Advanced Manufacturing and Automation IX (Lecture Notes in Electrical Engineering, 634) 9789811523403, 9811523401

Table of contents :
Preface
Organization
Organized and Sponsored by
Co-organized by
Honorary Chairs
General Chairs
Local Organizing Committee
International Programme Committee
Secretariat
Contents
About the Editors
Design and Optimization
Performance Analysis of Ball Mill Liner Based on DEM-FEM Coupling
Abstract
1 Introduction
2 Typical Biological Structure Analyses
3 Lifting Bar Structure Design
4 Simulation Parameter Definitions
4.1 Particle Model Establishment
4.2 The Meshing of Geometry
4.3 Setting of Simulation Model
5 Simulation Result Analysis
6 Conclusions
Acknowledgement
References
Performance of Spiral Groove Dry Gas Seal for Natural Gas Considering Viscosity-Pressure Effect of the Gas
Abstract
1 Introduction
2 Model Description
2.1 Geometry Model
2.2 The Model of Natural Gas Viscosity-Pressure Effect
2.3 The Real Gas Model of Natural Gas
2.4 Modified Gas Film Pressure Governing Equations
2.5 Solution of Gas Film Pressure Governing Equations
3 Analytical Model and Verification
3.1 Model Verification
3.2 Property Parameter
3.3 Relative Errors
4 Results and Discussion
4.1 Leakage Rate
4.2 End Face Opening Force
5 Conclusions
Acknowledgement
References
Analysis of Residual Stress for Autofrettage High Pressure Cylinder
Abstract
1 Introduction
2 Residual Stress Analysis
2.1 Analysis of Residual Stress in Self-reinforced High Pressure Cylinder
2.2 Finite Element Analysis of Residual Stress
2.3 Analysis of Working Stress of Non-autofrettage High Pressure Cylinder
3 FEA of Residual Stress
3.1 Stress Distribution of the BLH Model
3.2 Stress Distribution of Autofrettage Material Model
3.3 Autofrettage Stress Optimization
4 Conclusions
References
Study on the Detection System for Electric Control Cabinet
Abstract
1 Introduction
2 System Introduction
3 Detection Module
4 Data Analysis Module
5 System Development
6 Result
Acknowledgements
References
Effects of Remelting on Fatigue Wear Performance of Coating
Abstract
1 Introduction
2 Experimental Procedure
2.1 Preparation of Spraying Material and Coating Samples
2.2 Test Method
3 Test Results and Analysis
3.1 Analysis of Coating Wear Test Results
3.2 Wear Performance of Coating Layer
4 Wear Fatigue Life Prediction of Coated Parts
5 Conclusions
References
Design of Emergency Response Control System for Elevator Blackout
Abstract
1 Introduction
2 The Main Content of the Subject Research
3 Functional Requirements of the System
4 Control Circuit Design of the System
4.1 The Circuit Structure of the System
4.2 Design of Control Schematic Diagram of the System
4.3 Schematic Design of Wiring Between System and Control Cabinet
5 Summary
References
Effect of Cerium on Microstructure and Friction of MoS2 Coating
Abstract
1 Introduction
2 The Material and Layer Process
2.1 Matrix
2.2 Coating Preparation Process
3 Experiment
4 Results and Analysis
4.1 The Microstructure of Coating Layers
4.2 The Friction Coefficient Curves of Coating Layers
4.3 Wear Morphology of Layers
5 Conclusion
Acknowledgments
References
A Machine Vision Method for Elevator Braking Detection
Abstract
1 Introduction
2 Basic Theory
2.1 Two Dimensional Discrete Fourier Transform
2.2 Fourier Spectrum Analysis of Images
2.3 Calculation of Rotation Angle
3 Experiment and Result Analysis
4 Conclusion
References
Remote Monitoring and Fault Diagnosis System and Method for Traction Elevator Cattle Dawn
Abstract
1 Introduction
2 Classify According to the Test Data
3 Remote Monitoring and Fault Diagnosis Methods for Traction Elevators Include the Following Steps
4 Conclusion
Acknowledgements
References
Soil Resistance Computation and Discrete Element Simulation Model of Subsoiler Prototype Parts
Abstract
1 Introduction
2 Design and Process a Sample
3 Force Analysis
4 Discrete Element Method Simulation
4.1 The Establishment of Discrete Element Model
4.2 Determination of Discrete Element Simulation Parameters
4.3 Simulation
5 Test
5.1 Design and Build Test System
5.2 Test and Analysis
6 Discussion
7 Conclusions
Acknowledgements
References
Simulation Analysis of Soil Resistance of White Asparagus Harvesting End Actuator Baffle Parts Based on Discrete Element Method
Abstract
1 Introduction
2 End Effector Baffle Structure Design
3 Simulation Parameter Setting
4 Analysis of Simulation Results
4.1 Analysis of the Resistance of Different Models in the Same Speed
4.2 Analysis of the Resistance of the Same Baffle at Different Speeds
5 Conclusions
Acknowledgements
References
Simulation and Experimental Study of Static Porosity Droplets Deposition Test Rig
Abstract
1 Introduction
2 Construction of Space Droplets Deposition Test Device Based on Static Porosity
3 Static Porosity Space Droplet Deposition Distribution Test
3.1 Experimental Method and Environment
3.2 Combination of Test Schemes
4 CFD Simulation of Droplet Deposition in Static Porosity Droplet Tester
4.1 Spray Calculation Simulation of Static Porosity Tester
4.2 Simulation and Experimental Analysis of Spatial Droplet Deposition Distribution Under Static Porosity
5 Conclusions
Acknowledgements
References
Effect of Heat Treatment on the Ductility of Inconel 718 Processed by Laser Powder Bed Fusion
Abstract
1 Introduction
2 Experimental Method
3 Results and Discussion
3.1 Heat Treatment Condition 1
3.2 Heat Treatment Condition 2
4 Discussion
5 Summary and Conclusions
Acknowledgements
References
Comparative Study of the Effect of Chord Length Computation Methods in Design of Wind Turbine Blade
Abstract
1 Introduction
2 Chord Length Distribution Calculation Methods
2.1 Chord Length Distribution Comparison
3 Blade Design and Simulation Using Qblade Software
3.1 Qblade Software
3.2 Blade Design and Modelling in Qblade Software
3.3 Blade Element Analysis and Different Comparison
4 Conclusion
References
On Modelling Techniques for Mechanical Joints: Literature Study
Abstract
1 Introduction
2 Modelling Approaches, Typical Test Methods and Models
2.1 Stochastic Models and Methods
2.2 Deterministic Method
3 Discussions and Outlooks
4 Conclusion
References
Research on Magnetic Nanoparticle Transport and Capture in Impermeable Microvessel
Abstract
1 Introduction
2 Theoretical Model
2.1 Fluid Control Equation
2.2 Magnetic Field and Magnetic Force Equation
2.2.1 External Magnetic Field Acts on the Magnetic Force of the Magnetic Particle
2.2.2 Additional Magnetic Field Acts on the Magnetic Force of the Magnetic Particles
2.3 Magnetic Particle Distribution Control Equation–Fock-Plank Equation
2.4 Magnetic Particle Capture Rate
3 Numerical Calculation Results and Analysis
3.1 Magnetic Field Distribution
3.2 Magnetic Particle Distribution
3.3 Magnetic Particle Capture Rate
4 Conclusion
References
Analysis of Drag of Bristle Based on 2-D Staggered Tube Bank
Abstract
1 Introduction
2 Model
2.1 Simulation Model
2.2 Condition Assumptions
3 Mesh Generation
4 Computing Method
4.1 Governing Equations
4.2 Determination of Reynolds Number
4.3 Solver
5 Theoretical Analysis of Drag
6 Discussion
7 Conclusion
Acknowledgement
References
Design of Large Tonnage Lift Guide Bracket
Abstract
1 Introduction
2 Primary Design of Guide Bracket
3 Calculation and Check of Car Guide Bracket
4 Structural Optimization and Finite Element Analysis
5 Conclusions
Acknowledgements
References
Stress Analysis of Hoist Auto Lift Car Frame
Abstract
1 Introduction
2 Primary Design of Car Frame
3 Material Selection and Check
4 Finite Element Analysis
5 Conclusions
Acknowledgements
References
Research on Lift Fault Prediction and Diagnosis Based on Multi-sensor Information Fusion
Abstract
1 Introduction
2 Multi-sensor Synthesis Analysis
3 D-S Theory
4 Fusion of Neural Network, DS and Sensor Data
5 Conclusions
Acknowledgements
References
Design of Permanent Magnet Damper for Elevator
Abstract
1 Introduction
2 Structure and Principle
3 The Design and Analysis of Magnet
3.1 The Design of Eddy Current Reducer
3.2 Design of Permanent Magnet Spring
4 Results and Discusses
4.1 Effect of Magnetic Arrangement
4.2 Effect of Falling Speed
4.3 Permanent Magnet Spring Support Effect
5 Conclusion
Acknowledgments
References
Design and Simulation of Hydraulic Braking System for Loader Based on AMESim
Abstract
1 Introduction
2 Hydraulic Braking System Simulation
3 Mathematical Model of Hydraulic Braking System
3.1 Simplification of Braking System
3.2 Mathematical Model of Simplified System
4 Results and Discussion
5 Conclusions
References
Dynamic Modeling and Tension Analysis of Redundantly Restrained Cable-Driven Parallel Robots Considering Dynamic Pulley Bearing Friction
Abstract
1 Introduction
2 Dynamic Modeling
3 Friction Model Construction and Simulation
3.1 Coulomb Friction Model
3.2 Simulation of Coulomb Friction Model
3.3 Dahl Friction Model
3.4 Simulation of Dahl Friction Model
4 Conclusion
Acknowledgements
References
Industry 4.0
Knowledge Discovery and Anomaly Identification for Low Correlation Industry Data
Abstract
1 Introduction
2 Data Correlation Analysis
2.1 Data Integration and Normalization
2.2 Data Analytics for Impending Failure Prediction
2.3 Correlation Validation
3 Knowledge Discovery and Anomaly Identification
4 Discussion and Conclusion
Acknowledgment
References
A Method of Communication State Monitoring for Multi-node CAN Bus Network
Abstract
1 Introduction
2 Scheme Design of Monitoring
2.1 CAN Bus Network
2.2 Principle of Monitoring
3 Software Design of Lower Computer
4 Software Design of Upper Computer
5 Experimental Results
6 Conclusion
References
Communication Data Processing Method for Massage Chair Detection System
Abstract
1 Introduction
2 Detection System Master-Slave Information Interaction
3 Message Broadcast Information Mark
4 Message Broadcast Information Filtering and Identification
5 Transmission of Long Message
6 Real-Time Transmission of Message Information
7 Conclusion
References
Development of Data Visualization Interface in Smart Ship System
Abstract
1 Introduction
2 System Architecture and Functional Structure Analysis
2.1 Design of System Architecture Design
2.2 System Function Requirements Analysis and Communication Protocol
3 Design and Implementation of System Dynamic Webpage
4 Conclusions
References
Feature Detection Technology of Communication Backplane
Abstract
1 Introduction
2 System Design
3 Image Enhancement Processing
3.1 Image Color Merging Using K-Means Algorithm
3.2 Spatial Domain Image Enhancement
4 Deep Learning and Data Processing
4.1 Image Recognition Based on CNN
4.2 Label the Results on the Original Image
5 Conclusions
References
Research on Data Monitoring System for Intelligent Ship
Abstract
1 Introduction
2 Hardware Platform Architecture
3 Basic Software Platform Architecture
4 Application Software Platform
5 Development of Data Monitoring System
6 Conclusions
References
Research on Fault Diagnosis Algorithm Based on Multiscale Convolutional Neural Network
Abstract
1 Introduction
2 Multiscale Convolutional Neural Network Model
2.1 Multiscale Convolutional Neural Network Model Structure
3 Rolling Bearing Fault Diagnosis Algorithm Based on Multiscale Convolutional Neural Network
3.1 Selection of Data Sources and Network Parameters
3.2 Visualization of the Network Structure
3.3 Model Comparison
4 Conclusions
Acknowledgements
References
Proactive Learning for Intelligent Maintenance in Industry 4.0
Abstract
1 Introduction
2 Industry 4.0: Enabling Technologies
3 Predictive Maintenance Supportive Structure
4 Adaptive Learning Approach and Applications
5 Conclusion
Acknowledgement
References
An Introduction of the Role of Virtual Technologies and Digital Twin in Industry 4.0
Abstract
1 Introduction
2 Cyber Physical System
3 The Role of Virtual Technology
4 The Role of Digital Twin
5 Conclusion
Acknowledgment
References
Model Optimization Method Based on Rhino
Abstract
1 Introduction
2 Software Basic Introduction
2.1 3D Modeling Software—Rhino
2.2 3D Engine—Unity 3D
3 Model Optimization Method Based on Rhino
3.1 Optimization Method Based on Plug-in
3.2 Optimization Method Based on Rhino’s Own Tools
3.3 Rhino’s Own Tools Improvements
4 Comparative Analysis
5 Conclusion
Acknowledgements
References
Construction of Equipment Maintenance Guiding System and Research on Key Technologies Based on Augmented Reality
Abstract
1 Preface
2 Equipment Maintenance Guiding System Based on Augmented Reality
3 Key Technologies of Equipment Maintenance Guiding
3.1 Tracking Registration Technology
3.2 Virtual-Real Masking Consistency Technology
3.3 Human-Computer Interaction Technology
4 Research Content and System Operation Process of Virtual-Real Fusion Maintenance Guiding
4.1 Research Content
4.2 System Operation Process
5 Summary
Acknowledgements
References
A New Fault Identification Method Based on Combined Reconstruction Contribution Plot and Structured Residual
Abstract
1 Introduction
2 Reconstruction Contribution Method
3 Structured Residual
3.1 The Principle of Structured Residual
4 The Fault Identification Algorithm Flow Based on Reconstruction Contribution Plot and Structured Residual
5 Simulation Validation and Analysis
5.1 Simulation Environment Setting
5.2 The Analysis and Comparison for Simulation
6 Conclusion
Acknowledgements
References
Prediction of Blast Furnace Temperature Based on Improved Extreme Learning Machine
Abstract
1 Introduction
2 Improved Extreme Learning Machine
2.1 Flower Pollinate Algorithm
2.2 Extreme Learning Machine
2.3 Improved ELM Based on FPA (FPA-ELM)
3 Prediction Model for Blast Furnace Temperature
4 Simulation Results
5 Conclusions
Acknowledgements
References
The Economic Dimension of Implementing Industry 4.0 in Maintenance and Asset Management
Abstract
1 Introduction
2 Value Driven Maintenance and Sensitivity Analysis
3 Industry 4.0
4 Discussion
5 Conclusion
References
Manufacturing System
Common Faults Analysis and Detection System Design of Elevator Tractor
Abstract
1 Elevator Tractor
2 Analysis of Common Faults of Elevator Tractor
3 Vibration Analysis of Elevator Tractor
3.1 Electromagnetic Vibration
3.2 Mechanical Vibration
4 Selection of Vibration Detection Points for Elevator Tractor
5 Design of Detection System for Elevator Tractor
5.1 Overall Design of Elevator Detection System
5.2 Design of Detection Software
5.3 Main Interface for Traction Machine Detection
References
Balanced Maintenance Program with a Value Chain Perspective
Abstract
1 Introduction
2 A Value Chain Perspective for Smart Maintenance
3 Balanced Maintenance Program
4 Conclusion
References
Construction Design of AGV Caller System
Abstract
1 Introduction
2 Construction of AGV Caller System
2.1 Overall Framework
2.2 Design of Caller
2.3 Design of CCS
2.4 Introduction to AGV
2.5 Stock Management
3 Experiment
4 Conclusion
Acknowledgements
References
A Transfer Learning Strip Steel Surface Defect Recognition Network Based on VGG19
Abstract
1 Introduction
2 Strip Steel Surface Defect Recognition Based on Convolutional Neural Network
2.1 Data Background Introduction and Model Performance Index Evaluation System
2.2 Convolutional Neural Network Structure Design and Performance Analysis
3 Strip Steel Surface Defect Recognition Network Based on Transfer Learning
3.1 Transfer Learning Network Based on VGG19
3.2 Improvement of Transfer Learning Network Based on VGG19
4 Comparison with Machine Learning Algorithms
5 Conclusions
Acknowledgements
References
Visual Interaction of Rolling Steel Heating Furnace Based on Augmented Reality
Abstract
1 Introduction
2 System Framework
3 System Structure Function Design and Implementation
3.1 Equipment Motion Control
3.2 Equipment Parameter Information Monitoring
3.3 Virtual Line Scheduling
4 Effect Analysis of Practical Application
5 Conclusion
Acknowledgements
References
Design and Test of Electro-Hydraulic Control System for Intelligent Fruit Tree Planter
Abstract
1 Introduction
2 Mechanical Structure and Control Principle of Planting System
3 Overall Scheme Design of Control System
3.1 Hardware Design of Control System
3.2 Automatic Planting Operation Subsystem
3.3 Data Acquisition Subsystem
3.4 PID Intelligent Feedback Subsystem
3.5 Hydraulic Transmission Control Subsystem
4 Test and Result Analysis
5 Conclusions
Acknowledgements
References
Lean Implementing Facilitating Integrated Value Chain
Abstract
1 Introduction
2 Theoretical Background
2.1 Integrated Value Chain
2.2 Lean
2.3 Factors Influencing Lean and Integration
3 Research Design
4 Results and Discussion
4.1 Management Commitment
4.2 Standardization
4.3 Visualization Tools
4.4 Employee Involvement
5 Conclusions
Acknowledgements
References
Developing of Auxiliary Mechanical Arm to Color Doppler Ultrasound Detection
Abstract
1 Function Requirements Analysis
2 Design of the Mechanical Arm
3 Simulation of the Mechanical Arm
3.1 Detection Simulation
3.2 Dynamics Simulation
4 Conclusions
References
The Importance of Key Performance Indicators that Can Contribute to Autonomous Quality Control
Abstract
1 Introduction
2 Zero Defect as a Purpose for Quality Planning and Data Collection
3 KPI’s for Autonomous Quality Control in Relation to a Workflow Software Engine
4 Results and Discussion
5 Conclusions
Acknowledgements
References
Collaborative Fault Diagnosis Decision Fusion Algorithm Based on Improved DS Evidence Theory
Abstract
1 Introduction
2 Collaborative Fault Diagnosis Decision Fusion Model Based on DS Evidence Theory
3 Decision Fusion Algorithm Based on Improved DS Evidence Theory
4 Numerical Simulation Analysis
5 Conclusions
Acknowledgements
References
Hybrid Algorithm and Forecasting Technology of Network Public Opinion Based on BP Neural Network
Abstract
1 Introduction
2 State of the Art
3 Methodology
3.1 Clustering Method Analysis
3.2 Algorithm and Optimization of BP Neural Network
4 Result Analysis and Discussion
5 Conclusions
Acknowledgements
References
Applying Quality Function Deployment in Smart Phone Design
Abstract
1 Introduction
2 Literature Review
3 Critical Evaluation
4 Conclusion
References
eQUALS: Automated Quality Check System for Paint Shop
Abstract
1 Introduction
2 State of the Art
3 The eQUALS System
3.1 Proof of Concept
3.2 Virtual Validation
3.3 Phase II
4 Results
5 Conclusions
Acknowledgments
References
Equipment Fault Case Retrieval Algorithm Based on Mixed Weights
Abstract
1 Introduction
2 CBR Based Fault Diagnosis Method
3 CRAMW Algorithm
3.1 Determination of Attribute Weight Based on Entropy Weight Method
3.2 Equipment Fault Case Retrieval Algorithm Based on Mixed Weights
4 Results and Discussion
5 Conclusions
References
Cascaded Organic Rankine Cycles (ORCs) for Simultaneous Utilization of Liquified Natural Gas (LNG) Cold Energy and Low-Temperature Waste Heat
Abstract
1 Introduction
2 Process Description
3 Process Optimization
4 Results and Discussion
5 Conclusion
References
Manufacturing Technology
Effect of T-groove Parameters on Steady-State Characteristics of Cylindrical Gas Seal
Abstract
1 Introduction
2 Model
2.1 T-groove Cylinder Gas Film Seal
2.2 Model Structure
2.3 Mesh Structure
2.4 Model Assumptions
2.5 Boundary Conditions
2.6 Solution Set
3 Results
3.1 Effect of Number of Grooves
3.2 Effect of Groove Depth
3.3 Effect of Groove Width Ratio (γ)
4 Summary
Acknowledgement
References
Simulation Algorithm of Sample Strategy for CMM Based on Neural Network Approach
Abstract
1 Introduction
2 Artificial Neural Network Approach
3 Model Implementation
4 Simulation Procedure
5 Results and Discussion
6 Conclusion
References
Digital Modeling and Algorithms for Series Topological Mechanisms Based on POC Set
Abstract
1 Introduction
2 Digital Modeling of Series Mechanism Topologies
2.1 Sports Subtype and Scale Constraint
2.2 A Digital Matrix Description of Series Mechanism POC Set
3 Planar Substring and Its POC Set
4 Automatic Generation of Series Mechanism POC Sets
4.1 Identification of Planar Substrings in Series Branches of Automatic Generation Algorithm Flow and Extraction of POC Set Matrix
5 Conclusion
References
Optimization of Injection Molding for UAV Rotor Based on Taguchi Method
Abstract
1 Introduction
2 Construction of Injection Molding Systems
2.1 Process Analysis
2.2 Establishment of a Finite Element Model
3 Experimental Design by Taguchi Method
4 Analysis of Experimental Data
4.1 Experimental Results
4.2 Effect of Process Parameters on Deformation of Plastic Parts
5 Verification and Analysis
6 Conclusion
References
Assembly Sequence Optimization Based on Improved PSO Algorithm
Abstract
1 Introduction
2 Problem Statement
2.1 Constraints
2.2 Fitness Function
3 Algorithmic Design
3.1 Basic PSO Algorithms
3.2 Improved PSO Algorithm
4 Examples Verification
4.1 Experimental Settings
4.2 Running Results
5 Conclusions
Acknowledgements
References
Influence of Laser Scan Speed on the Relative Density and Tensile Properties of 18Ni Maraging Steel Grade 300
Abstract
1 Introduction
2 Method
3 Results and Discussion
4 Conclusions
Acknowledgements
References
Application of Automotive Rear Axle Assembly
Abstract
1 Introduction
2 System Overall Technical Framework
2.1 Lightweight Treatment of Automobile Rear Axle Assembly Line
2.2 Data Acquisition During Assembly Execution
2.3 Interaction Mechanism Between Physical Space and Virtual Space
3 Framework Application Examples and Effects
4 Conclusions
Acknowledgements
References
Improvement of Hot Air Drying on Quality of Xiaocaoba Gastrodia Elata in China
Abstract
1 Introduction
2 Materials and Methods
2.1 Raw Materials
2.2 Main Equipment
2.3 Hot Air Drying
2.4 The Evaluation Index
2.5 The Technological Process
3 Results and Discussion
3.1 The Appearance
3.2 Dry Base Moisture Content
3.3 Gastrodin
3.4 Polysaccharide
4 Conclusions
Acknowledgements
References
Installation Parameters Optimization of Hot Air Distributor During Centrifugal Spray Drying
Abstract
1 Introduction
2 CFX Simulation and Verification
3 Optimization and Result Verification
3.1 CCD-RSM Parameters Optimization
3.2 Verify Optimized Process Parameters
3.3 Optimization Results
4 Conclusions
References
Wear Mechanism of Curved-Surface Subsoiler Based on Discrete Element Method
Abstract
1 Introduction
2 Establishment of Discrete Element Simulation Model
2.1 Subsoiler Model
2.2 Soil Model
2.3 Subsoiling Model
3 Results and Analysis of Discrete Element Simulations
3.1 Analysis of the Wear Surface of the Subsoiler
3.1.1 The Wear Surface of Subsoiler Tip
3.1.2 The Wear Surface of Subsoiler-Surface
3.1.3 The Wear Surface of Subsoiler Handle
3.2 Verification of the Subsoiler Wear
4 Conclusions
Acknowledgements
References
Development Status of Balanced Technology of Battery Management System of Electric Vehicle
Abstract
1 Introduction
2 Equalization Circuit Structure
2.1 Passive Equilibrium
2.2 Active Equilibrium
3 Balance Criteria
3.1 Equilibrium Variable
3.2 Equilibrium Strategy Method
4 Conclusion
References
Application Analysis of Contourlet Transform in Image Denoising of Flue-Cured Tobacco Leaves
Abstract
1 Introduction
2 General Model of Image Denoising
3 Image Denoising Based on Contourlet Transform
4 Analysis and Comparison of Simulation Results
5 Conclusions
References
Monte Carlo Simulation of Nanoparticle Coagulation in a Turbulent Planar Impinging Jet Flow
Abstract
1 Introduction
2 Numerical Methodology
2.1 Governing Equations for Gas Phase
2.2 Governing Equations for Particle Phase
3 Algorithmic Implementation
4 Results and Discussion
4.1 Configuration and Model Description
4.2 Evolution of Coherent Structures
4.3 Evolution of Nanoparticles
5 Conclusions
Acknowledgements
References
Structural Damage Detection of Elevator Steel Plate Using GNARX Model
Abstract
1 Introduction
2 Expression of GNARX Model
3 Identification of GNARX Model
3.1 Parameter Estimation of GNARX Model
3.2 Structure Identification of GNARX Model
4 Structural Damage Detection of Elevator Steel Plate
4.1 KNN Algorithm
4.2 Data Acquisition
4.3 Result and Discussion
5 Conclusions
References
Production Management
The Innovative Development and Application of New Energy Vehicles Industry from the Perspective of Game Theory
Abstract
1 Introduction
2 Literature Review
3 Case Analysis and Propositions
3.1 Model Assumptions and Definitions
3.2 Model Analysis and Recommendations
3.2.1 Game Model Analysis of Supply Side and Demand Side in New Energy Vehicle Market
3.2.2 Game Model Analysis of Supply Side in New Energy Vehicle Market
3.2.3 Game Model Analysis Between Government and Enterprise in New Energy Vehicle Market
4 Conclusions
Acknowledgement
References
Survey and Planning of High-Payload Human-Robot Collaboration: Multi-modal Communication Based on Sensor Fusion
Abstract
1 Introduction
2 Related Research
3 Scenarios
4 Discussion
5 Conclusion
Acknowledgements
References
Research on Data Encapsulation Model for Memory Management
Abstract
1 Introduction
2 Data Storage Modeling
3 Memory Management and Dynamic Storage
4 Application Example
5 Conclusions
References
Research on Task Scheduling Design of Multi-task System in Massage Chair Function Detection
Abstract
1 Introduction
2 The Principle of Task Scheduling
3 The Logical Design of Task Scheduling for Massage Chair Function Detection
4 The Verification of Massage Chair Function Detection Logic Task Scheduling
5 Conclusions
References
A Stochastic Closed-Loop Supply Chain Network Optimization Problem Considering Flexible Network Capacity
Abstract
1 Introduction
2 Mathematical Model
3 Algorithm
4 Experiment and Discussion
5 Conclusions
References
Solving the Location Problem of Printers in a University Campus Using p-Median Location Model and AnyLogic Simulation
Abstract
1 Introduction
2 Method
2.1 p-median Location Problem
2.2 AnyLogic Simulation
3 Case Study
4 Result and Discussion
5 Conclusions
References
Intelligent Workshop Digital Twin Virtual Reality Fusion and Application
Abstract
1 Introduction
2 System Framework
3 System Structure Function Design and Implementation
3.1 Multi-source Data Collection Based on Intelligent Workshop
3.2 Intelligent Workshop Data Fusion Model Based on Digital Twins
3.2.1 Intelligent Shop Floor Data Filtering Based on Digital Twins
3.2.2 Intelligent Workshop Data Fusion Modeling Based on Digital Twins
4 Effect Application Examples and Effect Analysis
5 Conclusions
Acknowledgements
References
Harvesting Path Planning of Selective Harvesting Robot for White Asparagus
Abstract
1 Introduction
2 Global Path Planning Algorithm
2.1 Harvesting Path Planning
2.2 Global Optimal Path Planning Algorithm
3 Path Planning Algorithm Based on Sequential Harvesting
4 Simulation Analysis of Path Planning Algorithm
4.1 Simulation Analysis of Global Optimal Path Planning Algorithm
4.2 Simulation Analysis of Path Planning Algorithm Based on Sequential Harvesting
4.3 Comparison Between Global Optimal Path Planning Algorithm and Path Planning Algorithm Based on Sequential Harvesting
5 Conclusion
Acknowledgements
References
Optimization of Technological Processes at Production Sites Based on Digital Modeling
Abstract
1 Introduction
2 Representation of an Initial Technological Route as an MSC Diagram
3 Optimization of a Technological Route
4 Digital Modeling and Simulation
5 Conclusions
Acknowledgements
References
Smart Maintenance in Asset Management – Application with Deep Learning
Abstract
1 Introduction
2 Smart Maintenance in Asset Management
2.1 The Trend Towards Smart Maintenance
2.2 Smart Maintenance Framework
3 Smart Maintenance Planning with Criticality Assessment
4 Discussion and Concluding Remarks
Acknowledgements
References
Maintenance Advisor Using Secondary-Uncertainty-Varying Type-2 Fuzzy Logic System for Offshore Power Systems
Abstract
1 Introduction
2 Reliability Models in System Maintenance Optimizer
3 Intelligent Maintenance Advisor with Type-2 Fuzzy Logic System
4 Results and Discussion
4.1 Case Study and Parameters
4.2 Advantage of Secondary-Uncertainty-Varying Type-2 Fuzzy Logic in Reducing Computational Complexity
4.3 Impacts of Operational Variations and Uncertainties on Optimization of Maintenance Schedules
5 Conclusions
Acknowledgements
References
Determine Reducing Sugar Content in Potatoes Using Hyperspectral Combined with VISSA Algorithm
Abstract
1 Introduction
2 Materials and Methods
2.1 Sample Preparation and Determination
2.2 Hyperspectral Acquisition System
2.3 Variable Iterative Space Shrinkage Algorithms
2.4 Running Environment and Software Code
3 Results and Analysis
3.1 Spectral Analysis and Pretreatment
3.2 Selection of Characteristic Wavelength
3.3 Establishment of Prediction Model
4 Conclusions
Acknowledgements
References
Game Theory in the Fashion Industry: How Can H&M Use Game Theory to Determine Their Marketing Strategy?
Abstract
1 Introduction
2 Literature Review
3 Game Theory in the Fashion Industry
4 Critical Analysis of Game Theory
5 Conclusion
References
Multidimensional Analysis Between High-Energy-Physics Theory Citation Network and Twitter
Abstract
1 Introduction
2 Shortest Path Length
3 Clustering Coefficients Comparison
4 Conclusion
References
Application of Variable Step Size Beetle Antennae Search Optimization Algorithm in the Study of Spatial Cylindrical Errors
Abstract
1 Introduction
2 Establishment of Mathematical Model of Spatial Cylindrical
3 Proposed Algorithm
3.1 Algorithm Flow
3.2 Algorithm Test
4 Experimental Results and Discussion
5 Conclusions
Acknowledgements
References
A Categorization Matrix and Corresponding Success Factors for Involving External Designers in Contract Product Development
Abstract
1 Introduction
2 Supplier Involvement
2.1 Relevant Supplier Categories for Contract Product Development
2.2 Success Factors
3 Methodology
4 A Categorization Approach/Matrix
5 Success Factors
6 Conclusion
Acknowledgements
References
Engineering Changes in the Engineer-to-Order Industry: Challenges of Implementation
Abstract
1 Introduction
2 Methodology
3 Engineering Change Management
4 Results
5 Discussion
6 Conclusion
Acknowledgements
References
Impact of Carbon Price on Renewable Energy Using Power Market System
Abstract
1 Introduction
2 Data and Methodology
2.1 Power System Modeling
2.2 Carbon Price and Scenarios Design
3 Results and Discussions
4 Conclusions and Discussion
References
Author Index

