steel is carried out using grinding wheel made of Aluminiun Oxide (A60L5V). The machining parameters investigated are grinding speed, workpiece speed, and depth of cut. It is revealed that grinding speed has the strongest influence on the surface roughness. Inversely, workpiece speed, depth of cut, as well as the interaction between these parameters have insigniﬁcant influences on the surface roughness. Moreover, a predicting surface roughness model was proposed. Response surface method is also used in a study by Periyasamy S et al. [2], in which surface grinding for AISI 1080 steel was conducted by using grinding wheel made of Aluminiun Oxide (A60V5V). Depth of cut, feed rate, and dressing depth are the considered machining parameters. Their results showed that surface roughness increases with increasing in feed rate, while depth of cut has a complicated impact on the surface roughness. Dressing depth has little effect on the surface roughness. Moreover, a regression model with polynomial degree 2, expressing the relation of the machining parameters, was suggested. Binu. T et al. [3] performed the grinding of ceramic materials using diamond grinding wheels. Design of Experiment was carried out according to response surface method with 27 tests to investigate the influences of machining parameters (workpiece speed, cutting speed, and depth of cut) on the surface roughness. It was reported that three parameters have important impacts on the surface roughness, and the workpiece speed has greater effects than two remaining parameters. Grinding operations of EN31steel were executed by Waikar. O et al. [4]. In this study, machining quality characterized by surface roughness was analyzed in terms of investigating influences of cutting parameters such as workpiece speed, depth of cut, and coolant flowrate. A plan of experiments based on Taguchi method (L9) was designed. The influencing order of machining parameters on the surface roughness is workpiece speed, depth of cut, and coolant flowrate. Surface roughness increases with increasing in depth of cut, and decreasing in workpiece speed and coolant flowrate. Nurul. A.Y et al. [6] used the response surface method to design testing plan for grinding of Inconel 718 steel. The dependence of surface roughness on workpiece speed, depth of cut, and number of passes. The results showed that both workpiece and depth of cut has bigger influence on surface roughness than number of passes. Nguyen H.S et al. [7] studied the impacts of workpiece speed, feed rate, and depth of cut on surface roughness resulting from grinding process of SUJ2 steel using CBN grinding wheel. Fifteen tests based on BoxBehnken design were performed. Their results detailed that the mentioned parameters have important effect on the surface roughness where the feed rate has the strongest impact, and the depth of cut has the weakest effect. The interactions between the investigated parameters also have signiﬁcant effects on the surface roughness. Other studies of Nguyen H.S et al. [8, 9] conducted the grinding process of 3X13 and SKD11 steels. Workpiece speed, feed rate, and depth of cut were selected to check their influences on the surface roughness. Cutting speed and workpiece speed are found to have strongest influences on the surface roughness for 3X13 and SKD11 steels, respectively. The lowest impacts belong to depth of cut and workpiece speed corresponding to 3X13 and SKD11 steels. The optimum input process parameters for minimum surface roughness when surface grinding 90CrSi tool steel were also reported in [10, 11]. In addition, the optimum
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dressing factors have been proposed to get the minimum surface roughness when grinding 90CrSi steel [12, 13]. Based on the previously mentioned studies, it is realized that the influencing degrees of cutting parameters on the surface roughness are different due to dissimilar machining conditions. Moreover, response surface methods have been utilized in most mentioned studies. This means that this method is highly suitable to design the testing plans. AISI 5120 steels include low carbon percent but possess high strength. This kind of steel has been widely used to fabricate mechanical parts in automobile such as cam shaft, transmission shafts, tube, rods, etc. The mentioned parts are necessary to grind with the good surface quality before using in services. Regarding the materials of grinding wheels, CBN is one of the most effective grinding wheels strongly recommended in industry due to its properties and machinability. For example, this kind of grinding wheel materials can be used for dry grinding which is not able to conduct by other materials. The grinding process of AISI 5120 steels using CBN grinding wheel has not attracted research community so far. In this study, the influences of workpiece speed, feed rate, depth of cut, and dressing depth on the surface roughness (Ra) will be conducted when grinding operation of AISI 5120 steels. The materials of grinding wheels are CBN. Design of experiments will be carried out to develop a predicting surface roughness model according to response surface method. The Box–Cox transformation will be used to improve the accuracy of the proposed model. The predicted values of surface roughness will be compared with experiments to validate the ratability of the proposed models.
2 Grinding Test 2.1
Material Preparation
The tested specimens have a rectangular shape with dimension of 60 mm, 40 mm, and 10 mm corresponding to the length, the width, and the height. Specimens made AISI 5120 steels exhibit the hardness of 56–58 HRC after Carburizing process. The chemical components of specimens are presented in Table 1 (Table 2). Table 1. Denotation of AISI 5120 steels in various standards USA Germany China Japan France England ISO AISI DIN GB JIS AFNOR BS ISO 5120 SCr4204 20Cr Scr420 18C3 527A20 SCr4204
2.2
Machine Tool and Grinding Wheel
Grinding machine used in this study is a surface type, APSG820/2A (Taiwan origin). The CBN grinding wheel, HY100# (South Korea origin) is selected herein. The dimensions of external diameter, thickness, and hole diameter of grinding wheel are
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orderly 180 mm, 13 mm, and 31.75 mm. The grinding tests will be conducted by ﬁxing following parameters: cutting speed of 26 m/s, feed rate of 100 mm/min, and coolant of emulsion with concentration of 6%  flowrate of 10 (l/min) (Fig. 1).
Fig. 1. Grinding machine
2.3
Design of Experiment
The testing plan is designed according to response surface method. The input parameters are workpiece speed, feed rate, depth of cut, and dressing depth. The testing matrix, therefore, are 2 k = 16 (k = 4), 2 k = 8. The values of parameters at different levels are listed in Table 3. The testing plan is shown in Table 4.
Table 3. Testing levels of input parameters Parameters
Denotation Level −2 −1 0 1 2 Workpiece speed, m/min v 5 10 15 20 25 Feed rate, mm/stroke f 2 4 6 8 10 Depth of grinding d 0.005 0.01 0.015 0.02 0.025 Dressing depth t 0.005 0.01 0.015 0.02 0.025
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Table 4. Experimental matrix and results No Code v f 1 0 0 2 0 2 3 −2 0 4 0 0 5 0 0 6 0 −2 … 29 1 1 30 1 1
2.4
d
t
0 0 0 −2 0 0
0 0 0 0 2 0
Real value v (m/min) 15 15 5 15 15 15
−1 1 20 −1 −1 20
Ra (µm) f (mm/stroke) 6 10 6 6 6 2 8 8
d (mm) 0.015 0.015 0.015 0.005 0.015 0.015
t (mm) 0.015 0.015 0.015 0.015 0.025 0.015
0.920 1.260 0.530 0.847 1.886 0.760
0.01 0.01
0.02 0.01
0.670 1.112
Roughness Tester
A Roughness tester, SJ201 (Mitutoyo – Japan), is adopted to measure the surface roughness of machined specimens (c.f. Fig. 2). The measuring direction is perpendicular to that of the cutting speed direction.
Fig. 2. Roughness tester SJ201
3 Results and Discussion The surface roughness values listed in Table 4 are averagely calculated by three continuous measurements. The results are analyzed based on Minitab@19, and shown in Table 5, Fig. 3, and Fig. 4. It is observed from Table 5 that the dressing depth has the strongest impact, i.e. the surface roughness increases with increasing in dressing depth. The similar tendencies are also visualized for the cutting speed and the feed rate. However, totally, the workpiece speed, the feed rate and the depth of cut has little effects on the surface
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Fig. 3. Influences of cutting parameters on the surface roughness.
Table 5. Analysis of variance Coefﬁcients Intercept 0.90367 v 0.08138 f 0.04538 d −0.00354 t 0.32121 v2 −0.03911 f2 0.03589 d2 −0.02824 t2 0.05501 v*f 0.02831 v*d −0.01919 v*t 0.03019 f*d 0.13106 f*t −0.07831 d*t 0.07844 Multiple R R Square
Standard error t Stat Pvalue Lower 95% 0.10294 8.77834 0.00000 0.68425 0.05147 1.58097 0.13474 −0.02833 0.05147 0.88156 0.39192 −0.06433 0.05147 −0.06881 0.94605 −0.11325 0.05147 6.24052 0.00002 0.21150 0.04815 −0.81240 0.42927 −0.14174 0.04815 0.74533 0.46759 −0.06674 0.04815 −0.58653 0.56624 −0.13086 0.04815 1.14255 0.27114 −0.04761 0.06304 0.44912 0.65976 −0.10605 0.06304 −0.30437 0.76503 −0.15355 0.06304 0.47887 0.63894 −0.10418 0.06304 2.07906 0.05518 −0.00330 0.06304 −1.24228 0.23321 −0.21268 0.06304 1.24426 0.23250 −0.05593 0.88371 Adjusted R Square 0.78095 Standard Error
Upper 95% 1.12308 0.19108 0.15508 0.10617 0.43092 0.06351 0.13851 0.07438 0.15763 0.16268 0.11518 0.16455 0.26543 0.05605 0.21280
roughness. The surface roughness progressively increases when these parameters increase. The details can be graphically presented in Fig. 3. The interaction between the input parameters are found to be unimportant. The influencing degrees on surface roughness reduce in the following order: feed rate and depth of cut; depth of cut and dressing depth; workpiece speed and feed rate; workpiece speed and depth of cut. The
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Workpiece speed – feed rate interaction
Feed speed – depth of cut interaction
Workpiece speed – depth of cut interaction
Workpiece speed – dressing depth interaction
Feed rate – dressing depth interaction
Depth of cut – dressing depth interaction
385
Fig. 4. Interaction impact between input parameters on surface roughness.
previous statements can be seen in Fig. 4. Based on the results listed in Table 5, a regression model is proposed and described by Eq. (1) to predict the surface roughness. The determination coefﬁcient in this case is 0.7809. Ra ¼ 0:90367 þ 0:08138 v þ 0:04538 f 0:00354 d þ 0:32121 t 0:03911 v2 þ 0:03589 f 2 0:02824 d2 þ 0:05501 t2 þ 0:02831 v f 0:01919 v d þ 0:03019 v t þ 0:13106 f d 0:07831 f t þ 0:07844 d t
ð1Þ
4 Developing Surface Roughness Model Using BoxCox Transformation In order to improve the accuracy of predicting surface roughness model, BoxCox transformation is used to transform nonnormal dependent data into a normal shape (normal distribution) [14]. Figure 5 shows the shape of data before using BoxCox transformation. It is seen that the data are not located inside the reference lines (blue lines). On the other hand, Pvalue of 0.01 is smaller than signiﬁcance of 0.05. Hence, it can be said that the data are nonnormal distribution. Hence, they can be transformed by BoxCox technique to get the normal distribution ones.
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Fig. 5. Distribution shape of data before using BoxCox transformation.
The Box  Cox transformation can be executed by formula as Eq. (2):
X 0 ¼ X k when k 6¼ 0 X 0 ¼ lnð X Þ when k ¼ 0
ð2Þ
Where: X and X′ are the data before and after using transformation respectively; k is the power parameter. The values of k can be found based on the standard that standard deviation of X′ are minimized. Typically, in the process of BoxCox transformation, the values of k can lie in the range between “−5” and 5. After that, the ﬁnal values can be rounding to the popular values which are listed in Table 6. The values of k are determined to be zero as shown in Fig. 6. The proposed model using BoxCox transformation is described by Eq. (3) which exhibits the determination coefﬁcient of 0.7930. The surface roughness given by model (1) and model (4) are presented in Table 7. Table 6. The popular value of power parameters in Box – Cox transformation process k k¼2 k ¼ 0:5
Transformation formula
X0 ¼ X2 pﬃﬃﬃﬃ X0 ¼ X k¼0 X 0 ¼ ln X pﬃﬃﬃﬃ k ¼ 0:5 X 0 ¼ 1= X k ¼ 1 X 0 ¼ 1=X
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Fig. 6. Diagram of Box–Cox transformation of surface roughness.
Figure 7 presents the distribution shape of data before using BoxCox transformation. It is observed that the data are placed inside the reference lines. The Pvalue of 0.463 is hugely larger than signiﬁcance of 0.05. Hence, it can be concluded that the data using BoxCox transformation are normal distribution. Ln ðRaÞ ¼ 0:1014 þ 0:0795 v þ 0:0506 f 0:0122 d þ 0:3549 t 0:0507 v2 þ 0:0379 f 2 0:0294 d2 0:0335 t2 þ 0:0327 v f 0:0393 v d þ 0:0076 v t þ 0:1065 f d 0:1010 f t þ 0:0873 d t
ð3Þ Or: Ra ¼ EXPð 0:1014 þ 0:0795 v þ 0:0506 f 0:0122 d þ 0:3549 t 0:0507 v2 þ 0:0379 f 2 0:0294 d2 0:0335 t2 þ 0:0327 v f 0:0393 v d þ 0:0076 v t þ 0:1065 f d 0:1010 f t þ 0:0873 d tÞ
ð4Þ In order to investigate the accuracy of two proposed models, the mean absolute errors – MBA and mean square errors – MSE given by prediction will be compared with the experiments in terms of determination coefﬁcient and listed in Table 7. %MBE ¼
%MSE ¼
! n 1X e p i i 100% n i ei n 1X j e i pi j 2 n i
ð5Þ
! 100%
ð6Þ
Where: e is denoted for the experimental data, p is representative for the predicted data, n is the number of tests.
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Order
Experimental surface Roughness Ra (µm)
Predicted surface roughness Ra (µm)
Absolute error
Without transformation (Eq. 1)
With Transformation (Eq. 2)
Without transformation
Square error
1
0.920
0.904
0.904
1.78%
1.79%
0.03%
2
1.260
1.138
1.163
9.68%
7.66%
0.94%
0.59%
3
0.530
0.584
0.629
10.28%
18.73%
1.06%
3.51%
4
0.847
0.798
0.823
5.81%
2.81%
0.34%
0.08%
5
1.886
1.766
1.607
6.36%
14.79%
0.40%
2.19%
6
0.760
0.956
0.950
25.85%
25.04%
6.68%
6.27%
7
0.660
0.784
0.784
18.73%
18.78%
3.51%
3.53%
29
0.670
1.169
1.111
74.42%
65.76%
55.38%
43.25%
30
1.112
0.779
0.784
29.92%
29.51%
8.95%
8.71%
With transformation
Without transformation
With transformation 0.03%
…
Fig. 7. Distribution shape of data after using BoxCox transformation.
Table 8 shows the comparison between the surface given by model (1) and model (4) compared with experiments. It is seen that the model using Box  Cox transformation and the other display the errors of 13.05% and 17.2% respectively when compared with experiments. Regarding the mean square errors, the corresponding values are in order 6.88% and 3.77%. Based on the above analysis, it can be concluded that the surface roughness model using Johnson transformation can predict more exactly than the other model. Moreover, we can see that the determination coefﬁcient of model (1) is larger than that given by model (4).
Table 8. Comparison between two proposed model of surface roughness Models % Mean absolute error % Mean square error R2 With Box  Cox transformation 13.05% 3.77% 0.7930 Without Box  Cox transformation 17.21% 6.88% 0.7809
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5 Conclusions In this study, surface grinding process of AISI 5120 steel by grinding wheel of CBN. The following conclusions can be drawn: The dressing depth has the strongest influence on the surface roughness when compared to two others. The surface roughness increases with increasing in dressing depth. The workpiece speed, the feed rate and the depth of cut have little influences. The interaction between the workpiece speed, the feed rate, the depth of cut, and the dressing depth have no influence on surface roughness. The accuracy of the proposed model has improved when using BoxCox transformation. The model with and without using BoxCox transformation give differential errors when compared with experiments. Acknowledgements. This work was supported by Thai Nguyen University of Technology.
References 1. Kumar, P., Kumar, A., Singh, B.: Optimization of process parameters in surface grinding using response surface. Methodology 3(2), 245–252 (2013) 2. Periyasamy, S., Aravind, M., Vivek, D., Amirthagadeswaran, K.S.: Optimization of surface grinding process parameters for minimum surface roughness in AISI 1080 using response surface methodology. Adv. Mater. Res. 984–985, 118–123 (2014) 3. Thomas, B., David, E., Manu, R.: Modeling and optimization of surface roughness in surface grinding OFSiC advanced ceramic material. In: 5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014), Assam, India, pp. 333.1–311.7 (2014) 4. Sarika, S., Manikantesh, K., Pavan Kumar, D.: Experimental investigation of surface roughness and temperature on surface grinding of AISI1040 steel using MQL technique. Int. J. Sci. Eng. Technol. Res. 6(2), 2364–2369 (2017) 5. Waikar, O., Nikam, S., More, R., Shinde, S., Chakule, R.: Optimization of machining parameter for surface roughness. IOSR J. Mech. Civ. Eng. 25–30 (2017) 6. Yaakob, N.A., Ganesan, H.N., Harun, N.H., Abdullah, R.I.R., Kasim, M.S.: Influence of grinding parameters on surface ﬁnish of inconel 718. J. Mech. Eng. 3(2), 199–209 (2014) 7. Son, N.H., Trung, D.D.: Analysis on the effects of cutting parameters on surface roughness of workpiece in surface grinding. Int. J. Sci. Res. Sci. Eng. Technol. 6(5), 277–282 (2019) 8. Son, N.H., Trung, D.D.: Investigation of The effects of cutting parameters on surface roughness when grinding 3X13 steel using CBN grinding wheel. J. Multidisc. Eng. Sci. Technol. 6(10), 10919–10921 (2019) 9. Hong Son, N., Duc Trung, D., Nguyen, N.T.: Surface roughness prediction in grinding process of the SKD11 steel by using response surface method. IOP Conf. Ser. Mater. Sci. Eng. 758 (2020). https://doi.org/10.1088/1757899x/758/1/012029 10. Tung, L.A., Pi, V.N., Hung, L.X., Banh, T.L.: A study on optimization of surface roughness in surface grinding 9CrSi tool steel by using Taguchi method. In: International Conference on Engineering Research and Applications, pp. 100–108. Springer, December 2018 11. Hong, T.T., Cuong, N.V., Ky, L.H., Tung, L.A., Nguyen, T.T., Vu, N.P.: Effect of process parameters on surface roughness in surface grinding of 90CrSi tool steel. In: Solid State Phenomena, vol. 305, pp. 191–197. Trans Tech Publications Ltd. (2020)
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12. Tu, H.X., Hong, T.T., Nga, N.T.T., Gong, J., Pi, V.N.: Influence of dressing parameters on surface roughness of workpiece for grinding hardened 9XC tool steel. In: IOP Conference Series: Materials Science and Engineering, vol. 542, no. 1, p. 012008). IOP Publishing, June 2019 13. Trung D.D., Son N.H., Hong T.T., Van Cuong, N., Vu, N.P.: Calculating effects of dressing parameters on surface roughness in surface grinding. In: Sattler, K.U., Nguyen, D., Vu, N., Tien Long, B., Puta, H. (eds.) Advances in Engineering Research and Application, ICERA 2019. Lecture Notes in Networks and Systems, vol 104. Springer, Cham (2019). https://doi. org/10.1007/9783030374976_19 14. Van Du, N., Binh, N.D.: Design of experiment techniques, Science and technics publishing House, Ha Noi, Viet Nam (2011)
Ensemble of Deep Learning Models for InHospital Mortality Prediction Quang H. Nguyen1(&) and Quang V. Le1,2 1
School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected] 2 ARS Viet Nam Co., Ltd., Ho Chi Minh City, Vietnam [email protected]
Abstract. Using machine learning in health care for supporting doctors to diagnose diseases is receiving a lot of research interest. Currently, many hospitals use Electronic Health Records (EHR) to store medical records, which is an extremely valuable data source for machine learning. This paper uses the Medical Information Mart for Intensive Care (MIMICIII) dataset to solve the problem of predicting mortality in hospitals. We have proposed standardizing numerical attributes in two ways: normalizing using mean and variance on Training set and standardizing using mean and variance according to 48 h of each sample. Neural network architectures based on CNN models have been proposed and tested. Ensemble technique has been applied to each parameter representation type and each model type. Test results show that the ensemble method has improved the performance of the system. Test results on the Test set achieve AUROC is 0.851, AUPRC is 0.452 and min (Se, + P) is 0.459. Keywords: Information Mart for Intensive Care (MIMICIII) dataset Electronic Health Records (EHRs) Deep learning Inhospital mortality prediction Convolution Neural Network Longshort term memory Multihead attention
1 Introduction Nowadays, in Vietnam, electronic medical record technology has been used in some large hospitals and the Ministry of Health is expected to deploy to all hospitals by 2020 [13]. This is an opportunity for researchers to exploit medical data for helping diagnose diseases in Vietnam. In the world, the use of Machine Learning to help doctors diagnose the disease has been proven to be very effective than traditional evaluation methods. There are many medical problems that can be solved by machine learning methods. This paper proposes Deep Learning models to handle the Inhospital mortality prediction problem. In fact, many patients in the intensive care unit want to spend the ﬁnal time with their loved ones instead of being in the hospital. At the same time, the patient holding in the ICU (Intensive care unit) for a long time can be very difﬁcult for the patient’s family members, while also making it difﬁcult for the local hospital to arrange the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 391–398, 2021. https://doi.org/10.1007/9783030647193_44
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manpower and equipment for those patients. It is, therefore, necessary to predict whether a patient will survive a hospital stay. To be able to use Machine Learning models, especially Deep Learning, we need enough data. Many large electronic health data sets (EHRs) have been published recently by the community, typically Medical Information Mart for Intensive Care (MIMICIII) [12]. In this study, we use the standard data processing method proposed by Hrayr Harutyunyan et al. [1]. Data were extracted from the MIMICIII data set with 17 attribute ﬁelds such as pH, Heart Rate, Oxygen saturation, Weight, Glucose … over time series. After preprocessing the data, we obtained 17 attributes per hour of hospitalization. This data is collected during the ﬁrst 48 h when the patient enters the ICU. From this data, we need to build a model that predicts the patient’s mortality. The use of Recurrent Neural Network (RNN) models for time series data processing issues often yields the best results [1, 8–10] because this model helps to retain information from previous time points. But training these models is often quite complex and timeconsuming. Another idea is to treat data as a 2dimensional matrix with one dimension being time and one dimension being attributes. Then deep learning Convolutional Neural Network (CNN) model can be applied to this problem. The remaining of the report will be organized in the following order: Section 2 will describe pros and cons of previous research on the problem of predicting mortality in hospitals. Section 3 describes how to extract information from the MIMICIII dataset, standardization and parameter enhancement methods and CNN architecture for this problem. Section 4 will be a summary of the empirical and evaluation results. And ﬁnally, it will be conclusions and further researches on this topic.
2 Related Works In recent years, there have been many studies related to the problem of predicting mortality in hospitals. These studies suggest different directions from the proposed new model [5–10], suggesting new data processing [1–4], methods for training with fewer data problems and faster ways for models to converge [2]. Here are some previous studies on this issue. First of all, the research of Hrayr Harutyunyan and colleagues [1] simultaneously solved all 4 tasks: risk of mortality, forecasting length of stay, detecting physiologic reverse, and phenotype classiﬁcation. The model proposed by this paper is the Multitask LSTM network, which means that instead of using LSTM for individual tasks, they proposed implementing a network architecture that can do all these problems. An important contribution of this study was that they proposed a standard data processing for 4 tasks. With the same MIMICIII data set, Brandon Malone and his colleagues proposed a new data processing method aimed at solving the problem of “missing data” using representative communicationbased learning diagrams [2]. This method is particularly effective in processing missing data and reducing noise but does not increase the effectiveness of the prediction. Along with that, Yanbo Xu and his colleagues also proposed a different way of processing data on the MIMICIII data set [3]. While this data processing is intended to be a nonfatal problem, the paper proposes a data
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processing method that relates to data processing on the MIMICIII large data set. The data processing model mentioned is the Recurrent Attentive and Intensive Model (RAIM), which integrates continuously monitored data with separate clinical event sequences and develops predictive clinical models in ICU. Sanjay Purushothama and colleagues also proposed a new method of data processing [4], by taking all the raw attributes of the patient, through many steps of data cleansing and trying to retain the majority of the attributes. The Deep Neural Network is used to ﬁnd automatically the hidden properties of the data instead of manually selecting [1, 2, 7]. This study proposes a variety of data processing methods using various Deep Neural Network models, all of which show that using the Deep Neural Network is more effective than the conventional attribute selection. But the problem with this method is that using all the patient attributes on all tables of the MIMICIII data set makes it extremely difﬁcult to clean the data. Especially studied by Jeeheh Oh et al. [11] using the CNN1D model and the data processing method proposed by Hrayr Harutyunyan [1] and there was a slight change in the reprocessing of data for quite satisfactory results. The same problem of predicting mortality in hospital but not on MIMICIII but on other electronic medical data sets, Bryan Lim and Mihaela van der Schaar [5] proposed the timetoevent model to address constraints due to computational difﬁculties when applying to height data sets. But because the authors use unpublished datasets for the community, evaluating and comparing the results in this report is difﬁcult. In a paper published in January 2018, Alvin Rajkomar et al. [8] demonstrated the use of machine learning models in predicting mortality. They used an extremely large dataset of data provided by the US academic medical center with 216,221 patients and up to 46,864,534,945 data items. In another study, Fengyi Tang et al. [6] made comparisons on the use of traditional models such as SAPS and SOFA used to assess mortality risk and the most popular Machine Learning models such as LSTM, CNN, etc. Studies show that SAPSII predicts in a wide range while Machine Learning models provide better efﬁciency in a smaller and more accurate range. This conﬁrms that the use of machine learning models is better than traditional assessments. A number of other papers, such as those of Manahil Sadiq BSc [9], propose new models for processing time series data, by Priyanka Gupta [10] using the Transfer Learning technique for RNN models.
3 Proposed Approach 3.1
MIMICIII Dataset
MIMICIII (Medical Information Mart for Intensive Care III) is a large database of data related to the health of more than 40,000 patients cared for in the Health Care Center’s intensive care units of Beth Israel Deaconess from 2001 to 2012. This database includes information such as demographics, key marker measurements taken at the bed (*1 data point per hour), review results, laboratory tests, procedures, medications, caregiver notes, visual reports and mortality (both inside and outside the hospital). MIMICIII can be used to support studies including epidemiological analysis,
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improving clinical decision rules, and developing electronic tools. MIMICIII has great advantages, which are free for researchers around the world, including a large number of patients, containing high time resolution data [12]. In this study, we only used a single pretreatment dataset provided by Hrayr Harutyunyan for the prediction problem [1]. This data included 20,139 patients of whom 2,797 died. In order to compare with the results of other studies, we keep the same Test set as in [1]. The details of the training set, validation set, and test set are described in Table 1. Looking at this table, we see a data imbalance between two classes: mortality (*10%) and nonmortality (*90%). Table 1. Data set distribution for Inhospital mortality detection problem Dataset Mortality Nonmortality Total Training set 1919 12403 14322 Validation set 504 3077 3581 Test set 374 2862 3236
3.2
Feature Engineering
The extraction of features for the mortality prediction problem was used based on the benchmarks published by Hrayr Harutyunyan et al. [1]. This data set contains information of patients located in the ICU department of more than 40,000 patients. Patients under 18 were then removed from the dataset. It will also eliminate all patients hospitalized for multiple stays with ICU stays or transitions between different ICU units. The determination of death in hospital or not is done by comparing the date of death of the patient with the time of admission and discharge. These extracted properties are based on suggestions from Ikaro Silva et al. [7]. The standard data will include 17 timevarying data including a number of attributes such as pH, Heart Rate, Oxygen saturation, Weight, Glucose, etc. These attributes are quite closely related to the patient’s ability to survive. The total number of survey hours is the ﬁrst 48 h when patients enter the ICU. A preprocessing operation is performed to ensure each data is valid for each hour of the 48 h. The method is as follows: in the case of multiple measurements in an hour, the last value will be used, and if the value is missing during that hour, the value of the last measurement was used, if there is still no measured value, the default value (the value of a normal person) will be used. In these 17 attributes, there are 5 categorical attributes, meaning each of them has only a certain number of options. These properties will be encrypted in onehot form. Therefore, the total number of attributes will be 59. In addition, each attribute is also checked to see if it is measured in each hour. The test results will be coded with 1 if this attribute is measured in that hour, and 0 if not. From there, we propose the standardization and data enhancement methods for each of the 48 h as follows: • PARAMTYPE1: calculates the mean and variance of each of the 12 noncategorical attributes with Training set data, using them for normalization to both Training set, Validation set and Test set. The total number of features per hour is 59 and each sample (per patient) will be represented by the 48 59 matrix (48 h).
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• PARAMTYPE2: added 17 properties reflecting the presence/absence of each feature in each hour of measurement in addition to PARAMTYPE1. The total number of features per hour is 76 and each sample (per patient) will be represented by the 48 76 matrix. • PARAMTYPE3: add 12 noncategorical attributes normalized to 48 h of each sample to PARAMTYPE2. The total number of features per hour is 88 and each sample (per patient) will be represented by the 48 88 matrix. 3.3
CNN Architecture
The ﬁrst approach, when treating time data as an image, we use the Convolution Neural Network (CNN) to extract hidden properties about the space inside the data that normal data processing cannot be extracted (our model was built based on the VGG 16 architecture). The CNN architecture is depicted in Fig. 1.
Fig. 1. Architecture of the system
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In the network architecture described in Fig. 1, Convolution 1D and Max Pooling layers use parameters like kenel_size = 3, pool_size = 2, strides = 2. The Convolution 1D and Dense layers use the nonlinear activation functions ReLU. To speed up the convergence of the models, alternating between Convolution 1D layers is Batch Normalization layers. Also, to avoid overﬁting for the model, we added Dropout layers between blocks of Convolution 1D and Dense layers. 3.4
Training Deep Learning Models
During training, we use the following techniques: • When designing deep neural networks, the number of parameters is chosen to ensure the models have roughly the same number of parameters. • Use the Loss function as a binary cross entropy function. • The Adam optimal technique is used with a learning rate starting at 0.001. • In addition, the Reduce Learning Reate On Plateau technique is also used to gradually reduce the learning rate when the Loss function of the Validation set starts to increase. • The Early Stopping method is used to ﬁnd the optimal model during training: the model with the smallest Loss function value on the Validation set. • To solve the problem of data imbalance between mortality (about 10% in the data set) and nonmortality (about 90% in the data set), we use classweight method during the training phase with weight of 0.9 for mortality data and weight of 0.1 for nonmortality data. • The above neural network models are implemented on Keras which is a toolkit that provides a lot of APIs that make it easy to build and deploy deep neural networks. The test was performed on computers with Intel Core i7 processor, GPU 2080TI, 32 GB RAM. 3.5
Model Evaluation
Because the proportion of data samples between deaths and nondeaths is very large imbalanced, so we cannot use accuracy to evaluate that need some other measurement. Measures used to evaluate the performance of models include: AUROC (Area Under Receiver Operating Characteristic), AUPRC (Area Under PrecisionRecall Curve and min (Se, P+) [1]. 3.6
Ensemble Models
We have proposed a method to ensemble the deep learning models: • Each model we designed to return P (Inhospital mortality  X). • Suppose there are N models, then Pensemble (Inhospital mortality  X is the average of the probability distributions of the component models. • Tests are performed on each type of parameter representation as well as each type of deep learning model.
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4 Experiments and Discussions Experimental results are summarized in Table 2. From the results in Table 2, we have the following comments: Table 2. Experimental results of different parameter types Param type PARAMTYPE1 PARAMTYPE2 PARAMTYPE3 Ensemble CNN models
AUCROC 0.836 0.819 0.826 0.851
AUCPR 0.431 0.398 0.433 0.452
Min (Se, P+) 0.445 0.413 0.464 0.459
• The results have shown the effectiveness of the ensemble deep learning models method. • The results are equivalent to Hrayr Harutyunyan [1] when tested with multitask channelwise LSTM (In this test, they used all data from all 4 tasks: risk of mortality, forecasting length of stay, detecting physiologic reverse, and phenotype classiﬁcation to training model whereas we only use data set of only one task of risk of mortality).
5 Conclusion and Future Works In this paper, we have presented the method of predicting risk of mortality of patients in ICU. The data were extracted from MIMICIII dataset. We have proposed standardizing numerical attributes in two ways: normalizing on the Training set and standardizing 48 h of each sample. We have experimented with deep neural network based CNN model. The ensemble method on the full parameter set gave results equivalent to stateoftheart results when they used data from four different tasks whereas we only use data set of only one task of risk of mortality in the training process. In the future, we will apply the multihead attention model by adding parameters describing the chronological order of the attributes. We will then try to build an integrated model of the above models as endtoend models. Acknowledgment. Quang H. Nguyen gratefully acknowledges the support of BKAV Corporation, Viet Nam with the donation of the Server and GPU used for this research. The research was partially supported by ARS Viet Nam, Co. Ltd. from Hanoi, Viet Nam. We thank our colleagues from ARS Viet Nam Company, who provided insight and expertise that greatly assisted the research.
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References 1. Harutyunyan, H., Khachatrian, H., Kale, D.C., et al.: Multitask learning and benchmarking with clinical time series data. Sci. Data 6, 96 (2019). https://doi.org/10.1038/s4159701901039 2. Malone, B., GarciaDuran, A., Niepert, M.: Learning representations of missing data for predicting patient outcomes. arXiv preprint arXiv:1811.04752 (2018) 3. Xu, Y., et al.: Raim: Recurrent attentive and intensive model of multimodal patient monitoring data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2565–257. ACM (2018) 4. Purushothama, S., Mengb, C., Che, Z., Liu, Y.: Benchmark of deep learning models on large healthcare mimic datasets. J. Biomed. Inf. 83, 112–134 (2018) 5. van der Schaar, B.L.M.: Diseaseatlas: navigating disease trajectories using deep learning. In: Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR, vol. 85, pp. 137–160 (2018) 6. Tang, F., Xiao, C., Wang, F., Zhou, J.: Predictive modeling in urgent care: a comparative study of machine learning approaches. JAMIA Open 1(1), 87–98 (2018). https://doi.org/10. 1093/jamiaopen/ooy011 7. Silva, I., et al.: Predicting inhospital mortality of ICU patients: the physionet/computing in cardiology challenge 2012. In: 2012 Computing in Cardiology, pp 245–248. IEEE (2012) 8. Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Hardt, M., Liu, P.J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G.E., Irvine, J., Le, Q., Litsch, K., Mossin, A., Tansuwan, J., Wang, S., Wexler, J., Wilson, J., Ludwig, D., Volchenboum, S.L., Chou, K., Pearson, M., Madabushi, S., Shah, N.H., Butte, A.J., Howell, M.D., Cui, C., Corrado, G.S., Dean, J.: Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1(1) (2018) 9. Sadiq, M., Field, P., Pentyala, S.K.: 2017 interpretable deep learning framework for predicting allcause 30day ICU readmissions. Technical report (2018) 10. Gupta, P., Malhotra, P., Vig, L., Shroff, G.: Transfer learning for clinical time series analysis using recurrent neural networks. J. Healthc. Inf. Res. 4(2), 112–137 (2020) 11. Oh, J., Wang, J., Wiens, J.: Learning to exploit invariances in clinical timeseries data using sequence transformer networks. In: Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR, vol. 85, pp. 332–347 (2018) 12. Johnson, A.E.W., Pollard, T.J., Shen, L., Lehman, L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L.A., Mark, R.G.: MIMICIII, a freely accessible critical care database. Sci. Data (2016). https://doi.org/10.1038/sdata.2016.35 13. List of hospitals implemented Electronic Health Record Management. https://ehealth.gov.vn/ Index.aspx?action=Detail&MenuChildID=391&Id=4369, Electronic Health Administration, Ministry of Health, Vietnam 14. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Adv. Neural. Inf. Process. Syst. 2017, 5998–6008 (2017) 15. Hochreiter, S., Schmidhuber, J.: Long shortterm memory. Neural Comput. 9(8), 1735–1780 (1997)
Evaluating the Impact of Demand Response in Planning Microgrids Considering Uncertainties V. V. Thang(&) and N. H. Trung Faculty of Electrical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam {thangvvhtd,nguyenhientrung}@tnut.edu.vn
Abstract. This study proposes a planning framework to optimize the conﬁguration of gridconnected microgrids under the impact of the demand response program. The uncertain parameters are integrated into the optimal model by different discrete states that are divided by the clustering technique from the probability distribution functions. A life cycle cost objective function including investment cost, operation cost, energy cost, and emission cost of the system together with constraints are presented in a mixedinteger programming model. The simulation result by GAMS/CPLEX for the test microgrid shows the effect of the proposed model for microgrids planning problems which include uncertain parameters. Moreover, the proposed model allows for analyzing the performance of the demand response program during the planning period. By performing the demand response program, both life cycle cost and invested power of RS together with emission decreased. Keywords: Demand response Uncertainty
Life cycle cost Microgrid Planning
1 Introduction Demand response (DR) is deﬁned as the shifting of electricity demand by enduse customers from their normal consumption patterns in response to changes in the price of electricity over time that can increase flexibility on the demand side by reducing peak demand or temporarily shifting to avoid the equipment investments and high electricity purchase price [1]. Additionally, the advantages of DR in improving system reliability and security, reducing electricity price, reduction in transmission line bottlenecks are also introduced in [2]. DR programs positively impact on the users that participate in a DR program as well as the distribution companies and this is a developing trend for the future of energy systems planning and operating [3, 4]. In recent years, more numbers of researchers have an interest in modeling and integrating the DR programs in energy systems [5, 6]. In [7], a sizing optimization model for considering the demand response of household appliances on an island microgrid is proposed with micro pumped storage used as an energy storage system. DR can improve the utilization of PV and WT and reduce storage costs. Similarly, a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 399–410, 2021. https://doi.org/10.1007/9783030647193_45
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novel model for designing and operation of flexible microgrids is proposed to optimize the sizing and siting of distributed generation as well as performing the DR programs [8]. The power of renewable sources, energy demand and price are uncertain parameters and thus it is considered in energy system planning and operation problems and is reflected by probabilistic distribution functions (pdf). In [9], a stochastic framework is proposed to expansively plan of energy storage in microgrids, which contain renewable sources (RS) and DR program. Similarly, the demand response program is also integrated into a multiobjective optimization framework for optimal planning of the microgrid. Moreover, DR programs are utilized to determine the size and place of different components in microgrid which helps to consider uncertainties, improve reliability and power quality, and minimize power loss as shown in [10] while [11] introduces a novel approach of DR program in the optimal planning of multicarrier microgrids. The results show that considering DR programs changes the size of components together with the conﬁguration of microgrids signiﬁcantly and enhances the effects of these systems. However, the different lifetime and uptime of equipment in microgrids are ignored lead to analysis results incorrect because of the nonconformity of computed parameters with reality. To consider the different lifetime and uptime of equipment, the objective function of the life cycle cost (LCC) has been introduced in the recent year to ﬁnd the optimal size of battery energy storage systems (BESS) [12], the capacity conﬁguration optimization for island microgrid [13], and optimal designing of hybrid renewable energy systems [14]. Besides, the energy resources optimization to enhance the efﬁciency, reduce the pollutant emissions, and impact on climate changes of energy systems should be considered in microgrid planning problems. Therefore, a planning framework is proposed in this study to optimize the conﬁguration of gridconnected microgrids under the impact of the DR program. Not only the lifetime and uptime of RS considered by the objective function LCC but also the rated power of them with discrete values are integrated into the model. The uncertainty of parameters is analyzed by pdf and thus increases in the accuracy of planning results. A mixedinteger problem model is programmed by GAMS and is solved by CPLEX solver [15]. The rest of the paper is organized as follows: Sect. 2 presents the structure of the microgrid and mathematics model of the planning framework. Section 3 discusses the simulation results, and Sect. 4 presents the conclusive remarks and a few insights for future work.