Citation preview

Lecture Notes in Electrical Engineering 634

Yi Wang Kristian Martinsen Tao Yu Kesheng Wang   Editors

Advanced Manufacturing and Automation IX

Lecture Notes in Electrical Engineering Volume 634

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martin, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Junjie James Zhang, Charlotte, NC, USA

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Yi Wang Kristian Martinsen Tao Yu Kesheng Wang •

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Editors

Advanced Manufacturing and Automation IX

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Editors Yi Wang School of Business Plymouth University Plymouth, UK Tao Yu Shanghai Second Polytechnic University Shanghai, China

Kristian Martinsen Department of Manufacturing and Civil Engineering NTNU Gjøvik, Norway Kesheng Wang Department of Mechanical and Industrial Engineering Norwegian University of Science and Technology Trondheim, Sør-Trøndelag Fylke, Norway

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-15-2340-3 ISBN 978-981-15-2341-0 (eBook) https://doi.org/10.1007/978-981-15-2341-0 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

IWAMA—International Workshop of Advanced Manufacturing and Automation— aims at providing a common platform for academics, researchers, practising professionals and experts from industries to interact, discuss current technology trends and advances and share ideas and perspectives in the areas of manufacturing and automation. IWAMA began in Shanghai University 2010. In 2012 and 2013, it was held at the Norwegian University of Science and Technology, in 2014 at Shanghai University again, in 2015 at Shanghai Polytechnic University, in 2016 at Manchester University, in 2017 at Changshu Institute of Technology and in 2018 at Changzhou University. The sponsors organizing the IWAMA series has expanded to many universities throughout the world; including Plymouth University, Changzhou University, Norwegian University of Science and Technology, SINTEF, Manchester University, Shanghai University, Shanghai Polytechnic University, Changshu Institute of Technology, Xiamen University of Science and Technology, Tongji University, University of Malaga, University of Firenze, Stavanger University, The Arctic University of Norway, Shandong Agricultural University, China University of Mining and Technology, Indian National Institute of Technology, Donghua University, Shanghai Jiao Tong University, Changshu Institute of Technology, Dalian University, St. Petersburg Polytechnic University, Hong Kong Polytechnic University, Lingnan Normal University, Civil Aviation University of China and China Instrument and Control Society. As IWAMA becomes an annual event, we are expecting more sponsors from universities and industries, who will participate in the international workshop as co-organizers. Manufacturing and automation have assumed paramount importance and are vital for the economy of a nation and the quality of daily life. The field of manufacturing and automation is advancing at a rapid pace, and new technologies are also emerging. The main challenge faced by today’s engineers, scientists and academics is to keep on top of the emerging trends through continuous research and development.

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IWAMA 2019 takes place in Plymouth University, UK, 21–22 November 2019, organized by Plymouth University, Norwegian University of Science and Technology and Lingnan Normal University. The programme is designed to improve manufacturing and automation technologies for the next generation through discussion of the most recent advances and future perspectives and to engage the worldwide community in a collective effort to solve problems in manufacturing and automation. The main focus of the workshop is focused on the transformation of present factories, towards reusable, flexible, modular, intelligent, digital, virtual, affordable, easy-to-adapt, easy-to-operate, easy-to-maintain and highly reliable “smart factories”. Therefore, IWAMA 2019 has mainly covered five topics in manufacturing engineering: 1. 2. 3. 4. 5.

Industry 4.0 Manufacturing Systems Manufacturing Technologies Production Management Design and optimization

All papers submitted to the workshop have been subjected to strict peer review by at least two expert referees. Finally, 84 papers have been selected to be included in the proceedings after a revision process. We hope that the proceedings will not only give the readers a broad overview of the latest advances, and a summary of the event, but also provide researchers with a valuable reference in this field. On behalf of the organization committee and the international scientific committee of IWAMA 2019, I would like to take this opportunity to express my appreciation for all the kind support, from the contributors of high-quality keynotes and papers and all the participants. My thanks are extended to all the workshop organizers and paper reviewers, to Plymouth University and NTNU for the financial support and to co-sponsors for their generous contribution. Thanks are also given to Jian Wu, Jin Yuan, Yun Chen, Bo Chen and Tamal Ghosh, for their hard editorial work of the proceedings and arrangement of the workshop.

Yi Wang Chair of IWAMA 2019

Organization

Organized and Sponsored by PLYU (Plymouth University, UK) NTNU (Norwegian University of Science and Technology, Norway)

Co-organized by LNU (Lingnan Normal University, China) SSPU (Shanghai Second Polytechnic University, China) TU (Tongji University, China) SHU (Shanghai University, China) SJTU (Shanghai Jiao Tong University, China)

Honorary Chairs Minglun Fang, China Kesheng Wang, Norway Jan Ola Strandhagen, Norway

General Chairs Yi Wang, UK Kristian Martinsen, Norway Tao Yu, China

Local Organizing Committee Yi Wang (Chair) Jonathan Moizer Stephen Childe Tamal Ghosh Oleksandr Semeniuta Ivanna Baturynska Jian Wu

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International Programme Committee Jan Ola Strandhagen, Norway Kesheng Wang, Norway Asbjørn Rolstadås, Norway Per Schjølberg, Norway Knut Sørby, Norway Erlend Alfnes, Norway Heidi Dreyer, Norway Torgeir Welo, Norway Leif Estensen, Norway Hirpa L. Gelgele, Norway Wei D. Solvang, Norway Yi Wang, UK Chris Parker, UK Jorge M. Fajardo, Spain Torsten Kjellberg, Sweden Fumihiko Kimura, Japan Gustav J. Olling, USA Michael Wozny, USA Wladimir Bodrow, Germany Guy Doumeingts, France Van Houten, Netherlands Peter Bernus, Australia Janis Grundspenkis, Latvia George L. Kovacs, Hungary Rinaldo Rinaldi, Italy Gaetano Aiello, Italy Romeo Bandinelli, Italy Jinhui Yang, China

Secretariat Jian Wu Tamal Ghosh

Dawei Tu, China Minglun Fang, China Binheng Lu, China Xiaoqien Tang, China Ming Chen, China Yun Chen, China Henry Xinguo Ming, China Keith C. Chan, China Xiaojing Wang, China Jin Yuan, China Bo Chen, China Shouqi Cao, China Shili Tan, China Ming Li, China Cuilian Zhao, China Chuanhong Zhou, China Jianqing Cao, China Yayu Huang, China Shirong Ge, China Jianjun Wu, China Guijuan Lin, China Shangming Luo, China Dong Yang, China Zumin Wang, China Guohong Dai, China Sarbjit Singh, India Vishal S. Sharma, India

Organization

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Contents

Design and Optimization Performance Analysis of Ball Mill Liner Based on DEM-FEM Coupling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhen Xu, Junfeng Sun, Taohong Liao, Xiangping Hu, and Xuedong Zhang

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Performance of Spiral Groove Dry Gas Seal for Natural Gas Considering Viscosity-Pressure Effect of the Gas . . . . . . . . . . . . . . . . . . Xuejian Sun, Pengyun Song, and Xiangping Hu

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Analysis of Residual Stress for Autofrettage High Pressure Cylinder . . . Guiqin Li, Yang Li, Jinfeng Shi, Shijin Zhang, and Peter Mitrouchev

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Study on the Detection System for Electric Control Cabinet . . . . . . . . . Lixin Lu, Weicong Wang, Guiqin Li, and Peter Mitrouchev

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Effects of Remelting on Fatigue Wear Performance of Coating . . . . . . . Zhiping Zhao, Xinyong Li, Chao Wang, and Yang Ge

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Design of Emergency Response Control System for Elevator Blackout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Dou, Wenmeng Li, Jiaxin Ma, and Lanzhong Guo

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Effect of Cerium on Microstructure and Friction of MoS2 Coating . . . . Wu Jian, Xinyong Li, Ge Yang, Lanzhong Guo, Cao Jie, and Peijun Jiao

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A Machine Vision Method for Elevator Braking Detection . . . . . . . . . . Yang Ge, Jian Wu, and Xiaomei Jiang

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Remote Monitoring and Fault Diagnosis System and Method for Traction Elevator Cattle Dawn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuguang Niu, Junjie Huang, and Zhiwen Ye

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Soil Resistance Computation and Discrete Element Simulation Model of Subsoiler Prototype Parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gong Liu, Zhenbo Xin, Ziru Niu, and Jin Yuan

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Simulation Analysis of Soil Resistance of White Asparagus Harvesting End Actuator Baffle Parts Based on Discrete Element Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haoyu Ma, Liangliang Zou, Jin Yuan, and Xuemei Liu Simulation and Experimental Study of Static Porosity Droplets Deposition Test Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laiqi Song, Xuemei Liu, Xinghua Liu, and Haishu Zhang Effect of Heat Treatment on the Ductility of Inconel 718 Processed by Laser Powder Bed Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Even Wilberg Hovig, Olav Åsebø Berg, Trond Aukrust, and Harald Solhaug

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Comparative Study of the Effect of Chord Length Computation Methods in Design of Wind Turbine Blade . . . . . . . . . . . . . . . . . . . . . . 106 Temesgen Batu and Hirpa G. Lemu On Modelling Techniques for Mechanical Joints: Literature Study . . . . 116 Øyvind Karlsen and Hirpa G. Lemu Research on Magnetic Nanoparticle Transport and Capture in Impermeable Microvessel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Jiejie Cao and Jian Wu Analysis of Drag of Bristle Based on 2-D Staggered Tube Bank . . . . . . 134 Xiaolei Song, Meihong Liu, Xiangping Hu, Yuchi Kang, and Baodi Zhang Design of Large Tonnage Lift Guide Bracket . . . . . . . . . . . . . . . . . . . . 142 Lanzhong Guo and Xiaomei Jiang Stress Analysis of Hoist Auto Lift Car Frame . . . . . . . . . . . . . . . . . . . . 151 Xiaomei Jiang, Michael Namokel, Chaobin Hu, Jinzhong Dong, and Ran Tian Research on Lift Fault Prediction and Diagnosis Based on Multi-sensor Information Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 Xiaomei Jiang, Michael Namokel, Chaobin Hu, and Ran Tian Design of Permanent Magnet Damper for Elevator . . . . . . . . . . . . . . . . 169 Xinyong Li, Jian Wu, Jianfeng Lu, Peijun Jiao, and Lanzhong Guo Design and Simulation of Hydraulic Braking System for Loader Based on AMESim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 Junjun Liu, Lanzhong Guo, Jiaxin Ma, Yang Ge, Jian Wu, and Zhanrong Ma

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Dynamic Modeling and Tension Analysis of Redundantly Restrained Cable-Driven Parallel Robots Considering Dynamic Pulley Bearing Friction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Zhenyu Hong, Xiaolei Ren, Zhiwei Xing, and Guichang Zhang Industry 4.0 Knowledge Discovery and Anomaly Identification for Low Correlation Industry Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Zhe Li and Jingyue Li A Method of Communication State Monitoring for Multi-node CAN Bus Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Lu Li-xin, Gu Ye, Li Gui-qin, and Peter Mitrouchev Communication Data Processing Method for Massage Chair Detection System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Lixin Lu, Yujie Jin, Guiqin Li, and Peter Mitrouchev Development of Data Visualization Interface in Smart Ship System . . . 219 Guiqin Li, Zhipeng Du, Maoheng Zhou, Qiuyu Zhu, Jian Lan, Yang Lu, and Peter Mitrouchev Feature Detection Technology of Communication Backplane . . . . . . . . . 227 Guiqin Li, Hanlin Wang, Shengyi Lin, Tao Yu, and Peter Mitrouchev Research on Data Monitoring System for Intelligent Ship . . . . . . . . . . . 234 Guiqin Li, Xuechao Deng, Maoheng Zhou, Qiuyu Zhu, Jian Lan, Hong Xia, and Peter Mitrouchev Research on Fault Diagnosis Algorithm Based on Multiscale Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 Xiaolong Li, Lilan Liu, Xiang Wan, Lingyan Gao, and Qi Huang Proactive Learning for Intelligent Maintenance in Industry 4.0 . . . . . . . 250 Rami Noureddine, Wei Deng Solvang, Espen Johannessen, and Hao Yu An Introduction of the Role of Virtual Technologies and Digital Twin in Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 Mohammad Azarian, Hao Yu, Wei Deng Solvang, and Beibei Shu Model Optimization Method Based on Rhino . . . . . . . . . . . . . . . . . . . . . 267 Mengyao Dong, Zenggui Gao, and Lilan Liu Construction of Equipment Maintenance Guiding System and Research on Key Technologies Based on Augmented Reality . . . . . 275 Lingyan Gao, Fang Wu, Lilan Liu, and Xiang Wan

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A New Fault Identification Method Based on Combined Reconstruction Contribution Plot and Structured Residual . . . . . . . . . . 283 Bo Chen, Kesheng Wang, Xiue Gao, Yi Wang, Shifeng Chen, Tianshu Zhang, Kristian Martinsen, and Tamal Ghosh Prediction of Blast Furnace Temperature Based on Improved Extreme Learning Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 Xin Guan The Economic Dimension of Implementing Industry 4.0 in Maintenance and Asset Management . . . . . . . . . . . . . . . . . . . . . . . . . 299 Tom I. Pedersen and Per Schjølberg Manufacturing System Common Faults Analysis and Detection System Design of Elevator Tractor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Yan Dou, Wenmeng Li, Yang Ge, and Lanzhong Guo Balanced Maintenance Program with a Value Chain Perspective . . . . . 317 Jon Martin Fordal, Thor Inge Bernhardsen, Harald Rødseth, and Per Schjølberg Construction Design of AGV Caller System . . . . . . . . . . . . . . . . . . . . . . 325 Zhang Xi, Wang Xin, and Yuanzhi Xu A Transfer Learning Strip Steel Surface Defect Recognition Network Based on VGG19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Xiang Wan, Lilan Liu, Sen Wang, and Yi Wang Visual Interaction of Rolling Steel Heating Furnace Based on Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Bowen Feng, Lilan Liu, Xiang Wan, and Qi Huang Design and Test of Electro-Hydraulic Control System for Intelligent Fruit Tree Planter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 Ranguang Yin, Jin Yuan, and Xuemei Liu Lean Implementing Facilitating Integrated Value Chain . . . . . . . . . . . . 358 Inger Gamme, Silje Aschehoug, and Eirin Lodgaard Developing of Auxiliary Mechanical Arm to Color Doppler Ultrasound Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 Haohan Zhang, Zhiqiang Li, Guowei Zhang, and Xiaoyu Liu The Importance of Key Performance Indicators that Can Contribute to Autonomous Quality Control . . . . . . . . . . . . . . . . . . . . . . 373 Ragnhild J. Eleftheriadis and Odd Myklebust

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Collaborative Fault Diagnosis Decision Fusion Algorithm Based on Improved DS Evidence Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Xiue Gao, Bo Chen, Shifeng Chen, Kesheng Wang, Yi Wang, Wenxue Xie, Jin Yuan, Kristian Martinsen, and Tamal Ghosh Hybrid Algorithm and Forecasting Technology of Network Public Opinion Based on BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . 388 Ronghua Zhang, Changzheng Liu, and Hongliang Ma Applying Quality Function Deployment in Smart Phone Design . . . . . . 396 Taylor Barnard and Yi Wang eQUALS: Automated Quality Check System for Paint Shop . . . . . . . . . 402 Angel Dacal-Nieto, Carmen Fernandez-Gonzalez, Victor Alonso-Ramos, Gema Antequera-Garcia, and Cristian Ríos Equipment Fault Case Retrieval Algorithm Based on Mixed Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 Yonglin Tian, Chengsheng Pan, Yana Lv, and Bo Chen Cascaded Organic Rankine Cycles (ORCs) for Simultaneous Utilization of Liquified Natural Gas (LNG) Cold Energy and Low-Temperature Waste Heat . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418 Fuyu Liu, Xiangping Hu, Haoshui Yu, and Baosheng Zhang Manufacturing Technology Effect of T-groove Parameters on Steady-State Characteristics of Cylindrical Gas Seal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 Junfeng Sun, Meihong Liu, Zhen Xu, Taohong Liao, and Xiangping Hu Simulation Algorithm of Sample Strategy for CMM Based on Neural Network Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 Petr Chelishchev and Knut Sørby Digital Modeling and Algorithms for Series Topological Mechanisms Based on POC Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442 Lixin Lu, Hehui Tang, Guiqin Li, and Peter Mitrouchev Optimization of Injection Molding for UAV Rotor Based on Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 Xiong Feng, Zhengqian Li, and Guiqin Li Assembly Sequence Optimization Based on Improved PSO Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 Xiangyu Zhang, Lilan Liu, Xiang Wan, Kesheng Wang, and Qi Huang Influence of Laser Scan Speed on the Relative Density and Tensile Properties of 18Ni Maraging Steel Grade 300 . . . . . . . . . . . . . . . . . . . . 466 Even Wilberg Hovig and Knut Sørby

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Application of Automotive Rear Axle Assembly . . . . . . . . . . . . . . . . . . . 473 Shouzheng Liu, Lilan Liu, Xiang Wan, Kesheng Wang, and Fang Wu Improvement of Hot Air Drying on Quality of Xiaocaoba Gastrodia Elata in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 Xiuying Tang, Chao Tan, Bin Cheng, Xuemei Leng, Xiangcai Feng, and Yinhua Luo Installation Parameters Optimization of Hot Air Distributor During Centrifugal Spray Drying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488 Yunfei Liu and Jingjing Xu Wear Mechanism of Curved-Surface Subsoiler Based on Discrete Element Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 Jinguang Li, Liangliang Zou, Xuemei Liu, and Jin Yuan Development Status of Balanced Technology of Battery Management System of Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . 504 Xiupeng Yan, Jianjun Nie, Zongzheng Ma, and Haishu Ma Application Analysis of Contourlet Transform in Image Denoising of Flue-Cured Tobacco Leaves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Li Zhang, Haohan Zhang, Hongbin Liu, Sen Wang, and Xiaoyu Liu Monte Carlo Simulation of Nanoparticle Coagulation in a Turbulent Planar Impinging Jet Flow . . . . . . . . . . . . . . . . . . . . . . . 517 Hongmei Liu, Weigang Xu, Faqi Zhou, Lin Liu, Jiaming Deng, Shuhao Ban, and Xuedong Liu Structural Damage Detection of Elevator Steel Plate Using GNARX Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Jiaxin Ma and Yan Dou Production Management The Innovative Development and Application of New Energy Vehicles Industry from the Perspective of Game Theory . . . . . . . . . . . . 535 Jianhua Wang and Junwei Ma Survey and Planning of High-Payload Human-Robot Collaboration: Multi-modal Communication Based on Sensor Fusion . . . . . . . . . . . . . . 545 Gabor Sziebig Research on Data Encapsulation Model for Memory Management . . . . 552 Lixin Lu, Weixing Zhao, Guiqin Li, and Peter Mitrouchev Research on Task Scheduling Design of Multi-task System in Massage Chair Function Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 560 Lixin Lu, Leibing Lv, Guiqin Li, and Peter Mitrouchev

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A Stochastic Closed-Loop Supply Chain Network Optimization Problem Considering Flexible Network Capacity . . . . . . . . . . . . . . . . . . 567 Hao Yu, Wei Deng Solvang, and Xu Sun Solving the Location Problem of Printers in a University Campus Using p-Median Location Model and AnyLogic Simulation . . . . . . . . . . 577 Xu Sun, Hao Yu, and Wei Deng Solvang Intelligent Workshop Digital Twin Virtual Reality Fusion and Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 Qiang Miao, Wei Zou, Lilan Liu, Xiang Wan, and Pengfei Wu Harvesting Path Planning of Selective Harvesting Robot for White Asparagus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 Ping Zhang, Jin Yuan, Xuemei Liu, and Yang Li Optimization of Technological Processes at Production Sites Based on Digital Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600 Pavel Drobintsev, Nikita Voinov, Lina Kotlyarova, Ivan Selin, and Olga Aleksandrova Smart Maintenance in Asset Management – Application with Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 Harald Rødseth, Ragnhild J. Eleftheriadis, Zhe Li, and Jingyue Li Maintenance Advisor Using Secondary-Uncertainty-Varying Type-2 Fuzzy Logic System for Offshore Power Systems . . . . . . . . . . . . . . . . . . 616 Haitao Sang Determine Reducing Sugar Content in Potatoes Using Hyperspectral Combined with VISSA Algorithm . . . . . . . . . . . . . . . . . . 625 Wei Jiang, Ming Li, and Yao Liu Game Theory in the Fashion Industry: How Can H&M Use Game Theory to Determine Their Marketing Strategy? . . . . . . . . . . . . . . . . . . 633 Chloe Luo and Yi Wang Multidimensional Analysis Between High-Energy-Physics Theory Citation Network and Twitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 Lapo Chirici, Yi Wang, and Kesheng Wang Application of Variable Step Size Beetle Antennae Search Optimization Algorithm in the Study of Spatial Cylindrical Errors . . . . 646 Chen Wang, Yi Wang, and Kesheng Wang A Categorization Matrix and Corresponding Success Factors for Involving External Designers in Contract Product Development . . . 654 Aleksander Wermers Nilsen and Erlend Alfnes

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Engineering Changes in the Engineer-to-Order Industry: Challenges of Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 662 Luis F. Hinojos A., Natalia Iakymenko, and Erlend Alfnes Impact of Carbon Price on Renewable Energy Using Power Market System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671 Xiangping Hu, Xiaomei Cheng, and Xinlu Qiu Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679

About the Editors

Yi Wang obtained his PhD from Manufacturing Engineering Centre, Cardiff University in 2008. He is a lecturer in Business School, Plymouth University, UK. Previously, he worked in the Department of Computer Science, Southampton University and at the Business School, Nottingham Trent University. He holds various visiting lectureship in several universities worldwide. Dr. Wang has special research interests in supply chain management, logistics, operation management, culture management, information systems, game theory, data analysis, semantics and ontology analysis, and neuromarketing. Dr. Wang has published 26 technical peer-reviewed papers in international journals and conferences. He co-authored two books: Operations Management for Business and Data Mining for Zero-defect Manufacturing. Kristian Martinsen took his Dr. Ing. degree at the Norwegian University for Science and Technology (NTNU) in 1995, with the topic “Vectorial Tolerancing in Manufacturing”. He has 15 years’ experience from the manufacturing industry. He is a professor at faculty of Engineering and Department for Manufacturing and Civil Engineering, the Norwegian University for Science and Technology (NTNU), and is the manager of the Manufacturing Engineering research group in this department. He is a corporate member of the International Academy for Production Engineering and a member of the High-Level Group of the EU technology platform for manufacturing, MANUFUTURE. He is the manager of the Norwegian national infrastructure for manufacturing research laboratories, MANULAB, and is the international co-ordinator for the Norwegian Centre for Research-based Innovation SFI MANUFACTURING. He has published many papers in international journals and conference. His major research area is within the field of measurement systems, variation/quality management, tolerancing and industry 5.0.