2 Planning Framework of Microgrids Integrated Demand Response 2.1
Structure of Microgrids
Two structures of the microgrids introduced are the gridconnected and the islanded types [16]. In the gridconnected type, the loads are simultaneously supplied from both distributed generators and utility grid through the point of common coupling (PCC). The RS such as the PV and WT as well as BESS have been successfully developed and
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are applied in this structure because they improve the efﬁciency and reliability and reduce the investment cost of microgrid shown in Fig. 1 [17]. In this structure, the uncertainty of RS is remedied by BESS and utility grid through PCC. Hence, gridconnected microgrids can optimize the electric energy purchased from utility gird and RS is invested to improve the effectiveness of the microgrids. Bus
PCC Utility Grid
WT
PV
DC AC
DC AC BESS Electrical Load
Fig. 1. The structure of gridconnected microgrids
2.2
Demand Responses Modeling
The DR programs can be classiﬁed into two types, consisting of pricebased DR and incentivebased DR [18], of which the pricebased mechanism is the most important for the successful implementation of DR. Hence, in this study, the pricebased DR is chosen to evaluate the effects of DR on the optimal planning of microgrids. The change in demand under the impact of DR is presented in Eq. (1) [19, 20]. Plh ¼ Pl0 ð1 þ kDR Þ :
qh q0 q0
ð1Þ
where Pl0 ; q0 are the initial electricity demand and price. qh is the spot electricity price which can be calculated with realtime prices. kDR is the demandprice elasticity coefﬁcient which depends on the customer types and historical load demand data. 2.3
Uncertain Parameters Modeling
The uncertain parameters consist of the output power of RS, electricity prices, and electrical demand. These parameters are often modeled by PDFs and are divided into different states by kmean clustering technique. There is a speciﬁc value with the related probability in each state [21]. The randomness of solar radiation is expressed by beta PDF as Eq. (2) with the Iir (the intensity of solar radiation), l (mean) and r (the standard deviation) [22, 23]. The value and probability of intensity of solar radiation in each state are determined, and hence the output power of each module PV is calculated as expression (3). In the
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equation, kF.s is the ﬁll factor, and n is the number of modules [24, 25]. Is and Us are the generated current and voltage. ISC and UOC are short circuit current and open circuit voltage. UMPP and IMPP are current and voltage at the maximum power point, respectively. kU and kI are coefﬁcients while hA, hN are the ambient and nominal operating temperature. Similarly, the wind speed v is represented by Rayleigh PDF which is a special case of Weibull PDF as Eq. (4) with the scale index c. The output power of WT in each state wt is calculated as Eq. (5) [26] with output power Pwt s , rated power Pr together with cutin speed vci, rated speed vcr, and cutoff speed vco.
ða1Þ
½Cða þ bÞ=CðaÞCðbÞ : Iir 0 else b ¼ ð1 lÞ : ½l : ð1 þ lÞ=r2 1;
fb ðIir Þ ¼
: ð1 Iir Þðb1Þ if 0 Iir 1; a; b
ð2Þ
a ¼ l : b=ð1 lÞ
Ppv s ðIir Þ ¼ n : kF : s : Us ðIir Þ : Is ðIir Þ; kF : s ¼ UMPP : s : IMPP : s =UOC : ISC Us ðIir Þ ¼ UOC kU : ½hA þ Iir : s : ðhN 20Þ=0:8 Is ðIir Þ ¼ Iir : s : ½ISC þ kI : ðhA þ Iir : s : ðhN 20Þ=0:8 25Þ h i fr ðvÞ ¼ ð2v=c2 Þ exp ðv=cÞ2 8 vs vci or vco vs (pm + n) ( pm + n+1) data samples in order to obtain the result of this linear equation. Now, we proposed the modiﬁed Qleaning algorithm for online implementation. Algorithm 1: Modified Qlearning algorithm 1.
Initialization: The stabilizing policy uK0
2.
Policy Evaluation: Use LeastSquares to solve the equation j vecv( Kj )T vecv( Kj 1 )T vecs( H j 1 ) K
3.
0 L102 xK is chosen (21)
Policy Update: Update control policy using
L12j 1
uKj
1
H 22j 1
L j 1 xK
1
H 21j 1
0 L12j 1 xK
(22) (23)
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In this policy evaluation stage, the Bellman Eq. (20) is implemented for vecsðH j þ 1 Þ under the data collection along the system trajectories to establish the data matrices. The solution of (21) can be achieved by using the LeastSquares: 1 vecsðH j þ 1 Þ ¼ ðZ j ÞT Z j ðZ j ÞT Y j In the policy update stage, an improved policy ujKþ 1 is computed through minimizing the Qfunction of the jth policy. It should be noted that according to (21) we j12þ 1 . Therefore, we have to form complete feedonly obtain nonzero elements, i.e. L back matrix (27). The PE condition [1, 4, 15] need to be satisﬁed to guarantee the convergence of Algorithm 1 in the optimal policy. Therefore, we need to add more a PE probing noise so that the optimal policy can be precisely learned. The works in [8, 9] shown that the additional term of PE probing noise does not cause bias in Qfunction estimation. The convergence of the proposed algorithm is expressed in the following theorem. 2; B 2 Þ be controllable and u0K be an initially stabilizing control. Theorem 2. Let ðA 1 Then, the sequence fH j gj¼0 convergences to the optimal matrix kernel H as j ! 1 j ! L as j ! 1. and the feedback gain L Proof. In the previous works [1, 4, 9, 13], the convergence of Qlearning algorithm is B Þ is controllable, i.e. all of poles are conshown due to the couple of matrices ðA; trollable and we can change all of them. For linear periodic discretetime system (1), B Þ is stabilizable under Assumption 1. But both the optimal feedback matrices (11) ðA; and the jth iterative feedback gain (22) only change controllable poles of the modiﬁed system according to Remark 4. Therefore, Algorithm 1 satisﬁes conditions of proofs in [1, 4, 9, 13]. Additionally, the convergence of the Algorithm 1 is guaranteed. This completes the proof of the convergence. ∎ Remark 5. Algorithm 1 is implemented online in real time using only the state variables data, which collected along the state trajectories without requiring any knowledge of system matrices.
4 Simulation Results In this section, a simulation example is developed to illustrate the effectiveness and convergence of the modiﬁed Qlearning algorithm. Consider a linear periodic discretetime system (1) with several terms: x ð k þ 1Þ ¼
1 0:1 cosð0:2pkÞ
0:2 sinð0:2pkÞ 0 xð k Þ þ uðk Þ 0:9 0:1 cosð0:2pkÞ
The initial state is given as x0 ¼ ½3; 2 T . The periodic of system is p ¼ 10. The inﬁnite performance index is deﬁned as (3) with Qk ¼ Q0 ¼ I2 ; Rk ¼ R0 ¼ 1.
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To verify the effectiveness of the proposed algorithm, we use the Matlab function “DARE” to solve the Riccati Eq. (9). Then, the optimal feedback gain is obtained as 12 ¼ L
0:7035
0:4002
0:0322
0:0962
0:4853
0:6575
0:3806
0:7127
0:4866
0:1329
0:0786
0:1056
0:0858
0:0921
0:0382
0:0574 0:0757 0:4053 T
0:0822 0:2594
We add a random noise to control input to ensure PE condition until the learning process is successful. Figure 1 shows norm of the difference between optimal feedback and the obtained feedback matrix L j . Figure 1 also shows state trajectory matrix L curves and the control signal. We see that the feedback gain sequence converges to the optimal gain and the system is stable in learning process. After 6 iterations, the feedback gain converges to 12 ¼ L
0:7035 0:7127
0:4002 0:4866
0:0322 0:1329
0:0962 0:0786
0:4853 0:1056
0:6575 0:0858
0:3806 0:0921
0:0382 0:0574 0:0757 0:4053 T
0:0822 0:2594
to its optimal values, state variables, and control signal Fig. 1 Convergence of matrix L
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which is the same as the optimal gain which is determined by system matrices and Matlab function. The method introduced in [18] has to use the system matrices to determine the optimal controller, while proposed method only use state variable data without any knowledge of system matrices. According to the simulation results, we can conclude that the proposed algorithm for linear periodic discretetime systems is appropriate.
5 Conclusion In this paper, we presented a modiﬁed Qlearning strategy using a lifting method to deal with the periodic LQR problem for linear periodic discretetime system. The proposed algorithm overcame the controllability condition of the existing algorithms as well as ensured the convergence. Moreover, the algorithm is implemented online without depending any knowledge of the system matrices. The simulation results have determined that the proposed algorithm guarantees convergence in the optimal solution. As no prior knowledge of system dynamics is required during the learning process, the proposed strategy provides a potentially feasible solution to various control applications. Acknowledgements. This research was supported by Research Foundation funded by Thai Nguyen University of Technology.
References 1. AlTamimi, A., Lewis, F.L., AbuKhalaf, M.: Modelfree Qlearning designs for linear discretetime zerosum games with application to Hinﬁnity control. Automatica (2007) 2. Allen, M.S., Sracic, M.W., Chauhan, S., Hansen, M.H.: Outputonly modal analysis of linear timeperiodic systems with application to wind turbine simulation data. Mech. Syst. Signal Process. 25(4), 1174–1191 (2011) 3. Bittanti, S., Laub, A.J., Willems, J.C.: The Riccati Equation. Springer, Heidelberg (1991) 4. Bradtke, S.J., Ydstie, B.E.: Adaptive linear quadratic control using policy iteration. In: Proceedings of 1994 American Control Conference, June, vol. 3, pp. 3475–3479 (1994) 5. Chauvin, J., Corde, G., Petit, N., Rouchon, P.: Periodic input estimation for linear periodic systems: automotive engine applications. Automatica 43(6), 971–980 (2007) 6. Grasselli, O.M., Longhi, S.: Pole placement for nonreachable periodic discretetime systems. Math. Control. Signals Syst. 4, 439–455 (1991) 7. Hench, J.J., Laub, A.J.: Numerical solution of the discretetime periodic Riccati equation. IEEE Trans. Autom. Control 39(6), 1197–1210 (1994) 8. Jiang, Y., Kiumarsi, B., Fan, J., Chai, T., Li, J., Lewis, F.L.: Optimal output regulation of linear discretetime systems with unknown dynamics using reinforcement learning. IEEE Trans. Cybern. 50, 1–10 (2019) 9. Landelius, T.: Reinforcement learning and distributed local model synthesis. Ph.D thesis, Linkoping University, Sweden (1997) 10. Lewis, F.L., Vrabie, D.: Reinforcement learning and adaptive dynamic programming for feedback control. IEEE Circuits Syst. Mag. 9(3), 32–50 (2009)
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11. Li, J., Chai, T., Lewis, F.L., Ding, Z., Jiang, Y.: Offpolicy interleaved Qlearning: optimal control for afﬁne nonlinear discretetime systems. IEEE Trans. Neural Netw. Learn. Syst. 30(5), 1308–1320 (2019) 12. Meyer, R.A., Burrus, C.S.: A uniﬁed analysis of multirate and periodically timevarying digital ﬁlters. IEEE Trans. Circuits Syst. 22, 162–168 (1975) 13. Rizvi, S.A.A., Lin, Z.: Output feedback Qlearning control for the discretetime linear quadratic regulator problem. IEEE Trans. Neural Netw. Learn. Syst. 30(5), 1523–1536 (2019) 14. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Iintroduction. MIT Press, Cambridge (2011) 15. Vrabie, D., Pastravanu, O., AbuKhalaf, M., Lewis, F.L.: Adaptive optimal control for continuoustime linear systems based on policy iteration. Automatica 45(2), 477–484 (2009) 16. Wei, Q., Liu, D., Lin, Q., Song, R.: Adaptive dynamic programming for discretetime zerosum games. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 957–969 (2017) 17. Yang, Y.: An efﬁcient algorithm for periodic Riccati equation with periodically timevarying input matrix. Automatica 78, 103–109 (2017) 18. Yang, Y.: An efﬁcient LQR design for discretetime linear periodic system based on a novel lifting method. Automatica 87(301), 383–388 (2018)
Multi Response Optimization of Dressing Conditions for Surface Grinding SKD11 Steel by HaiDuong Grinding Wheel Using Grey Relational Analysis in Taguchi Method Tran Thi Hong1, Ngo Ngoc Vu2, Nguyen Huu Phan3, Tran Ngoc Giang2, Nguyen Thanh Tu2, Le Xuan Hung2, Bui Thanh Danh4, and Luu Anh Tung2(&) 1
Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 3 Faculty of Mechanical Engineering, Hanoi University of Industry, Ha Noi, Vietnam 4 University of Transport and Communications, Ha Noi, Vietnam
Abstract. This paper presents a multi response optimization for dressing parameters for surface grinding SKD11 steel using HaiDuong grinding wheel by grey relational analysis (GRA) in the Taguchi method. The input parameters including feed rate (S), depth of rough dressing (ar), rough dressing times (nr), depth of ﬁne dressing (af), ﬁne dressing times (nf) and nonfeeding dressing (nnon) to flatness tolerance (Fl) were investigated and optimized to obtain the smallest flatness tolerance and the highest material removal rate (MRR). The experimental results showed that optimum caculation model with dressing process for surface grinding SKD11 steel using Haiduong grinding wheel is suitable. Keywords: Surface grinding ANOVA Optimization
Orthogonal array Grey relational analysis
1 Introduction Dressing is an important work in grinding operation. Up to now, there have been a variety of researches relating dressing. This work includes two steps which are dressing and cleaning operations. Dressing is to generate a deﬁned workpiece proﬁle and microtopography for grinding wheel, as shown in Fig. 1. In grinding process, grinding wheel is affected by cutting forces, cutting temperature and complex chemical interactions, so grinding wheel is worn. Here, perimeter and edge wear are the macro grinding wheel wear and wear of abrasive grains is micro wear. It includes 4 types, as shown in Figs. 2 and 3. Therefore, the minimum depth of dressing must be double wear types [1, 2].
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 560–571, 2021. https://doi.org/10.1007/9783030647193_62
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There are some methods to measure topography of grinding wheel. One of the methods uses sensors [3–6]. Accordingly, the times of dressing is based on the relationship between the force of the grinding process and the machined surface roughness. The depth of dressing bases on topography analysis of grinding wheel. The wear of grinding wheel and optimum dressing conditions were also presented [7]. In another study [8], optimum dressing conditions were investigated to reduce cutting forces and enhance surface roughness for Al2O3 grinding wheel with SPK12080 hardening workpiece material. The influence of grinding ratio and cutting forces on grinding life was presented by [9]. In a study [10], authors compared effective of dressing using diamond dressing tool with dressing using laser method for grinding 100Cr6 hardening material using SiC grinding wheel.
Fig. 1. Dressing process [1].
Fig. 2. Macro grinding wheel wear [2].
Fig. 3. Micro grinding wheel wear [2].
The dressing conditions for external grinding were also presented in many researches. Milton C. Shaw et al. [11] proposed the dressing conditions for external grinding as follows: the singlepoint diamond dressing tool with a = 100 200; rough dressing with depth of dressing dd 25 µm and feed rate S 500 µm/rev; ﬁne dressing with depth of dressing dd 12.5 µm and feed rate S 125 µm/rev. In another study, L.M. Kozuro et al. [12] also proposed the dressing conditions for surface
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grinding to obtain surface roughness Ra = 0.32−1.25 lm including the multipoint diamond dressing tool, feed rate S = 1.5 (m/min); four times with depth of dressing dd = 0.03 mm/single stroke and four times of the nonfeeding dressing; with the singlepoint diamond dressing tool, feed rate S = 1.0 (m/min); six times with depth of dressing dd = 0.02 mm/single stroke and four times of the nonfeeding dressing. Besides, the dressing conditions also were presented in other researches [13, 14]. Based on the literature review, it can be said that until now, there has not been research on the optimum dressing conditions for surface grinding SKD11 steel quenching using HaiDuong grinding wheel. Therefore, this study focuses on the optimum dressing conditions for surface grinding SKD11 steel using HaiDuong grinding wheel through 3 steps: rough dressing, ﬁne dressing and nonfeeding dressing. Besides, to examine the impact of process factors on the responses, the Taguchi method is very common. It has been used successfully in several studies [15–24]. Therefore, the method has been selected for designing and analyzing the experiment in this work.
2 Experimental Setup and Research Methodology 2.1
Experimental System
In this study, the workpiece material was SKD11 (C: 1.4 1.6%, Si 0.4%, Mn 0.6%, Cr: 11.0 13.0%, P, S 0.03%, Mo: 0.8 1.2%, W: 0.2 0.5%, Cu 0.25%, V 0.25%) with the dimensions of 70 40 25 mm and the hardness of 58–62 HRC. The experimental equipment speciﬁcations are shown in Table 1 and the grinding conditions are shown in Table 2. Table 1. Experimental equipment. Equipment type Equipment name Grinding machine MOTO –YOKOHAMA Grinding wheel Cn46TB2GV1 Dressing tool (singlepoint type) 39080088C type 2 Measuring equipment for flatness tolerance Mitutoyo CMM544 Lubricant Cantext Aquatext 381 Concentration of lubricant 3% Flow rate of lubricant 10 l/min
Table 2. Grinding conditions. Dressing conditions Depth of cut (mm) Table velocity (m/min) Radial feed (mm/stroke) Cutting speed (m/s)
Values 0.01 8 8 26.7
Country Japan Vietnam Russian Japan
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The flatness tolerance is determined by the difference between the highest and lowest values of 27 points which were measured on the surface of the workpiece after grinding. The material removal rate (MRR) is determined by the material removal volume in a unit of grinding life. 2.2
Experimental Design
The factors and their levels considered in this study are shown in Table 3. Minitab 19 software was used to design experiment based on Taguchi method. A designed experiment plan with 6 factors including 4 factors at 4 levels and 2 factors at 2 levels (44x22) was established, as shown in Table 3. Each experiment was repeated three times with the following dressing process: rough dressing depth trd with rough dressing times nrd; ﬁne dressing depth tfd with ﬁne dressing times nfd; and nonfeeding dressing nnon. The results of MRR and Fl also are shown in Table 4. Table 3. Factors and levels. Parameters
Unit
Levels 1 2 Dressing feed rate (S) m/min 1.6 1.8 0.015 0.02 Rough dressing depth (trd) mm Rough dressing times (nrd) times 1 2 Fine dressing depth (tfd) mm 0.005 0.01 Fine dressing times (nfd) times 0 1 Nonfeeding dressing (nnon) times 0 1
3 – 0.025 3 – 2 2
4 – 0.03 4 – 3 3
Table 4. L16 orthogonal array with factors and responses. TT trd
nrd nnon nfd tfd
S
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1.6 1.8 1.6 1.8 1.8 1.6 1.8 1.6 1.8 1.6 1.8 1.6 1.6 1.8 1.6 1.8
0.015 0.015 0.015 0.015 0.02 0.02 0.02 0.02 0.025 0.025 0.025 0.025 0.03 0.03 0.03 0.03
0 1 2 3 1 0 3 2 2 3 0 1 3 2 1 0
0 1 2 3 2 3 0 1 3 2 1 0 1 0 3 2
0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005 0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005
Fl (µm) MRR (mm3/s) Trial 1 Trial 2 Trial 3 Trial 1 Trial 2 11.8 12.7 13.5 2.44 2.42 24.6 25 24.8 2.05 2.00 14.5 14.8 14.2 1.92 1.89 20.8 21.2 21.8 1.83 1.82 13.3 13.9 14.2 1.84 1.79 10.9 10.3 11.2 2.40 2.36 12.9 13.9 13.5 2.06 2.05 11.3 11.5 12 1.93 1.90 10.5 10.9 11 1.87 1.91 8.8 9 9.5 2.30 2.25 9.5 10.1 10.5 1.75 1.93 17.3 17.6 17.8 1.92 1.88 11.8 11.5 12.1 1.98 1.92 13.5 13.9 13.3 1.89 1.86 19.6 19 19.5 1.91 1.87 11.8 12 12.5 1.83 1.81
Trial 3 2.38 1.94 1.88 1.85 1.80 2.37 2.04 1.64 1.92 2.27 1.92 1.89 1.95 1.87 1.86 1.79
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3 Optimization Steps Using Grey Relational Analysis for S/N Ratio Step 1.