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About the Editors

Tao Yu is the president of Shanghai Second Polytechnic University (SSPU), China and professor of Shanghai University (SHU). He received his PhD from SHU in 1997. Professor Yu is a member of the Group of Shanghai manufacturing information and a committee member of the International Federation for Information Processing IFIP/TC5. He is also an executive vice president of Shanghai Science Volunteer Association and executive director of Shanghai Science and Art Institute of Execution. He managed and performed about 20 national, Shanghai, enterprises commissioned projects. He has published hundreds of academic papers, of which about thirty were indexed by SCI, EI. His research interests are mechatronics, computer integrated manufacturing system (CIMS) and grid manufacturing. Kesheng Wang holds a PhD in production engineering from the Norwegian University of Science and Technology (NTNU), Norway. Since 1993, he has been appointed as a professor at the Department of Mechanical and Industrial Engineering, NTNU. He is also an active researcher and serves as a technical adviser in SINTEF. He was elected member of the Norwegian Academy of Technological Sciences in 2006. He has published 21 books, 10 book chapters and over 270 technical peer-reviewed papers in international journals and conferences. Professor Wang’s current areas of interest are intelligent manufacturing systems, applied computational intelligence, data mining and knowledge discovery, swarm intelligence, condition-based monitoring and structured light systems for 3D measurements and RFID, predictive/cognitive maintenance and Industry 4.0.

Design and Optimization

Performance Analysis of Ball Mill Liner Based on DEM-FEM Coupling Zhen Xu1, Junfeng Sun2(&), Taohong Liao3, Xiangping Hu4, and Xuedong Zhang5 1

Faculty of Mechanical and Electrical Engineering, Yunnan Open University, Kunming, China 2 Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650504, China [email protected] 3 Department of Marine Technology, Norwegian University of Science and Technology, Trondheim, Norway 4 Industrial Ecology Programme, Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway 5 SAIC Iveco Hongyan Commercial Vehicle Co., Ltd., Chongqing 401122, China

Abstract. In the paper, based on the finite element-discrete element method coupling and the coupled bionics theory, four lifting bar models with coupled bionic surface structures, such as smooth, transverse stripe, longitudinal stripe and convex hull, are established. The particle model with different geometric features is established and the coupled simulations using finite element-discrete element method are carried out. Results show that the lift bar with the coupled bionic structure with soft matrix-hard bearing unit can effectively reduce the equivalent stress of the lifting bar during grinding. This coupled bionic structure can also reduce wear and tear of the lifting bar, and therefore, the lifting bar is protected and the grinding effect is improved. Results also indicate that the transverse stripe-coupled bionic structure has the best wear resistance and grinding effect comparing among the four kinds of lifting bars. Keywords: Ball mill
Lifting bar stress
Coupled bionic structure


DEM-FEM
Liner wear
Equivalent

1 Introduction As important equipment in the field of mineral processing, ball mills play crucial roles in normal operation of the national economy [1–3]. For ball mills, wear and tear of the lifting bars is the main causes of failure of the liner of the ball mill. According to historical data [4], wet ball mills consumed more than 110,000 tons of liners in metal mines during 2004 in China. However, green environmental protection is the mainstream trend of world economic development, which adds new requirements on equipment efficiency to save energy. Therefore, selecting a suitable wear-resistant © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 3–10, 2020. https://doi.org/10.1007/978-981-15-2341-0_1

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material and designing a reasonable structure of the liner is of great importance for the ball mill.

2 Typical Biological Structure Analyses Two biological characteristics of the buttercup and the ostrich toe make them to have excellent wear and impact resistance [5]. These characteristics are closely related to the surface topography, hierarchy, and materials of the organism. The unit shape of the wear-resistant part of the ostrich toe is a spherical crown type convex body. This part of the toe is in contact with the ground and has a high frequency of abrasive wear with sand. By measuring the cross section, it is found that the ratio of height, width and adjacent spacing of the bottom of the convex body is about 5:3:1. Similarly, the ratio of the intercostals grooves, rib width and rib height can be found from the surface morphology of the clam shell and it is about 4:3:1.

3 Lifting Bar Structure Design In this paper, the ball mill prototype with the cylinder size 600 mm  400 mm is used as the research platform and the size of the incoming ore is 4 mm–12 mm. The proportional relationship discussed in Sect. 2 between the different parameters of the extracted biometrics is used to design the lifting bars with different characteristic surfaces. According to the relationship between the stripe and the direction of motion of the cylinder, the stripe is divided into horizontal stripe and vertical stripe. The threedimensional model of the lifting bar with the stripe feature is shown in Fig. 1. In the lift bar model, the width of the horizontal stripes and vertical stripes is M = 6 mm, the height is H = 2 mm, and the space between stripes is L = 8 mm. At the same time, according to the wear-resisting characteristics of ostrich toes, a lifting bar with convex features on the surface is designed. The unit of the convex hull feature as a diameter of M = 6 mm, a height of H = 2 mm, and the space between stripes is L = 8 mm. The designed lifting bar with convex hull feature is shown in Fig. 2.

Fig. 1. Lifting bar with stripe features

Fig. 2. Lifting bar with convex hull feature

Performance Analysis of Ball Mill Liner Based on DEM-FEM Coupling

5

4 Simulation Parameter Definitions The parameters of the mill in the simulation are as follows: the filling rate is 0.4 [6]; the diameter of the selected steel balls is 40 mm, 30 mm, 20 mm, and they match the medium using the equal number method. The material of the steel ball is ZGMn13, and the selected mill rotation rate is 65%. 4.1

Particle Model Establishment

Table 1. The mass distribution of ore and grinding medium of each particle in the simulation Particle size (mm) Category 4 Ore

Quality (kg) 8.568

7.5

11.424

12 20, 30, 40

Shape Sphere Tetrahedron Sphere Tetrahedron Hexahedron Diamond Sphere Tetrahedron Grinding medium Sphere

8.568 216

The outlines of the particles are limited to spheres, tetrahedrons, hexahedrons, and diamonds. The mass distribution of particles and the shape in the simulation model are shown in Table 1. 4.2

The Meshing of Geometry

Tetrahedral mesh is used for the liner, the lifting bar and its bionic unit. A single liner was analyzed when the amount of wear was counted. 4.3

Setting of Simulation Model

Set the motion of the geometric model to a linear rotation, and the speed is 35.55 rpm. The geometric model starts from 1.45 s, and the total simulation time is 2.9 s. The motion of materials in the ball mill at different time points are shown in Fig. 3, where Fig. 3(a) shows the static accumulation state after blanking is completed, and Fig. 3(b) shows the motion state of the material at 2.9 s.

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(a)Static accumulation state after blanking

(b)The motion state of the material at 2.9s

Fig. 3. The motion of materials in the ball mill at different time points

5 Simulation Result Analysis The static analysis of the liner is based on the DEM-FEM coupling method. The force obtained in EDEM soft and its simulation files are imported into the EDEM module. Therefore, the meshing in the coupling analysis is consistent with the discrete element simulation. In such a way, a more accurate solution can be obtained. The wear of the lifting bar is related to the impact of the material and the medium on the lifting surface. Therefore, the equivalent stress distribution on the surface of the lifting bar is analyzed in detail. Figures 4, 5, 6 and 7 shows the equivalent stress distribution of the ball mill cylinder model with four different surface feature lifting bars in a certain period. Results in Fig. 4 illustrate that the smooth surface of lifting bar is most concentrated in the joint portion of the lifting bar and the substrate of liner during a certain period of mill operation. Secondly, Stress concentration occurs at the adjacent part of the surface of lifting bar and the upper surface. This is because the contacts between the material and the medium in these places are the severest, and it is known that the stresses at both ends of the lifting bar are also higher than the stress in the middle of the bar. It can be seen from Fig. 5 that the equivalent stress on the surface of the lateral stripe surfacing feature lift bar is mainly concentrated in the adjacent part of the lift bar and the liner base, and there is no obvious stress concentration. The stress between the bionic features is lower than that of the bionic features. From the stress and its distribution, the value is smaller than that of the smooth surface features. Figure 6 shows the stress distribution of the lifting bar of the longitudinal stripe feature. The stress is mainly distributed at the end of the longitudinal feature of lifting bar and near the substrate of the liner. This is related to the geometric dimension of the end of the feature and the size of the material. Since the particles stay between the bionic feature and the substrate of the liner, the stress becomes larger. In general, the stress value of the longitudinal feature of lifting bar is smaller than that of the smooth surface. In Fig. 7, the distribution of overall stress is uniform compared to the other three lift bars. At the same time, the stress value on the feature unit is smaller than the stress value of the lateral feature bar. These results show that the stress relieving effect of the convex hull type bionic feature is better than other bionic feature in the simulation. However, on the matrix between the features, there is few difference between the vertical stripes lifting bar and even the smooth surface lifting bar, so that the protection

Performance Analysis of Ball Mill Liner Based on DEM-FEM Coupling

7

Fig. 4. Equivalent stress distributions of smooth surface lifting bars and its surrounding area

Fig. 5. Equivalent stress distributions of horizontal stripes surface lifting bars

Fig. 6. Equivalent stress distributions of vertical stripes lifting bars

Fig. 7. Equivalent stress distributions of convex hull lifting bars

effect on the substrate is limited. To make a more accurate study of the wear zone distribution of each lifting bar during the grinding process, the EDEM post-processing module is used to select the “Geometry Bin” option. The concentrated area of the liner and lifting bars are divided into two parts, and the areas are numbered and shown in Fig. 8. In the post-processing module of EDEM, the “Arc hard Wear” option in the geometry section is selected in “File Export-Result Date”, since this option evaluates the amount of wear based on the accumulated energy. The trend can also verify the cumulative energy received by the liner and the lifting bar in this simulation. The accumulated energy received by the liner and the lifting bar is plotted after the ball mill model with different surface feature operated for a period, and this result can be used to intuitively compare the amount of wear in the same area. The relationship is shown in Fig. 9.

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Fig. 8. The divided area of the liner and the lifting bars

Fig. 9. The wear distribution of specified area in different liner and lifting bars

As shown in Fig. 9, for the liner where the smooth surface feature lifting bar is mounted, the main wear position is located between the upper surface of the lifting bar and the non-lifting surface, i.e., between the region 9 and the region 14. The amount of wear in the region 9 is 5.19E−7 mm. This is because of the right-angled trapezoidal liner used in this simulation. Therefore, stress concentration appears at the common edge of the non-lifting surface (region 9) and the upper surface. At the same time, the transition region between the upper surface and the lifting surface (region 13) undergoes relatively intense friction during the grinding process with the material raised to the highest point, and this is the reason why the amount of wear is correspondingly high. The reason why the amount of wear of the area 10 to area 12 is also relatively large is that from the time when the upper surface is in contact with the material, and the time when the material begins to drop, the material does more work for the location than the work done on the lifting surface, so the amount of wear is slightly larger than the area 13 to area 15. The wears of other areas are significantly lower than the ones in area 9 to area 14.

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9

6 Conclusions In this paper, the stress distribution of the lifting bar and its surrounding area during the grinding process is analyzed. At the same time, the wear areas of the lifting bar of different surface features are compared. The results are summarized as follows: (1) During the grinding process, the stress of the lifting bar is mainly concentrated in the adjacent part of the lifting bar and the liner, and the values in the two ends are larger than the one in intermediate part. The smooth surface lifting bar has obvious stress concentration during the grinding process, and the concentrated area is the widest among the four lifting bars. The stress relaxation effect occurs in all three different bionic feature units, and this reduces the equivalent stress of the lifting bar to a certain extent and improves the wear resistance of the lifting bar. (2) Although the stress on the convex hull type bionic feature unit is significantly reduced, the effect of the release of the equivalent stress on the lifting bar base is lower than that of the horizontal stripe. The order of the stress relieving effect of the bionic feature unit, from high to low, is horizontal stripe, convex hull feature and vertical stripe. (3) During the grinding process, the wear of the liner surface and the lifting surface of the right angle trapezoidal lifting bar is significantly higher than that of the liner between the lifting bar, and the most severely worn area are mainly concentrated near the common side of upper surface and lifting surface. (4) Comparing with the lifting bar on the smooth surface, the liner with the bionic feature lifting bar has better wear resistance, and the order of the wear resistance of the liner, from low to high, is horizontal stripes, vertical stripes, and convex hull. (5) The horizontal stripes bionic feature lifting bar can extend the replacement period of the mill liner and avoid the mill downtime caused by the inconsistent replacement period between the liner and the lifting bar. It can also reduce the number of liner replacements and extend the liner life cycle. Acknowledgement. The work was fully supported by the Youth Project of Science and Technology Department of Yunnan Province (No. 2017FD132). We gratefully acknowledge the relevant organizations.

References 1. Zhao, M., Lu, Y., Pan, Y.: Review on the theory of pulverization and the development of pulverizing equipment. Min. Metall. 10(2), 36–41 (2001) 2. Zhang, G.: Current status and development of crushing and grinding equipment. Powder Technol. 4(3), 37–42 (1998) 3. Belov, Brandt: Grinding. Liaoning People’s Publishing House, Liaoning (1954)

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4. Li, W.: Market and production of wear-resistant steel parts. In: Proceedings of the Annual Meeting of Yunnan Wear-Resistant and Corrosion-Resistant Materials 2004, Kunming, pp. 7–12 (2004) 5. Cao, Z., Wang Dapeng, G.: Research progress of Marsh’s mother-of-pearl and seawater pearls. J. South. Agric. Sci. 40(12), 1618–1622 (2009) 6. Zhang, X., Dong, W., Zhou, H., et al.: Numerical simulation of wear resistance of ball mill lifting bars with biomimetic characteristics. Nonferrous Metals (Mineral Process.) 6, 56–62 (2017)

Performance of Spiral Groove Dry Gas Seal for Natural Gas Considering Viscosity-Pressure Effect of the Gas Xuejian Sun1, Pengyun Song2(&), and Xiangping Hu3(&) 1

2

Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China Chemical Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China [email protected] 3 Industrial Ecology Programme, Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway [email protected]

Abstract. Centrifugal compressors used for transporting natural gas are usually equipped with dry gas seals. The working medium of the seal is usually the delivered gas, that is, natural gas. In this paper, the natural gas viscosity-pressure equation is derived from the Pederson mixed gas viscosity model and Lucas viscosity-pressure model, and the real gas property of natural gas is expressed by Redlich-Kwong equation. The gas film pressure governing equations proposed by Muijderman for narrow grooves are modified and solved for the seal faces. The influences of natural gas viscosity-pressure effect on the sealing characteristics, such as leakage rate and opening force, of spiral groove dry gas seal are analyzed. Results show that the viscosity-pressure effect has significant influence on spiral groove dry gas seal. This effect reduces the leakage rate but increases the opening force, compared to the situation without considering the viscosity-pressure effect. With the pressure up to 4 MPa, the viscosity-pressure effect of natural gas is weak and negligible. As the pressure increases, the viscosity-pressure effect increases. At 12 MPa, the relative deviations of leakage rate and opening force caused by the viscosity-pressure effect are respectively −30.6% and 1.65%. Therefore, the analyses indicate that the viscosity-pressure effect of natural gas needs to be considered when used in high pressure situation. Keywords: Dry gas seal
Natural gas
Analytical method
Viscosity-pressure effect

1 Introduction In natural gas long-distance pipelines, the compressors used for transporting natural gas are usually equipped with dry gas seals as their shaft end seals. The working medium of the seal is usually the delivered gas, that is, natural gas. Typically, the natural gas in the pipeline is a mixture of different gases, and its components is different from the natural gas sources. The physical property is different from each other. Viscosity is an © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 11–19, 2020. https://doi.org/10.1007/978-981-15-2341-0_2

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important physical property for dry gas seal, and this property of the natural gas is closely related to gas components, temperature and pressure. In general, when the isothermal flow is assumed, the viscosity of the natural gas is a function of composition and pressure. Daliri et al. [1] analyzed the viscosity variation with pressure to obtain squeeze film characteristics by modified Reynolds equation and Stoke’s microcontinuum. Lin et al. [2] analyzed the effects of viscosity-pressure dependency and studied the impacts of squeezed films between parallel circular plates of non-Newtonian coupled stress fluid lubrication. According to their results, the influences of viscosity-pressure dependency raise the load capacity and lengthen the approaching time of the plates. As to the viscosity-pressure effect on the dry gas seal, Song et al. [3] analyzed the effect of viscosity-pressure of nitrogen gas on the sealing performance by the Lucas model. Their results show that high pressure has significant effects on the opening force, the leakage rate and the gas pressure at the spiral groove root radius. However, nitrogen is a pure gas and does not involve the viscosity relationship of a mixed gas such as the natural gas. As to the spiral groove dry gas seal, the Pederson mixed gas viscosity model and Lucas viscosity-pressure model are used to express the natural gas viscosity-pressure effect, and the real gas property of natural gas is expressed by Redlich-Kwong equation. The gas film pressure governing equations proposed by Muijderman for narrow grooves are modified and solved for the dry gas seal faces. The dry gas sealing characteristic parameters such as the opening force and leakage rate are obtained.

2 Model Description 2.1

Geometry Model

The structural model of the spiral groove dry gas seal and geometric model of seal face are shown in Fig. 1. In the geometric model, ri and ro are the inner and outer radii of the sealing ring, respectively, and rg is the radius at the root of the spiral groove; x is the angular velocity of the sealing ring; pi and po are the inlet and outlet pressure, and a is the helix angle.

Fig. 1. Structural model of the spiral groove dry gas seal (a) and geometric model of seal face (b)

Performance of Spiral Groove Dry Gas Seal for Natural Gas

2.2

13

The Model of Natural Gas Viscosity-Pressure Effect

Lucas natural gas viscosity-pressure equation [4] has the following form, go ðpo ; To Þ ¼

1 þ a1 p1:3088 r

a4 1 a2 pa5 r þ ð1 þ a3 pr Þ

g0

ð1Þ

Equation (1) is substituted to the Pederson mixed gas viscosity expression [5], which yields the model of natural gas viscosity-pressure effect:
gmix ¼

Tc;mix Tco

16


pc;mix pco

23


l Mo

12

amix 1 þ a1 p1:3088 r g a4 1 0 ao a2 pa5 r þ ð1 þ a3 pr Þ

ð2Þ

The po, To, Tc,mix, pc,mix and l are expressed as follow: po ¼

pc;mix

ppco
ð1:0 þ 0:031q1:847 Þ TTco
ð1:0 þ 0:031q1:847 Þ r r ; To ¼ 3 1:847 0:5173 Tc;mix ð1:0 þ 7:378  103 q1:847 ð1:0 þ 7:378  10 qr l Þ l0:5173 Þ r

C P C P

Tc;mix ¼

i¼1 j¼1

ni nj

 1 3

PP i

j

Tci pci

ni nj

þ

 13 3 

 1 3

0 l ¼ 1:304  104 @

Tci pci

Tcj pcj

þ

c X i¼1

Tci Tcj

12

 13 3

8 ; pc;mix ¼

C P C P i¼1 j¼1

(

Tcj pcj

ni nj

PP i

ni Mi2 =

c X

!2:303 ni M i

i¼1



c X

j

  1 3

ni nj

Tci pci

þ

 1 3 Tci pci

 13 3  Tcj pcj

þ

Tci Tcj

 13 3

12

)2

Tcj pcj

!2:303 1 c X Aþ ni M i ni M i

i¼1

i¼1

Where, {ai, i = 1, …, 5} are correction factors, and Pc, Tc are the critical pressure and critical temperature obtained from the literature [5], respectively. 2.3

The Real Gas Model of Natural Gas

The present research adopts the Redlich-Kwong Equation [6]: p¼

RT a  V  b T 0:5 V ðV þ bÞ

ð3Þ

And the Gas state equation can be written as: PV ¼ ZRT

ð4Þ

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Substituting Eqs. (3) to (4) yields: 2 N Z ¼ 4 þ 2

3 2 3 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2
3 1=3
2
3 1=3 N M 5 N N M 5 þ1 þ þ 4  þ 2 3 2 2 3 3

ð5Þ

The real gas state equation is: q ¼ pM=ZRT

ð6Þ

The density of the natural gas of Eqs. (5) and (6) are expressed as: qmix ¼   N2

pM=RT 1=3   qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi M 3 M 3ffi 1=3 N 2 N N 2 þ þ 3 þ 2 þ 3 þ 2 2

ð7Þ 1 3

M, N, a, b, aij, ai and bi are expressed as follow:
 1 p2 b2 ap bp þ 2 2  2 2:5 þ M ¼  3 RT R T
R T  2 1 p2 b2 ap bp abp2  N ¼   þ  3 3:5 2 2 2 2:5 27 3 R T R T RT R T a ¼

n X n X i¼1 j¼1

yi yj aij b ¼

n X

 0:5  yi bi aij ¼ ai aj 1  kij

i¼1

ai ¼ 0:42748R2 Tc2:5 =Pc Tc0:5 bi ¼ 0:08664RTc =Pc where, ai, aj are pure material parameters, yi, yj are the mole fraction of the mixture of the pure substances i and j; kij is the binary interaction coefficient of the i, j pure substances. These parameters can be found in the literature [6]. 2.4

Modified Gas Film Pressure Governing Equations

The modified gas film pressure governing equations can be obtained by substituting Eqs. (2) and (6) to the gas film pressure governing equations which are based on Muijderman narrow groove theory [7]. For sealed dam area, the equation has the form: dp 6g St RT 1 ¼ mix 3 dr ph qmix r

ð8Þ

Performance of Spiral Groove Dry Gas Seal for Natural Gas

15

For sealed groove area, the equation can be written as: dp 6g1 gmix xr 6gmix St g7 RT 1 ¼ þ dr h2 ph1 h2 g5 qmix r

ð9Þ

St is the mass flow rate of the gas passing through the sealing surface; h and h1 are, respectively, the film thickness of the groove and non-groove area, and they fulfill the relationship h1 = h + t, where t is the groove depth of the spiral groove; x is the angular velocity of rotation of the sealing ring; g1, g5, and g7 are the spiral groove coefficients which can be obtained from the literature [7]. 2.5

Solution of Gas Film Pressure Governing Equations

Boundary conditions of Eqs. (8) and (9) are: pjr¼ri ¼ pi ; pjr¼ro ¼ po The pressure distribution p(r) of end face film is obtained, and the end face opening force F is obtained by integrating over the entire end face: Z F¼

ro

pðr Þ2prdr

ð10Þ

  ph3 p2g  p2i   St ¼ r 12gRc T ln rgi

ð11Þ

ri

The leakage rate St is expressed as:

3 Analytical Model and Verification 3.1

Model Verification

The results from Eqs. (2) and (7) obtained in this paper are compared with the literature data, and they are illustrated in Fig. 2 with different pressure conditions. The results show that the average deviation of the natural gas compressibility factor, viscosity with the National Institute of Standards and Technology database (NIST) [8] are 0.344% and 1.45%, respectively.

X. Sun et al. 1.00

Current data NIST database GERG-2008 natural gas data

0.98

Compressibility factor/Z

0.96 0.94 0.92 0.90 0.88 0.86

Current data Ref [9] RealPipe data NIST database

2.4

Natural gas viscosity ηmix/10-5Pa.s-1

16

2.2 2.0 1.8 1.6 1.4 1.2

0.84 0

3

6

9

P/MPa

12

15

0

2

4

6

8

P/MPa

10

12

14

16

Fig. 2. The comparison between the current data and references data [9, 10]

3.2

Property Parameter

The parameters of natural gas components and seal face geometric are listed in Tables 1 and 2, and these parameters are from the literature [5, 11]. Table 1. Natural gas components and parameters Component CH4 C2H6 C3H8 I- C4H10 N- C4H10 CO2 N2 Molar 0.812 0.043 0.009 0.0015 0.0015 0.076 0.057

Table 2. Basic parameters of numerical calculation Parameter Outer radius, ro/mm Inner radius, ri/mm Number of groove, n Film thicknesses, h0/lm

3.3

Value 77.78 58.42 12 3.0

Parameter Value Radius of groove root, rg2/mm 69 Spiral groove angle, a1/° 15 Groove depth, hg1/lm 5 Groove width ratio, c 1

Relative Errors

As shown in Sect. 2.5, the leakage rate St and opening force F of the natural gas with the viscosity-pressure effect and the real gas property can be obtained from Eqs. (9) and (10). Furthermore, two additional cases are analyzed, i.e., ideal gas case by setting Z = 1 and viscosity-pressure effect ignorance case with constant viscosity. To express the effect of natural gas viscosity-pressure on spiral groove dry gas seals, the relative errors are used: E1 = ((the leakage rate of G1-the leakage rate of G3)/(the leakage rate of G3))  100%. E2 = ((the leakage rate of G2-the leakage rate of G4)/(the leakage rate of G4))  100%. E3 = ((the opening force of G1-the opening force of G3)/(the opening force of G3))  100%.

Performance of Spiral Groove Dry Gas Seal for Natural Gas

17

E4 = ((the opening force of G2-the opening force of G4)/(the opening force of G4))  100%. where G1 is the ideal gas with the viscosity-pressure effect; G2 is the real gas with the viscosity-pressure effect; G3 is the ideal gas without viscosity-pressure effect; G4 is the real gas without viscosity-pressure effect.

4 Results and Discussion The boundary condition of internal pressure is 0.1013 MPa and the external pressure po is respectively 0.6 MPa, 4 MPa, or 12 MPa. The sealing performance is calculated at different film thicknesses. The results are shown in Figs. 3 and 4. 4.1

Leakage Rate

The leakage rates of G1 to G4 under different pressures and film thicknesses are shown in Fig. 3. It can be seen from the Fig. 3(a)–(c) that the leakage rate increases with the increase of film thickness and pressure. Figure 3(d) is a three-dimensional (3-D) map of pressure, film thickness, leakage rate, from which it can be seen that the interaction between the leakage rate and the two parameters, i.e. the film thickness and pressure is obvious. When the pressure reaches 0.6 MPa, the averages of E1, E2 are, −0.070% and −1.193%, respectively. Negative values of E1 and E2 indicate that the viscosity-

Fig. 3. Leakage at different film thicknesses and different po

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pressure effect reduces the leakage rate. The reason is that as the pressure increases, the viscosity increases and the gas flow decreases, which results in the decrease in the leakage rate. The value of │E2│ is greater than the │E1│ indicates that the viscosity-pressure effect induces a stronger influence on real natural gas spiral groove dry gas seal compared with the assumptions of ideal gas. When the pressure reaches 12 MPa, the averages of E1 and E2 are −28.622% and −30.6%, respectively. The results show that the viscosity-pressure effect has influence on the leakage rate of dry gas seal. 4.2

End Face Opening Force

The opening force of G1 to G4 under different pressures and film thicknesses are shown in Fig. 4. The result in Fig. 4(a)–(c) show that the opening force increases with the increase of pressure but decreases with the increase of the film thickness. From the three-dimensional (3-D) map of pressure, film thickness and opening force, it can be seen that the effect of pressure on the opening force is more obvious compared with the film thickness. E3, E4 is greater than 0, indicating that the viscosity-pressure effect of natural gas raises the opening force. At 0.6 MPa, the opening forces of the G1 to G4 almost overlap. As the pressure increases, the relative error of the opening force of G1 to G4 increases. When the pressure reaches 4 MPa, the average values of E3, E4 are 0.503%, 0.8120%, respectively. When the pressure reaches 12 MPa, the average values of E3, E4 are 0.901%, 1.6472%, respectively. It is shown that under 4 MPa, the effect of natural gas viscosity-pressure effect on the opening force is negligible.

Fig. 4. Opening force for kinds of gas at different film thicknesses

Performance of Spiral Groove Dry Gas Seal for Natural Gas

19

5 Conclusions For spiral groove dry gas seal of conveying natural gas centrifugal compressor, the natural gas viscosity-pressure effect is analyzed based on the narrow groove theory of the spiral groove. The conclusions of the present research are listed as follows: (1) The viscosity-pressure effect reduces the gas leakage rate but increases the opening force. (2) Up to 4 MPa, natural gas viscosity-pressure effect is weak. As the pressure increases, the viscosity-pressure effect increases. (3) At 12 MPa, the relative deviations of leakage rate and opening force caused by the viscosity-pressure effect are respectively 30.6% and −1.6472%. The viscosity-pressure effect of natural gas needs to be considered when used for high pressure situation. Acknowledgement. The research is supported by National Natural Foundation of China (granted no. 51465026)

References 1. Daliri, M., Jalali-Vahid, D.: Investigation of combined effects of rotational inertia and viscosity-pressure dependency on the squeeze film characteristics of parallel annular plates lubricated by couple stress fluid. J. Tribol.-Trans. 137(3), 1–23 (2015) 2. Lin, J.-R., Chu, L.-M., Liang, L.-J.: Effects of viscosity-pressure dependency on the nonnewtonian squeeze film of parallel circular plates. Lubr. Sci. 25(1), 1–6 (2013) 3. Song, P., Ma, A., Xu, H.: The high pressure spiral groove dry gas seal performance by considering the relationship of the viscosity and the pressure of the gas. In: 23rd International Conference on FLUID SEALING, pp. 36–72 (2016) 4. Poling, B.E., Prausnitz, J.M., John, P.O.C., et al.: The Properties of Gases and Liquids. Mcgraw-Hill, New York (2001) 5. Zhang, S., Ma, I., Xu, Y.: Natural Gas Engineering Handbook, pp. 32–44. Petroleum Industry Press, Beijing (2016). (in Chinese) 6. Chen, Z., Gu, Y., Hu, W.: Chemical Thermodynamics, Third edn. pp. 184–185. Chemical Industry Press, Beijing (2011). (in Chinese) 7. Muijderman, E.A.: Spiral Groove Bearings, pp. 17–21. Springer, New York (1966). Bathgate R.H. Trans. 8. National Institute of Standards and Technology: NIST Chemistry Webbook [EB/OL] 9. Sun, H., Wang, J.: Design Manual for Flowmeter Measurement Throttling Device, Second edn., p. 4. Chemical Industry Press, Beijing (2005). (in Chinese) 10. Deng, J., Song, F., Chen, J.: Research the simulation software of Realpipe-gas in natural gas long-distance pipelines. Oil Gas Storage Transp. 9(30), 659–662 (2011). (in Chinese) 11. Sun, X., Song, P.: Analysis on the performance of dry gas seal in centrifugal compressor for transporting natural gas. J. Drain. Irrig. Mach. Eng. 1(36), 55–62 (2018). (in Chinese)

Analysis of Residual Stress for Autofrettage High Pressure Cylinder Guiqin Li1(&), Yang Li1, Jinfeng Shi1, Shijin Zhang1, and Peter Mitrouchev2(&) 1

Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China [email protected] 2 University Grenoble Alpes, G-SCOP, 38031 Grenoble, France [email protected]

Abstract. Based on the elastoplastic theory of materials, the distribution of residual stress is analyzed by the Huang’s model calculation to obtain the optimum autofrettage pressure considering the strain hardening effect of the plastic material and the Bauschinger effect. The BLH simulating model is set up by applying 16 different autofrettage stresses from 400 MPa to 1200 MPa. The optimized autofrettage stress distribution was achieved by comparing the working stress of 16 groups of experiments. Then, the Huang’s model is simplified, and the trend of working stress with residual stress is obtained. The reliability of the simplified model is verified, which provides a basis for autofrettage high-pressure cylinders design. Keywords: Bauschanger effect
Autofrettage
Residual stress
High pressure cylinder

1 Introduction The essence of self-enhancement technology is the rational use of residual compressive stress field. Therefore, the study of residual stress field is the basis of autofrettage technology to improve the fatigue characteristics of ultra-high pressure parts. For ultra-high pressure pumps, it is an indisputable fact that the fatigue characteristics of the components can be greatly improved after the introduction of the residual stress field. However, how to quantify the change in fatigue characteristics of the residual stress field? Over the years, many Chinese and foreign researchers have conducted a lot of exploration. The feasibility of finite element analysis in the application of prestressed scene analysis is demonstrated by Parker [1], including the plastic deformation of metals with highly nonlinear stress-strain curves. Bhatnagar [2] presents a new concept of selfreinforced composite tube and models the autofrettage process by considering the Bauschinger effect. Gibson [3] proposes a swage-like model, which applies deformation through pressure belt to investigate the effect of local load and shear stress on residual stress field. Peng [4] considers the energy stored in the residual micro-stress field in the plastically deformed material and its influence on the subsequent plastic deformation and derives a 3D constitutive equation for large elastic deformation. © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 20–28, 2020. https://doi.org/10.1007/978-981-15-2341-0_3

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Domestic researchers have also done a lot of work on the autofrettage model of the high pressure cylinder. Huang [5] proposes an autofrettage model considering the material strain-hardening relationship and the Bauschinger effect, based on the actual tensile and compressive stress-strain curve of material, plane-strain, and modified yield criterion. Yuan [6, 7] studied the effects of end conditions of the vessel and material parameters on the residual stress. ZHU [8] Based on the 3rd strength theory, the theoretical relations among the equivalent stress of total stresses at elastoplastic juncture, depth of plastic zone and reverse yielding, and load-bearing capacity for an autofrettaged cylindrical pressure vessel are analyzed and demonstrated by using combined image and analytical methods. On the basis of a nonlinear kinematic hardening model, FU and YANG [9] present a nonlinear combined hardening model which is used to describe the mechanical properties of autofretted barrel and calculate the residual stress distribution. Yang [10] based on the energy dissipation method, derived the fatigue damage and fatigue life function of composite thick-walled cylinders.

2 Residual Stress Analysis 2.1

Analysis of Residual Stress in Self-reinforced High Pressure Cylinder

According to the experimental data, the fatigue life of the high-pressure cylinder is only 500–600 h. In order to improve the fatigue life of the high-pressure cylinder, this study intends to use the self-enhancement technology to treat the high-pressure cylinder. The essence of self-enhancement technology is the rational use of residual compressive stress field. Therefore, the study of residual stress field is the basis of autofrettage technology to improve the fatigue characteristics of ultra-high pressure parts. The material of the high-pressure cylinder is a plastic material, and the plastic material has a Bauschinger effect and a strain hardening behavior. Therefore, if the EPP model is used to calculate the process, there will be a large deviation. In this paper, the optimal autofrettage stress is solved by the Huang’s model. which defines the critical self-reinforced stress, that is, the optimal self-reinforced stress, when the reverse yield occurs exactly at the inner wall of the self-reinforcing cylinder. It defines that the stress when the reverse yield happens exactly at the inner wall of the self-reinforcing cylinder is the critical autofrettage stress, that is the optimum autofrettage stress. According to the formula (2-1): Pzzq

"
2 # arE ri 1 ¼ 2 ro

Pzzq : Optimum autofrettage stress; ri : Autofrettage cylinder inner diameter; ro : Autofrettage cylinder outer diameter; a: Yield criterion parameter.

ð2  1Þ

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The Bauschinger effect is represented by the rE parameter, which can be obtained from the tensile-compressive stress-strain curve of the material. In this paper, rE = 1.684rS , a = 1.21. It is calculated that the optimum self-reinforced stress is 861.167 MPa. In order to verify the reliability of the calculation results, this paper will carry out finite element virtual simulation calculation. 2.2

Finite Element Analysis of Residual Stress

In order to facilitate the simulation of the self-enhancement process, the reverse hardening stage and replaces the original curve with a multi-segment polyline has been optimized in this paper. The tensile-compression curve of the newly constructed selfreinforced wall material is shown in Fig. 1 where the DEFG section is the reverse hardening stage, which characterizes the effect of the Bauschinger effect on the material.

Fig. 1. Material model

2.3

Analysis of Working Stress of Non-autofrettage High Pressure Cylinder

Taking a high pressure cylinder of waterjet cutting machine as the research object for modeling, in order to facilitate the calculation, the author simplified the model and only relied on the external dimensions for modeling. Since the model is relatively simple, it can be modeled directly in abaqus to avoid unpredictable errors during the import of the model. The working condition of the high-pressure cylinder is 420 MPa. The stress-strain cloud diagram of the high-pressure cylinder under the working pressure is shown in Fig. 2. It can be seen that the maximum stress is 699 MPa (Fig. 3).

Analysis of Residual Stress for Autofrettage High Pressure Cylinder

Fig. 2. 420 Mpa stress contour

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Fig. 3. Plastic deformation contour

3 FEA of Residual Stress 3.1

Stress Distribution of the BLH Model

The high-pressure cylinder is approximately subjected to the ultra-high internal pressure load of the pulsation cycle. When the simulation calculation is performed, the pulsating cyclic load is calculated by the working load of 420 MPa, and the calculation result is safe. By applying different self-reinforcing stresses, it was divided into 16 groups from 400 MPa to 1200 MPa. Only the self-enhancement pressure is different between experiments, so this paper uses python to program the simulation process to reduce the amount of repetitive work. The law of distribution obtained by the simulation experiment is shown in Fig. 4. As the autofrettage pressure increases, the mises stress increases slowly, and the radial stress has a significant improvement. Analysis of the residual stress curve reveals that residual stress begins to occur when the selfreinforcing pressure is 550 MPa. When the autofrettage pressure reaches 600 MPa, the thick-walled cylinder begins to yield, and the residual stress increases significantly. Comparing the radial residual stress with the tangential residual stress, it can be found that the radial residual stress is almost negligible.

Fig. 4. Equivalent stress contour

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The PEEQ diagram of the thick-walled cylinder is shown in Fig. 5. As the selfreinforcing pressure increases, the equivalent elastic-plastic strain gradually increases. When the autofrettage pressure is 550 MPa, the elastic-plastic strain begins to appear, and the 800 MPa front elastic-plastic strain increase is slower. The strain increase is slower, and the elastic-plastic strain starts to increase sharply after 800 MPa. When the self-reinforcing pressure increases to 1200 MPa, it begins to enter the fully plastic shaped state.

Fig. 5. PEEQ contour

he working load of the water jet high pressure cylinder studied in this paper is 420 MPa, and the maximum stress under the working load of the high pressure cylinder without self-reinforcing treatment is 699.6 MPa. The maximum stress after self-reinforcing treatment is shown in Fig. 6. It can be seen from the figure that the maximum working stress begins to decrease after the self-reinforcing pressure is higher than 550 MPa. When the self-reinforced pressure increases to 800 MPa, the working stress drops to a minimum of 495.6 MPa. Then, if the self-reinforced stress continues to increase, the working stress begins to rise. Therefore, it can be concluded that the working stress near 800 MPa takes the lowest value.

Fig. 6. Residual stress and working stress

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25

It can be found that the optimal self-enhancement pressure calculated by the formula method is quite different from the optimal self-enhancement pressure calculated by the bilinear hardening model. Therefore, in the engineering design process, it is not advisable to simulate the autofrettage high-pressure cylinder using the BLH. 3.2

Stress Distribution of Autofrettage Material Model

The Bauschinger effect refers to the phenomenon that plastic strain strengthening caused by forward loading during metal plastic processing leads to plastic strain softening during subsequent reverse loading. When the metal material is first stretched to the plastic deformation stage, unloaded to zero, and then reverse loaded, that is, when the compression deformation is performed, the compressive yield limit of the material is significantly lower than the original yield limit. The object of this paper is the high-pressure cylinder of water jet cutting machine. The load is pulsed and has the deformation process of reciprocating loading, unloading and reloading. Therefore, the Bauschinger effect needs to be considered. According to the reference [5], the autofrettage residual stress distribution can be obtained by subtracting the corresponding unloading stress from the loading stress, and the calculation of the residual stress can be divided into two cases and three regions for discussion. The first case is a completely elastic unloading, and no reverse yielding occurs, so the Bauschinger effect is negligible. The second case is elastoplastic unloading, which consists of three regions, namely, loading elastic zone, unloading elastic zone, loading plastic zone, unloading elastic zone, loading plastic zone, and unloading plastic zone. The analysis can be such that when the reverse yield occurs exactly at the inner wall of the self-reinforcing cylinder, the corresponding selfreinforcing pressure is the optimum autofrettage pressure. This paper combines the field variable multi-analysis step to model the self-reinforced material model. The simulation process can be roughly divided into three steps, self-reinforced stress loading, self-reinforced stress unloading, and working load loading. rs , E1 , E2 are measured by the tensile and compressive tests of the material. For the simplified calculation, E3 ¼ E2 , E4 ; E5 ; E6 are obtained by interpolation.

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Fig. 7. Radial distribution of tangential stress

The radial distribution of the maximum tangential residual stress is shown in Fig. 7 above, where green, blue, yellow, and brown represent the tangential residual stress distribution at 800 MPa, 850 MPa, 900 MPa, and 950 MPa, respectively. It can be seen that the maximum tangential residual stress is obtained at the inner wall. The trend of the residual stress along the radial direction is firstly sharply decreased with the increase of the thickness, and after reaching the wall thickness of 7 mm, it enters a relatively gentle phase.

Max working Stress

450

AutofreƩage model

BLH model

Fig. 8. Max working stress change trend

The working stress diagram is shown in the above Fig. 8. The orange curve is the maximum working stress under the ideal elastoplastic model. It can be seen that when the self-reinforcing stress is less than 550 MPa, the material does not have any strengthening effect. When it is greater than 550 MPa, the working stress begins to have a significant drop interval. The blue curve is the maximum working stress contour under the autofrettage material model. The two are approximately coincident before

Analysis of Residual Stress for Autofrettage High Pressure Cylinder

27

750 MPa, and after more than 750 MPa, the two begin to separate. It can be seen that because of the existence of the Bauschinger effect, the self-reinforcing effect is weakened, so the influence of the Bauschinger effect must be considered in the design process, otherwise there will be significant losses. 3.3

Autofrettage Stress Optimization

It can be concluded from the calculation and simulation experiments that the selfreinforcing stress is 850 Mpa, which is the optimum autofrettage pressure. At this time, the distribution of equivalent residual stress and equivalent working stress along the cross section of the high-pressure cylinder is shown in Figs. 9 and 10 is shown. It can be seen from the above analysis that the application of the self-enhancement technology effectively reduces the working stress of the high-pressure cylinder during operation, thereby improving its working life. When the autofrettage stress reaches 750 MPa, the change of working stress tends to be gentle with the increase of selfreinforcing pressure, and the increase of residual stress is relatively large. Large residual stress can reduce the working stress and achieve the purpose of increasing fatigue life. However, since the high-pressure cylinder is under high stress for a long time, the probability of initial cracking also increases.

Fig. 9. Equivalent residual stress

Fig. 10. Equivalent working stress

4 Conclusions (1) By simplifying the water jet high pressure cylinder model, the parameters of the self-reinforced model are determined, and the distribution of residual stress is calculated. The value of the best self-reinforced stress is obtained. (2) The Huang’s model is simplified to facilitate finite element analysis calculations. Based on abaqus software, a three-dimensional finite element parametric model of high-pressure cylinder is established. The method of simulating the selfenhancement process of high-pressure cylinder by multi-load step and field variable method is given. The finite element simulation of the BLH model and the self-enhancement model is carried out, and the simulation results are obtained. The maximum residual stress and working stress under different self-reinforced stresses are analyzed. The optimal self-reinforced pressure is obtained by

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comparison, and the distribution of tangential residual stress along the radial direction is obtained under the optimal self-reinforced stress. The simulation results show that the BLH model does not consider the Bauschinger effect and the reverse yielding effect, and the results obtained deviate greatly from the real situation. The results of the self-enhanced model simulation more accurately reflect the distribution of residual stress.

References 1. Parker, A.P.: Autofrettage of open-end tubes—pressures, stresses, strains, and code comparisons. J. Press. Vessel Technol. 123(271), 8 (2001) 2. Bhatnagar, R.M.: Modelling, validation and design of autofrettage and compound cylinder. Eur. J. Mechan. 39, 17–25 (2013) 3. Gibson, M.C.: Determination of Residual Stress Distributions in Autofrettaged ThickWalled Cylinders. Cranfield University, Cranfield (2008) 4. Peng, X., Balendra, R.: Application of a physically based constitutive model to metal forming analysis. J. Mater. Process. Technol. 145(2), 180–188 (2004) 5. Huang, X.P., Cui, W.: Effect of bauschinger effect and yield criterion on residual stress distribution of autofrettaged tube. ASME J. Press. Vessel Technol. 128(2), 212–216 (2006) 6. Yuan, G.: Analysis of residual stress for autofrettaged ultrahigh pressure vessels. Zhongguo Jixie Gongcheng/China Mech. Eng. 22(5), 536–540 (2011) 7. Yuan, G.: Numerical analysis of residual stress and strength of autofrettage high pressure cylinder finishing. Mech. Des. Manuf. (03), 229–232 (2015) 8. Zhu, R.: Study on autofrettage of cylindrical pressure vessels. J. Mech. Eng. 46(6), 126–133 (2010) 9. Fu, S., Yang, G.: A nonlinear combined hardening model for residual stress analysis of autofretted thick-walled cylinder Acta Armamentarii 07 (2018) 10. Yang, Z.Y., Qian, L.F.: Research on fatigue damage of composite thick wall cylinder. Chin. J. Appl. Mech. 30(03), 378–383 (2013)

Study on the Detection System for Electric Control Cabinet Lixin Lu1, Weicong Wang1, Guiqin Li1(&), and Peter Mitrouchev2(&) 1

Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China [email protected] 2 University Grenoble Alpes, G-SCOP, 38031 Grenoble, France [email protected]

Abstract. The electric control cabinet for massage armchair, as a control unit, has a crucial influence on the normal operation of massage armchair, so the manual detection of electric control cabinet should be replaced by an intelligent detection system with high efficiency to improve the reliability of detection results. By combining the characteristics of electric control cabinet and several detection methods widely used at present, this paper proposes a study on the detection system for electric control cabinet in massage armchair to meet the urgent demand of precise and efficient detection of electric control cabinets, with LabVIEW as the software foundation and ADAM data acquisition module as the hardware supports. Keywords: Cabinet


Detection
LabVIEW
ADAM module

1 Introduction Nowadays, the application of automated detection systems plays an important role in promoting the development of enterprises [1]. Lately the increasing popularity of massage chairs has led to the formation of an independent industrial chain and hence the relevant standards and industry, norms for massage chairs have officially appeared [2]. The need for massage chair testing is going to be more urgent [3]. For the detection of the power plant control cabinet, Yang [4] proposed to check the analog output signal of the power station by providing the analog input signal. As for the detection system of subway control system, Lan [5] proposed a hardware system that can directly connect the tested cabinet and give test input to the cabinet and the response. This paper takes the electric control cabinet as the detection object and designs a detection system for electric control cabinet based on distributed network.

2 System Introduction The system mainly consists of detection module and data analysis module. The detection system of electric control cabinet mainly aims to confirm whether the control function of cabinet is normal or not. Therefore, this paper regards the electric control © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 29–36, 2020. https://doi.org/10.1007/978-981-15-2341-0_4

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cabinet as “black box” so that it should pay less attention to the internal structure and electric control principle of the electric control cabinet but emphasize the output signal generated by the electric control cabinet when the input signal has been given. From this perspective, all the electric control cabinets of massage armchair have a common characteristic regardless of various types. In short, the input and output signals of electric control cabinet are both constituted by analog quantity signal and control signal. The upper computer of this system is programmed through LabVIEW, controlling the operation of electric control cabinet through the module from RS-232 to TTL in an industrial personal computer (IPC) and collecting the pressure value, analog voltage and control peripheral through RS485 bus and Advantech ADAM module. As shown in the Fig. 1, the control system controls the data acquisition module respectively through the bus and achieves the control of detection system and data management based on LabVIEW.

Fig. 1. Topology of control network

3 Detection Module Generally, different signal pathways will be used to enter the controller according to different signal types, roughly including digital input, digital output, analog input, analog output, technology/frequency inputs and other modules. As shown in the Fig. 2, the most common method for system structure is to control the ADAM module (I/O module from Advantech Co., Ltd) on the whole network of RS485 through IPC and RS232/RS485 conversion modules. Each ADAM module is connected to RS485 and operate independently without any data exchange between modules to achieve data transfer and data receive.

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In this detection system, the electric control cabinet has the output signals including pressure value, analog voltage and digital quantity, which can be acquired through I/O module. Therefore, it’s considerately appropriate to apply the structures of distributed data acquisition and control system constituted by RS485 bus to this system.

Fig. 2. Common system structure of ADAM module

As for the detection of airbag, measure the output pressure of air pump with an air gauge and judge the working condition of each airbag based on the high or low levels controlled by solenoid valve. As for the detection of movement, a set of load resistance in the detection system is used to simulate the working condition of electric control cabinet, so as to detect the output voltage in the case of analog load and further judge whether the corresponding function can meet requirements. Finally, after the detection is completed, the data will be stored in local database to generate a report and the system will be reset for the next detection.