Determine the S/N ratios
In this study, the desired flatness tolerance is ``smaller is better’’, thus the ratio can be determined as follows [25]: SN ¼ 10log10 ð
n 1X y2 Þ n i¼1 i
ð1Þ
The desired material removal rate is “larger is better”, the S/N ratio can be determined as follows: SN ¼ 10log10 ð
n 1X 1 Þ n i¼1 y2i
ð2Þ
Where n is the repeated times at each experiment and yi is the measured value at measuring times i = 1, 2, …n (n = 3). Table 5. Values of SN ratio, normalized values of S/N ratio and the absolute values. TT S/N Fl
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
−22.07 −27.89 −23.23 −26.56 −22.8 −20.67 −22.57 −21.29 −20.67 −19.19 −20.04 −24.89 −21.44 −22.65 −25.74 −21.66
Zij MRR Fl MRR Reference value 1.000 1.000 7.66 0.67 1.00 6.00 0.00 0.34 5.56 0.54 0.16 5.27 0.15 0.05 5.16 0.59 0.00 7.51 0.83 0.94 6.23 0.61 0.43 5.15 0.76 0.00 5.57 0.83 0.16 7.14 1.00 0.79 5.39 0.90 0.09 5.56 0.34 0.16 5.79 0.74 0.26 5.46 0.60 0.12 5.47 0.25 0.13 5.15 0.72 0.00
Dj(k) Fl MRR
0.33 1.00 0.46 0.85 0.41 0.17 0.39 0.24 0.17 0.00 0.10 0.66 0.26 0.40 0.75 0.28
0.00 0.66 0.84 0.95 1.00 0.06 0.57 1.00 0.84 0.21 0.91 0.84 0.74 0.88 0.87 1.00
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The most reliable results are at the larger S/N ratio and they are insigniﬁcantly affected by noise. This ratio is normalized by Zij (0 Zij 1). It can be calculated as follows: SNij min SNij ; j ¼ 1; 2; ::k Zij ¼ max SNij ; j ¼ 1; 2; ::n min SNij ; j ¼ 1; 2; ::n
ð3Þ
Where j is the number of the experimental data items (j = 16). The S/N ratio and the normalized value Z corresponding each output objective are shown in Table 5.
Table 6. Grey relational coefﬁcient and grade. TT Grey relational coefﬁcient ci Fl MRR 1 0.602 1.000 2 0.333 0.430 3 0.518 0.374 4 0.371 0.344 5 0.546 0.334 6 0.746 0.898 7 0.563 0.468 8 0.674 0.333 9 0.746 0.375 10 1.000 0.707 11 0.837 0.356 12 0.433 0.374 13 0.659 0.402 14 0.557 0.363 15 0.399 0.364 16 0.638 0.333
Step 3.
c
0.801 0.382 0.446 0.357 0.440 0.822 0.515 0.504 0.560 0.853 0.596 0.403 0.531 0.460 0.382 0.486
Calculate the interaction coefﬁcient in fuzzy relational coefﬁcient for the normalized S/N ratio cðkÞ ¼
Dmin þ fDmax Df ðkÞ þ fDmax
ð4Þ
Where: þ Þ j ¼ 1; 2; . . .n; k ¼ 1; 2; . . .:m; n is the number of experimental data items, m is the number of responses.
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þ Þ D0j ¼ Z0 ðkÞ Zj ðkÞ is the absolute value of the difference between Z0(k) (reference sequence) and Zj(k) (the speciﬁc comparison sequence). þ Þ Dmin ¼ min min Z0 ðk Þ Zj ðkÞ is the smallest value of D0j 8j2i
8k
8j2i
8k
þ ÞDmax ¼ max max Z0 ðkÞ Zj ðkÞ is the largest value of D0j +) f is the distinguish coefﬁcient, which can be determined in range 0 f 1. This value can be adjusted based on the requirement of system. In this study, f = 0.5. Step 4. Determine the grey relational grade cj ¼
m 1X c k i¼1 ij
ð5Þ
This is the average value of interaction in the grey relational grade determined at step 3. k is number of performance characteristics. Table 6 shows the grey relational grade corresponding with objective and average value of grey relational grade. Table 7. Influence degree of parameters on fuzzy relational coefﬁcient. Response Table for Means nrd nnon Level trd 1 0.4965 0.5830 0.6763 2 0.5702 0.6292 0.4016 3 0.6035 0.4849 0.4925 4 0.4644 0.4375 0.5642 Delta 0.1391 0.1917 0.2746 Rank 3 2 1 c ¼ 0:534
nfd 0.5450 0.5030 0.5563 0.5304 0.0533 6
tfd S 0.5603 0.5927 0.5070 0.4746
0.0533 0.1181 5 4
Fig. 4. Main effects plot for means data means.
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Step 5.
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Determine the optimal factor and its level combination
The higher grey relational grade implies the better product quality. Table 6 shows the grey relational grade for each experiment and interaction of the grey relational grade. In Table 6, the 10th experiment including two times of the rough dressing with dressing depth of 0.005 mm, three times of the nonfeeding dressing with feed rate of 1.6 m/min has the highest interaction grey relational grade of 0.853. This shows that the 10th experiment has a S/N ratio similar to the normalized S/N ratio and it has various good characteristics among sixteen experiments. However, this level is not the optimum one of all factors. Therefore, the average grey relational grade of each factor at different levels needs to be determined and they are shown in Table 7 and Fig. 3. A factor is optimum if its grey relational grade at any level is the highest. Therefore, based on Table 7 and Fig. 3, the optimum parameters of the dressing process for surface grinding are suitable with both MRR with “larger is better’’ and Fl with “smaller is better”, as follows: trd3/nrd3/nnon1/nfd3/tfd1/S1 corresponding with two times of the rough dressing and depth of cut trd = 0.025 mm; two times of the ﬁne dressing and depth of cut trd = 0.005 mm with feed rate S = 1.6 m/min; the nonfeeding dressing is not used. Step 6.
Perform ANOVA for identifying the signiﬁcant factors
The main purpose of the analysis of variance (ANOVA) is the application of a statistical method to identify the effect of individual factors. The regression analysis results of variance are shown in Table 8 and Fig. 4.
Table 8. Results of ANOVA on grey relational grade. Analysis of Variance for Means Source DF Seq SS Adj SS 3 0.049591 0.049591 trd nrd 3 0.092794 0.092794 nnon 3 0.161580 0.161580 nfd 3 0.006359 0.006359 tfd 1 0.011369 0.011369 S 1 0.055755 0.055755 Residual Error 1 0.003860 0.003860 Total 15 0.381308 Model Summary S RSq 0.0621 98.99%
Adj MS F 0.016530 4.28 0.030931 8.01 0.053860 13.96 0.002120 0.55 0.011369 2.95 0.055755 14.45 0.003860
P C% 0.338 13.01 0.253 24.34 0.194 42.38 0.730 1.67 0.336 2.98 0.164 14.62 1.01 100.00
RSq(adj) 84.82%
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Table 8 shows that the output parameters are signiﬁcantly affected by the nonfeeding dressing times nnon (42.38%), followed by the rough dressing times (24.34%), the feed rate S (14.62%), the depth of the rough dressing trd (13.01%), the depth of the ﬁne dressing tfd (2.98%) and the ﬁne dressing times nfd (1.67%). Step 7.
Calculate the predicted optimum values
The fuzzy relationship value is determined as follows: cop ¼ gm þ R5i¼1 ðg gm Þ ¼ trd3 þ nrd2 þ nnon1 þ nfd3 þ tfd1 þ S1 5 T
ð6Þ
Where T is the average grey relational grade T = 0.534; trd3, nrd2, nnon1, nfd3, tfd1, S1 are the grey relational grade of factors corresponding with the optimum levels in Table 6. Where by, cop ¼ 0:950. Conﬁdence interval CI can be calculated as follows: sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ 1 1 þ CI ¼ Fa ð1:fe Þ:Ve : ¼ 0:315 Ne R
ð7Þ
Where F/ ð1; fe Þ ¼ 39:864 is the coefﬁcient with signiﬁcance level a% = 90%, fe = 1 is the degree of freedom of error, Ve = 0.00386 is the average deviation of error, Ne is the number of effective iterations, R = 3 is the number of iterations of an experiment. Total experiments 1 þ number of degree of freedom of input parameters 48 ¼ 3:2 ¼ 1þ3þ3þ3þ3þ1þ1
Ne ¼
Therefore, when a = 90%, the grey relational grade can be predicted with the suitable levels of input parameters trd3/nrd2/nnon1/nfd3/tfd1/S1 as follows: 0:6 cop 1:0 Based on the optimum levels of input parameters, the optimum values of output parameters (MRR and Fl) are determined as follows: ðFl; MRRÞop ¼ trd3 þ nrd2 þ nnon1 þ nfd3 þ tfd1 þ S1 5 T
ð8Þ
Where: + ðFl; MRRÞop is the flatness tolerance or the optimum material removal rate. þ trd3 is the average flatness tolerance or the material removal rate when depth of rough dressing is at level 3. þ nrd2 is the average flatness tolerance or the material removal rate when the rough dressing times is at level 2.
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þ nnon1 is the average flatness tolerance or the material removal rate when the nonfeeding dressing is at level 1. þ nfd3 is the average flatness tolerance or the material removal rate when the ﬁne dressing times is at level 3. þ tfd1 is the average flatness tolerance or the material removal rate when depth of ﬁne dressing is at level 1. þ S1 is the average flatness tolerance or the material removal rate when feed rate is at level 1. þ T is the average flatness tolerance or the material removal rate of all experiments. Thus: ðFlÞop ¼ 6:87 lm ðMRRÞop ¼ 2:37 mm3 =s To investigate the accuracy of calculation process, the optimum dressing parameters are as follows: two times of the rough dressing with trd = 0.025 mm; two times of the ﬁne dressing with tfd = 0.005 mm and feed rate S = 1.6 m/min; the nonfeeding dressing was not used. The experimental and predicted values are shown in Table 9.
Table 9. Compared values between experimental and predicted ones. Characteristics
Fl (µm) MRR (mm3/s) Fuzzy relational grade
Optimum parameters Predicted values trd3, nrd2, nnon1, nfd3, tfd1, S1 6.87 2.37 0.950
Experimental values trd3, nrd2, nnon1, nfd3, tfd1, S1 6.15 2.12
% deviation 10.48 10.54
The experimental results show that the maximum deviation compared with the predicted values is 10.54% corresponding with the flatness tolerance, therefore, this calculation method can be used to accurately predict two characteristics, MRR and Fl, at the same time.
4 Conclusions In this study, the material removal rate and flatness tolerance have been predicted by analyzing the grey relational grade using Taguchi method. This study showed that the output parameters is signiﬁcantly affected by the nonfeeding times nnon (42.38%),
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followed by the rough dressing times nrd (24.34%), feed rate S (14.62%), depth of rough dressing trd (13.01%), depth of the ﬁne dressing tfd (2.98%), and the ﬁne dressing times nfd (1.67%). The optimum parameters of the dressing process for surface grinding suitable for both MRR with “larger is better” and Fl with “smaller is better” are trd3/ nrd3/nnon1/nfd3/tfd1/S1 corresponding with two times of the rough dressing with trd = 0.025 mm, two times of the ﬁne dressing with tfd = 0.005 mm, feed rate S = 1.6 m/min, and the nonfeeding dressing was not used. Acknowledgements. This work was supported by Thai Nguyen University of Technology.
References 1. Klocke, F.: Manufacturing Processes 2 – Grinding, Honing, Lappin. Springer, Heidelberg (2009) 2. Wegener, K., Hoffmeister, H.W., Karpuschewski, B., Kuster, F., Hahmann, W.C., Rabiey, M.: Conditioning and monitoring of grinding wheels. CIRP Ann. – Manufact. Technol. 60, 757–777 (2011) 3. Kim, S.H., Ahn, J.H.: Decision of dressing interval and depth by the direct measurement of the grinding wheel surface. J. Mater. Process. Technol. 88, 190–194 (1999) 4. Deng, H., Chen, G.Y., Zhou, C., Li, S.C., Zhang, M.J.: Processing parameter optimization for the laser dressing of bronzebonded diamond wheels. Appl. Surf. Sci. 290, 475–481 (2014) 5. Xie, J., Wei, F., Zheng, J.H., Tamaki, J., Kubo, A.: 3D laser investigation on micronscale grain protrusion topography of truncated diamond grinding wheel for precision grinding performance. Int. J. Mach. Tools Manuf 51, 411–419 (2011) 6. Klink, A.: Wire electro discharge trueing and dressing of ﬁne grinding wheels. CIRP Ann. – Manuf. Technol. 59, 235–238 (2010) 7. Chen, X., Allanson, D.R., Rowe, W.B.: Life cycle model of the grinding process. Comput. Ind. 36(1–2), 5–11 (1998) 8. Baseri, H.: Simulated annealing based optimization of dressing conditions for increasing the grinding performance. Int. J. Adv. Manuf. Technol. 59, 531–538 (2012) 9. Kwak, J.S., Ha, M.K.: Evaluation of wheel life by grinding ratio and static force. KSME Int. J. 16(9), 1072–1077 (2002) 10. Rabiey, M., Walter, C., Kuster, F., Stirnimann, J., Pude, F., Wegener, K.: Dressing of hybrid bond cbn wheels using shortpulse ﬁber laser. J. Mech. Eng. 58(7–8), 462–469 (2012) 11. Shaw, M.C.: Principles of Abrasive Processing. Oxford University Press, UK (1996) 12. Kozuro, L.M., Panov, A.A., Remizovski, E.I., Tristosepdov, P.S.: Handbook of Grinding. Publish Housing of Higheducation, Minsk (1981). (in Russian) 13. Malkin, S.: Grinding Technology: Theory and Applications of Machining with Abrasives. Ellis Horwood Limited, Endland (1989) 14. SainGobain, W.: Catalogue No.5 Dressing Tools: WINTER diamond tools for dressing grinding wheels (2015) 15. Hoang, X.T., Pi, V.N., Jun, G.: A Study on determination of optimum parameters for lubrication in external cylindrical grinding base on taguchi method. Key Eng. Mater. 796, 97–102 (2019). https://doi.org/10.4028/www.scientiﬁc.net/KEM.796.97 16. Hung, L.X., Hong, T.T., Ky, L.H., Tung, L.A., Nga, N.T.T., Pi, V.N.: Optimum dressing parameters for maximum material removal rate when internal cylindrical grinding using taguchi method. Int. J. Mech. Eng. Technol. (IJMET) 9(12), 123–129 (2018)
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17. Tran, T.H., Hoang, T.D., Le, H.K., Do, T.T., Bui, T.H., Nguyen, M.C., Luu, A.T., Vu, V.P.: Analysis of effects of machining parameters on surface roughness in electrical discharge machining tablet shape punches using taguchi method. Mater. Sci. Forum 977, 12–17 (2020) 18. Hung, L.X., Pi, V.N., Hong, T.T., Ky, L.H., Lien, V.T., Tung, L.A., Long, B.T.: Multiobjective optimization of dressing parameters of internal cylindrical grinding for 9CrSi aloy steel using taguchi method and grey relational analysis. Mater. Today Proc. 18, 2257–2264 (2019) 19. Hong, T.T., Cuong, N.V., Ky, L.H., Nguyen, Q.T., Long, B.T., Tung, L.A., Nguyen, T.T., Pi, V.N.: Multicriteria optimization of dressing parameters for surface grinding 90CrSi tool steel using taguchi method and grey relational analysis. Mater. Sci. Forum, 998, 61–68 (2020). https://doi.org/10.4028/www.scientiﬁc.net/msf.998.61 20. Tung, LA et al.: Optimization of dressing parameters of grinding wheel for 9CrSi tool steel using the taguchi method with grey relational analysis. In: 10th International Conference on Mechatronics and Manufacturing (ICMM 2019), IOP Conference Series: Materials Science and Engineering, Vol. 635, Bangkok, Thailand 21–23 January 2019 21. Ky, L.H., Hong, T.T., Dung, H.T., Tuan, N.A., van Tung, N., Tung, L.A., Pi, V.N.: Optimization of dressing parameters for grinding table shape punches by CBN wheel on CNC milling machine. Int. J. Mech. Eng. Technol. 10, 960–967 (2019) 22. Vu, N.P., Nguyen, Q.T., Tran, T.H., Le, H.K., Nguyen, A.T., Luu, A.T., Nguyen, V.T., Le, X.H.: Optimization of grinding parameters for minimum grinding time when grinding tablet punches by CBN wheel on CNC milling machine. Appl. Sci. 9, 957 (2019) 23. Son, N.H., Hong, T.T., Van Cuong, N., Vu, N.P.: Calculating effects of dressing parameters on surface roughness in surface grinding. In: International Conference on Engineering Research and Applications, pp. 164–169 (December 2019) 24. Hong, T.T., Cuong, N.V., Ky, L.H., Tung, L.A., Nguyen, T.T., Vu, N.P.: Effect of process parameters on surface roughness in surface grinding of 90CrSi tool steel. Solid State Phenom. 305, 191–197 (2020). Trans Tech Publications Ltd 25. Jeyapaul, R., Shahabudeen, P., Krishnaiah, K.: Quality management research by considering multiresponse problems in the Taguchi method  a review. Int. J. Adv. Manuf. Technol. 26, 1331–1337 (2005)
Multiobjective Optimization of Process Parameters During Electrical Discharge Machining of Hardened 90CrSi Steel by Applying Taguchi Technique with Grey Relational Analysis Tran Thi Hong1, Nguyen Manh Cuong2, Nguyen Dinh Ngoc2, Luu Anh Tung2, Tran Ngoc Giang2, Le Thu Quy3, Nguyen Thanh Tu2, and Do Thi Tam2(&) 1
3
Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] National Research Institute of Mechanical Engineering, Ha Noi City, Vietnam
Abstract. The aim of this study is to optimize output responses as minimum surface roughness and maximum material removal rate (MRR) of Electrical Discharge Machining (EDM) process when machining hardened 90CrSi steel. Thank to this process the combination of machining parameters obtained by minimizing problems is found. The machining parameters for EDM process under consideration include concentration of powder, pulse – on  time, pulse off  time, current, and voltage. The experiments are set up based on the L18 orthogonal array of Taguchi method, and analysis has been carried out using Grey Relational Analysis (GRA) to ﬁnd the optimal set of machining parameters. ANOVA is also used to determine which parameters have the signiﬁcant effects on the output responses. Finally, the experiments are performed to validate the optimal set of machining parameters. The results show that Current has the strongest influence (32,63%), and Pulse off time has the smallest effects (1,04%) on the grey grade value. The conﬁrmation experiment and ANOVA show the reliability of the proposed model which can be considered as an effective method to predict the surface roughness and material removal rate. Keywords: EDM Hardened 90CrSi steel Taguchi method Optimization
Grey Relational Analysis
1 Introduction EDM (Electrical Discharge Machining) is one of the nonconventional operations for machining materials. The principles of this process are removing electrically conductive materials by using precisely controlled sparks appearing between an electrode and a workpiece in a dielectric fluid. This process is specially needed for cutting © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 572–583, 2021. https://doi.org/10.1007/9783030647193_63
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difﬁculttomachining materials due to their high strengths, high hardness and great brittleness in the highspeed machining (see Fig. 1) [1].
Fig. 1. Schematic illustration of electrical discharge machining [1]
In EDM, the input process parameters are normally the current, the pulse on time, the pulse off time and the voltage [2–7] which have important impacts on various output responses, i.e. surface roughness, tool wear rate, material removal rate [3, 6, 8, 9]. These responses also strongly impact on the cost of EDM process. Hence, in order to reduce the cost as well as to improve the quality product, there have been many studies dealing with ﬁnding optimum input parameters which can minimize the cost of EDM operation. Optimizing the output parameters leads to the fact that multiple objectives are naturally derived based on investigating various input parameters with differential levels. To meet this requirement, Grey relation analysis (GRA) coupled with Taguchi technique shows a distinct selection [1, 8, 10–14]. The Taguchi method has been used effectively to ﬁnd the effects of input factors on the responses in many experimental works [15–17]. The effectiveness of this method is higher when it is combined with GRA such as when ﬁnding optimum dressing regimes in internal grinding [18] and surface grinding [19, 20] or determining optimum input factors in powdermixed electrical discharge machining [21]. The obtained results presented in [10] show that the Taguchi Grey Relational Analysis is an effective technique to optimize the machining parameters for EDM process. In the study, some process parameters like current, pulse on time, and pulse off time are used to ﬁnd the evolution of three responses such as material removal rate (MRR), wear rate of cutting tool, and surface roughness with machined materials of mild steel IS 2026 using copper electrode. The combination between GRA and Taguchi method (using L9 orthogonal array) is utilized to analyze the experimental results to ﬁnd the optimal levels of machining parameters which can generate higher material removal rate, lower wear rate of cutting tool, and lower surface roughness. The predicted results are checked with the experimental ones and a good agreement is visualized. Subrata et al. [14] investigated the influences of some process parameters such as laser power, scan speed, and powder speed rate on laser machining operation. L9 orthogonal array is adopted to perform the experiments. The GRA is utilized to ﬁnd out the optimum parameters which are power of 1.25 kW, scan speed of
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0.8 m/min, and a powder feed rate of 11 gm/min. The responses of optimization are selected in study of [22] are surface roughness and MRR. In this study, an attempt has been carried out to optimize the multiple objective responses, e.g. surface roughness and material removal rate resulting from EDM process of hardened SKD11 steel. The investigated parameters are the powder concentration, the pulse on time, the pulse off time, the servo current, and the servo voltage. Among those, the ﬁrst parameter will be considered at six levels, and the others at three levels. Taguchi method coupled with GRA has been applied to ﬁnd the combination of machining parameters generating the optimum responses. ANOVA will be performed by Minitab@18 to determine which parameters have signiﬁcant effects on the multiple responses. Furthermore, experiments will be conducted to validate the combination of machining parameters which are predicted by Taguchi method coupled with GRA.
2 Experimental Preparation 2.1
Design of Experiments
In order to evaluate the performance of output responses, surface roughness and MRR, ﬁve machining parameters are selected such as Concentration of powder, Pulse on time, Pulse off time, Current, and Voltage. The ﬁrst parameter will be considered at six levels, and the others at three levels. The information of machining parameters is listed in Table 1. A Taguchi L18 (6^1 3^4) orthogonal array is designed with 18 runs to perform experiments.
Table 1. Investigated factors and their levels Variable
Level 1 2 Concentration of powder Cp [g/l] 0 2.0 Pulse on time Ton [µs] 6 10 14 21 Pulse off time Toff [µs] Current IP [A] 4 8 Voltage SV [V] 3 4
2.2
3 2.5 14 30 12 5
4 3.5 – – – –
5 4.0 – – – –
6 4.5 – – – –
Materials and Specimens
The specimens are in shapes and dimension as shown in Fig. 2. The materials used are hardened 90CrSi steel with a hardness of 5862 HRC. The chemical composition of specimens can be seen in Table 2. The EDM process is performed using electrode of copper, and dielectric solution mixed with nanopowder of SiC 500 nm. The principles of EDM process can be schematically described in Fig. 3. In order to keep nanopowder of SiC consistent when contained in the dielectric solution, the speed of stirring reaches 90 r/min. The EDM
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process can be kept stable by using a nozzle and a pump. Chip generated during EDM can be collected by the magnetic plated. The details of EDM process are further consulted in Table 3 and in [3].
Fig. 2. Shape and dimension of machined workpiece.
Table 2. Chemical composition of 90CrSi steel (%) C Si 0.85* 1.2 0.95 * 1.6
Mn 0.3 * 0.6
P S Cr Mo Ni V W Others 0.03 0.03 0.95 * 1.25 0.2 0.35 0.15 0.2 Cu 0.3; Ti 0.03
Fig. 3. Shema of EDM process [3]
The surface roughness Ra is measured by using the roughness tester of Mitutoyo 1789232A, SJ201. The surface roughness values are averagely calculated by three times of measurements. MRR is determined by removed material volume per time unit of pulse. Each experimental run was repeated three times to lessen the experimental error. The EDM process is carried out according to the experiment design as previously mentioned and the results of Ra and MRR are listed in Table 4.
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T. T. Hong et al. Table 3. EDM parameters Items EDM machine Electrode Workpiece Workpiece dimensions Dielectric fluid Surf – test instrument Nanopowder
Description Sodick A30 Copper 90CrSi alloy steel; 5862 HRC hardness 60 mm 35 mm 25 mm Diel MS 7000 oil SV 3100 SiC 500 nm
3 Grey Relational Analysis and Discussion on Experiment Results The signal to noise (S/N) ratio for each test can be determined based on the expectation for surface roughness that is “the smaller is the better’’, S=N ¼ 10log10 ð
1 Xn 2 yÞ i¼1 i n
ð3:1Þ
And conversely, that is “the larger is better’’ for MRR[16]: SN ¼ 10log10 ð
1 Xn 1 Þ i¼1 y2 n i
ð3:2Þ
In which, n is the total of tests, yi the observed data. The normalized values of grey relation for both surface roughness and MRR can be determined by Zij (0 Zij 1): SNij min SNij ; j ¼ 1; 2; ::k Zij ¼ max SNij ; j ¼ 1; 2; ::n min SNij ; j ¼ 1; 2; ::n
ð3:3Þ
The values of S/N ratio and normalized values of grey relation are shown in Table 5. The grey relation coefﬁcient, cðkÞ, is calculated to express the relationship between reference and experimental data normalized data as follows: cð k Þ ¼
Dmin þ fDmax Dj ðkÞ þ fDmax
ð3:4Þ
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Where: Δoj is the deviation of reference data. Dj ðkÞ ¼ Z0 ðkÞ Zj ðk Þ and Z0(k) is the reference data or best data. þ Þ Dmin ¼ min min Z0 ðkÞ Zj ðk Þ minimum value of Dj ðkÞ. 8j2i
8k
þ ÞDmax ¼ max max Z0 ðkÞ Zj ðkÞ maximum value of Dj ðkÞ. 8j2i
8k
+) f is distinguishing or identiﬁcation coefﬁcient and its value varies from 0 to 1. We select its value of 0.5 herein.
Table 4. Orthogonal array with factors and responses TT Cp Ton Toff IP SV Ra (µm) Trial 1 Trial 2 1 0 6 14 4 3 2.960 2.930 2 0 10 21 8 4 2.239 2.161 3 0 14 30 12 5 5.066 5.117 4 2 6 14 8 4 2.411 2.434 5 2 10 21 12 5 2.749 2.839 6 2 14 30 4 3 4.942 5.200 7 2.5 6 21 4 5 2.158 2.232 8 2.5 10 30 8 3 3.895 3.882 9 2.5 14 14 12 4 3.840 3.733 10 3.5 6 30 12 4 2.791 2.620 11 3.5 10 14 4 5 3.421 3.559 12 3.5 14 21 8 3 2.685 3.068 13 4 10 30 4 4 2.959 2.795 14 4 14 14 8 5 2.646 2.670 15 4 6 30 8 5 1.614 1.655 16 4.5 10 14 12 3 3.752 3.613 17 4.5 14 21 4 4 4.404 4.298 18 4.5 14 30 8 3 2.864 2.732
Trial 3 2.928 2.383 5.125 2.482 2.601 5.174 2.196 3.868 3.790 2.528 3.490 2.906 2.763 2.785 1.741 3.926 4.491 2.795
MRR (g/h) Trial 1 Trial 2 0.0192 0.0193 0.0101 0.0101 0.2475 0.2465 0.0034 0.0034 0.3304 0.3302 0.0321 0.0321 0.0018 0.0018 0.0392 0.0392 0.3384 0.3384 0.4537 0.4523 0.0505 0.0506 0.0025 0.0025 0.3252 0.3249 0.0078 0.0078 0.0089 0.0088 0.3102 0.3099 0.0070 0.0070 0.0080 0.0080
Trial 3 0.0193 0.0101 0.2470 0.0034 0.3302 0.0322 0.0018 0.0391 0.3381 0.4528 0.0505 0.0025 0.3255 0.0077 0.0089 0.3102 0.0070 0.0080
Determining the gray relational grade: cj ¼
m 1X c k i¼1 ij
ð3:5Þ
Based on this equation, the average value of interactions of grey relation is found at third step; k is the number of objectives optimized. Table 6 shows the values of grey relational coefﬁcient of both surface roughness and MRR.
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T. T. Hong et al. Table 5. S/N ratio values, normalized S/N ratio values and the absolute value. TT S/N Ra
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
−9.365 −7.093 −14.156 −7.757 −8.728 −14.163 −6.831 −11.780 −11.568 −8.460 −10.858 −9.220 −9.067 −8.631 −4.459 −11.517 −12.866 −8.936
MRR
−34.316 −39.896 −12.147 −49.361 −9.623 −29.859 −54.958 −28.143 −9.415 −6.879 −25.928 −51.916 −9.758 −42.212 −41.064 −10.170 −43.082 −41.951
Zij Ra MRR Reference data 1.000 1.000 0.494 0.429 0.729 0.313 0.001 0.890 0.660 0.116 0.560 0.943 0.000 0.522 0.756 0.000 0.246 0.558 0.267 0.947 0.588 1.000 0.341 0.604 0.509 0.063 0.525 0.940 0.570 0.265 1.000 0.289 0.273 0.932 0.134 0.247 0.539 0.271
Dj(k) Ra MRR
0.506 0.271 0.999 0.340 0.440 1.000 0.244 0.754 0.733 0.412 0.659 0.491 0.475 0.430 0.000 0.727 0.866 0.461
0.571 0.687 0.110 0.884 0.057 0.478 1.000 0.442 0.053 0.000 0.396 0.937 0.060 0.735 0.711 0.068 0.753 0.729
The higher value of grey relation refers to the better quality of product. Hence, it can be estimated the influence of factors and the optimum degree for each controlled factor. Table 6 presents the values of grey relation for each speciﬁc test and predicted grey relation values. It is observed that the tenth test (Cp4, Ton1, Toff3, IP3, SV2) corresponding to machining conﬁguration of Cp = 3.5 (g/l), T = 6 (µs), Toff = 30 (µs), IP = 12 (A), SV = 4 (V) exhibits the highest value of predicted grey relation, 0.774. This refers that at the tenth test the value of S/N ratio reaches approximately normalized ones and better than other tests shown in Table 6. However, it is noticed that this test is not the best one. For this reason, it is needed to calculate the average values of each factor at different levels. The main effects of each factor with different levels on grey grade are presented in tabular form and graphical form corresponding to Table 7 and Fig. 4 respectively. It is noticed that the highest grade value of each factor refers to the optimum value for corresponding factor. Hence, it is observed that the optimum set combining all machining parameters which makes the smaller is better for the surface roughness and the larger is the better for MRR is Cp5/Ton1/Toff3/IP3/SV3 or for speciﬁc value: Cp = 4.0 (g/l), Ton = 6 (µs), Toff = 30 (µs), IP = 12 (A), SV = 5 (V). In order to
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determine which factor have signiﬁcant effects on the grey grade, Analysis of Variance (ANOVA) is performed (c.f. Table 8).
Table 6. Grey relational coefﬁcient and grey grade values TT Grey relational coefﬁcient ci Ra MRR 1 0.497 0.467 2 0.648 0.421 3 0.333 0.820 4 0.595 0.361 5 0.532 0.898 6 0.333 0.511 7 0.672 0.333 8 0.399 0.531 9 0.406 0.905 10 0.548 1.000 11 0.431 0.558 12 0.505 0.348 13 0.513 0.893 14 0.538 0.405 15 1.000 0.413 16 0.407 0.880 17 0.366 0.399 18 0.520 0.407
c
0.482 0.535 0.577 0.478 0.715 0.422 0.503 0.465 0.655 0.774 0.495 0.426 0.703 0.471 0.706 0.643 0.382 0.463
Table 7. Main effects on grey grades. Level Cp 1 0.5313 2 0.5383 3 0.5410 4 0.5650 5 0.6267 6 0.4960 Delta 0.1307 Rank 2 c ¼ 0:55
Ton 0.5972 0.5105 0.5415
Toff 0.5330 0.5575 0.5587
IP 0.4727 0.5422 0.6343
SV 0.4800 0.5627 0.6065
0.0867 0.0257 0.1617 0.1265 4 5 1 3
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Fig. 4. Factor effects on grade values. Table 8. Results of ANOVA on grey grade Source DF 5 Cp Ton 2 2 Toff IP 2 SV 2 Residual Error 4 Total 17
Seq SS 0.028752 0.023142 0.002521 0.078922 0.049515 0.058983 0.241834
Adj SS 0.028752 0.023142 0.002521 0.078922 0.049515 0.058983
Adj MS 0.005750 0.011571 0.001260 0.039461 0.024757 0.014746
F 0.39 0.78 0.09 2.68 1.68
P C% 0.835 11.89 0.516 9.57 0.920 1.04 0.183 32.63 0.296 20.47 24.39 100.00
According to the presented results, it is visualized that Current (IP) has the strongest influence on the grey grade corresponding to 32.63%. This percentage contribution is followed by Voltage (SV), Concentration of Powder (Cp), Pulse on time (Ton), and Pulse off time (Toff) corresponding to 20.47%, 11.89%, 9.71%, and 1.04%, respectively. The optimum grey grade value can be conﬁrmed by following process: cop ¼ gm þ
4 X
ðg gm Þ ¼ Cp5 þ Ton1 þ Toff 3 þ IP3 þ SV3 4 T
ð3:5Þ
i¼1
Where: T is the average grey grade, T = 0.55; Cp5, Ton1, Toff3, IP3, SV3 present the optimum values listed in Table 5. Hence, cop ¼ 0:824. Conﬁdence interval can be determined as: sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ 1 1 ¼ 0:218 CI ¼ Fa ð1:fe Þ:Ve : þ Ne R
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Where: F/ ð1; fe Þ ¼ 5:448 is the tabular constant with the signiﬁcant level a% = 90%, fe = 4 is denoted for the degrees of freedom of errors, Ve = 0.014746 is the average deviation of errors, Ne is the effective repetition; R = 3 represents for the repetition of each test. Total number of test 54 ¼ 1 þ total number of degrees of fredom of factors 1 þ 5 þ 2 þ 2 þ 2 þ 2 ¼ 3:857
Ne ¼
Based on the above results, the values of grey grade can vary in the following range: 0:606 cop 1:00 The optimum values of surface roughness and MRR can be found by following equation: ðRa; MRRÞop ¼ Cp5 þ Ton1 þ Toff 3 þ IP3 þ SV3 4 T In which: Cp5 is the average value of concentration of powder at level 5 Ton1 is the average value of Pulse on time at level 1 Toff 3 is the average value of Pulse off time at level 3 IP2 is the average value of Current at level 3 SV3 is the average value of Voltage at level 3 T is the average value of surface roughness and/or MRR. As a result, we can get: ðRaÞop ¼ 3:009lm ðMRRÞop ¼ 0:4348g=h In order to validate the reliability of the proposed regression model, the optimal set of parameters as previously analyzed is selected to perform the conﬁrmation experiment. The chosen parameters are Cp = 4.0 g/lit, Ton = 6 µs, Toff = 30 µs, IP = 12 A, SV = 5 V. The experimental results and predicted ones are compared and listed in Table 9. Table 9. Comparison of prediction and experiment Prediction Cp5/Ton1/Toff3/IP3/SV3 Surface roughness, Ra (µm) 3.009 Material removal rate (g/h) 0.4348 Grey grade value 0.824
Experiment Error (%) Cp5/Ton1/Toff3/IP3/SV3 2.783 7.51 0.4016 7.64
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Based on the results of comparison, it is seen that the maximum error between the experiment and the prediction is 7.64% belonging to MRR. This means that the proposed model and calculating process are signiﬁcantly reliable and it could be adopted for simultaneously and accurately predicting the optimum values of surface roughness and MRR.
4 Conclusions In the current research work, the EDM process of hardened 90CrSi steel is performed. The electrode is made of copper, while the dielectric solution is mixed with the powder of nano SiC 500. The Taguchi technique is combined with Grey relational analysis to minimize the surfaces roughness and maximize the material removal rate. Based on this aim, the optimum set of machining parameters such as Concentration of powder, Pulse on time, Pulse off time, Current, and Voltage is found. The following conclusions are made: – Current has the strongest influence on the grey grade value (32.63%), and it is sequentially followed by Voltage (20.47%), Concentration of powder (11.89), Pulse on time (9.57%), and Pulse off time (1.04%). – The optimum set of machining parameters for EDM process of hardened 90CrSi steel is Cp = 4.0 (g/l), Ton = 6 (µs), Toff = 30 (µs), IP = 12 (A), SV = 5 (V). – The conﬁrmation experiment and ANOVA show the reability of the proposed model which can be considered as an effective method to predict the surface roughness and material removal rate when conducting EDM process of hardended 90CrSi steel using nano SiC 500 nm powder. Acknowledgements. This work was ﬁnancially supported by the scientiﬁc project No. B2019TNA03.