4 Data Analysis Module The system processes the measured data through a barometer to determine whether the air pump function of the electric control cabinet is normal. However, due to the interference of various external factors, the collected signals will inevitably have various burrs. Therefore, the signal should be filtered to overcome the sudden disturbance caused by the abrupt disturbance or the sensor. When the sampling rate of the ADC is higher than the actual demand and the controller has enough actual filtering of the sensor for multiple acquisitions, the median filtering method is generally adopted, which essentially determines the filter output response by minimizing the absolute value of the error box. Its mathematical principle is that let a set of observations be fxi gð1  i  NÞ and find the best approximation x of fxi g to minimize the sum of the absolute errors. Then there is

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Q¼

N X

j x  xi j

ð1Þ

i¼1 N dQ dX ¼ jx  xi j dx dx i¼1

¼

¼

N h i1=2 dX ð x  xi Þ 2 dx i¼1

N N X X x  xi ¼ signðx  xi Þ ¼ 0 j x  xi j i¼1 i¼1

ð2Þ

To satisfy the formula (1), x should take the value of fxi g in the order of the size of the middle position.In this system, the ADC sampling rate is set to 4.4 kHz. After each time the data acquisition ends, a DMA interrupt is generated, and the sensor data of different channels is copied to the elements of the corresponding label of the array ADC1_ConvertedValue. And the elements in the array will be updated again every 8 times. At this point, the array is placed in a two-dimensional buffer as a column of a two-dimensional array. The buffer uses the data structure of the heap (first-in first -out) so that the five latest 8-channel sensor values are always stored in the buffer and refreshed at a frequency of 4.4 kHz. According to the control instruction of the host computer, when the controller needs to collect and filter data for different channels, the program calls the filter. And if the data in the buffer does not overflow, it will directly return the collected 8-channel value. Otherwise, it will perform 5 cycles of bubble sorting for each row of 5 data in the buffer and 8 rows of data will be sorted to return the median value of each row. The data is stored in the global variable ADC1_filterValue and the data is stored after a data acquisition is completed. The exact delay is based on the acquisition frequency in the instruction and the filter is called again after the time has elapsed until all points are collected. The data acquisition frequency of different detection functions is also different. When the data sampling rate is low, the newly acquired elements in the buffer are obtained by taking the median value of the five elements continuously sampled, which achieves that the element time interval is shorter, the sampling value of pressure is more accurate. When the sampling frequency is 4.4 kHz, one column of data is added each time in the filter buffer and the median value is extracted by scrolling, which does not limit the sampling rate while ensuring the filtering effect. Following figures is a comparison of the collected pressure waveform before and after filtering. Figure 3 is the original waveform acquired, and Fig. 4 is the effect after the filter is called. It can tell that the filter removes the pulse interference better and makes extraction of subsequent feature much smoother.

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Fig. 3. Pre-filtering signal

Fig. 4. Filtered signal

5 System Development The LabVIEW, which is significantly used for the detection system in the development environment, has its own unique advantages. Firstly, the establishment of humancomputer interface is easier and faster than that of other languages. Secondly, LabVIEW can provide serial communication control unit for researchers to develop the system program of serial communication rapidly. Besides, there is a large amount of convenience in its functions to facilitate the use of users. This system combines LabVIEW and distributed system to build a high-efficiency and practical detection system in an easy way. The modularized software design is used for the programming of control system, with the software structure shown as below (Fig. 5):

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Fig. 5. Software framework

As for the design and development of this system, the VISA serial communication control unit of LabVIEW is used to achieve the connection with hardware equipment and the communication protocol of ADAM is used to achieve the control of upper computer over peripheral equipment, such as reading the output value of air pump collected through air pressure sensor, etc. Meanwhile, ADAM has the functions of current and voltage measurements, properly achieving the direct measurement on current and voltage of analog load resistance and realize the precision and high efficiency of data acquisition (Fig. 6).

Fig. 6. VI of communication judgement

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The upper computer program is mainly divided into four parts: ADAM operation, function detection, cabinet command and custom control. Each part includes the corresponding VI module. The modular design is adopted in the design of the program. Each function detection item of electric control cabinet is set for a module (a subroutine). The advantage of this is that when the type of the electric control cabinet increased, the corresponding program module can be used as long as hardware support is satisfied, which is convenient for post-maintenance.

6 Result According to the demand analysis on electric control cabinet of massage armchair, this paper proposes the detection system structure of electric control cabinet based on distributed network. The LabVIEW is adopted as the upper computer to control the electric control cabinet and read the feedback signal through serial port and achieve the distributed control over each ADAM module through RS485 bus so that the detection system can have good real-time responsiveness and the capacity of resisting disturbance. In order to detect whether the electric control cabinet can drive the normal operation of a motor, the load resistance should be used to simulate the internal resistance of motor under working conditions so as to realize convenient and efficient

Fig. 7. Operation interface of system

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detection procedures, guarantee the accuracy of detection results and facilitate the use of users. The detection system for the electric control cabinet of massage armchair proposed in this paper provides an integrated environment for follow-up intelligent production (Fig. 7). Acknowledgements. This work is supported by the project under grant D.71-0109-18-167.

References 1. Wang, D.: Application of automated inspection in the era of “Industry 4.0”. Sci. Technol. (12), 109 (2016) 2. Chen, J.J.: Industrial automation technology in various engineering fields. Silicon Val. 5, 129 (2010) 3. Su, D.D.: Research and Design of Adaptive Control System of Massage Chairs. Anhui University of Technology (2017) 4. Yang, S.X., Wang, X., Wu, D.J.: The design of test system for power plant control panel. Mob. Power Veh. (01), 19–20+35 (2011) 5. Lan, J.: Design of Automatic Test System for Electrical Cabinet of Metro Vehicle Control System. University of Science and Technology, Nanjing (2014) 6. Mao, L., Sun, D.: A method of pressure sensor dynamic digital filter. Sens. Instrum. 24(12–1), 127–128 (2008) 7. Tian, Y.: Development of electronic control function inspection system for mining equipment. Ind. Miner. Autom. 37(8), 165–167 (2011) 8. Ping, Y.B.: Research on Control System Based on RS-485 Network. Southwest Jiaotong University (2003)

Effects of Remelting on Fatigue Wear Performance of Coating Zhiping Zhao(&), Xinyong Li, Chao Wang, and Yang Ge School of Mechanical Engineering, Changshu Institute of Technology, Changshu, Jiangsu, China [email protected]

Abstract. The thermal spraying technology is a kind of surface strengthening technology. With the development of science and technology, the thermal spraying technology has been made extensive popularization and application. To guarantee the service life of thermal spraying component and investigate the effects with various remelting time, specimens of 40Cr steel substrate which were thermally sprayed with Ni-based self-fluxing alloy coating were prepared. Fatigue wear performance and friction coefficient of the samples with different remelting time were investigated by the micro vibration friction and wear test machine. Wear volume were analyzed by non-contact three-dimensional surface contour graph measuring system. The results showed that a reasonable remelting time has a significant effect on the surface fatigue resistance and coating structure. At a reasonable remelting time, the fatigue resistance of the coating surface will be the strongest. Keywords: Coating


Remelting time
Wear volume
Wear properties

1 Introduction Flame spray welding (powder welding) is a method to make a coating by heating a selffluxing alloy powder on surface with an oxy-acetylene flame or other heat source. With this method, a spray coating on the surface of metal substrate could be obtained. The method is well to reduce the pores and oxide slag in sprayed layer. And, a fusion layer between metal substrate and coating would be generated and improve the compactness and bonding strength of the coating greatly. Moreover, the surface of workpiece will achieve an excellent corrosion resistance. Due to its performance and wear resistance and ability to withstand higher stresses, it is widely used in aerospace, mechanical engineering, petrochemical and other fields. In recent decades, many scholars [1–5] have studied the safety and reliability of the interface of powder flame spray weldments. While, the fatigue performance and wear resistance of flame spray weldments with various remelting times have few studies. In this study, 40Cr was metal substrate and Ni60A nickel-based self-fluxing alloy powder was coating material. After flame spray welding, flame remelting technology is adopted. In flame remelting process, the flame temperature is constant, various experiments were carried out to study the effect from remelting time. In previous study, the bending behavior and torsion fatigue properties of samples after various remelting © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 37–43, 2020. https://doi.org/10.1007/978-981-15-2341-0_5

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time treatments have been worked out [6–8]. The main job in this study is to investigate wear resistance of the treated coated surface. With parametric study and analyze, the optimization of process parameters, basic theory of wear resistance and prediction of fatigue wear life could be explored.

2 Experimental Procedure 2.1

Preparation of Spraying Material and Coating Samples

The spraying material of Ni60A alloy powder is used in this study, which is a kind of self-fluxing alloy powder. With this material, external weld flux is not necessary during flame spray welding. Moreover, the alloy composition shows the deoxidation and slagging performance during remelting, which could improve the wetting performance greatly. Meanwhile, a low melting point alloy which is well metallurgically bonded could be obtained. The melting point from 1050 to 1100 °C could ensure only the spray coating layer melts during remelting while have no influence on substrate metal. The hardness of coating layer was measured as 55–65 HRC. Also, a good solid-state flow property was observed and the particles are spherical with granularity of 200. The mass percentage of the chemical composition are 13.7% Cr, 2.96% B, 4.40% Si, 2.67% Fe, 0.60% C, and the rest is Ni. Before flame spray welding, the surface of the substrate metal was derusted and degreased. Then, the coating layer is prepared by sandblasted and roughened before flame thermal spraying. The process parameters are shown in Table 1 [9]. Table 1. Flame thermal spaying process parameters Fuel 1 (oxygen) press/MPa 0.5

Fuel 1 (acetylene) press/MPa 0.1

Spraying distance/mm

Preheat temperature/°C

Spraying temperature/°C

150

300

500

The total processes were surface pretreatment, preheating, pre-spraying and dusting. After spraying, the sample was processed into a standard sample of U24 mm  7.9 mm (thickness) with coating thickness of 1 mm. Then, the samples were proceeded by flame remelting. A constant flame temperature with four different remelting times were tested. The remelting time was 2 min, 5 min, 10 min and 12 min. After remelting, the samples were cooled slowly to room temperature surrounded by asbestos powder. 2.2

Test Method

The non-lubricating fatigue friction and wear test in room temperature was carried out by SRV-IV micro-vibration friction and wear tester of OPTIMEL, Germany. The motion form is reciprocating movement and the contact form is point contact. For the

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rubber ball sample, the ceramic ball Si3N4 with U10 mm with amplitude of 1 mm was used. The test load of 30 N with 20 Hz was applied in 30 min. The three-dimensional shape of the wear scar of the sample coating was reflected by the non-contact threedimensional surface profilometer of ADE, American. With this profilometer, the wear volume of coating could be calculated.

3 Test Results and Analysis 3.1

Analysis of Coating Wear Test Results

With observing the three-dimensional wear morphology of each coating layer (Fig. 1 is the three-dimensional wear morphology of the coating after remelting at different lengths of time), it is found that the wear volume of each typical sample coating after different remelting treatments is different. Different (as shown in Fig. 2, where the wear volume is organized as shown in Fig. 2). Among them: the coating wear volume after remelting for 2 min is the largest, the wear volume is 3.734  107 lm3; the second is the coating after remelting for 5 min, the wear amount is 3.50974  107 lm3; then it is after remelting for 12 min. The coating has a wear amount of 3.266  107 lm3; the smallest amount of wear is the coating after remelting for 10 min, and the wear amount is 3.029  107 lm3. It shows that the wear resistance of each coating is as follows: remelting 10 min > remelting 12 min > remelting 5 min > remelting 2 min coating. According to the wear test, the remelting time is 10 min for the wear resistance of the sample, which is a reasonable remelting time. Secondly, by observing the threedimensional wear morphology of each coating in Fig. 1, it was found that the bottom of the remelted 2 min and remelted 5 min coating had obvious lamellar peeling that was inconsistent with the grinding direction, and remelted for 10 min and remelted for 12 min. There is no obvious layering peeling marks at the bottom of the pit, but there are deep cutting furrow marks on the wear surface of the coating. In order to further understand the coating strength and influencing factors, we studied the microscopic morphology of each coating in 3.3. 3.2

Wear Performance of Coating Layer

In terms of coating layer, the particle deformation, furrow and adhesion are three basic mechanisms for generating friction force. The friction factor is mainly affected by factors such as coating morphology, coating adhesion and slip conditions. The relationship between the friction coefficient and the structure is related to factors such as the distribution of defects. Since the hardness of the ceramic ball is higher than that of the coating, the main factor which cause the change of the friction force include the change of the tangential furrow force and the contact area. Figure 3 shows the friction coefficient of the remelting coatings under different durations during the friction and wear test. It shows that each friction factor fluctuated with the continuation of time growing. After remelting for 2 min, remelting for 5 min, and remelting for 12 min, the friction coefficient of the coating was fluctuated with time. The friction factor after

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Fig. 1. Micro wear morphology of coating after different post-fusing: (a) 2 min; (b) 5 min; (c) 10 min; (d) 12 min

Fig. 2. Wear volume of coating after different post-fusing: (a) 2 min; (b) 5 min; (c) 10 min; (d) 12 min

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remelting for 10 min began to fluctuate after 200 s, while at the period from 800 s to 1200 s, the friction factor dropped back to a low value. And then it showed a volatility growth. To this end, the mean friction factor under different conditions were calculated. The mean friction coefficient after remelting for 2 min was 0.661842. The mean friction coefficient after remelting for 5 min was 0.7253625. The mean friction coefficient after remelting for 10 min was 0.6818065. And the mean friction coefficient after remelting for 12 min is 0.6893495. Hence, the order of friction factor could be determined as 2 min < 10 min < 12 min. Hence, the friction coefficient of remelting 2 min was minimal.

Fig. 3. Friction coefficient of coating after different post-fusing: (a) 2 min; (b) 5 min; (c) 10 min; (d) 12 min

Secondly, Fig. 3 shows that the friction factor is relatively stable during the previous 200 at test. While the mean value fluctuated slightly, it was indicated that the friction factor is mainly determined by the coating structure in this period. Moreover, the influence on friction factor from strength and defect was small. The mean value of friction coefficient of each coating layer could be obtained by calculation. The mean friction coefficient of coating layer after remelting for 2 min is 0.505657. And the mean friction coefficient of coating layer after remelting for 5 min is 0.52132. The mean friction coefficient of remelting for 10 min is 0.41205. And the mean value after remelting 12 min is 0.526977. Hence, the order of friction factor is 10 min < 2 min < 5 min < 12 min. With comparing the wear volume of coating, it was found that the friction coefficient by remelting 2 min was less than that of remelting 5 min.

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It was indicated that there has no absolute correspondence between wear resistance and friction factor. Hence, it is necessary to study the microscopic morphology of each coating layer.

4 Wear Fatigue Life Prediction of Coated Parts According to the wear formula proposed by Czichos [10], the wear rate remains constant during the wear stable period. And the wear volume is a function of time as W = Ct. Where W is wearing volume, t is wearing time and C is a constant. Therefore, we can fit the wear volume and time curve at wear stable period to obtain the constant C. Hence, the wear fatigue life of thermal sprayed parts under a same condition could be predicted. Meanwhile, the product can be repaired and replaced timely and the reliability and economic of the product could be ensured.

5 Conclusions (1) Under the same test conditions, the wear volume is directly related to the overall wear resistance of the coating. The smaller the wear volume means a better performance of wear resistance. (2) The friction factor is directly related to fractional wear resistance of the coating. The smaller value means a better performance of fractional wear resistance of the coating and it does not indicate the overall wear resistance of the coating. (3) When remelting time is reasonable, the wear resistance is best. While the remelting time is insufficient or too long, the wear resistance are not well. (4) Fitting the wear amount and time curve during the wear stabilization period will obtain the value of the constant c in the formula W = Ct. Thereby, the fatigue wear life of the thermally sprayed member can be predicted under the same conditions.

References 1. Zhang, X.C., Xu, B.S., Wang, Z.D., Tu, S.T.: Failure mode and fatigue mechanism of laserremelted plasma-sprayed Ni alloy coating in rolling contact. Surf. Coat. Technol. 205, 3119– 3127 (2011) 2. Berger, L.M., Lipp, J., Spatzier, J., Bretschneider, J.: Dependence of the rolling contact fatigue of HVOF-Sprayed WC-17%Co hardmetal coatings on substrate hardness. Wear 271, 2080–2088 (2011) 3. Wang, S.Y., GuoLu, L.I., Wang, H.D., et al.: Influence of remelting treatment on rolling contact fatigue performance of NiCrBSi coating. Trans. Mater. Heat Treat. 32(11), 135–139 (2011) 4. Wang, G., Sun, D., Wang, Y., et al.: Cavitation properties of Ni-based coatings deposited by HVAF and plasma cladding technology. Trans. Mater. Heat Treat. 28(6), 109–113 (2007)

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5. Akebono, H., Komotori, J., Shimizu, M.: Effect of coating microstructure on the fatigue properties of steel thermally sprayed with Ni-based self-fluxing alloy. Int. J. Fatigue 30, 814– 821 (2008) 6. Zhao, Z., Li, X., Li, Y., et al.: The analysis of fatigue properties and the improvement of process for plunger with different post-fused thermal spray. Trans. Mater. Heat Treat. 33(s1), 92–95 (2012) 7. Zhao, Z., Li, Y., Li, X.: Effects of remelting time on fatigue life and wear performance of thermal spray welding components. Trans. Mater. Heat Treat. 34(7), 169–174 (2013) 8. Zhao, Z., Li, X., Li, Y., et al.: Effect of remelting processing on fatigue properties of Ni based PM alloy parts with thermal spraying coating. Powder Metall. Technol. 1, 3–7 (2012) 9. Li, X., Zhao, Z.: The investigation and practice of flame thermal spray technology on piston. J. Lanzhou Polytech. Coll. 12(2), 9–11 (2005) 10. Yang, W., Wu, Y., Hong, S., et al.: Microstructure, friction and wear properties of HVOF sprayed WC-10Co-4Cr coating. J. Mater. Eng. 46(5), 120–125 (2018)

Design of Emergency Response Control System for Elevator Blackout Yan Dou1,2, Wenmeng Li3, Jiaxin Ma1,2, and Lanzhong Guo1,2(&) 1

School of Mechanical Engineering, Changshu Institute of Technology, Changshu, People’s Republic of China {jixiedouyan,guolz}@cslg.edu.cn, [email protected] 2 Jiangsu Elevator Intelligent Safety Key Construction Laboratory, Changshu, People’s Republic of China 3 Zhejiang Academy of Special Equipment Science, Hangzhou, People’s Republic of China [email protected]

Abstract. The emergency system will cooperate with an integrated controller with low-speed self-rescue function. When the elevator is powered off, the inverter power supply will supply power to the elevator controller. The controller will drive the traction machine to slowly stop the car to the nearest level position to open the door. During the normal operation of the elevator, the SCM should monitor the power supply situation at all times and control the system circuit to charge the battery. After the power failure of the elevator, the single chip should be able to respond in time, control the inverter power supply to supply power to the elevator controller and output emergency operation signals and phase sequence short circuit signals to the controller. After the emergency operation is finished, the single chip also needs to receive the stop signal output by the controller and then control the system to enter a standby state. The emergency system is one of the defense lines to ensure the safety of elevators. The elevators using the emergency system can effectively reduce the occurrence of trapped accidents and make the emergency rescue process more convenient. Keywords: Elevator power failure response
Control system


Automatic leveling
Emergency

1 Introduction The traditional rescue method requires manual turning, which takes a long time. In order to rescue trapped passengers from the car in time, emergency response control system came into being. When the city power supply is normal, the elevator emergency response control system charges the internal storage battery. Once the power grid is cut off or the elevator has an electrical fault, the system first isolates the external power grid from the elevator operation control system, and then inverts and outputs the electric energy in the storage battery to the frequency converter of the control cabinet so that it drives the traction machine to drive the car to slowly level off and release people. The elevator emergency response control system should be able to distinguish from the © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 44–51, 2020. https://doi.org/10.1007/978-981-15-2341-0_6

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time-consuming and labor-intensive manual operation, so as to cut off power and save oneself, reduce the trapped time of personnel and ensure the safety of passengers. At present, there are two kinds of elevator power failure emergency rescue devices on the domestic market. The first type is to install an electric brake release device. When the elevator is out of power, the device opens the traction brake independently from the control cabinet to make the car floor nearby, and then professional rescuers come to open the door and release the trapped passengers. The second type is the installation of uninterrupted power supply in the elevator control cabinet. When the elevator is out of power, supply power to the control cabinet and let the car automatically open and put people on the flat floor. This kind of rescue device is more convenient but not widely used at present [1] (Fig. 1). Interface of Elevator Control System Input power supply detection

Charging circuit

Storage battery

Emergency Control System

Control System for Door-Motor of Elevator

Elevator doormotor

DC Converter

Brake

Three-phase Inverter Circuit

Tractor

Fig. 1. Schematic diagram of emergency device

2 The Main Content of the Subject Research This design is an emergency response control system. The system needs to be able to detect the power supply situation of the elevator at all times and react in time to cooperate with the elevator controller inverter to drive the traction machine when power is cut off. During the normal operation of the elevator, the emergency response control system shall not interfere with the operation of the operation control system. The rated voltage of the inverter used to drive the traction machine of the elevator is usually 380 V AC. The controller used in this design must have the functions of lowspeed self-rescue, running direction self-identification and power failure rescue. After the power failure of the elevator, the controller can identify the load condition of the car and start the emergency power supply to slowly run the elevator to the level area to open the door in the most energy-saving way, thus safely and quickly rescuing the trapped personnel in the car.

3 Functional Requirements of the System Elevator power outage emergency response control system (abbreviated as emergency system). First of all, the system should have the function of detecting the external power grid so that the system can respond in time in case of power failure. In this

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system, a phase sequence relay needs to be placed, which mainly plays the role of phase sequence detection and phase interruption protection. When the power failure occurs, elevator controller will stop the operation when the power is lost. In order to realize the automatic landing release of the car, a storage battery is required to transmit power to the control cabinet. Under the condition of ensuring its power supply, an emergency operation start signal is output to the frequency converter of the control cabinet to make it operate, and the traction machine is driven to lift the car and control the door system to open the door to release the car. Since a battery is required during power failure, it is inevitable to have a charging circuit and a saturated discharging circuit in the circuit when power supply is normal. After the emergency rescue, the emergency system needs to enter the standby state to wait for the power supply to return to normal, which requires the controller to output a rescue stop signal to stop the system [2]. In this experiment, NICE3000new series elevator integrated controller is selected for the inverter part of the control cabinet. Its rated voltage is AC 380 V. After the emergency system outputs 380 V AC, the controller can realize the function of lowspeed self-rescue, control the car to slowly level the floor and then open the door. In addition to the safety switch circuit, it also includes the door lock circuit. When all the door electrical interlocking switches are closed, the controller’s main board can receive the signal that the safety circuit works normally. Since there is a safety switch of phase sequence relay in the series circuit of safety switch, when the elevator is out of power, the phase sequence relay will stop working, and its safety switch will be disconnected naturally, making the safety circuit invalid. In the emergency operation stage, the safety circuit can be connected only when the safety switch of phase sequence relay is closed, and the controller can control the emergency operation of the elevator. The emergency system of this experiment should not only be able to output the two-phase 380 V alternating current and the emergency operation start signal to the control cabinet inverter, but also be able to output the phase sequence short connection signal.

4 Control Circuit Design of the System 4.1

The Circuit Structure of the System

The microcomputer control module is necessary for the system to receive and output signals. When the elevator power supply is normal, the battery module should be charged, so there must be a charging power module in the circuit. In case of elevator power failure, the system shall supply ac 380 V to the control cabinet inverter, while the battery module outputs dc voltage, which requires an inverter module to invert the dc voltage output from the battery module into ac 380 V output (Fig. 2).

Design of Emergency Response Control System for Elevator Blackout

Incomplete sequence relay

Microcomputer Control

Battery charger

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Storage battery

Inverter

Elevator Controller

Fig. 2. The Hardware module structure diagram of the system

The microcomputer control module takes the single chip microcomputer as the core, its working voltage is generally 24 V direct current. When the elevator is running normally, the charging power supply module will supply dc 24 V to the power detection end of the microcomputer control module. After recognition by the singlechip microcomputer, the power supply of the elevator is confirmed to be normal. The phase sequence relay module has 3 input points to detect three-phase electricity, and one of its normally open contacts is connected to the microcomputer module power detection terminal and connected in series to the power supply circuit. When the elevator is out of power, the phase sequence relay stops working, the normally open contact of the phase sequence relay in the power supply circuit is disconnected, the power detection end cannot detect 24 V dc, and the single chip microcomputer can identify and confirm the elevator is out of power. Therefore, the basis of microcomputer control module to judge whether the elevator is out of power is whether 24 V dc is detected at the power detection end [3] (Fig. 3).

Storage battery Charging Power Supply

Control cabinet

Power supply detection The microcomputer control module

R

S

AC220V

T

Charging Power Supply Module

Incomplete sequence relay module

Stop signal

Charging

24V Power supply detection

Storage battery

Power supply COM

Microcomputer board

Fig. 3. The Microcomputer control module, The Incomplete sequence relay module and The Charging power supply module

4.2

Design of Control Schematic Diagram of the System

The battery module is charged by the charging power module when the power supply is normal, and the microcomputer control module and inverter module are supplied when the power is cut off. 48 V battery group is selected as the battery module. The charging

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power module outputs 55 V dc to charge the battery and 24 V dc to supply power to the microcomputer control module. The detailed design idea of the schematic diagram of the control circuit is as follows. Press the start switch of the device, and both FA normally open contacts in the circuit are closed. When the elevator power supply is normal, the phase sequence relay works normally, and the phase sequence relay at the power supply detection end of the microcomputer control board is often open and closed. The km coil connecting the three-phase electricity and the zero line is electrified. The KM is often open and the contact is closed and the circuit is turned on (Fig. 4).