References 1. Kolahan, F., Moghaddam, M.A.: The use of Taguchi method with grey relational analysis to optimize the EDM process parameters with multiple quality characteristics (2014) 2. Hoang, T.T., Duc, T.A., Cuong, N.M., Tung, L.A., Hung, L.X., Pi, V.N.: Modelling surface ﬁnish in electrical discharge machining tablet shape punches using response surface methodology. SSRG Int. J. Mech. Eng. 4(9), 28–30 (2017) 3. Tran, T.H., et al.: Electrical discharge machining with SiC powdermixed dielectric: an effective application in the machining process of hardened 90CrSi steel. Mach. 8(3), 36 (2020) 4. Tran, T.H., et al.: Analysis of effects of machining parameters on surface roughness in electrical discharge machining tablet shape punches using taguchi method. Mater. Sci. Forum 977, 12–17 (2020) 5. Duc, T.A., et al.: Effects of process parameters on machining time in wire electrical discharge machining of 9CrSi steel. Int. J. Eng. Trends Technol. 60(3), 168–171 (2018)
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6. Muthuramalingam, T., Saravanakumar, D., Babu, L.G., Huu Phan, N., Pi, V.N.: Experimental investigation of white layer thickness on EDM processed silicon steel using ANFIS approach. Silicon 12(8), 1905–1911 (2019). https://doi.org/10.1007/s12633019002872 7. Le Xuan, H., Tran Thanh, H., Vu Ngoc, P.: A Study on modelling surface ﬁnish in electrical discharge machining tablet shape punches using response surface methodology. J. Environ. Sci. Eng. B. 6(7), 387–390 (2017) 8. Kumar Chauhan, N., Kumar Das, A., Rajesha, S.: Optimization of process parameters using grey relational analysis and taguchi method during microEDMing. Mater. Today: Proc. 5(13), 27178–27184 (2018) 9. Huo, J., et al.: Influence of process factors on surface measures on electrical discharge machined stainless steel using TOPSIS. Mater. Res. Express 6(8) (2019) 10. Raghuraman, S., Thiruppathi, K., Panneerselvam, T., Santosh, S.: Optimization of EDM parameters using Taguchi method and grey relational analysis for mild steel IS 2026. Int. J. Innovative Res. Sci. Eng. Technol. 2(7), 3095–3104 (2013) 11. Prayogo, G.S., Lusi, N.: Application of Taguchi technique coupled with grey relational analysis for multiple performance characteristics optimization of EDM parameters on ST 42 steel (2016) 12. Achuthamenon Sylajakumari, P., Ramakrishnasamy, R., Palaniappan, G.: Taguchi grey relational analysis for multiresponse optimization of wear in cocontinuous composite. Materials 11(9), 1743 (2018) 13. Xie, Z.P., Zheng, J.M., Quan, B.L.: Optimization by grey relational analysis of EDM parameters on machining Ti6Al4V. Adv. Mater. Res. 139–141, 540–544 (2010) 14. Mondal, S., et al.: Application of Taguchibased gray relational analysis for evaluating the optimal laser cladding parameters for AISI1040 steel plane surface. Int. J. Adv. Manuf. Technol. 66(1–4), 91–96 (2012) 15. Hoang, X.T., Pi, V.N., Jun, G.: A study on determination of optimum parameters for lubrication in external cylindrical grinding base on taguchi method. Key Eng. Mater. 796, 97–102 (2019). https://doi.org/10.4028/www.scientiﬁc.net/KEM.796.97 16. Hung, L.X., Hong, T.T., Ky, L.H., Tung, L.A., Nga, N.T.T., Pi, V.N.: Optimum dressing parameters for maximum material removal rate when internal cylindrical grinding using taguchi method. Int. J. Mech. Eng. Technol. (IJMET) 9(12), 123–129 (2018) 17. Tran, T.H., et al.: Analysis of effects of machining parameters on surface roughness in electrical discharge machining tablet shape punches using taguchi method. Mater. Sci. Forum 977, 12–17 (2020) 18. Hung, L.X., et al.: Multiobjective optimization of dressing parameters of internal cylindrical grinding for 9CrSi aloy steel using taguchi method and grey relational analysis. Mater. Today Proc. 18, 2257–2264 (2019) 19. Hong, T.T., et al.: Multicriteria optimization of dressing parameters for surface grinding 90CrSi tool steel using taguchi method and grey relational analysis. Mater. Sci. Forum 998, 61–68 (2020). https://doi.org/10.4028/www.scientiﬁc.net/msf.998.61 20. Tung, LA, et al.: Optimization of dressing parameters of grinding wheel for 9CrSi tool steel using the taguchi method with grey relational analysis. In: 10th International Conference on Mechatronics and Manufacturing (ICMM 2019), IOP Conference Series: Materials Science and Engineering, Volume 635, Bangkok, Thailand 21–23 January 2019 21. Pi, V.N., Tam, D.T., Cuong, N.M., Tran, T.H.: Multiobjective optimization of PMEDM process parameters for processing cylindrical shaped parts using Taguchi method and grey relational analysis. Int. J. Mech. Prod. Eng. Res. Dev. (IJMPERD) 10(2), 669–678 (2020) 22. Raut, V., Gandhi, M., Nagare, N., Deshmukh, A.: Application of Taguchi grey relational analysis to optimize the process parameters in wire electrical discharge machine (2016)
Multiobjective Optimization of Surface Roughness and MRR in Surface Grinding of Hardened SKD11 Using GreyBased Taguchi Method Tran Thi Hong1, Do The Vinh2, Tran Vinh Hung3, Tran Ngoc Giang2, Nguyen Thanh Tu2, Le Xuan Hung2, Bui Thanh Danh4, and Luu Anh Tung2(&) 1
Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen 23000, Vietnam [email protected] 3 Faculty of Mechanical Engineering and Mechatronics, PHENIKAA University, Hanoi 100000, Vietnam 4 University of Transport and Communications, Hanoi, Vietnam
Abstract. The dressing plays an important role in wheel preparation in the grinding process. In this study, Grey Relational Analysis (GRA) based Taguchi method is applied to optimize the dressing parameters for minimizing the surface roughness and maximizing the material removal rate (MRR) in surface grinding of hardened SKD 11 steel. The Taguchi technique L16 is used to organize experiments that include six input parameters of the dressing process. There are two twolevel parameters and four 4level parameters including dressing feed rate, rough dressing depth, rough dressing times, ﬁne dressing depth, ﬁne dressing times, and nonfeeding dressing. As shown in the result, the optimal dressing process for the minimum surface roughness and maximum MRR consists of 2 times of the rough dressing with a depth of 0.015 mm/stroke, a feed rate of 1.6 mm/min, 3 times nonfeeding dressing, and no ﬁne dressing was performed. Also, the dressing feed rate has the strongest impact on multiple performance characteristics (with 43.24% contribution), followed by the rough dressing depth (with 26.20% contribution). A veriﬁcation experiment has demonstrated the appropriateness of the predictive model to the measurement data. Keywords: SKD11
Surface grinding Taguchi GRA
1 Introduction Grinding is a metalworking process that is commonly used in roughing as well as ﬁnishing. During the grinding process, the dressing functions to sharpen the abrasive particles and remove metal debris adhering to the surface of the grinding wheel [1, 2]. During the working process, the grinding wheel is constantly worn. Hence, the dressing process is required to maintain the initial speciﬁcations of the grinding wheel. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 584–593, 2021. https://doi.org/10.1007/9783030647193_64
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The performance of the grinding wheel, that is reflected in proﬁle, topography, and wear behavior, is highly dependent on the dressing process [3–5]. In small scale production, dressing is often done empirically. Therefore, the efﬁciency of the process is not high. Optimizing the dressing process or ﬁnding out the effect of dressing parameters on quality and productivity as well as wheel life, wear of wheel have attracted many researchers [6–9]. However, there are many parameters of dressing process such as the coarse dressing, the ﬁne dressing and nonfeeding dressing, which have not been adequately studied. In metal cutting, surface roughness is an important indicator that reflects the quality of processed products. However, that reducing roughness and increasing machining productivity are carried out at the same time is a difﬁcult request. The process of simultaneously optimizing two or more goals is called multiobjective optimization. Greybased Taguchi method has been used to solve multiobjective optimization in many researches including grinding [4, 10–16], turning [17, 18], milling [19, 20], etc. In the grinding process, surface grinding is the most common process. Therefore, the improvement of the efﬁciency of the surface grinding process such as improving the roughness, increasing grinding performance, increasing the wheel life is the topic that attracts many researchers and manufacturers [21–28]. In this work, Greybased Taguchi method was used for multiobjective optimization to minimizing the surface roughness and maximizing MRR in surface grinding of hardened SKD 11 steel. The effect of dressing parameters on multiple performance characteristics was also analyzed by using ANOVA. Besides, a veriﬁcation experiment was performed to evaluate the results of the predictive model.
2 Experimental Procedures In this study, all experiments were carried out by using a surface grinding machine MOTO – YOKOHAMA (Japan). The workpiece was SKD 11 steel with chemical composition shown in Table 1. The workpieces have a dimension of 70 40 25 mm and hardness of 5862 HRC. Cantext Aquatex 3810 was used as cooling fluid with a concentration of 3% and a flow of 10 l/min. Besides, the grinding wheel was Cn46TB2GV1.300.32.127.30 m/s of Hai Duong company (Vietnam) and the dressing tool was 39080088C type 2 (Russia). Table 1. Chemical composition of SKD 11 steel. C 1.4 1.6%
Si Mn Cr P S Mo W Cu V 1113% 0.81.2% 0.20.5% 0.25% 0.25% 0.4% 0.6% 0.03% 0.03%
The grinding conditions ﬁxed for all experiments were cutting velocity of 26.7 m/s, depth of cut of 0.01 mm, longitudinal feed rate of 8 mm/stroke, and table speed of 8 m/min. The dressing parameters including rough dressing depth (trd), rough dressing times (nrd), nonfeeding dressing (nnon), ﬁne dressing times (nfd), ﬁne dressing depth
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(tfd), and dressing feed rate (S) were selected for multiobjective optimization to minimize the surface roughness and maximize MRR as shown in Table 2. The design of experiment is conducted by applying the L16 orthogonal array of Taguchi technique. Table 2. Factors and levels Parameters
Unit
Dressing feed rate S Rough dressing depth trd Rough dressing times nrd Fine dressing depth tfd Fine dressing times nfd Nonfeeding dressing nnon
m/min mm Times mm Times Times
Levels 1 2 1.6 1.8 0.015 0.02 1 2 0.005 0.01 0 1 0 1
3 – 0.025 3 – 2 2
4 – 0.03 4 – 3 3
3 Results and Discussion Table 3 indicates the experimental results. The surface roughness was measured by using an instrument of Mitutoyo company, model 1789232A, SJ201. MRR was determined by measuring the weight before and after each experiment using a highprecision scale (precision of 0.001 g). Table 3. L16 Orthogonal array with factors and responses No. trd
nrd nnon nfd tfd
S
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1.6 1.8 1.6 1.8 1.8 1.6 1.8 1.6 1.8 1.6 1.8 1.6 1.6 1.8 1.6 1.8
0.015 0.015 0.015 0.015 0.02 0.02 0.02 0.02 0.025 0.025 0.025 0.025 0.03 0.03 0.03 0.03
0 1 2 3 1 0 3 2 2 3 0 1 3 2 1 0
0 1 2 3 2 3 0 1 3 2 1 0 1 0 3 2
0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005 0.005 0.005 0.01 0.01 0.01 0.01 0.005 0.005
Surface roughness Ra (µm)
T1 0.661 0.471 0.433 0.452 1.225 1.267 0.984 0.524 1.286 0.73 0.757 0.51 0.363 0.591 0.841 0.998
T2 0.685 0.485 0.45 0.517 1.042 1.322 1.104 0.453 1.362 0.74 0.731 0.465 0.358 0.613 0.848 1.027
T3 0.647 0.516 0.413 0.516 1.252 1.35 1.06 0.528 1.332 0.767 0.756 0.464 0.462 0.609 0.833 1.105
Material Remove Rate MRR (mm3/s) T1 T2 2.44 2.42 2.05 2.00 1.92 1.89 1.83 1.82 1.84 1.79 2.40 2.36 2.06 2.05 1.93 1.90 1.87 1.91 2.30 2.25 1.75 1.93 1.92 1.88 1.98 1.92 1.89 1.86 1.91 1.87 1.83 1.81
T3 2.38 1.94 1.88 1.85 1.80 2.37 2.04 1.64 1.92 2.27 1.92 1.89 1.95 1.87 1.86 1.79
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Table 4. SN ratio values, normalized SN ratio values and the absolute value. TT S/N Ra
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
3.55 6.18 7.29 6.09 −1.41 −2.37 −0.43 5.97 −2.46 2.55 2.52 6.37 8.02 4.37 1.51 0.38
Zij MRR Ra Z0(k) 1.000 7.66 0.57 6.00 0.82 5.56 0.93 5.27 0.82 5.16 0.10 7.51 0.01 6.23 0.19 5.15 0.80 5.57 0.00 7.14 0.48 5.39 0.48 5.56 0.84 5.79 1.00 5.46 0.65 5.47 0.38 5.15 0.20
Dj(k) MRR Ra MRR 1.000 1.00 0.34 0.16 0.05 0.00 0.94 0.43 0.00 0.16 0.79 0.09 0.16 0.26 0.12 0.13 0.00
0.43 0.18 0.07 0.18 0.90 0.99 0.81 0.20 1.00 0.52 0.52 0.16 0.00 0.35 0.62 0.80
0.00 0.66 0.84 0.95 1.00 0.06 0.57 1.00 0.84 0.21 0.91 0.84 0.74 0.88 0.87 1.00
After the data collection process, Greybased Taguchi technique was used for multiobjective optimization. This technique has been used very successfully in many studies [29–32]. The basic steps of the optimization process according to Greybased Taguchi method are given as follows: In the ﬁrst step of Greybased Taguchi method, S/N is calculated for corresponding responses. The goal of this study is to reduce the roughness and increase MRR. Therefore, the smaller is the better type of S/N ratio is selected for surface roughness and the larger is the better type is selected for MRR as calculated in following: The smaller is the better S/N: SN ¼ 10log10
X 1 n 2 y i¼1 i n
ð1Þ
X 1 1 n i¼1 y2 n i
ð2Þ
The larger is the better S/N: SN ¼ 10log10
Where: yi is the data received by experiment, n is the number of experiments.
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c
0.770 0.585 0.626 0.537 0.346 0.617 0.425 0.526 0.354 0.598 0.422 0.567 0.701 0.476 0.405 0.359
In the second step, a data preprocessing is conducted to normalize the raw data (S/N data). The linear normalization of the S/N ratio is carried out by the grey relational generating (a range between zero and unity). The normalized S/N ratio Zij for the ith performance characteristic in the jth experiment can be determined by (3). Table 4 shows the calculated values of S/N, normalized Zij and Δj(k): SNij min SNij ; j ¼ 1; 2; ::k Zij ¼ max SNij ; j ¼ 1; 2; ::n min SNij ; j ¼ 1; 2; ::n
ð3Þ
The grey relation coefﬁcient is calculated in a third step as expressed in Eq. (4) cð k Þ ¼
Dmin þ fDmax Dj ðkÞ þ fDmax
ð4Þ
Where j = 1, 2…n; k = 1, 2…m, n is the number of experiments, k is the number of objectives. Dj(k) is the deviation sequence and calculated by DjðkÞ ¼ Z0 ðkÞ Zj ðk Þ. And: Dmin ¼ min8j2i min8k Z0 ðkÞ Zj ðkÞ; Dmax ¼ max8j2i max8k kZ0 ðkÞ Zj ðkÞk. f is the distinguishing coefﬁcient 0 f 1. In this case, f = 0.5.
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After averaging the grey relation coefﬁcients, the grey relational grade ci is calculated as Eq. (5): cj ¼
1 Xm c i¼1 ij k
ð5Þ
Where cj is the grey relational grade for the jth experiment; k is the number of objectives (in this study, k = 2) The values of the grey relational coefﬁcient and grey relational grade are shown in Table 5. In the next step, Taguchi method is used to determine the effects of the input factors on the grey relational grade. Table 6 shows the main effect of the input factors on the grey relational grade. Figure 1 indicates main effects plot for the grey relational grade. As shown in Table 6 and Fig. 1, the optimal dressing process for the minimum surface roughness and maximum MRR consists of 2 times of the rough dressing with a depth of 0.015 mm/stroke, a feed rate of 1.6 mm/min, 3 times nonfeeding dressing, and no ﬁne dressing performed. Table 6. Main effects on the grey relational grade Level trd 1 0.6294 2 0.4784 3 0.4852 4 0.4852 Delta 0.1511 Rank 2 c ¼ 0; 520
nrd 0.5426 0.5689 0.4694 0.4973 0.0995 3
nnon 0.5418 0.4757 0.4954 0.5653 0.0896 4
nfd 0.5596 0.5584 0.4819 0.4783 0.0813 5
tfd S 0.5027 0.6011 0.5364 0.4380
0.0337 0.1631 6 1
Fig. 1. Main effects plot for the grey relational grade
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The result of variance analysis for the grey relational grade is shown in Table 7. Based on the ANOVA, the dressing feed rate has the strongest impact on multiple performance characteristics with 43.24% percent contribution, followed by the rough dressing depth with 26.20% percent contribution. Fine dressing times contributes 10.13%, rough dressing times contributes 9.71%, and nonfeeding dressing contributes 8.27%. The effect of ﬁne dressing depth is negligible with 1.84%. Table 7. Analysis of variance for the grey relational grade Source trd nrd nnon nfd tfd S Re. Error Total
DF 3 3 3 3 1 1 1 15
Seq SS 0.064517 0.023906 0.020368 0.024940 0.004540 0.106470 0.001477 0.246217
Adj SS 0.064517 0.023906 0.020368 0.024940 0.004540 0.106470 0.001477
Adj MS F 0.021506 14.56 0.007969 5.40 0.006789 4.60 0.008313 5.63 0.004540 3.07 0.106470 72.10 0.001477
P C% 0.190 26.20 0.304 9.71 0.327 8.27 0.298 10.13 0.330 1.84 0.075 43.24 0.60 100.00
The grey relation grade is determined by the following equation: cop ¼ gm þ
X5 i¼1
ðg gm Þ ¼ trd1 þ nrd2 þ nnon4 þ nfd1 þ tfd2 þ S1 5 T
According to Table 6, trd1 ¼ 0:6294 nrd2 ¼ 0:5689 nnon4 ¼ 0:5653 nfd1 ¼ 0:5596 tfd2 ¼ 0:5364 S1 ¼ 0:6011 T ¼ 0:520 By (6): cop ¼ 0:863
ð6Þ
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Based on the result of the optimization process, the minimal value of output factor is determined by Eq. (7): ðRa; MRRÞop ¼ trd1 þ nrd2 þ nnon4 þ nfd1 þ tfd2 þ S1 5 T
ð7Þ
Where trd1 is the average of output response for trd at level 1, nrd2 is the average of output response for nrd at level 2, nnon4 is the average of output response for nnon at level 4, nfd1 is the average of output response for nfd at level 1, tfd2 is the average of output response for tfd at level 2, S1 is the average of output response for S at level 1, and T is the average of output response in all experiments. Table 8. The results from model and experiment Properties
Optimal parameters predicted result measured result Error (%) trd1, nrd2, nnon4, nfd1, tfd2, S1 trd1, nrd2, nnon4, nfd1, tfd2, S1 Ra (µm) 0.211 0.231 9.48 2.17 8.13 MRR (mm3/s) 2.362 GRA value 0.863
So: ðRaÞop ¼ 0:211 lm ðMRRÞop ¼ 2:362 mm3 =s A veriﬁcation experiment was conducted with the optimal dressing parameters consisting of 2 times of the rough dressing with a depth of 0.015 mm/stroke, a feed rate of 1.6 mm/min, 3 times nonfeeding dressing, and no ﬁne dressing performed. A comparison between the predicted result and measured results is shown in Table 8. The error between the predicted result and measured result is small. It means that the research result is reliable.
4 Conclusions In this study, Greybased Taguchi method was applied for multiobjective optimization to minimize the surface roughness and maximize MRR in surface grinding of hardened SKD 11 steel. Some main conclusions can be given as following: – The optimal dressing process for the minimum surface roughness and maximum MRR consists of 2 times of the rough dressing with a depth of 0.015 mm/stroke, a feed rate of 1.6 mm/min, 3 times nonfeeding dressing, and no ﬁne dressing performed. – The dressing feed rate has the strongest impact on multiple performance characteristics with 43.24% percent contribution, followed by the rough dressing depth with 26.20% percent contribution.
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– A veriﬁcation experiment has demonstrated the appropriateness of the predictive model to the measurement data. Acknowledgements. This work was supported by Thai Nguyen University of Technology.
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Multiresponse Optimization in PMSEDM Process Using TaguchiGrey Method Tran Thi Hong1, Nguyen Manh Cuong2, Tran Ngoc Giang2, Nguyen Anh Tuan3, Le Thu Quy4, Thangaraj Muthuramalingam5, Nguyen Thanh Tu2, and Do Thi Tam2(&) 1
Center of Excellence for Automation and Precision Mechanical Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam 2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected] 3 University of Economic and Technical Industries, Ha Noi, Vietnam 4 National Research Institute of Mechanical Engineering, Ha Noi, Vietnam 5 Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
Abstract. The optimization of input parameters on Powder Mixed Sinking Electrical Discharge Machining (PMSEDM) is critical for manufacturing industries for achieving higher productivity and product quality. In this work, a multitarget optimization of input factors for minimizing the surface roughness and maximizing the material removal rate (MRR) in PMSEDM process using TaguchiGrey approach is presented. The ﬁve input parameters including the pulseontime (Ton), the pulseofftime (Toff), the pulse current (IP), the server voltage (SV), and the powder concentration (Cp) are chosen for optimizing two responses i.e. the surface roughness and MRR. The results show that the optimal input parameters are Ton of 6 ls, Toff of 30 ls, IP of 4 A, SV of 5 V, and Cp of 6%. The optimized surface roughness obtained is 3.527 µm and the optimized MRR is 0.0724 g/min. The discovered technology mode has been applied to the real machining process and the outcome shows a considerable improvement in comparison with the default setting modes. Keywords: PMSEDM Taguchigrey method
Silicon carbide powder Multiobjective
1 Introduction Electrical Discharge Machining (EDM) plays an important role in modern industry due to the continuous improvement of part accuracy [1, 2]. In the EDM process, it is very important to enhance the input parameters to improve the process efﬁciency [2]. Therefore, the analysis of their impact on machining characteristics is essential. The general process parameters to control the machining operation in EDM are Ton, Toff, IP, SV, and Cp. Subsequently, surface roughness (Ra) and MRR are the vital responses to decide the productivity and product quality [3]. Thus, the optimization of these parameters is especially important in EDM process because of its direct impact on the efﬁciency of the machining process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 594–606, 2021. https://doi.org/10.1007/9783030647193_65
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Currently, numerous researches have been carried out to optimize input parameters which contribute considerably to enhance the efﬁciency of the EDM process [4–10]. In 2017, Luboslav Straka [4] performed a study on the impact of the input parameters on the surface quality after diesinking EDM with a SFCu electrode. Another remarkable research was conducted by Alexia Torres Salcedo [5], where a model of energy density and optimum input factors of Inconel 600 was carried out using CuC electrodes. Based on that, the optimal input parameters were the current intensity of 8 A, the pulse time of 100 µs, and the duty cycle of 0.6 to gain a maximum MRR of 30.49 (mm3/min) [5]. Furthermore, Mustufa Haider Abidi [6] analyzed the impact of three important input parameters on various output factors, such as overcut, taper angle and surface roughness in microEDM process for a nickeltitanium shape memory alloy. In this work, a methodology of multiresponse optimization has been developed by using Grey PCA. In addition, based on response surface methodology, Tran Thanh Hoang and Vu Ngoc Pi investigated the surface roughness of part in EDM tablet shape punches [7, 8]. In the study, the optimum input parameter values were determined to minimize surface roughness [7, 8]. Similarly, Phan H. Nguyen [9] analyzed the impacts of input parameters on surface quality after EDM process. By using TaguchiTOPSIS technique, the optimal process factors were also determined to minimize surface hardness and surface roughness [9]. Recently, the effects of input parameters in EDM process have been explored to achieve better surface quality [10–13]. Based on TOPSIS method, the optimal set of input parameters has been determined as a duty factor of 0.6, voltage of 80 V, current of 15 A, and tool of tungsten carbide [13]. However, the study on multiobjective optimization in PMSEDM process for 90CrSi steel to maximize the surface quality and MRR has not before been published. Therefore, this work proposes an experimental study on the influences of the input factors including Ton, Toff, IP, SV and Cp on MRR and surface roughness in PMSEDM process. In addition, the optimal values of these input parameters were determined to maximize the surface quality and MRR by using TaguchiGrey method.
2 Methodology 2.1
Experimental Setup
An experiment was designed to maximize the surface quality and MRR in PMSEDM process. The description of experimental machine and equipment is presented in Table 1. In addition, the diagram of the experimental setup is illustrated in Fig. 1. In this setup, an AG40L CNC sinker EDM machine was used to perform experiments. 90CrSi steel with a hardness of 55–60 HRC with the dimensions of 13 13 30 (mm) was used as the workpiece material. The electrode material was copper and the dielectric fluid was HD1 oil. Furthermore, 45–55 (nm) silicon carbide powders were applied to add to the dielectric fluid.
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Equipment and materials Machine for sinking EDM Electrode material Dielectric fluid Workpiece material Workpiece dimensions Roughness measurement machine
Speciﬁcations AG40L CNC sinker EDM (Sodick Europe Ltd. UK) Copper EDM oil HD1 90CrSi 13 13 30 mm SURFTEST SV3100 (Japan)
Fig. 1. The diagram of the experimental setup: 1) machining tank; 2) workpiece; 3) electrode; 4) stirring; 5) magnets
For the experiment, ﬁve input parameters (Table 2) were selected to assess their effects on the surface roughness and the MRR in the PMSEDM process. The selected three levels for each of the ﬁve input parameters are shown in Table 2. Table 2. Input factors and their levels No. Input factors Unit Experimental levels Low level (1) Base level (2) High level (3) 1 Ton ls 6 14 – 2 Cp g/l 0 0.03 0.06 3 Toff ls 14 21 30 4 IP A 4 8 12 5 SV V 3 4 5
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Design of Experiment
In this work, the Taguchi method and an orthogonal array L18 (21 and 34) are applied to design the experiments [13–16]. The experimental plan with eighteen experiments is demonstrated in Table 3. Based on Minitab_18 statistical software, an analysis is implemented. Table 3. Standard L18 (21 and 34) orthogonal matrix Experiment number Factors Ton Cp 1 6 0 2 6 0 3 6 0 4 6 0.03 5 6 0.03 6 6 0.03 7 6 0.06 8 6 0.06 9 6 0.06 10 14 0 11 14 0 12 14 0 13 14 0.03 14 14 0.03 15 14 0.03 16 14 0.06 17 14 0.06 18 14 0.06
Toff 14 21 30 14 21 30 14 21 30 14 21 30 14 21 30 14 21 30
IP 4 8 12 4 8 12 8 12 4 12 4 8 8 12 4 12 4 8
SV 3 4 5 4 5 3 3 4 5 5 3 4 5 3 4 4 5 3
The Taguchi method is employed to analyze the obtained results by using signaltonoise ratio. In PMSEDM process of 9CrSi steel, the key target is to decrease the surface roughness and increase the MRR. With the target function of the MRR, the Signal to Noise ratio is determined by the following equation [13–16]: X 1 n S=N ¼ 10log10 1=y2i Þ i¼1 n
ð1Þ
With the target function of surface roughness, the Signal to Noise ratio is determined by the following equation [10–13]: S=N ¼ 10log10 ð
1 Xn 2 y Þ i¼1 i n
ð2Þ
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Where n is the number of tests in trials and yi is the result of a particular experiment. In this work, the number of tests in trials has been chosen as ‘3’. 2.3
Multicriteria Optimization
For multicriteria optimization, TaguchiGrey method has been exploited. Based on the experimental results, the grey relational analysis is utilized to convert the multicriteria optimization into the onecriterion optimization of the grey relational grade. The purpose of this work is to solve the multicriteria optimization problem in which the surface roughness is minimized and the MRR is maximized instantaneously. The optimization procedure, using TaguchiGrey method, is explained below: Step 1: Normalizing signal to noise ratio of experimental results for all output responses; Step 2: Determining grey relational coefﬁcient; Step 3: Determining grey relational grade (GRG); Step 4: Evaluating experimental results by using GRG and ANOVA; Step 5: Selecting optimal value of input parameters;
3 Results and Discussion After selecting input parameters and their ranges, the experimental results were obtained. To increase the accuracy of the experiment, each trial was repeated 3 times. All the gathered data from experiments are illustrated in Table 4. Based on that, the S/N ratio for various response parameters was determined by using Eqs. (1) and (2) as illustrated in Table 4. Table 4. Experimental results of L18 (21 and 34) and their S/N ratios No Factors and their levels Mean and S/N ratio of experimental results MRR (g/min) Ton Cp Toff IP SV Ra (lm) Mean S/N Mean S/N 1 6 0 14 4 3 3.075 −9.758 0.0111 −39.125 2 6 0 21 8 4 2.330 −7.348 0.0026 −51.629 3 6 0 30 12 5 3.124 −9.894 0.0701 −23.082 4 6 0.03 14 4 4 3.177 −10.040 0.0113 −38.954 5 6 0.03 21 8 5 2.434 −7.725 0.0033 −49.511 6 6 0.03 30 12 3 3.211 −10.132 0.0712 −22.949 7 6 0.06 14 8 3 2.512 −8.000 0.0065 −43.728 8 6 0.06 21 12 4 3.925 −11.876 0.0114 −38.876 9 6 0.06 30 4 5 2.273 −7.131 0.0477 −26.423 10 14 0 14 12 5 7.408 −17.394 0.0768 −22.298 (continued)
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Table 4. (continued) No Factors and their levels Mean and S/N ratio of experimental results MRR (g/min) Ton Cp Toff IP SV Ra (lm) Mean S/N Mean S/N 11 14 0 21 4 3 5.882 −15.391 0.0207 −33.685 12 14 0 30 8 4 3.869 −11.752 0.0071 −42.938 13 14 0.03 14 8 5 4.341 −12.751 0.0063 −43.966 14 14 0.03 21 12 3 6.862 −16.729 0.0720 −22.855 15 14 0.03 30 4 4 5.476 −14.769 0.0160 −35.910 16 14 0.06 14 12 4 7.027 −16.936 0.0827 −21.647 17 14 0.06 21 4 5 5.273 −14.441 0.0194 −34.260 18 14 0.06 30 8 3 7.027 −16.936 0.0071 −43.031
3.1
Normalization of S/N Ratio and Computation of Grey Relational Grade
3.1.1 Normalization of S/N Ratio of Experimental Results for All Output Responses The ﬁrst step of the optimization procedure using TaguchiGrey method is the normalization of S/N ratio. The formula for normalizing the output characteristic with “larger the better” as below [17–20]: SNij min SNij Zij ¼ max SNij min SNij
ð3Þ
The formula for the normalization of the output characteristic with “lower the better” as below [17–19]: max SNij SNij Zij ¼ max SNij min SNij
ð4Þ
Where: Zij is the normalized S/N ratio; SNij is the S/N ratio; min(SNij) and max(SNij) are respectively minimum and maximum values of S/N ratio; j is the number of experiments (j = 18). The normalization values for each output target are shown in Table 5.
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MRR −39.125 −51.629 −23.082 −38.954 −49.511 −22.949 −43.728 −38.876 −26.423 −22.298 −33.685 −42.938 −43.966 −22.855 −35.910 −21.647 −34.260 −43.031
Zij Ra 0.744 0.979 0.731 0.717 0.942 0.708 0.915 0.538 1.000 0.000 0.195 0.550 0.452 0.065 0.256 0.045 0.288 0.045
MRR 0.417 0.000 0.952 0.423 0.071 0.957 0.264 0.425 0.841 0.978 0.599 0.290 0.256 0.960 0.524 1.000 0.579 0.287
Di(j) Ra 0.256 0.021 0.269 0.283 0.058 0.292 0.085 0.462 0.000 1.000 0.805 0.450 0.548 0.935 0.744 0.955 0.712 0.955
MRR 0.583 1.000 0.048 0.577 0.929 0.043 0.736 0.575 0.159 0.022 0.401 0.710 0.744 0.040 0.476 0.000 0.421 0.713
3.1.2 Calculation of Grey Relational Coefﬁcient Grey relational coefﬁcient illustrates the relationship between ideal conditions and the actual conditions of the normalized characteristic. The following formula is utilized to determine the value of the grey relational coefﬁcient [17–20]: ci ð k Þ ¼
Dmin þ fDmax Di ðkÞ þ fDmax
ð5Þ
Where: ci ðkÞ is the grey relational coefﬁcient; n is the number of experiments (n = 18); m is the number of output targets (m = 2); f is the distinguishing coefﬁcients ð0 f 1Þ. Because multiresponsive features include both largerthebetter and smallerthebetter, f is chosen as 0.5 in this case [17–20]; Di ðkÞ is the deviation sequence of reference sequence Z0 ðkÞ and comparability sequence Zj ðk Þ, i.e.D0j ¼ Z0 ðk Þ Zj ðkÞ is absolute value of deviation between Z0 ðkÞ and Zj ðkÞ.Dmin and Dmax are the minimum and maximum absolute values of D0j . Based on Eq. (5), the values of the grey relational coefﬁcient are determined as illustrated in Table 6.
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Table 6. Grey relational coefﬁcient and grey grade values No Grey relational coefﬁcient (ci ðkÞ) Ra MRR 1 0.661 0.462 2 0.959 0.333 3 0.650 0.913 4 0.638 0.464 5 0.896 0.350 6 0.631 0.920 7 0.855 0.404 8 0.520 0.465 9 1.000 0.758 10 0.333 0.958 11 0.383 0.555 12 0.526 0.413 13 0.477 0.402 14 0.348 0.925 15 0.402 0.512 16 0.344 1.000 17 0.412 0.543 18 0.344 0.412
Grey relational grade (ci Þ
0.562 0.646 0.781 0.551 0.623 0.776 0.630 0.492 0.879 0.646 0.469 0.470 0.440 0.637 0.457 0.672 0.478 0.378
3.1.3 Calculation of Grey Relational Grade The grey relational grade exposed in the ﬁnal column of Table 6 is the average value of the grey relational coefﬁcients, and these values are determined by the following formula [20, 21]: ci ¼
1 Xm c ðk Þ i¼1 i m
ð6Þ
Where m is the number of output targets. Based on Eq. (6), they are calculated as illustrated in Table 6. 3.2
Determining Optimum Input Parameters
Based on the above results, it has been observed that experiment trial No. 9 has the highest value of grey relational grade ðcmax ¼ c9 ¼ 0:879Þ. Thus, it indicates the best combination of multiobjective responses. However, MRR at this setting is low. To identify the optimal level of parameters, the analysis of variance is used on grey relational grade (GRG). Because higher GRG is desirable, the higherthebetter S/N quality feature is used to get the optimal combination for multiobjective optimization.
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Thus, S/N ratios of multi response features are determined by using Eq. (1) and illustrated in Table 7. The level of a parameter with the largest S/N ratio shows the optimal level. Table 7. Main effects on grey relation grades Level Ton 1 −4.794 2 −7.080 3 Delta 2.286 Rank 1
Cp −5.961 −5.974 −5.877 0.096 5
Toff −6.279 −6.725 −4.808 1.917 2
IP −5.493 −7.006 −5.313 1.693 3
SV −6.020 −6.536 −5.255 1.281 4
Fig. 2. Main effects plot for grey relational grade
Figure 2 illustrates the main influence for the grey relational grade. The deviation from the mean line indicates the signiﬁcance of factors on quality [22, 23]. It can be clearly seen that the optimum input factors are the 1st level of Ton (6 ls), the 3rd levels of Cp (0.06 g/l), Toff (30 ls), IP(4 A) and SV(5 V). These optimum values of the input parameters would help in minimizing the surface roughness and maximizing MRR. 3.3
Analysis of Variance (ANOVA)
This method indicates the numerical contribution of factors on measures [24, 25]. The signiﬁcant contribution of each input factor on the responses in PMSEDM process is studied by using ANOVA. The results of the analysis are displayed in Table 8. It is revealed that Ton is the most influential element among the ﬁve input factors studied in this work. The impact steadily decreased in the following sequence order of the ﬁve input factors: Ton, IP, SV, Toff, and CP. Accordingly, the smallest influential element is CP.
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Table 8. Results of ANOVA on grey relational grade Source DF Ton 1 CP 2 Toff 2 IP 2 SV 2 Residual Error 8 Total 17
3.4
Seq SS 0.093151 0.000680 0.013235 0.060121 0.027483 0.116660 0.311331
Adj SS 0.093151 0.000680 0.013235 0.060121 0.027483 0.116660
Adj MS 0.093151 0.000340 0.006618 0.030060 0.013742 0.014583
F 6.39 0.02 0.45 2.06 0.94
P 0.035 0.977 0.651 0.190 0.429
Determining Predict Model
From the above experimental data, the predicted optimum value of GRG is found as: lop ¼ Ton1 þ Cp3 þ Toff 3 þ IP3 þ SV 3 4gm
ð7Þ
In which: ηm is the average of grey relation grades of the whole experiment (ηm = 0.588); Ton1 ; Cp3 ; Toff 3 ; IP3 , and SV3 are the mean values of the grey relational grade where Ton is at level 1, CP, Toff, IP and SV are at level 3. Therefore: lop ¼ 0:66 þ 0:5881 þ 0:6235 þ 0:6673 þ 0:6411 40:588 ¼ 0:828 From the optimum level of input factors, the optimal value of the Ra and MRR outputs is determined by: ðRa; MRRÞop ¼ Ton1 þ Cp3 þ Toff 3 þ IP3 þ SV3 4T
ð8Þ
Where: Ton1 ; Cp3 ; Toff 3 ; IP3 ; SV3 are the average surface roughness or MRR with Ton at level 1, CP, Toff, IP and SV at level 3. T is the average surface roughness or MRR of the whole experiment. Therefore: ðRaÞToiuu ¼ 2:896 þ 4:673 þ 4:163 þ 5:259 þ 4:142 44:401 ¼ 3:527 lm ðMRRÞop ¼ 0:026141 þ 0:029129 þ 0:036545 þ 0:064031 þ 0:037276 40:0301 ¼ 0:0724 g=min From the above equation, the predicted values of output parameters have been determined with optimal parameter settings as 3.527 (Ra) and 0.0724 (MRR).
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Evaluating the Predict Model
To evaluate the accuracy of the predict model, a veriﬁed experiment with three repetitions has been carried out. The experimental input factors are the optimum input parameters including Ton = 6 ls, Cp = 0.06 g/l, Toff = 30 ls, IP = 4 A, and SV = 5 V. Table 9 shows the experimental and calculated values of output factors corresponding to the optimal input value. The obtained result shows that the difference between calculation results and experiments is within 8.56% of the range, indicating that the models which are proposed in the study are reliable. Table 9. For evaluating the proposed model Output parameters
Surface roughness – Ra (µm) MRR (g/min) Grey relation grade value ( lop Þ
Optimum parameters Prediction value Ton1/Cp3/Toff3/IP3/SV3 3.527 0.0724 0.828
Experimental value Ton1/Cp3/Toff3/IP3/SV3 3.367
Error (%)
0.0786
8.56
4.54
4 Conclusions In this work, the TaguchiGrey method has been utilized to determine the optimal values of input parameters in PMSEDM process for achieving better multiobjective responses. The research has obtained the following optimum parameters: Ton = 6 ls, Toff = 30 ls, IP = 12 A, SV = 5 V, CP = 6 g/l. In the optimal technology condition, the optimized surface roughness is 3.527 µm and the optimized MRR is 0.0724 g/min. In addition, it has been found that Ton is the most influential element among the ﬁve input parameters. The factor with the smallest influence is Cp. Such research results would help manufacturers to determine and set up optimal input parameters in order to enhance economic and technical effectiveness of PMSEDM process. Acknowledgments. This work was ﬁnancially supported by the scientiﬁc project No. B2019TNA03.
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Numerical Identiﬁcation of the Mechanical Behaviour of a Fluoroelastomer (FKM) Using Nanoindentation Test Florent Chalon1(&), Julie Pepin1, Nathan Le Pennec1, TienDung Do2, Stéphane Meo1, Clémence Fradet1, Gaelle Berton1, and Florian Lacroix1 1
Université de Tours, Université d’Orléans, INSA Centre val de Loire, Laboratoire de Mécanique Gabriel Lamé, 7 Avenue Marcel Dassault, 37000 Tours, France [email protected] 2 Thai Nguyen University of Technology, Thai Nguyen City, Vietnam
Abstract. The aim of the paper is to provide a numerical model of nanoindentation tests carried out on a synthetic elastomer. Some works deal with such numerical model but on classical elastomer like silicon rubber. Our study focuses on a ﬁlled fluoroelastomer (FKM). At ﬁrst, a 2D numerical model equivalent to a Berkovich test is built. The law of behaviour used in the simulation is obtained from the results of a single traction test. Then a numerical nanoindentation test can be carried out and compared with experimental nanoindentation curves of a previous study. The relevance of the most suitable laws of behaviour is deduced. Keywords: Nanoindentation
Numerical simulation Elastomer FKM
1 Introduction Nanoindentation is now commonly used to obtain local mechanical properties of materials. The metal or classical material with an elastoplastic behavior are widely studied. Numerical models are elaborated to provide tools which allow to lead easier numerical campaign of tests. The relevance of the numerical approach is also applied for example to biosourced material [1], human bones [2], hardbrittle material [3]…. Hereby, the point is to focus on rubbery materials. Some studies can be found in the literature and it often concerns unﬁlled rubber without a viscous component [4] or unﬁlled and/or silicone rubber [5, 6]. A 2D model, which saves computing time, can be used to lead our study. Firstly, the parameters of the mechanical behavior are extracted by a comparison with experimental uniaxial tests. Then these properties are used to simulate a nanoindentation test and the numerical indentation curves are compared with the experimental ones. The difﬁculty in this study is to match with the behavior of a synthetic ﬁlled elastomer, the fluoroelastomer (FKM) in the case of a nanoindentation tests. The future aim of the study is to model the viscosity of the FKM by comparison with nanoindentation curves following the proﬁle loadingholdingunloading. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 607–612, 2021. https://doi.org/10.1007/9783030647193_66
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2 Material Fluoroelastomer (FKM) is a special rubber designed to withstand severe chemical and thermal conditions. The raw rubber blend was elaborated by introducing all the ingredients into an internal mixer and was then pressed and cured so as to get 2mmthick sheets. The curing process was carried out for 20 min at 180 °C within the mold of the press and was followed by a postcuring step realized in an oven for 2 h at 200 ° C. Samples were ﬁnally extracted from the 2mmthick sheets.
3 Experimental Nanoindentation Tests Indentation tests have been performed [7] using a Bruker’s Hysitron TI98O Triboindenter allowing forces and displacements up to 10 mN and 15 µm respectively, on which a diamond Berkovich tip was mounted. To limit measurements artifacts linked to the surface quality, it has been decided to carry out indentation test on surfaces prepared with razor blades. It allows to reduce ﬁve times the roughness in comparison with a molded sample (molded sample: Ra = 547 ± 100 nm, razor blade sample: Ra = 100 ± 32 nm). Samples were glued on a support which was itself maintained on the machine by vacuum. All tests were made with the force controlled quasistatic mode applying a trapezoidal load function. A maximal force Pmax of 1 mN has been chosen which is small enough to extract the local mechanical response. The load function was as follow: Loading time 100 s Holding time 300 s Unloading time 100 s Fives tests are carried out to verify the reproducibility (Fig. 1).