Fig. 4. The Control circuit diagram of the system

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The charging power supply outputs 24 V DC to the power supply detection terminal of the PC control board. The MCU recognizes that the elevator power supply is normal, then the K1 and K3 terminals have no signal output, and the K1 and K3 coils are not charged. K1 and K3 normally closed contacts are closed to maintain circuit connectivity; K1 and K3 normally open contacts are disconnected to maintain standby state; K1 often open contacts are disconnected to maintain standby state; K1 often open contacts are disconnected without starting signal and phase sequence short connection signal are issued; K3 normally closed contacts are kept closed so that power supply control terminal continues to monitor power supply. The 48 V output terminal of the charging power supply charges the battery, and the QF1 (48 V air switch) is turned on to prevent the short circuit of the inverter. Every charging period, the battery is close to saturation. The signal is sent from the K2 terminal of the PC control board. The action coil of the K2 relay is energized. The K2 single pole double throw switch disconnects the charging circuit. The battery and the resistance are discharged in series. At the same time, the inverter is charged and recharged after a period of time. Because the KM contactor coil is powered up, the KM closed contacts at the output end of the inverters are disconnected to isolate the power supply [4, 5]. When the elevator is out of power, the coil of KM contactor loses power, which causes the open contacts of KM to be disconnected and the circuit to be disconnected. The phase sequence relay stops working, its normal open contacts are disconnected in the power supply circuit, and there is no 24 V direct current input in the power supply detection terminal. The single chip computer identifies and confirms the elevator blackout, the output signals of K1 and K3 terminals, and the action coils of K1 and K3 relays are powered up. The normal closed contacts are disconnected and isolated from the market electricity, so as to prevent the sudden calls to make the KM contactor act during emergency operation. During the period, the computer module is supplied with 24 V DC by the battery, and the QF2 (24 V air) switch is closed. K1 and K3 normally open contacts are closed, batteries output 48 V DC to the inverters, QF1 closed, and inverters output 380 V AC (KM normally closed due to coil power loss has been disconnected) to the elevator control cabinet frequency converter NICE3000 new. K1 usually opens contacts to close and output emergency operation start signal and phase sequence short connection signal. In safety circuit, phase sequence relay often opens contacts to close and the safety circuit works normally. The controller drives the tractor to release the car slowly near the flat floor. During this period, because the K3 normally closed contacts are disconnected, even if the phase sequence relay operates the computer module, the power signal will not be detected, which ensures the stability of emergency operation. After the rescue, the controller outputs emergency stop signal to MCU for identification. The MCU controls K1 and K3 relays to stop, and the system enters standby state to wait for elevator power supply recovery. 4.3

Schematic Design of Wiring Between System and Control Cabinet

When the elevator is out of power, the emergency system provides the 380 V ac working voltage to the control cabinet. The single-chip microcomputer provides

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emergency operation signals to the main board X20 of the control cabinet, and at the same time provides phase sequence short connection signals to the phase sequence relay contacts of the safety circuit, and the elevator enters emergency operation. After the end of emergency operation, the elevator is located at the horizontal position. The horizontal sensor photoelectric switch X1 receives the horizontal signal, which is used as the stop signal to output to the emergency system. The system enters the standby state and waits for the power supply to resume (Fig. 5).

Fig. 5. Wiring Diagram of Emergency system and Control Cabinet

During emergency operation, 380 V AC power is output from the inverter to the elevator control cabinet. After K1 relay action, 24 V DC power is detected at the X20 end of the integrated controller. The 11 and 14 contacts of the phase sequence relay in the safety circuit are short connected, and the frequency converter drives the tractor operation.

5 Summary Emergency system and elevator control cabinet frequency converter matching use. When the abnormal elevator power supply is detected, the system provides 380 V AC to the controller and sends a start signal, and the elevator car will open slowly on the flat floor. After the operation, the controller feeds back a stop signal to the system and waits for power supply to be restored. Nowadays, most of the converters in the elevator

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control cabinet are integrated controllers, which have rich varieties and powerful functions, and have the function of low-speed self-rescue.

References 1. Wang, G., Zhang, G., Yang, R., et al.: Robust low-cost control scheme of direct-drive gearless traction machine for elevators without a weight transducer. IEEE Trans. Ind. Appl. 48(3), 996–1005 (2012) 2. Weiss, G., Felicito, N.R., Kaykaty, M., et al.: Weight-transducerless starting torque compensation of gearless permanent-magnet traction machine for direct-drive elevators. IEEE Trans. Ind. Electron. 61(9), 4594–4604 (2014) 3. Rashad, E.M., Radwan, T.S., Rahman, M.A.: A maximum torque per ampere vector control strategy for synchronous reluctance motors considering saturation and iron losses. In: Conference Record of the Industry Applications Conference, 2004. IAS Meeting, vol. 4, pp. 2411–2417. IEEE (2004) 4. Wang, A., Wang, Q., Jiang, W.: A novel double-loop vector control strategy for PMSMs based on kinetic energy feedback. J. Power Electron. 15(5), 1256–1263 (2015) 5. Zhang, Y.B., Pi, Y.G.: Fractional order controller for PMSM speed servo system based on Bode’s ideal transfer function. 173(6), 110–117 (2014)

Effect of Cerium on Microstructure and Friction of MoS2 Coating Wu Jian1,2,3(&), Xinyong Li1,2, Ge Yang1,2, Lanzhong Guo1,2, Cao Jie1,2, and Peijun Jiao1,2 1

School of Mechanical Engineering, Changshu Institute of Technology, Changshu, China [email protected] 2 Jiangsu Elevator Intelligent Safety Key Construction Laboratory, Changshu, China 3 Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway

Abstract. The MoS2 coatings with different Cerium contents (0.5%, 1%, 2%, 4%) were prepared based on the titanium alloy (Ti811) by a mixing method. The surface microstructure and metallographic structure of the MoS2 coatings were characterized by scanning electron microscopy (SEM). And the friction coefficient and wear texture were analyzed of four kinds of MoS2 coating layer. The results have shown the increase of Cerium content can refine the microstructure of the MoS2 coating, thereby inhibiting the growth of the crystal grains, improving the wear resistance, and lower the friction coefficient. The minimum friction coefficient is 0.055 at the Ce (1%). With the increase of Ce content, hydrogenation occurs, resulting in the crystallization of the MoS2 coating, which reduces the friction coefficient and affects the wear resistance. Keywords: MoS2
Cerium Morphology of wear


Microstructure
Friction coefficient


1 Introduction Tribology [1] is an applied discipline developed in the 1940s that focuses on friction, wear, and lubrication caused by relative motion between contact surfaces. Fretting wear can not only cause occlusion, looseness, noise increase between the contact surfaces but also may cause cracks on the surface of the test piece, which is greatly reducing the service life of the device [2]. As a convenient surface treatment technology, the bonded solid lubricant coating (such as MoS2) can reduce the friction and wear of parts [3–5]. Alnabulsi, Luo et al. studied the properties of fretting friction with MoS2 and analyzed the mechanism of friction [6, 7]. Zhu et al. tested the frictional properties of the lateral and radial loads on the MoS2 coating surface and compared the damage characteristics of the MoS2 coating under two different loads [8]. In this paper, the MoS2 coatings with different Cerium contents (0.5%, 1%, 2%, 4%) were prepared based on the titanium alloy (Ti811) by mixing method. The surface © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 52–58, 2020. https://doi.org/10.1007/978-981-15-2341-0_7

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microstructure and metallographic structure of the MoS2 coatings were characterized by scanning electron microscopy (SEM). And the friction coefficient and wear texture were analyzed of four kinds of MoS2 coating layer.

2 The Material and Layer Process 2.1

Matrix

The matrix material is the titanium alloy (Ti811) which is more common in aerospace. It is characterized by good thermal stability, strong corrosion-resistance, and high specific strength. Generally higher performance aircraft engine compressor components mainly choose this material. In this study, the titanium alloy (Ti811) sample processing size is u22 mm  7 mm, Using a double annealing treatment, and mechanical polishing to a surface roughness of Ra = 0.6 lm. The chemical components and the properties of Ti811 as shown Tables 1 and 2. Table 1. The chemical components of Ti811 (wt%) Al Mo V Fe C N H O Ti Ti811 7.9 1.0 0.99 0.05 0.1 0.01 0.001 0.06 89.889

Table 2. The properties of Ti811 Sb (MPa) S0.2 (MPa) HRC d (%) w (%) Ti811 931 890 32–38 23 46

2.2

Coating Preparation Process

The coating layer was prepared by solid lubricant, adhesive, curing agent, diluent, and Ce. The process of coating layer as follows: (a) The solid lubricant is selected from MoS2 and graphite, and the ratio is 1:1. The adhesive is a mixed resin of 601 and 618, and the mixing ratio is 3:1. The curing agent selects MHHPA, which has a relatively high curing temperature and the best solubility. The diluent is a mixed solvent of xylene, ethylbenzene and N-methyl pyrrolidone. The additive selects the rare earth element cerium (10 lm) with good modification properties. The amount of Cerium added to the coating is shown in Table 3. (b) Weigh the components according to the above ratio, mix and stir, then transfer to the grinder container for 24–48 h. Use a thinner to adjust the viscosity, and use ultrasonic cleaning to make the paint mix more evenly. After the treatment is finished, let it stand for a while and wait for the spray preparation. (c) The surface roughness of the sample after grinding is about 0.6 lm. The surface of the substrate was cleaned with ultrasonic waves, dried and sprayed. Basic parameters of spraying as followed: temperature is 18–25 °C, humidity less than

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80%, spray pressure is 0.2–0.4 MPa, Spray angle is 70°–90°, the thickness is 8– 15 lm. (d) Place the sample at room temperature for 1–2 h, then put it into the oven and gradually heat it. Curing time is 130 °C for half an hour; 220 °C for 0.5 h to 1 h. The picture of four coating sample as shown in Fig. 1.

Table 3. Coating ratio of Ce C1 C2 C4 C6 Ce % 0.5 1 2 4

Fig. 1. Picture of four sample

3 Experiment The fretting friction and wear test is carried out on self-made fretting friction and wear tester. The schematic diagram of the fretting friction test device is shown in Fig. 2. The motor drives the table to swing, and the three-axis micro force sensor is mounted on the upper test ball to measure the friction during the sliding process (Fig. 3).

Fig. 2. The schematic of wear tester

Fig. 3. Photo of wear tester

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In this test, the test parameters are: motor speed is 60 r/min, test load is 50 N, reciprocating distance amplitude is 200 lm, the test period is 2000 times, and each group of tests is done 3 times, and the average value is taken.

4 Results and Analysis 4.1

The Microstructure of Coating Layers

The structure of the four MoS2 lubricated coatings in the metallographic microscope (200) is shown in Fig. 4. The coating consists of black, green and gray phases, of which black is Cerium. Adding 0.5% rare Cerium (Fig. 4a), The coating structure is flat, but there are also very few particles that are bundled into a bundle. The coating is relatively uniform and dense, and the layered structure is more obvious. When the cerium particles with a content of 1% (Fig. 4b), the coating structure is uniform, the layer structure is uniform and compact, and the gray flat particles are obviously refined. With the increase of the Cerium, the coating structure is no longer uniform, begins to show agglomeration, and the surface protrusion of the coating increases, as shown in Fig. 4c and d.

Fig. 4. Microstructure topography of different MoS2 coatings

4.2

The Friction Coefficient Curves of Coating Layers

Figure 5 shows the friction coefficient of 4 coatings under the same test condition. The friction coefficient of different coatings has a similar trend. At the beginning of the test, the friction coefficient may change significantly in the initial stage of friction due to the unevenness of the surface of the coating. As the test progresses, the friction coefficient

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will gradually stabilize and rise and fall within a certain fixed range. The coefficient of friction of 0.5 content is the highest, which is 0.062–0.064, and the 1.0 content is the lowest, which is 0.056–0.058. As the content is increased, the coefficient of friction does not decrease and is basically between 0.056 and 0.064. Combined with microstructure, MoS2 containing Ce can reduce the friction coefficient of the surface. The friction coefficient is related to the distribution of Ce in MoS2. The more uniform Ce is inside MoS2, the lower the friction coefficient. But with the increase of Ce content, Ce accumulates in MoS2, which reduces the frictionreducing characteristics. However, if the Ce content is increased, Ce accumulates in MoS2, which in turn reduces the wear reduction characteristics. A Ce content of 1% allows Ce to be uniformly distributed in MoS2, thus obtaining the lowest coefficient of friction.

Fig. 5. Friction coefficient curve of four samples

4.3

Wear Morphology of Layers

The prepared samples were tested using self-made friction and wear test apparatus, and the results are shown in Fig. 6. Figure 6a shows the friction and wear morphology of a MoS2 layer coating with 0.5% Ce particles added. It can be seen from the figure that the surface of the coating has obvious wear marks, the surface of the wear scar has heavier furrow and falls off, and the base material is clearly visible. Figure 6b shows the friction and wear morphology of a MoS2 layer coating with 1.0% Ce. It can be seen that the wear profile of the coating is relatively light and the coating surface is smooth and complete. Figure 6c shows the friction and wear morphology with 2.0% Ce. The wear marks are deeper, the furrows are more obvious, and the central part is seriously worn. The last one (Fig. 6d) is to add 4%. It can be seen that the wear scars are more serious than 2%, the surface has heavier furrow marks and the lubrication film is lifted.

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Considering four microscopic of wear, the MoS2 coating with Ce can alleviate wear. A coating with a small amount of Ce added does not have a good anti-wear effect with the MoS2 coating. As the Ce content increases, the distribution in the MoS2 coating becomes more and more uniform, and the friction characteristics are effectively improved. As the Ce particle content increases, Ce particles begin to build up inside the coating, forming hard particles. The hard particles fall off during the movement, and at the same time increase the damage to the surface of the friction pair, and the surface does not form a good lubricating film.

Fig. 6. Different coating wear morphology of four samples

5 Conclusion (1) Cerium can improve the structure of the dry film coating. The MoS2 coating has a flat particle shape. After adding 1% Ce, the coating structure is refined, the coating structure is uniform, and there is no obvious particle agglomeration. (2) The addition of Cerium to the MoS2 dry layer can improve the friction and wear properties of the coating, and make the coating more wear-resistant and wearreducing. By comparing the fretting friction test of dry with different Cerium amount, with 1% Ce added was obtained, and the wear resistance of MoS2 dry film coating was the best. Acknowledgments. The work is supported by Jiangsu Government Scholarship for Overseas Studies, Open Project of Jiangsu Elevator Intelligent Safety Key Construction Laboratory (JSKLESS201703), and The Doctoral Science Foundation of Changshu Institute of Technology (No. KYZ2015054Z).

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References 1. Stachowiak, G., Batchelor, A.W.: Engineering tribology. Butterworth-Heinemann, Oxford (2013) 2. Zhu, M., Xu, J., Zhou, Z.: Alleviating fretting damages through surface engineering design. China Surf. Eng. 20(6), 5–10 (2007) 3. Asl, K.M., Masoudi, A., Khomamizadeh, F.J.M.S., et al.: The effect of different rare earth elements content on microstructure, mechanical and wear behavior of Mg-Al–Zn alloy. Mater. Sci. Eng.: A 527(7–8), 2027–2035 (2010) 4. Yan, M., Zhang, C., Sun, Z.J.A.S.S.: Study on depth-related microstructure and wear property of rare earth nitrocarburized layer of M50NiL steel. Appl. Surf. Sci. 289, 370–377 (2014) 5. Li, B., Xie, F., Zhang, M., et al.: Study on tribological properties of Nano-MoS2 as additive in lubricating oils. Lubr. Eng. 39(9), 91–95 (2014) 6. Alnabulsi, S., Lince, J., Paul, D., et al.: Complementary XPS and AES analysis of MoS3 solid lubricant coatings. Microsc. Microanal. 20(S3), 2060–2061 (2014) 7. Luo, J., Zhu, M., Wang, Y., et al.: Study on rotational fretting wear of bonded MoS2 solid lubricant coating prepared on medium carbon steel. Tribol. Int. 44(11), 1565–1570 (2011) 8. Zhu, M., Zhou, H., Chen, J., et al.: A comparative study on radial and tangential fretting damage of molybdenum disulfide-bonded solid lubrication coating. Tribology 22(1), 14218 (2002)

A Machine Vision Method for Elevator Braking Detection Yang Ge1,2(&), Jian Wu1,2(&), and Xiaomei Jiang1,2 1

School of Mechanical Engineering, Changshu Institute of Technology, Changshu 215500, Jiangsu, China [email protected], [email protected] 2 Jiangsu Key Laboratory for Elevator Intelligent Safety, Changshu Institute of Technology, Changshu 215500, Jiangsu, China

Abstract. Brake is one of important safety equipment in an elevator. An elevator brake force detection method based on machine vision is presented in this paper. The Fourier transform theory is used to transform an image from spatial domain to frequency domain, then power spectrum of the image is calculated, the main direction of the image is determined, and finally, rotation angle between two images is calculated. The experiment results show the proposed method has very high detection accuracy. Keywords: Machine vision spectrum
Rotation angle


Elevator braking
Fourier transform
Power

1 Introduction As special equipment, an elevator can quickly carry out vertical transportation, providing a great convenience for transportation. Elevator has a safety system to ensure safe operation, and the brake is an important device of the elevator safety system. It can effectively prevent and control the occurrence of accidents such as slipping and falling, which greatly improves the safety of elevator operation and enables the elevator to stop at exactly the position set by the program. Brake force is an important technical index of a brake, whose schematic diagram of the structure is shown in Fig. 1. The field measurement of braking force basically relies on manual measurement. The general practice is to load the car with a certain load, after the elevator runs smoothly, cut off the traction electromechanical source, the brake starts to break at the same time, until the elevator stops completely, the distance of the car runs during this period is the braking distance. The manual measurement procedure is tedious and the accuracy is poor. This paper presents a machine vision elevator braking force detection method, which can automatically calculate the braking distance, simplify the braking detection process and improve the detection accuracy. As shown in Fig. 1, the braking distance is the running distance of lift car during test, which can be calculated by knowing the diameter of traction wheel and the rotation angle of traction wheel during a test. Therefore, the main task of this paper is to propose a machine vision method to detect the rotation angle of the traction wheel during the test. After obtaining the rotation angle of traction wheel, the length of wire © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 59–66, 2020. https://doi.org/10.1007/978-981-15-2341-0_8

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Fig. 1. Simplified diagram of the elevator brake

rope movement can be calculated using the diameter of the traction wheel, and that is the braking distance. The calculating rotation angle of two images belongs to image registration technology. Image registration refers to the geometric alignment of two or more images in the same scene with different shooting time, a field of view or imaging mode. Image registration technology is widely used in medical image processing, remote sensing image processing, and computer vision. The main image registration methods are methods based on image grayscale [1], e.g. mutual information, methods based fast Fourier transform [2], methods based on image features, e.g. feature points and edge detection [3]. Ofverstedt et al. [4] proposed an affine registration framework based on a combination of strength and spatial information, which is symmetric and without strength interpolation. This method shows stronger robustness and higher accuracy than the commonly used similarity measure. Zhu et al. [5] proposed a robust non-rigid body feature matching method based on geometric constraints. The non-rigid feature matching method is transformed into the maximum likelihood (ML) estimation problem. The experimental results show that the method has good performance. Alahyane et al. [6] proposed a new fluid image registration method based on lattice point Boltzmann method (LBM). Chen et al. [7] proposed a multistage optimization method based on two-step Gauss-Newton method to minimize continuously differentiable functions obtained by discretization model for image registration. Wu et al. [8] proposed an aerial image registration algorithm based on gaussian mixture model, established an image registration model based on Gaussian mixture model (GMM), and solved the transformation matrix between two aerial images. This paper presents a method for detecting the rotation angle of traction wheel using a Fourier transform. This paper is organized as follows. Section 1 is an introduction. Section 2 introduces the basic theory used in this paper. Section 3 verifies the proposed method by an example. Finally, the conclusions are given in Sect. 4.

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2 Basic Theory Fourier transform is employed for detecting the rotation angle of two images. 2.1

Two Dimensional Discrete Fourier Transform

Let f ðx; yÞ denotes image in the spatial domain, the size is M  N and Fðu; vÞ denotes the Fourier transform result of the image, as shown in Formula (1). rffiffiffiffiffiffiffiffi h xu yvi 1 XM1 XN1 Fðu; vÞ ¼ þ f ð x; y Þexp j2p x¼0 y¼0 MN M N

ð1Þ

Where, u ¼ 0; 1;


; M  1, v ¼ 0; 1;


; N  1. Its phase spectrum U and power spectrum P can be calculated by Formulas (2) and (3). ( Uðu; vÞ ¼

Im½F ðu;vÞ arctan Re ½F ðu;vÞ ; Re½F ðu; vÞ 6¼ 0 0 Re½F ðu; vÞ ¼ 0

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pðu; vÞ ¼ ðRe½F ðu; vÞÞ2 þ ðIm½F ðu; vÞÞ2

ð2Þ

ð3Þ

Where Re½
 and Im½
 represent the operation of the real and imaginary parts of the function. 2.2

Fourier Spectrum Analysis of Images

According to the corresponding relationship between image spatial domain and frequency domain, the central brightness part of the spectrum map corresponds to the lowfrequency energy of the whole image, while the bright straight line reflects main texture direction of the whole image. According to the Fourier transform, when image rotates, it can be represented by f1 ðx; yÞ ¼ f ðx; yÞejðmx þ nyÞ . Its Fourier transform is F1 ðu; vÞ ¼ F ðu  m; v  nÞ. The power spectrum Pðu; vÞ will also change after rotation. So the Fourier transform is sensitive to rotation. As shown in Fig. 2, the four images are respectively original image and its Fourier spectrum, and the original rotated counterclockwise by 30° and its Fourier spectrum. In each spectrum diagram, there are two lines with high brightness and passing through the center, the directions indicated by the two lines are the texture directions of the image. As can be seen from Fig. 2, the Fourier transform is sensitive to rotation, and image rotation in the spatial domain will be directly reflected in the spectrum diagram after the Fourier transform. So, the rotation angle of the image can be detected using the spectrum diagram after the Fourier transform.

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Fig. 2. Image and its Fourier spectrum

2.3

Calculation of Rotation Angle

By observing the spectrum diagram in Fig. 2, it can be found that one or more bright lines will appear in the spectrum diagram after the Fourier transform of the image. In this paper, a rotation angle between two images can be calculated by locating the direction of the line with the highest superposition of brightness in the spectrum image. As shown in Fig. 3, determine a circular region in the spectrum graph, with ðM=2; N=2Þ as circle center and R ¼ minðM=2; N=2Þ as radius. Let angle of right horizontal radius to 0°, and denote angle of radius through circle center as h0 , h0 2 ½p; p. Accumulate pixel value of pixel points passing radius in the circular area, radius direction corresponding to the maximum accumulation result is the main direction of the image. The relationship between any point on the radius and the image coordinates ðx; yÞ can be expressed in Formula (4). 

x ¼ rcosðh0 Þ y ¼ rsinðh0 Þ

ð4Þ

Where r denotes the distance from the point on a radius to circle center, r 2 ½0; R. In calculating superimposed pixel values of points on the radius, h0 and r are

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Fig. 3. A circular area in the spectrum graph

discretized, and the corresponding coordinate can be calculated by the exhaustive method. When the result is a decimal, it needs to be rounded. Then add up these pixel coordinates values, results corresponding to the maximum h0 value is the main direction of the image. After main directions h1 and h2 of two images are determined respectively, rotation angle between two images can be calculated by Formula (5). h ¼ h2  h1

ð5Þ

The rotation angle of Fig. 2 is 30.2755° calculated using the proposed method, and the actual rotation angle is 30°. It can be seen that the error of the machine vision method proposed is very small, which can meet the requirements of some engineering calculations.

3 Experiment and Result Analysis As shown in Fig. 4, there are the first and last frames of video from an elevator brake test beginning to complete stop. Due to space limitation, all the frames are not listed, only rotation angle between the first frame and the last frame is calculated. It should be noted that the traction wheel rotation angle is less than a week during this period. For the convenience of measuring traction wheel rotation angle and determine the main direction of images, a black reflective strip was attached to traction wheel during the experiment.

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The first frame

The last frame

Fig. 4. Brake braking force test

In order to reduce the influence of image background and improve the calculation accuracy of the rotation angle, only the center part of images is shotted, as shown in Fig. 5.

The first frame

The last frame

Fig. 5. Cutting processing of images

Using the proposed method, Fourier transforms for images, then draw the spectrum diagram, as shown in Fig. 6.

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Fig. 6. Brake detection picture and its Fourier spectrum

As shown in Fig. 6, there is a line with higher brightness through the center of the image, that is the main direction of the image. After calculating the rotation angle, the rotation angle of the traction wheel is 48.2197°, and actual measurement of rotation angle is 47.5°, the error is 0.7197°. When traction wheel diameter is known, Formula (6) can be used to calculate braking distance. L ¼ phD=360

ð6Þ

Where L denotes braking distance, D denotes traction wheel diameter. Traction wheel diameter is 600 mm, bringing it to Formula (6), braking distance can be calculated as 252.4778 mm.

4 Conclusion An elevator brake force detection method based on machine vision is presented in this paper. The Fourier transform theory is used to transform the image from spatial domain to frequency domain, then power spectrum of the image is calculated, the main direction of the image is determined, and finally, rotation angle between two images is calculated. The proposed method can reduce the workload of elevator brake force detection and improve detection accuracy.