Fig. 1. Reproducibility of the indentation hysteresis for FKM. Maximal force of 1 mN, loading rate 0.01 mN/s, holding time 300 s and unloading rate 0.01 mN/s.
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4 Numerical Models The objective is here to provide a numerical model of a nanoindentation test which will be used to determine the mechanical parameters of the FKM. To obtain an accurate evaluation of the parameters of the mechanical model, it is usually necessary to proceed to four tests on the material: an uniaxial test, a planar test, a biaxial test and a volumetric test. The aim of the approach is to predict the indentation test using the macroscopic law of behavior obtained by identiﬁcation from uniaxial tests. Then these coefﬁcients could be improved by an optimization scheme. To rise this objective a numerical simulation of a nanoindentation test is carried out. To reduce the time of simulation, a 2D axisymmetric model is realized. Indeed, it has been shown, in previous studies, that the 3D indentation test with a Berkovich tip can be carried out with an axisymmetric conﬁguration [8, 9]. To perform the simulation, the FEM software Abaqus® 6.14 is used. The indenter is assumed to be a rigid body which is a commonly hypothesis for indentation of soft polymers. The geometrical size of the sample is a radius of 100 µm and 100 µm of thickness which is large enough to avoid the boundary effects [9] (see Fig. 2). A 2D ﬁnite element mesh is built with quadrangle elements with quadratic shape function. To obtain an accurate description of the sample deformation, it is important that the density of nodes under the indenter is high enough. The rigid indenter is ﬁxed in the horizontal direction and a load is applied in the vertical direction. The bottom nodes of the sample are ﬁxed and the boundary conditions to consider the axis symmetry are applied. The maximum displacement rises less than 6% at the maximum load. The friction between the indenter and the elastomer is neglected [10].
Fig. 2. Representation (a) of the tip and mesh of the sample, (b) zoom of the mesh near the tip
To carry out the simulation, uniaxial tests are realized and the mean is drawn on the Fig. 3.
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5 Comparison of the Numerical and Experimental Results
Fig. 3. Uniaxial tests
In help with the module of Abaqus® it is possible to evaluate the coefﬁcients of different hyperelastic models (Fig. 3). There are numerical speciﬁc forms of strainenergy functions to describe hyperelastic properties. We only focus on isotropic and nearly incompressible ones such as Neo Hooke, MooneyRivlin, Ogden…. To validate the numerical model, a load of 1mN is applied. The depth of the tip is measured and the curve of the load as a function of displacement is drawn. On the Fig. 4, the experimental curve [7] is superimposed with the numerical ones. We can observe that two models feat well with experimental data: NeoHooke and MooneyRivlin. The NeoHooke curve is closer to the experimental curve at the end of the loading whereas the MooneyRivlin curve has a good ﬁtting at the beginning of the loading but the gap is more important at the end of the loading. Let focus on these two models. The MooneyRivlin free energy is written as follow: W¼
X2 i þ j¼1
i j 1 Cij I 1 3 I 2 3 þ ð J 1Þ 2 D1
ð1Þ
And for NeoHooke: 1 W ¼ C10 I 1 3 þ ð J 1Þ 2 D1 Where I 1 and I 2 are the ﬁrst and second principle invariants
ð2Þ
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T I 1 ¼ tr FF 1 T 2 T 2 tr FF I2 ¼ tr FF 2
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ð3Þ ð4Þ
F is the deformation gradient, J the jacobian, F ¼ JF and Cij and D1 are material constants.
Fig. 4. Numerical and experimental loading curves
Table 1. MooneyRivlin and NeoHooke coefﬁcients. Model C10 C01 C11 C20 C02 D1 NeoHooke 0,2348 – – – – 0,1726 MooneyRivlin −0,5923 0,906 9,65E−2 −1,88E−2 0,1656 0,1291
The value of the parameters estimated by the module of Abaqus® are listed in the Table 1. As seen on the Fig. 4, the second order MooneyRivlin model does not provide a better description than the NeoHooke model. It shows that an uniaxial test is insufﬁcient to provide a satisfying description of a nanoindentation test even if it is acceptable. The discrepancy between the numerical and experimental nanoindentation curves comes from the approximation of the simulation (conical tip, frictionless) and imperfect identiﬁcation of the mechanical behavior.
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6 Conclusion Using a single and simple uniaxial traction test allows to carry out a numerical nanoindentation test on a fluoroelastomer. But the prediction is not enough accurate. To improve the model, the next step will be to optimize the coefﬁcients of the law of behaviour. To achieve this aim, a script could calculate the difference between numerical and experimental nanoindentation curves and minimize it by correcting the parameters of the law. To improve the model the viscosity has also to be considered. To validate the viscosity effect a simulation of the loadingholdingunloading will be performed and compared to the experimental unloading nanoindentation curves.
References 1. Girard, M., Gaillard, Y., Burr, A., DarqueCeretti, E., Felder, E.: Nanoindentation of biosourced adhesive 75% rosin/25% beeswax: experiment results and modelisation. Mech. Mater. 69, 185–194 (2014) 2. Remache, D., Semaan, M., Rossi, J.M., Pithioux, M., Milan, J.L.: Application of the JohnsonCook model in the ﬁnite element simulations of the nanoindentation of the cortical bone. Biomed. Mater. 101, 103426 (2020) 3. Youn, S.W., Kang, C.G.: FEA study on nanodeformation behaviors of amorphous silicon and borosilicate considering tip geometry for pit array fabrication. Mater. Sci. Eng., A 390, 233–239 (2005) 4. Le Saux, V., et al.: Identiﬁcation of constitutive model for rubber elasticity from microindentation tests on natural rubber and validation by macroscopic tests. Mech. Mater. 43, 775–786 (2011) 5. Kohl, J.G., Singer, I.L., Simonson, D.L.: Determining the viscoelastic parameters of thin elastomer based on materials using continuous microindentation. Polym. Test. 27(6), 679– 682 (2008) 6. Bles, G., Le Saux, V., CastroLopez, C., Morvan, B., Marco, Y., Prisacariu, G.: Instrumented microhardness measurements used to identify the local viscohyperelastic parameters of Polyurethane elastomers. In: GilNegrete, N., Alonso, A. (eds.) Constitutive Models for Rubber VIII, pp. 177–182. Taylort & Francis Group, London (2013) 7. Fradet, C., Lacroix, F., Berton, G., Méo, S., Le Bourhis, E.: Instrumented indentation of an elastomeric material, protocol and application to vulcanization gradient. Polym. Test. 81, 106278 (2020) 8. Lichinchi, M., Lenardi, C., Haupt, J., Vitali, R.: Simulation of Berkovich nanoindentation experiments on thin ﬁlms using ﬁnite element method. Thin Solid Films 312, 240–248 (1998) 9. Chen, Z., Scheffer, T., Seibert, H., Diebels, S.: Macroindentation of a soft polymer: identiﬁcation of hyperelasticity and validation by uni/biaxial tensile tests. Mech. Mater. 64, 111–127 (2013) 10. Chen, Z., Diebels, S., Schmitt, J.: Frictional nanoindentation of hyperelastic polymer layers: a numerical study. In: Proceeding of 3rd ECCOMAS, Thematic Conference on the Mechanical Response of Composites, pp 229–236 (2011)
On RoomTemperature Electrodeposition of Cobalt from a Deep Eutectic Solvent: A Study of Electronucleation and Growth Mechanisms Thao Dao Vu Phuong1,2, Hoang Thi Thanh Thuy3, Phuong Dinh Tam1, and Tu Le Manh1(&) 1
Faculty of Materials Science and Engineering, Phenikaa University, Hanoi 12116, Vietnam [email protected] 2 Advanced Institute for Science and Technology (AIST), Hanoi University of Science and Technology (HUST), No 01, Dai Co Viet Road, Hanoi, Vietnam 3 Faculty of Industrial Chemistry, Hanoi University of Industry, Cau Dien StreetBac Tu Liem District, Hanoi, Vietnam
Abstract. This paper demonstrates the capability of the cobalt electrodeposition from a eutectic mixture of choline chloride and urea at room temperature. Thermodynamic and kinetic aspects of cobalt electronucleation and growth mechanisms onto glassy carbon electrode were investigated. The cobalt electrodeposition, at 303 K, is dominated by progressive nucleation. The behavior of current density transients of the Co electrodeposition can be described by a model comprising the contribution due to threedimensional nucleation and diffusioncontrolled growth from metallic nuclei combined with the effect of the induction – time. The diffusion coefﬁcient of cobalt ions in deep eutectic solvent was determined by cyclic voltammetry and bestﬁt parameters obtained using the proposed model. Both values were in agreement with the results published in the literature. SEM and EDS veriﬁed the presence and the nucleation type of cobalt onto glassy carbon electrode. Keywords: Cobalt nucleation and growth solvent
Inductiontime Deep eutectic
1 Introduction Cobalt has been attracted by the scientists because of its strong magnetic and catalytic properties, which are applied in numerous ﬁelds such as magnetic sensors, chemical sensors, and catalysts [1, 2]. The cobalt electrodeposition is extensively studied in the aqueous media [2]. It is known that the use of aqueous media causes many problems (i.e. liberation of hydrogen, evaporation) and technological concerns (toxic solutions), which reduces the cobalt electrodeposition process efﬁciency [2]. Dealing with these problems, the deep eutectic solvent (DES) based on choline chloride [3] is a prospective candidate. Various metals and their alloys have been synthesized by © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K.U. Sattler et al. (Eds.): ICERA 2020, LNNS 178, pp. 613–618, 2021. https://doi.org/10.1007/9783030647193_67
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electrochemical method from DESs such as Ni [4], Zn [5], Co [6, 7], etc. They veriﬁed that the early stage of cobalt nuclei formation onto glassy carbon electrode (GCE) plays an important role in the cobalt electrodeposition from DES [6, 7]. However, since the viscosity of the DES is high, these works suggested to perform the experiment at relatively high temperature (above 343 K) to reduce the effect of DES’s viscosity. This causes the following problems: i) it requires more accessories for heating the DES and ii) it decreases the volume of DES due to the thermal decomposition [8], which results more difﬁcult in controlling the metal ions concentration during the electrodeposition process and decreasing the efﬁciency. Therefore, it is important to study the capability of the Co electrodeposition at room temperature and its thermodynamic and kinetic aspects. This paper focuses on studying mechanisms and kinetics of cobalt nucleation and growth onto glassy carbon from choline chloridebased DES at 303 K by means of cyclic voltammetry (CV) and chronoamperometry (CA) techniques. Scanning electron microscopy (SEM) and energy dispersive Xray spectroscopy (EDS) were used to characterize the morphology and chemical composition of surface electrodeposit, respectively.
2 Materials and Methods 2.1
Electrolyte Preparation
Choline Chloride and Urea are used to prepare DES in a 1:2 molar ratio at 363 K. This mixture was magnetstirred. The electrolyte solution was obtained by adding cobalt (II) chloride hexahydrate salt CoCl2.6H2O 50 mM to the DES kept stirring for 12 h at 343 K. 2.2
Electrochemical Experiments and Surface Characterization
The cobalt nucleation and growth mechanisms onto a glassy carbon electrode from DES were studied through CV and CA at room temperature. These tests were performed in a cell comprising three electrodes, using a VersaSTAT Potentiostat/Galvanostat, coupled to the VersaStudio software running on a PC to facilitate experimental control and data collection. The electrochemical cell was composed of a polished GCE with a surface area of 0.0707 cm2 as the working electrode, a platinum wire as counter, and a silver wire as quasi reference electrode. The surface morphology of the Co deposit onto GCE from DES was characterized by a ﬁeld emission scanning electron microscope (SEM JEOL 7000). The elementary analysis of the surface deposit was determined by EDS.
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3 Results and Discussion 3.1
Potentiodynamic Study
Figure 1 shows the CVs recorded on the GCE of DES and 50 mM Co(II) in DES at 303 K. No peak formation can be observed in black curve (in the absence of Co(II)) in both scan directions, while in the system GCE/50 mM Co(II) in DES, typical reduction and oxidation peaks are clearly formed. The quality of these peaks also veriﬁed the capability of DES to electrodeposit Co at room temperature. In the forward scan direction of CV, the reduction peak is located in the potential range from – 0.95 V to – 1.20 V. In the opposite direction, the oxidation peak located in the potential range of – 0.4 V to 0.1 V is supposed to the oxidation of Co metallic nuclei into Co (II) ion. The increase of current density at the potential lower than – 1.20 V is attributed to the decomposition of the solvent. To assess the thermodynamic and kinetic behavior of the system GCE/Co (II) in DES, CVs were performed at different scan rates (from 10 to 90 mVs−1). The inset of Fig. 1 indicates that the current density of cathode peak (jcp) displays a linear behavior with square root of the scan rate (v1/2), which demonstrates that the Co nucleation onto GCE from DES follows a diffusion – controlled mechanism described by the Randles – Sevcik equation [7]:
ð1Þ where n is the total number of electrons transferred during the overall electrochemical process, C0 (mol cm−3) is the reduced species bulk concentration, DCo(II) (cm2 s−1) is the diffusion coefﬁcient of Co(II) ions and T (K) is the temperature of medium. Also, the 3D nucleation behavior can be described by the model developed by Scharifker – Mostany (SM) [9]. From Eq. (1), DCo(II) was calculated to be 2.79 10−7 cm2 s−1, which is consistent with of the results published by Manh et al. [6]. 0.0015
DES Co(II) in DES
0.0000 0.005 1/2
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Fig. 1. Comparison between CVs recorded in the system GCE/DES in the absence (black curve) and in the presence of 50 mM Co (II) (red curve). In both cases the potential scan started at 0 V in the negative direction with a scan rate of 90 mVs−1. The inset displays jcp of the CVs as a function of v1/2.
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3.2
Nucleation and Growth Mechanisms of Co Electrodeposition
From CVs, experimental currenttime transients (jt plots) were recorded at – 0.95 V and – 0.96 V at 313 K. The recorded data from experiment was normalized through the maximum of current density for each curve (jm, tm) and then compared with theoretical plots for instantaneous and progressive nucleation [9] to investigate the Co nucleation and growth mechanism. Figure 2 shows the influence of the induction time by comparing the experimental normalized plot and the theoretical one in both cases (with and without t0). If ignoring the influence of t0, the normalized experimental curve is under the progressive curve (Fig. 2a), out of the validated zone that follows the behavior of the 3D nucleation and diffusioncontrolled growth described by the SM model [10]. 1.0
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Fig. 2. Comparison of the nondimensional plots of the experimental current density transients (black line) with the theoretical ones for instantaneous (blue) and progressive (red) nucleation at 303 K at 0.95 V (a) without inductiontime compensation and (b) with inductiontime compensation.
Therefore, in this situation, the classical form of the SM model cannot depict the nucleation and growth mechanism of Co. For this condition, it is signiﬁcant to consider the consequence of t0 in the SM model by replacing t = t – t0 [11]. As shown in Fig. 2b, after eliminating the inductiontime, the new normalized experimental curve is in the validated zone of SM model, and nearly ﬁt well to the progressive curve. This also evidences that the induction time plays the important part during the early stage of the Co electrodeposition process. Then, the SM model after modifying is below [11]:
ð2Þ where t = t – t0, q is the density of the Co deposit and M is its atomic mass, N0 is the number density of active sites on the electrode surface, and A is the nucleation frequency per active site.
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Fig. 3. (a) Comparison between the j – t plots of experimental CAs obtained in the system GCE/50 mM Co(II) in DES and the theoretical ones (solid lines) ﬁtting to the experimental data after subtracting the inductiontime at 303 K (b) The pictures of Co(II) salt solution in DES and Co electrodeposition on copper electrode, (c) SEM images of Co electrodeposited on the copper substrate at −0.96 V at 303 K and (d) EDS analysis of the electrode surface.
Figure 3a shows the comparison between the experimental j – t current density transients and the theoretical plots ﬁtting to the experimental data at 303 K using Eq. (2). It presents a good concordance in the shapes with the experimental j – t plots, which demonstrate that the modiﬁed SM model by adding the inductiontime correction can depict the nucleation and growth mechanism of Co. Electrodeposition of Co was performed at − 0.96 V at 303 K on copper electrode (Fig. 3b). The grey color able to see in naked eye of copper sheet indicates the formation of Co on the electrode, which is conﬁrmed by the presence of the Cu peak shown in EDS spectrum (Fig. 3d). Furthermore, SEM image in Fig. 3c reveals that the cobalt particles exhibit a hexagonal structure, with a relatively homogeneous distribution, which is consistent with the observation done in [8]. This evidences the ability of Co electrodeposition from DES at room temperature.
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4 Conclusions The capability of Co electrodeposition, at room temperature, onto glassy carbon from DES based on choline chloride was studied through the CV and CA analyses. It was found that the Co nucleation onto the GCE from the DES occurred by a diffusion – controlled mechanism, which can be described by adding the contribution of the inductiontime to the classic SM model. The diffusion coefﬁcient of Co(II) ions was determined to be 2.79 10−7 cm2 s−1. Based on the results, it was obvious to conclude that the early stage of the Co electrodeposition is dominant by progressive nucleation mechanism at 303 K. Finally, the ability of cobalt electrodeposition in DES at low temperature can expand the potential applications of DES in different engineering ﬁelds. Acknowledgement. This research is funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 103.022019.28.
References 1. LesliePelecky, D.L., Rieke, R.D.: Magnetic properties of nanostructured materials. Chem. Mater. 8, 1770–1783 (1996) 2. RiosReyes, C.H., GranadosNeri, M., MendozaHuizar, L.H.: Kinetic study of the cobalt electrodeposition onto glassy carbon electrode from ammonium sulfate solutions. Quim. Nova 32(9), 2382–2386 (2009) 3. Abbott, A.P., Capper, G., Davies, D.L., Rasheed, R.K., Tambyrajah, V.: Novel solvent properties of choline chloride/urea mixtures. Chem. Commun. (Camb), (1) 70–1 (2003) 4. Huang, P., Zhang, Y.: Electrodeposition of nickel coating in choline chlorideurea deep eutectic solvent. Int. J. Electrochem. Sci. 13, 10798–10808 (2018) 5. Vieira, L., Whitehead, A.H., Gollas, B.: Mechanistic studies of zinc electrodeposition from deep eutectic electrolytes. J. Electrochem. Soc. 161(1), D7–D13 (2014) 6. Manh, T.L., ArceEstrada, E.M., MejíaCaballero, M.I., AldanaGonzález, J., RomeroRoma, M., PalomarPardavé, M.: Electrochemical synthesis of cobalt with different crystal structures from a deep eutectic solvent. J. Electrochem. Soc. 165, 285–290 (2018) 7. Cao, X., Xu, L., Shi, Y., Wang, Y., Xue, X.: Electrochemical behavior and electrodeposition of cobalt from choline chloride urea deep eutectic solvent. Electrochim. Acta 295, 550–557 (2019) 8. Yizhak, M.: Properties of Deep Eutectic Solvents, pp. 45–110. Springer International Publishing (2019) 9. Scharifker, B., Hills, G.: Theoretical and experimental studies of multiple nucleation. Electrochim. Acta 28(7), 879–889 (1983) 10. Scharifker, B.R., Mostany, J.: Threedimensional nucleation with diffusioncontrolled growth. J. Electroanal. Chem. 177, 13–23 (1984) 11. Manh, T.L., ArceEstrada, E.M., MejıaCaballero, I., RodrıguezClemente, E., Sanchez, W., AldanaGonzalez, J., LartundoRojas, L., RomeroRomo, M., PalomarPardave, M.: Iron electrodeposition from Fe(II) ions dissolved in a choline chloride: urea eutectic mixture. J. Electrochem. Soc. 165(16), D808–D812 (2018)
Optimal Design of Cab’s Isolation System for a SingleDrum Vibratory Roller Le Van Quynh2(&), Nguyen Tien Duy2, Nguyen Van Liem1, Bui Van Cuong1, and Le Xuan Long1 1
2
Faculty of Automotive and Power Machinery Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam Faculty of Electronics Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam [email protected]
Abstract. The goal of this paper is to ﬁnd out the optimal parameters of cab’s isolation system to improve the ride comfort of the vibratory roller. A halfvehicle ride dynamic model of a single drum vibratory roller is established under various operating conditions. The design parameters of cab’s isolation system are optimized by the root mean square (rms) values of acceleration respo