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References 1. Barbara, Z., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003) 2. Reddy, B.S., Chatterji, B.N.: An FFT-based technique for translation, rotation, and scaleinvariant image registration. IEEE Trans. Image Process. 5(8), 1266–1271 (1996) 3. Wang, K., Shi, T., Liao, G.: Image registration using a point-line duality based line matching method. J. Vis. Commun. Image Represent. 24(5), 615–626 (2013) 4. Ofverstedt, J., Lindblad, J., Sladoje, N.: Fast and robust symmetric image registration based on distances combining intensity and spatial information. IEEE Trans. Image Process. 28(7), 3584–3597 (2019) 5. Zhu, H., Zou, K., Li, Y., et al.: Robust non-rigid feature matching for image registration using geometry preserving. Sensors 19(12), 1746–1752 (2019) 6. Alahyane, M., Hakim, A., Laghrib, A., et al.: A lattice Boltzmann method applied to the fluid image registration. Appl. Math. Comput. 349, 421–438 (2019) 7. Chen, K., Grapiglia, G.N., Yuan, J., et al.: Improved optimization methods for image registration problems. Numer. Algorithms 80(2), 305–336 (2019) 8. Wu, C., Wang, Y., Karimi, H.R.: A robust aerial image registration method using Gaussian mixture models. Neurocomputing 144, 546–552 (2014)

Remote Monitoring and Fault Diagnosis System and Method for Traction Elevator Cattle Dawn Shuguang Niu1,2(&), Junjie Huang1,2, and Zhiwen Ye1,2 1

Key Laboratory of Elevator Intelligent Safety in Jiangsu Province, Changshu, China [email protected] 2 Changshu Institute of Technology, Changshu, Jiangsu, China

Abstract. Remote monitoring and fault diagnosis system of traction elevator, temperature sensor is installed on traction engine brake, acceleration sensors are installed both on traction machine and lift car, photoelectric sensor is installed on car door, microprocessor and elevator monitoring center are installed on top of elevator; The sensors are connected to the microprocessor, which is connected to the elevator monitoring center. The elevator running process is monitored by sensors, microprocessors and the elevator monitoring center in real time, realizing that how the elevator runs and its running state is predicted by the system, so as to find out the hidden trouble in time and arrange the maintenance activities reasonably, thus avoiding accidents. Keywords: Remote monitoring
Fault diagnosis
Sensor
Monitoring center

1 Introduction With the rapid growth of the number of elevators, high load, large volume and long cycle of use of elevators become common, and the number of old elevators is surging. Due to the expansion of the number and scope of elevator using, the faults ,which become an important hidden danger in production safety have entered the stage of frequent occurrence—the time between faults has been significantly shortened. According to the statistics of elevators running for 5–10 years, an elevator has an average of mechanical and electrical failures for 36.5 times every year, and 3.3 times accidents that cause great harm to equipment and personal safety such as jacking and clamping. At present, there are nearly 700 elevator manufacturers in China, and thousands of elevator installation, transformation and maintenance units with hundreds of thousands of employees. In order to get a share from the limited market, many actions—vicious maintenance competition, bad currency drive good currency, the maintenance work out of place, grab resources—have been taken, which result in the maintenance market chaos, poor quality maintenance, directly affecting the safe operation of the elevator. These have a great impact on the safety of the elevator and elevator accidents and malfunctions occur from time to time.

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Aiming at the elevator safety, traditional method is as follows: first set up the elevator monitoring center and apply computer control technology and the elevator remote monitoring based on network communication technology, then use sensors to collect elevator operation data, analyzing abnormal data through the microprocessor. It can monitor the elevator in the network 24 h a day without interruption, analyze and record the running condition of the elevator in real time, and calculate the failure rate automatically according to the fault record. By means of GPRS network transmission, public telephone line transmission, LAN transmission and 485 communication transmission, it is a comprehensive elevator management platform that can realize elevator fault alarm, rescue of trapped people, daily management, quality assessment, hidden danger prevention and other functions. The method above solves the problem of knowing when, where, what happened and the development process of the accident in the first time, but it is the treatment after the occurrence of the fault, and cannot reduce the occurrence of the fault.

2 Classify According to the Test Data Related failure, equipment parameters are intermittent or permanently beyond the scope specified in technical standards. Unrelated failure, due to the failure of the test instrument and other reasons, the collected data exceed the specified value. Fatal failure, failures that can lead to significant loss of personal safety and property, identify the cause of failure and predict the change of function difference, according to the trend of failure and the prediction of equipment life to establish a benchmark, so as to carry out the corresponding warning. By means of vibration detection, modal analysis is carried out and the physical coordinates in the system of linear constant coefficient differential equations are transformed into modal coordinates, then derive out the system modal parameters: Fundamental equations for modal analysis: 



M x þ C x þ Kx þ f ðtÞ In the equation, M, C, K - mass matrix, damping matrix and stiffness matrix of the vibration system;   x; x; x – Displacement vector, velocity vector and acceleration vector in vibration system; For an undamped system, the free vibration equation is: 

M x þ Kx ¼ 0 Traction elevator operation monitoring system can carry out real-time monitoring of elevator status and fault warning or alarm, effectively ensuring the safety of elevator operation and solving the possibility of elevator failure. Is the traction elevator operation monitoring method. Remote monitoring and fault diagnosis system of traction

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elevator, temperature sensor is installed on traction wheel and motor brake, acceleration sensor is installed on the traction machine of the lift car, photoelectric sensor is installed on car door, The microprocessor and elevator monitoring center are installed on the top of the elevator; The sensor is connected with the elevator microprocessor, and the microprocessor is connected with the elevator monitoring center. Microprocessor, including central processing unit, the signal acquisition module, power module and storage module, signal acquisition module receives the acceleration sensor, photoelectric sensors, temperature sensors transmit signal data, signal acquisition module is connected to a central processing unit, central processing unit to analyze the signal data, determine whether the parameters of each sensor are within the specified range, power supply module and storage module respectively connected with the central processing unit (Fig. 1).

Fig. 1. Structure and layout

The central processing unit includes a first judge. After starting the motor and motor brake, the first judge is used to judge whether the acceleration signal data of the acceleration sensor exceeds the specified range when the photoelectric sensor has a signal. If it does, the central processing unit will send an alarm signal. The central processing unit also includes a second judge. After starting the motor and the motor brake, when there is a signal from the photoelectric sensor, the system calculates the real-time speed and position of the elevator car based on the signal data of the acceleration sensor, and determines whether the slip difference of the traction wheel and the traction rope exceeds the specified range. If the data exceeds the specified range, the central processing unit will send an alarm signal. Otherwise, the signal data

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store will be stored in the storage module it belongs to. The second discriminator can also be used when the photoelectric sensor has no signal but the acceleration sensor has signal data. It will activate the elevator’s safety clamps and the central processing unit will send out an alarm signal. The central processing unit also includes a third judge. It will determine whether the temperature exceeds the specified range according to the temperature data collected by the temperature sensor. Acceleration sensor is MEMS triaxial acceleration sensor. The monitoring center includes computers that are used to receive data from microprocessors and the data is displayed graphically on the computer screen.

3 Remote Monitoring and Fault Diagnosis Methods for Traction Elevators Include the Following Steps Install the temperature sensor on the traction wheel and motor brake, the acceleration sensor on the cage and the traction machine, and the photoelectric sensor on the door of the car; the elevator also installs microprocessors, used for analysis of abnormal data, to realize the early fault diagnosis of foresight. Elevator microprocessor in the central processing unit are connected to each sensor, used for collecting temperature and acceleration. Then it will determine whether the data is within the scope of the regulations, to the health of the elevator monitoring analysis; the microprocessor sends the monitoring analysis results to the monitoring center. When the motor and motor brake are started, if the photoelectric sensor has a signal, the first judge of the central processing unit will send an alarm signal according to whether the acceleration signal data of the acceleration sensor exceeds the specified range or not. The second judge of the central processing unit will calculate the instantaneous speed and position of the elevator car based on the acceleration signal data, and determine whether the slip difference between the traction wheel and the traction rope exceeds the specified range. If the specified range is exceeded, the central processing unit will send an alarm signal; Otherwise, the signal data is stored in the storage module; When the photoelectric sensor has no signal and the acceleration sensor has signal data, the elevator’s safety clamp will be activated and the central processing unit will send an alarm signal at the same time. The third judge of the central processing unit determines whether the temperature data is out of the specified temperature range (Fig. 2).

Fig. 2. Fault diagnosis

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4 Conclusion By installing sensors and elevator microprocessors on the running elevators, carrying out real-time monitoring and fault warning or alarm for the status of the elevators, and realizing the system can predict how the elevators run and their running status, so that we can find out the hidden trouble in time and arrange the maintenance activities reasonably. This effectively ensures the safety of elevator operation, solves the possibility of elevator failure, and thus avoids accidents, which is of great significance. Acknowledgements. This work is funded by Changshu Science and Technology Bureau (project no. CQ201702).

References 1. Wang, X., Niu, S.: Application of supercapacitor in elevator emergency and energy saving. J. Changshu Inst. Technol. 27(2), 72–74 (2013) 2. Shen, S., Zhao, G.: Research on elevator intelligent fault diagnosis system based on wireless cable. Mech. Eng. (2018) 3. Tian, M., Rui, Y.N.: Elevator safety remote monitoring technology and fault diagnosis. Chem. Ind. Manag. (2015) 4. Chen, G.: Mechanical fault diagnosis of elevator system based on vibration analysis, Master’s thesis. Soochow University. (2018)

Soil Resistance Computation and Discrete Element Simulation Model of Subsoiler Prototype Parts Gong Liu1, Zhenbo Xin2, Ziru Niu2, and Jin Yuan1(&)

2

1 College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China [email protected] Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment, Tai’an 271018, China

Abstract. Subsoiling operation can break the bottom layer of the soil, thicken the tillage layer, and increase crop yield. The subsoiling operation faces the problem of large resistance and serious wear of the soil parts, which greatly increases the cost of the subsoiling operation. In order to reduce tillage resistance, the active lubrication method is selected to study. According to the structure of the curved deep shovel handle, a simple sample is designed and processed. A theoretical calculation method for the resistance of the sample work is proposed. The finite element software EDEM is used to simulate the working condition of the sample, and it is tested in indoor soil trough and outdoor field. By comparing the simulation results with the real test, the correctness of the theoretical calculation and simulation model was verified, and the drag reduction effect of the active lubrication and drag reduction operation mode was proved. Keywords: Subsoil


Drag reduction
Discrete element simulation test

1 Introduction Subsoiling operation is an important part of conservation tillage, which has been widely promoted and applied in the world with less tillage and no tillage. Subsoiling operation can break the bottom layer of soil plough, thicken the tillage layer, store water and conserve moisture, which has a good effect on the increase of field crop yield [1]. At present, subsoiling operation is faced with problems such as large tillage resistance and serious wear of machinery and tools. Therefore, it is of great significance to reduce operation resistance, energy consumption and cost for the promotion and popularization of subsoiling operation and improve the quality of farmland in China. To solve the problem of large tillage resistance, experts at home and abroad have studied many ways to optimize the design of subsoiler, such as vibration resistance reduction, bionic resistance reduction, etc. Different from the above idea of reducing drag, the idea of an active lubrication method to reduce the resistance has not been fully studied in relevant academic conferences. © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 72–80, 2020. https://doi.org/10.1007/978-981-15-2341-0_10

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In order to study the drag reduction effect of the active lubrication mode, a sample was designed and processed in this paper. The theoretical analysis of the sample and the EDEM discrete element simulation model were established. The correctness of the theoretical analysis and simulation model was verified by experiments. The drag reduction effect of the active lubrication drag reduction mode was verified.

2 Design and Process a Sample In this paper, the shovel handle of the surface deep shovel is selected as the prototype of the sample. The prototype is optimized by the bionic method. By simulating the corrugated shape of the earthworm body surface, the back surface and ripple of the head and body of the scorpion to simulate, three back holes were designed on the side of the sample, and the three back holes were arranged vertically in a straight line on the side of the sample [2]. The arrangement of these back holes is the optimal arrangement of resistance reduction effect obtained by Bingxue Kou after the experiment. In this paper, the internal pipeline is designed in the sample, and the orifice is designed in the back hole of the foremost side in the forward direction of the sample as shown in Fig. 1. The orifice is evenly distributed in the back hole and connected to the pipe inside the sample.

Fig. 1. Sample design

Fig. 2. Sample

Fig. 3. Orifices

According to the sketch designed above, the test sample is processed. 65 Mn is selected as the material to obtain the sample as shown in Fig. 2 and the throttle hole as shown in Fig. 3.

3 Force Analysis In this paper, based on the experience and calculation methods of the predecessors, combined with the Kostritsyn calculation method, the theoretical analysis of the force of the sample is carried out. When Kostritsyn studied the cutting force of soil, he divided the subsoiler type cutter into two basic shapes, among which the front edge with sharp Angle is called the wedge blade, and the parallel blade is called the side edge [3]. The stress of the sample is analyzed, and its stress is shown in Fig. 4.

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Fig. 4. Stress condition of sample

Combined with Jingfeng Bai’s calculation method for subsoiler, the formula for calculating the resistance of the sample can be obtained as follows [4]:   a a F1 ¼ 2 N1 sin þ l N1 cos þ lN2 2 2
2S1 B cos a2 þ h N1 ¼ Kel þ d0 N2 ¼ Kel S2 cos

a 2

ð1Þ ð2Þ ð3Þ

where F1—total resistance of the sample (N) T—the positive force on the knife (N) P—the drag component acting in the normal direction (N) N1 —normal force of soil on wedge edge (N) N2 —normal force on the opposite edge of soil (N) a—sample wedge edge Angle (50°) l—Coefficient of friction between sample and soil (0.6) Kel—Stress due to elastic deformation of soil (4500 N/m2) S1—Wedge edge area of sample (0.003542 m2) S2—Area of side edge of sample (0.0106 m2) d′—Sample width (0.015 m) h—Sample work depth (0.15 m) L0—average deformation amount of soil h—Angle of friction between soil and metal (40°). When the subsoiler works, taking into account the complexity of the soil environment and the impact of the operating speed on the resistance, it is considered that the resistance also includes a part of the force including the undetermined coefficient, which is calculated as: F0 ¼ khd0 v2

ð4Þ

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where k—Undetermined coefficient v—Operating speed (0.5 m/s). According to the previous test, the value of the undetermined coefficient is taken as 120. Therefore, the average deformation of soil L0 = 0.0089 m. By arranging formula (1) to formula (4), the corresponding data can be substituted into the force of the sample: F2 ¼ F1 þ F 0 ¼ 911:55 N

ð5Þ

In summary, the total resistance of the sample is 911.55 N, and the direction is opposite to the direction in which the sample advances.

4 Discrete Element Method Simulation 4.1

The Establishment of Discrete Element Model

This paper adopts 3d CAD design software Solidworks2018 to carry out 3d modeling of the samples. After 1:1 modeling of the samples according to real objects, the sample is saved into the intermediate format of IGS. Discrete element method is used for simulation software EDEM2018. In order to simulate the soil environment of sample operation, it is very important to establish the soil model accurately. Contact model is an important basis of discrete element method, which is essentially the elastoplastic analysis results of contact mechanics of particles and solids under quasi-static state [5, 6]. The contact model determines the forces and torques suffered by the particle model. Due to the complexity of soil environment, selecting the appropriate soil model becomes a key step [7]. In this paper, soil parameters of the land environment were obtained through field testing of soil environment of Huanghuaihai regional maize technology innovation center, and microscopic parameters in EDEM simulation were finally determined by combining data. 4.2

Determination of Discrete Element Simulation Parameters

In discrete element simulation, the accuracy of parameters affects the simulation results [8]. According to the relevant literature, the parameters of microscopic soil required for simulation are referenced and fine-tuned, as shown in Table 1 [9].

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4.3

Value 1350 0.4 1.09 * 106 7830 0.3 7.9 * 106 0.3 0.5 0.2 0.5 0.6 0.2 3 214500 3.5

Simulation

In the Geometries function branch in the pre-processing module of EDEM2018 software, the Geometries sample in IGS format is imported into EDEM software, and the working depth of the Geometries sample is 150 mm by adjusting its position relative to the soil groove. The forward speed of the deep pine shovel is set to 0.5 m/s. The initial state of soil model simulation in EDEM software is shown in Fig. 5.

Fig. 5. Initial simulation state of simulation

In the EDEM solver module setup, this paper takes the experience of the predecessors and the way to find related documents, sets the fixed time step to 33%, saves the time every 0.0001 s. In the simulation process, the method of modifying the dynamic coefficient of rolling friction between the soil and the sample is used to simulate the lubrication and non-lubrication operations. The larger friction coefficient is

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used as the case of the sample operation without lubrication, and the smaller friction coefficient is used as the operation of lubricating the sample with the lubricating medium. After performing multiple simulations, through the analysis of the data, the resistance data in the steady state after the sampled material is placed in the soil, after processing the data, the working resistance of the sample is finally shown in Table 2. Table 2. Simulated resistance Lubrication No lubrication Coefficient of rolling friction 0.2 0.6 Average resistance (N) 830.59 961.36

It can be seen from the table that when coefficient is 0.6, the working resistance of the sample is 961.36 N, which corresponds to the resistance in the case of no lubrication. When coefficient is 0.2, the working resistance is 830.59 N, which corresponds to the resistance in the case of lubrication.

5 Test 5.1

Design and Build Test System

In order to test the sample, this paper designed a simple test system. The test system is shown in Fig. 6. The test system is provided with traction by electric capstan to drive the working platform forward. A water tank, a water pump, a battery, and a tension sensor and a pressure sensor are mounted on the work platform. One side of the platform is connected to the sensor via a paperless recorder, and the paperless recorder provides instant display and recording of test data.

Fig. 6. Assembly of test system

In order to realize the experiment of the sample conveniently and quickly, and to adjust the experimental scheme in real time, this paper built a simple soil trench test bench indoors. The design drawing is shown in Fig. 7. The test bench is shown in Fig. 8. The measurement of parameters is shown in Fig. 9.

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Fig. 7. Design drawing

Fig. 8. Test bench

Fig. 9. Measure parameters

After constructing the test bench, installing the rack and filling the soil in the soil tank, this paper collected the physical parameters of several fields in the Huanghuaihai area. The method of adding water and compacting the soil tank is adopted to make the soil physical property parameters in the soil tank consistent with the field parameters. 5.2

Test and Analysis

In order to fully test the samples, this paper carried out several experiments in the indoor soil tank test bed, the experimental field in the south campus of Shandong Agricultural University and the Huanghuaihai corn innovation center. Figures 10, 11 and 12 show the test.

Fig. 10. Indoor soil groove

Fig. 11. South school test field

Fig. 12. Huanghuaihai corn innovation center

The basic parameters of the soil tank and Daejeon are shown in Table 3 during the test. Table 3. Physical properties of the test site Physical parameters

Soil compactness (Kpa) Average soil moisture content (%)

Depth (mm) 100 200 300 100 200 300

Test site Indoor soil groove 71.21 172.64 285.65 18.63 19.11 19.85

South school test field 65.87 156.90 321.88 13.63 17.23 21.51

Huanghuaihai corn innovation center 79.30 170.25 347.10 23.20 28.31 27.78

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Fig. 13. Laboratory test result Fig. 14. South result

school

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test Fig. 15. Test result of Huanghuaihai corn innovation center

Figures 13, 14 and 15 show the test results of the multi-site test. Figure 13 shows the results of the lubrication drag reduction test conducted on the indoor soil test bench. The average resistance of liquid lubrication test is 750.73 N, and the average working resistance is 889.55 N under the condition of no liquid lubrication. The drag reduction rate is 15.61%. Figure 14 shows the test in the school test field. During the test, the soil moisture content is small and the soil is dry. In the case of liquid lubrication, the average operating resistance of the sample is 574.49 N. In the case of no liquid lubrication, the average operating resistance is 778.45 N. The drag reduction rate is 26.2%. Figure 15 shows the sample test in Huanghuaihai corn innovation center. The average resistance of liquid lubrication test is 835.11 N, and the average working resistance is 924.40 N under the condition of no liquid lubrication, and the drag reduction rate is 9.63%. At this time, the center’s field is in a state shortly after irrigation, and the soil moisture content is high.

6 Discussion After tests, according to the data of the tests and the comparison with the results of theoretical calculation, it is found that the error between the theoretical calculation results and the results of indoor tests is 2.4%, and the error between the results of Huanghuaihai regional tests is 1.4%. Therefore, it is considered that this theoretical calculation method is feasible. Meanwhile, comparing the simulation and experiment, it is found that the error of the simulated resistance is also within the acceptable range. Therefore, it is considered that the simulation model is correct, and it is feasible to adjust the rolling friction factor in the simulation to correspond to the watering condition.

7 Conclusions This paper chose the active lubrication drag reduction method for research and processed a sample. A theoretical calculation method of sample resistance is proposed, and a simulation model which can simulate the working resistance of the sample is established. The correctness of the theoretical calculation method and the simulation model is verified by experiments, and the drag reduction effect of the active lubrication and drag reduction operation mode is verified.

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Acknowledgements. This work was supported by National Key R&D Program of China (2017YFD0701103-3) and Key research development plan of Shandong Province (2018GNC11 2017, 2017GNC12108).

References 1. Zhang, J., Zhai, J., Ma, Y.: Design and experiment of biomimetic drag reducing deep loosening shovel. J. Agric. Mach. 45(04), 141–145 (2014) 2. Kou, B.: Resistance reduction by bionic coupling of earthworm lubrication function. Master’s thesis, Jilin University, pp. 19–24 (2011) 3. Gill, W.R., Vanden Berg, G.E.: Soil Dynamics in Tillage and Traction. China Agricultural Machinery Publishing House, Beijing (1983) 4. Bai, J.: Analysis of Anti-Drag Performance for Vibrating Bionic Subsoiler. Northwest Agriculture and Forestry University, Xianyang (2015) 5. Deng, J., Hu, J., Li, Q., Li, H., Yu, T.: Simulation and experimental study of deep loosening shovel based on EDEM discrete element method. Chin. J. Agric. Mech. 37(04), 14–18 (2016) 6. Deng, J.: Simulation and Experimental Study of the Subsoiler Tillage Resistance Based on Discrete Element Method. Heilongjiang Bayi Agricultural University, Daqing (2015) 7. Hu, J.: Simulation analysis of seed-filling performance of magnetic plate seed-metering device by discrete element method. Trans. Chin. Soc. Agric. Mach. 45(2), 94–98 (2014) 8. Wang, X.: Calibration method of soil contact characteristic parameters based on DEM theory. J. Agric. Mach. 48(12), 78–85 (2017) 9. Liu, X., Du, S., Yuan, J., Li, Y., Zou, L.: Analysis and experiment on selective harvesting mechanical end-effector of white asparagus. Trans. Chin. Soc. Agric. Mach. 49(04), 110–120 (2018)

Simulation Analysis of Soil Resistance of White Asparagus Harvesting End Actuator Baffle Parts Based on Discrete Element Method Haoyu Ma1, Liangliang Zou2, Jin Yuan1(&), and Xuemei Liu2

2

1 College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China [email protected] Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment, Tai’an 271018, China [email protected]

Abstract. In this paper, the baffle in the end effector of white asparagus harvest is taken as the research object, and five different baffles were designed. The discrete element method was used to analyze the resistance of different baffles when entering the soil. The simulation test results are as follows. The resistance of these five baffles depends on the depth and velocity of the incoming soil and is a quadratic function of these two factors. When the depth of the baffle into the soil is less than 6 cm and the velocity of the baffle into the soil is 0.2 m/s, the resistance of the inclined cylindrical baffle is minimal. When the speed is 0.4 m/s and 0.6 m/s, the rectangular inclined sheet-like baffle has the least resistance. When the depth of the baffle into the soil is greater than 6 cm, the resistance of the triangular plate baffle is minimal regardless of the speed. This study can provide a theoretical basis for structural parameter optimization of white asparagus harvest end effectors. Keywords: Discrete element simulation resistance
End effector


White asparagus harvest
Soil

1 Introduction White asparagus is a perennial herb with high nutritional value and anti-cancer health effects [1]. China is a big country for white asparagus cultivation, but the harvest of white asparagus is mainly based on artificial picking. There is no machinery for harvesting white asparagus in China. The harvest time of white asparagus is morning or evening, and the harvesting time is relatively concentrated. The manual harvesting workload is large and the efficiency is low. The high-efficiency and low-damage harvesting requirements of white asparagus have become the bottleneck restricting the development of China’s asparagus industry [2]. The mechanical harvesting of white asparagus can be divided into the following steps: the harvesting mechanism is inserted into the soil, the root of the white asparagus is cut, and the whole white asparagus is taken out [3]. The harvesting machine has a special baffle structure or clamping structure that prevents white asparagus from falling © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 81–88, 2020. https://doi.org/10.1007/978-981-15-2341-0_11

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during the harvesting process. These structures increase the resistance to the harvesting mechanism when it enters the soil, thereby increasing energy consumption. EDEM software is a software that uses discrete element technology to simulate and analyze the interaction of particles and particle clusters. EDEM software is a software for finite element simulation by synthesizing macroscopic objects through microscopic particles and imparting mechanical properties between the particles. It adopts advanced DEM algorithm and can simulate the finite element particles reliably. EDEM software has numerous applications in industry and agriculture, such as the study of viscous and non-viscous soils, crop harvesting and screening during crop harvesting, and the design and optimization of various knives and shovel [4–6]. In this paper, the five kinds of baffle structures in the self-designed white asparagus harvesting end effector are analyzed by discrete element method. Through simulation analysis, the variation of the resistance of different baffles with the velocity and depth of the baffle entering the soil is obtained. It is ultimately determined which baffle receives the least resistance to soil. This study can provide a theoretical basis for structural parameter optimization of white asparagus harvest end effectors.

2 End Effector Baffle Structure Design In this paper, five kinds of baffle structure models are designed with Solidworks. The various models are shown in Fig. 1, and are represented by A, B, C, D, and E, respectively. All baffles are attached to the same shelf. The A baffle is composed of two triangular hollow sheets. The B baffle is welded by two stainless steel cylinders with the same angle as the angle of the A baffle. The C-Baffle is formed by bending two stainless steel cylinders. The three baffles A, B, and C have the same length in the horizontal direction. The D baffle and the E baffle are welded at the same angle by a stainless steel plate and a cylinder. The width of the steel plate of the A baffle and the D baffle is 2 mm and the thickness is 1 mm. The stainless steel cylinders in the B, C and E baffles are both 2 mm in diameter. The distance between the uppermost and lowermost portions of the five baffles in the vertical direction is the same. In the following sections, the five types of baffles are represented by their corresponding uppercase English letters.

A

B

C Fig. 1. Five baffle models

D

E

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3 Simulation Parameter Setting In EDEM software, the interaction force between particles and particles, the interaction force between particles and boundaries, and the force of the particles themselves are generally analyzed using different contact models [7]. In this study, the ‘Hertz-Mindlin with bonding’ model in the EDEM2018 version was used to set up the soil model for simulation. The parameters that need to be set for this model are unit hardness, stress and bond radius [8]. This model can be used to simulate problems such as fracture and fracture, and the bond between the particles is destroyed by external forces. This model is used in this paper to simulate soil bonding and fracture. The simulation parameters of the model are shown in Table 1. Table 1. Model simulation parameter settings Parameter Poisson’s ratio of soil particles Shear modulus of soil particles/Pa Soil particle density (kg.m−3) Poisson’s ratio of stainless steel Shear modulus of stainless steel material/Pa Density of stainless steel material (kg.m−3) Restoration coefficient between soil particles and soil part Rolling friction coefficient between soil and soil Static friction coefficient between soil and soil Recovery coefficient between soil and stainless steel Rolling friction coefficient of soil and stainless steel Static friction coefficient between soil and stainless steel

Numerical value 0.4 1.09e+06 2600 0.3 8e+10 7800 0.3 0.3 0.5 0.3 0.13 0.6

The particle radius is 1.25 mm and the particle size ratio fluctuates between 0.8 and 1.2. The parameters of the contact model are shown in Table 2. The speed of the baffle is set to 0.2 m/s, 0.4 m/s and 0.6 m/s, and the direction is set to the direction of gravity acceleration. The depth of the baffle into the soil is 12 cm and the set time step is 30%. The discrete element model in the simulation process is shown in Fig. 2. Table 2. Model parameter settings Parameter Unit area method phase stiffness Tangential stiffness per unit area Critical phase stress Tangential critical stress Bonding radius Bonding time

Numerical value 2e+07 N/m2 1e+07 N/m2 7e+06 Pa 4e+06 Pa 2.2 mm 0s

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A

B

C

D

E

Fig. 2. Simulation model of five kinds of baffles

4 Analysis of Simulation Results 4.1

Analysis of the Resistance of Different Models in the Same Speed

Through the EDEM post-processor, the resistance of each model along the Z-axis is obtained and imported into an Excel spreadsheet. The force diagrams of the five models are plotted at different speeds, as shown in Figs. 3, 4, and 5.

Force (N)

13 11

A

9

B

7

C

5

D

3

E

1 -1 0

0.1

0.2

0.3

0.4

0.5

0.6

Time(s) Fig. 3. Force diagram when the speed is 0.2 m/s

When the baffle has just entered the soil, the forces of the three baffles A, B, and C are approximately equal and significantly larger than the resistance of the D and E baffles. When the baffle half enters the soil, the force of the B model increases significantly, reaching 13 N. The other four baffles are subjected to forces between 10 N and 11 N, and the difference in resistance does not exceed 1 N. Therefore, when the speed is 0.2 m/s, the D and E baffles can be used to reduce the resistance of the end effector into the soil.

Simulation Analysis of Soil Resistance

A B

Force(N)

17 15 13 11 9 7 5 3 1 -1

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C D E 0

0.05

0.1

0.15

0.2

0.25

0.3

Time (s) Fig. 4. Force diagram of each baffle at a speed of 0.4 m/s

In the first half of the simulation, the B and C baffles are subjected to a large force, the A-shaped baffle is centered, and the D and E baffles are less stressed. In the second half of the simulation, the B-plate is significantly increased in force. The resistance of the D and E baffles exceeds the A-type baffle within 0.2 s. The B and E baffles are subjected to a maximum force of 17 N, and the A baffle is subjected to a force of at least 14 N. Therefore, when the speed is 0.4 m/s, the A baffle can be selected. 24 21

A

Force (N)

18

B

15 12

C

9

D

6

E

3 0 0

0.025

0.05

0.075

0.1

0.125

0.15

0.175

0.2

Time (s) Fig. 5. Force diagram of each baffle at a speed of 0.6 m/s

When the speed is 0.6 m/s, the overall trend of the force of each baffle is the same as that of 0.4 m/s, but the interval between the force curves is obviously increased compared with the speed of 0.4 m/s, and this The trend gradually increases with the increase of the depth of the soil. B baffle force is up to 23.5 N, and A baffle force is only 18 N. By analyzing the force curves of the three speeds, it can be found that when the depth of the baffle entering the soil is less than 6 cm, the resistance of the D and E baffles is approximately the same, and is always smaller than the resistance of the other baffles.

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4.2

Analysis of the Resistance of the Same Baffle at Different Speeds

20 16 12 8 4 0 0

2

Force(N)

A

4

6

8

10

0

2

0.2m/s 0.4m/s 0.6m/s

0

12

2

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8

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0.2m/s 0.4m/s 0.6m/s

20 16 12 8 4 0 0

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D Depth(cm)

C Depth(cm)

Force(N)

6

B Depth(cm)

Depth(cm) 0.2m/s 0.4m/s 0.6m/s

20 16 12 8 4 0

24 20 16 12 8 4 0

Force(N)

0.2m/s 0.4m/s 0.6m/s

Force(N)

Force(N)

In order to determine the resistance of each baffle when the depth of the baffle entering the soil is greater than 6 cm, the force diagrams of the different baffles are plotted. As shown in Fig. 6. The trend of the resistance of each baffle as a function of speed is analyzed by Fig. 6.

0.2m/s

20 16 12 8 4 0

0.4m/s 0.6m/s

0

2

4

6

8

10

12

E Depth(N) Fig. 6. Force diagram of different speeds of each baffle

When the depth of the baffle into the soil is greater than 6 cm. The resistance of the B baffle increases rapidly with increasing speed. The maximum resistance of the B baffle is 24 N, and that of the other baffles is 20 N. When the soil depth is between 6 cm and 12 cm, the magnitude of the resistance of the remaining four baffles does not differ by more than 1 N. But different baffles have different trend of resistance change. As the speed and depth of entering the soil increase, the slope of the force curve of the C, D, and E baffles becomes larger and larger. Compared with other baffles, the force of the A baffle increases more slowly. By analyzing the previous single variable, it can be

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found that the relative magnitude of the resistance of each baffle changes when the depth of the soil is 6 cm. Therefore, the force of the soil depth of 6 cm and 12 cm is taken to determine the selection of the baffles at different depths and different speeds. When the depth of the soil is less than 6 cm, the force curve of the D and E baf-fles does not intersect with the force curve of the other baffles, and is always below the force curve of the other baffles. Table 3 can be used to determine the selection of baffles at different speeds. When the depth of the soil is greater than 6 cm, the resistance of the A baffle increases the slowest, and it can be seen from Table 4 that the resistance of the A baffle is the smallest, so the A-plate is the least stressed regardless of the speed. Table 3. Forces on baffles at different velocities at depths of 6 cm Force (N) Baffle Speed (m/s) A B 0.2 5.00 5.24 0.4 7.21 8.17 0.6 10.01 10.49

C 5.58 7.52 9.98

D 4.41 5.64 7.91

E 4.05 6.64 7.95

Table 4. Forces on baffles at different velocities at depths of 12 cm Force (N) Speed (m/s) 0.2 0.4 0.6

Baffle A 10.47 14.30 18.15

B 12.67 16.60 23.42

C 10.92 15.39 18.39

D 10.71 15.27 19.37

E 10.48 16.76 20.44

5 Conclusions The resistance of these five baffles depends on the depth and velocity of the incoming soil and is a quadratic function of these two factors. When the depth of entering the soil is less than 8 cm and the speed is 0.2 m/s, the E baffle receives the least resistance. At speeds of 0.4 m/s and 0.6 m/s, the D baffle receives the least resistance. When the depth of entering the soil is greater than 8 cm, the A baffle receives the least resistance regardless of the speed. Acknowledgements. This work is supported by Key R&D plan of Shandong Province (2017 GNC12110, 2017GNC12108), by National Natural Science Foundation of China (51675317) and by National Key R&D Program of China (2017YFD0701103-3).

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References 1. Huang, Z.: Key points of cultivation techniques of white asparagus in Dongshan County, Fujian Province. Agric. Eng. Technol. 39(05), 77+85 (2019) 2. Chen, D., Wang, S., Wang, X.: Analysis on the status quo and development of mechanized harvesting technology of asparagus. J. China Agric. Univ. 21(04), 113–120 (2016) 3. Du, S.: Optimization design and experimental study of selective harvesting end effector for white asparagus. Shandong Agricultural University (2018) 4. Fang, H., Ji, C., Chandio, F.A., Guo, J., Zhang, Q., Chaudhry, A.: Analysis of soil movement behavior in rotary cultivating process based on discrete element method. Trans. Chin. Soc. Agric. Mach. 47(03), 22–28 (2016) 5. Wang, J., Wang, Q., Tang, H., Zhou, W., Duo, T., Zhao, Y.: Design and experiment of deep burying and stalk returning device for rice straw. Trans. Chin. Soc. Agric. Mach. 46(09), 112– 117 (2015) 6. Zhang, Q., Liao, Q., Yu, W., Liu, H., Zhou, Y., Xiao, W.: Optimizati and experiment of the surface of the ditching plow of the rape direct seeding machine. Trans. Chin. Soc. Agric. Mach. 46(01), 53–59 (2015) 7. Wu, Y., Bai, X., Zhang, S., Zhai, J.: Discrete element analysis of material movement in rice mill based on discrete element EDEM. Cereal Process. 43(02), 52–55 (2018) 8. Wang, X., Yue, B., Gao, X., Zheng, Z., Zhu, R., Huang, Y.: Simulation and experiment of soil disturbance behavior when different heights of shingling shovel are installed. Trans. Chin. Soc. Agric. Mach. 49(10), 124–136 (2018)

Simulation and Experimental Study of Static Porosity Droplets Deposition Test Rig Laiqi Song1, Xuemei Liu2(&), Xinghua Liu2, and Haishu Zhang1 1

2

College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment, Tai’an 271018, China [email protected]

Abstract. In the process of field spraying, the migration and deposition of pesticide droplets in plant canopy are the key to ensure the high efficiency of application. This paper takes cotton as an example to study the spatial droplet deposition distribution based on the static porosity of cotton. The cotton canopy was regarded as porous medium from the point of droplet transportation in cotton canopy. Cotton canopy was divided into three layers and represented different cotton forms through the combination of different porosity. Set up a static porosity test device for cotton and conduct spray deposition test. At the same time, the spatial distribution of droplets under different porosity was obtained through CFD simulation. The results show that the static porosity droplet deposition test device can be used to analyze the spatial distribution of droplet deposition. Keywords: Static porosity


CFD simulation
Droplet deposition

1 Introduction In the field operation of the sprayer, the penetration and deposition of droplets are important indicators to measure the performance or the appropriateness of the parameters. The deposition and infiltration of droplets in the canopy of crops has a major impact on the control of pests and diseases as well as pesticide pollution. In order to improve the control effect, avoid environmental pollution and reduce the amount of pesticides used, it is particularly important to study the deposition distribution of droplets in plant canopies. At present, the test method for droplet osmosis deposition in crops is mainly to use computational fluid dynamics (CFD) simulation and field testing of real plants. Dekeyser et al. Used artificial trees to compare the sedimentary quality of seven orchard spray application techniques in trees [1]. Tonggui Wu et al. proposed a three-dimensional structural index that can represent the optical porosity of multi-row tree-shaped windbreaks, and applied it to wide-width tree-shaped windbreaks, revealing the relationship between the protective effect of tree windbreaks and optical porosity [2]. Cian James Desmond et al. found that an accurate assessment of the porosity of the canopy, and specifically the variability with height, improves simulation quality regardless of the © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 89–97, 2020. https://doi.org/10.1007/978-981-15-2341-0_12

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turbulence closure model used or the level of canopy geometry included [3]. Hong et al. established a comprehensive CFD model to predict displacement of pesticide spray droplets discharged from an air-assisted sprayer, depositions onto tree canopies, and offtarget deposition and airborne drift in an apple orchard [4, 5]. Endalew et al. proposed a new CFD integration method for airflow and droplet deposition simulation of airflow assisted spray, simulating orchard canopy targets, assisting airflow, complex interactions of air and mist flow, and proposing a random deposition model. This model was used for droplet deposition of leaves and verified the model to improve the design characteristics of nebulizer and the calibration of operating parameters to improve spray efficiency and reduce environmental impact [6–8]. As one of China’s important economic crops, cotton needs to be sprayed with pesticides during the growth process. As the cotton grows to the middle and late stages, the occlusion of the leaves is very serious, and the internal porosity is small, which is not conducive to the uniform deposition of droplets. In this paper, cotton is used as the research object and the research on spatial droplet deposition based on static porosity of cotton is carried out.

2 Construction of Space Droplets Deposition Test Device Based on Static Porosity This paper only discusses the static porosity of plant populations and there is no change in porosity during spraying. The canopy profile of multiple cottons is identical to the canopy profile of individual cotton and the porosity is also the same. The establishment process of the static porosity testing device for cotton is as follows. (1) Layering cotton plants. Four cotton plants with certain interaction between branches and leaves were selected as the droplet deposition test area. At the same time, along the vertical direction of the plants, they were divided into three different layers, namely the top layer, the middle layer and the lower layer. The height of each floor from the ground is shown in Fig. 1.

Fig. 1. Schematic diagram of cotton plant stratification

(2) Porosity measurement of cotton plants. Assume that the porosity of each layer of cotton plants in (1) is 60%, 50% and 35%, respectively.

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(3) Arrangement of porosity per layer in the test device. The porosity of each layer is described by using a 9 cm diameter disc in a square area of 1  1 m. According to the porosity assumed above, when the porosity is 60%, the number of discs is 64 and the actual calculated porosity is 59.28%. When the porosity is 50%, the number of discs is 81 and the actual porosity calculated is 48.47%. When the porosity is 35%, the number of discs is 100 and the actually calculated porosity is 36.38%. All the discs on each layer are evenly dispersed on the square area. (4) Construction of space droplet deposition test device based on static porosity. Aluminum profiles were used to build a three-layer disc placement platform to ensure that the heights of each layer from the ground were 0.9 m, 0.59 m and 0.28 m respectively. Finally, a static porosity similarity test device for cotton population plants was obtained, as shown in Fig. 2.

Fig. 2. Test device for static porosity of cotton plants

3 Static Porosity Space Droplet Deposition Distribution Test 3.1

Experimental Method and Environment

(1) The test site is the Spray Performance Laboratory of Shandong Agricultural University. The effect of natural wind is not considered during the test. As shown in Fig. 3. The porosity is adjusted by changing the size between the wires.

Fig. 3. Static porosity deposition test device and test process

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(2) Set the sampling point. According to the porosity of each layer of the prediction model, select different sampling points on the front of the disc. A 3  4 cm water-sensitive paper was used as a droplet deposition carrier and the watersensitive paper was fixed to a disk by a paper clip. It is randomly placed in the sampling point and numbered on the back of the water-sensitive paper. (3) Start spray test. The test used water instead of pesticide to spray. By adjusting the duty cycle and the nozzle flow rate was adjusted to 0.37 L/min. When the nozzle sprayed the droplets to a stable state, the mobile platform is controlled to pass through the spray zone at a speed of 0.65 m/s. (4) Collect water-sensitive paper. After the spray is complete, wait for the water sensitive paper to dry, place it in a sealed bag for post treatment. Repeat the above steps and adjust the combination of porosity conversion devices according to the test scheme. Each test combination was repeated three times. (5) The DepositScan software was used to process the water-sensitive paper. Then the analysis results are exported to Excel to obtain the unit deposition amount on each water-sensitive paper. The average deposition per unit area of each layer after treatment is calculated according to Eq. (1), and the deposition amount of the whole layer would be calculated by formula (2): q ¼

q1 þ q2 þ q3 þ


þ qm m Q ¼ q  S

ð1Þ ð2Þ

Where m—Number of sampling points per layer q—Average deposition at all sampling points in each layer (ll/cm2) S—Total area per layer (cm2) Q—Total deposition per layer (ll). 3.2

Combination of Test Schemes

In order to study the deposition characteristics of droplets under different porosity, according to the relationship between cotton plant type and porosity, the three kinds of porosity assumed according to different plant types are combined to obtain test devices with different static porosity. The combination of the three types of porosity on the droplet deposition device is shown in Table 1.

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Table 1. Three combinations of porosity on droplet deposition devices Porosity assemblage 1 2 3 4 5 6 7 8 9

Top porosity 59.28% 59.28% 59.28% 48.47% 48.47% 48.47% 36.38% 36.38% 36.38%

Middle porosity 36.38% 48.47% 59.28% 59.28% 36.38% 48.47% 48.47% 36.38% 59.28%

Lower porosity 59.28% 36.38% 48.47% 36.38% 48.47% 59.28% 48.47% 36.38% 59.28%

Middle and lower porosity 22.79% 33.06% 53.30% 29.47% 21.85% 37.78% 12.48% 22.42% 25.59%

Overall porosity 16.40% 18.25% 26.01% 14.95% 17.03% 28.90% 11.18% 12.16% 16.62%

For the above nine sets of porosity combinations, according to the relationship between different cotton canopy morphology and porosity, the corresponding canopy shapes of the nine devices are shown in Fig. 4.

Fig. 4. Schematic diagram of canopy morphology corresponding to nine groups of devices

According to Fig. 4, combination 1 represents fusiform plant, combination 2 represents tower plant, combination 9 represents inverted tower plant, combination 8 represents cylindrical plant. Other porosity combinations can be considered as transitional combinations of the above four basic forms.

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4 CFD Simulation of Droplet Deposition in Static Porosity Droplet Tester 4.1

Spray Calculation Simulation of Static Porosity Tester

The simulation conditions should be the same as the actual spraying experiment, so that the simulation test results can be comparable with the actual experimental results. Therefore, according to the actual situation, it is necessary to analyze the hypothesis of discrete phase model, droplet termination mode and boundary conditions in the simulation test. For droplets in the spray test, they are regarded as discrete phase because of their low volume concentration in the air. The Lagrangian discrete phase model is used to describe and track the spatial motion of droplets in the static porosity similarity test device. In this paper, a nozzle of a plate fan atomization model is set above the top 495 mm to simulate the nozzle in the actual spray process. The particle packet number is 200 and the particle type is inertial particle. The particle size of droplets is not considered. The atomization half Angle is 55° and the spray flow rate is 0.006167 kg/s. Setting of boundary conditions: Calculation area all sides set to free export (outflow), the discrete phase boundary condition is set to escape. The bottom surface of the device model was set as ground and the boundary condition was set as Wall and the boundary condition of discrete fog droplets was set as trap. The boundary conditions of the upper surface of each layer of the disc were set as Wall and the boundary conditions of discrete phase droplets were set as trap. CFD simulation process of the droplet deposition test device is shown in Fig. 5.

Fig. 5. CFD simulation process of static porosity droplet deposition test device

In the process of solving and calculation, the droplets are sprayed at the initial position for 50 steps, a total of 0.5 s, so that the drops fall to the ground. Then, the calculation area moves 145 steps, a total of 1.45 s, at the speed of 0.65 m/s. The particle spraying is completed and the motion stops. The rest of the particles that don’t

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fall off continue to calculate until all the particles fall off, complete the calculation. During the whole spray movement, the total spraying time of droplet particles was 1.95 s. 4.2

Simulation and Experimental Analysis of Spatial Droplet Deposition Distribution Under Static Porosity

In order to analyze the deposition and penetration of droplet in the test device, the amount of spray in the simulation process needs to be calculated. During the whole spray movement, the total spraying time of droplet particles was 1.95 s and the flow rate of the nozzle was 0.37 l/min. Equation (3) is used to calculate the amount of spray in the entire spray. Q ¼ q  t  106

ð3Þ

Where Q—The total amount of spray in the spray process ðL=min) q—Single nozzle flow ðL=min) t—Duration of spray process ðs) The calculation results show that the total injection volume in the simulation process is 12025 lL. In the simulation, the deposition amount of droplets in each layer is shown in Table 2.

Table 2. Statistical table of droplet deposition of each layer in the process of simulation Porosity assemblage Droplet deposition per layer (ll) Upper layer Middle layer Lower layer 1 4266.68 3472.28 881.6 2 4145.7 3342.44 2176.58 3 3855.67 2732.95 1132.56 4 5098.16 2562.66 1497.09 5 5094.98 2794.13 1237.62 6 5094.58 2292.17 1186.24 7 5587.17 2562.66 1090.65 8 5570.52 2232.16 1277.60 9 5557.00 2110.45 697.60

The deposition data obtained in laboratory tests are processed and the deposition per layer is expressed as a percentage of the deposition per layer relative to the total jet volume. The percentage of deposition in each layer and two kinds of test errors are shown in Table 3.

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layer (%) Exp ERR 30.48 14.09 29.78 13.63 26.96 15.91 38.80 8.49 38.27 9.68 38.17 9.91 43.16 7.10 43.52 6.04 42.21 8.65

Middle layer SL Exp 28.88 26.38 27.79 25.38 22.72 23.20 21.31 19.55 23.23 24.15 19.06 15.89 21.31 17.02 18.56 14.24 17.55 12.63

(%) ERR 8.80 8.69 11.13 5.26 8.96 10.13 10.74 12.53 10.92

Lower SL 7.33 18.10 9.41 12.45 10.29 9.86 9.07 10.62 5.80

layer (%) Exp ERR 5.27 28.10 15.12 16.44 8.11 13.86 12.14 2.51 10.27 0.21 9.99 13.16 7.63 15.82 9.87 7.07 4.47 23.01

It can be seen from Table 3 that the simulation results are basically consistent with the test results. Only in combination 1 and 9, the errors of the lower layer deposition amount are 28.1% and 23.01% respectively, which are mainly caused by the errors of the test system, and the rest are within 16.5%. It shows that the simulation basically reflects the actual spray situation, and the droplet deposition distribution test device with similar static porosity can be used to analyze the spatial distribution of droplet deposition.

5 Conclusions This paper describes the density of plant canopy by using porosity, and designs a kind of space droplet deposition test device which can replace the static porosity of plant canopy. Taking cotton as an example, a static porosity deposition test device was set up and spray deposition test was carried out. At the same time, the spatial distribution of fog droplets under different porosity was obtained through CFD simulation. The results show that the static porosity droplet deposition test device can be used to analyze the spatial distribution of droplet deposition. Acknowledgements. This work was supported by the National Natural Science Foundation of China (51475278) and the Agricultural Machinery Equipment R&D Innovation Project of Shandong Province (2018YF002).

References 1. Dekeyser, D.: Spray deposition assessment using different application techniques in artificial orchard trees. Crop Protect. 64(10), 187–197 (2014) 2. Wu, T.: Relationships between shelter effects and optical porosity: a meta-analysis for tree windbreaks. Agric. For. Meteorol. 259(9), 75–81 (2018)

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3. Desmond, C.J.: A study on the inclusion of forest canopy morphology data in numerical simulations for the purpose of wind resource assessment. J. Wind Eng. Ind. Aerodyn. 126 (03), 24–37 (2014) 4. Hong, S.-W.: CFD simulation of airflow inside tree canopies discharged from air-assisted sprayers. Comput. Electron. Agric. 149(06), 121–132 (2018) 5. Hong, S.-W., Zhao, L., Zhu, H.: CFD simulation of pesticide spray from air-assisted sprayers in an apple orchard: tree deposition and off-target losses. Atmos. Environ. 175(03), 109–119 (2018) 6. Endalew, A.M.: Modelling pesticide flow and deposition from air-assisted orchard spraying in orchards: a new integrated CFD approach. Agric. Forest Meteorol. 150(10), 1383–1392 (2010) 7. Endalew, A.M.: A new integrated CFD modelling approach towards air-assisted orchard spraying—part I: model development and effect of wind speed and direction on sprayer airflow. Comput. Electron. Agric. 71(02), 128–136 (2009) 8. Endalew, A.M.: A new integrated CFD modelling approach towards air-assisted orchard spraying—Part II: validation for different sprayer types. Comput. Electron. Agric. 71(02), 137–147 (2009)

Effect of Heat Treatment on the Ductility of Inconel 718 Processed by Laser Powder Bed Fusion Even Wilberg Hovig1(&) , Olav Åsebø Berg1, Trond Aukrust2, and Harald Solhaug3 1

3

SINTEF Manufacturing AS, Trondheim, Norway [email protected] 2 SINTEF Industry, Oslo, Norway Hydro Innovation & Technology, Finspång, Sweden

Abstract. Inconel 718 is a precipitation-hardening alloy, with a typical heat treatment consisting of solution annealing before a two-stage ageing process. Depending on the heat treatment procedure, strength or ductility can be enhanced, typically at the cost of the other. This study aims to determine a heat treatment procedure suitable for applications that require high ductility. Tensile tests of Inconel 718 processed by laser powder bed fusion additive manufacturing has been carried out on specimens subjected to different heat treatment procedures. The results show that solution annealing above 1010 °C followed by a two-stage ageing at 720 °C/8 h with furnace cooling to 620 °C/2 h and final holding at 620 °C/10 h, produce elongation at break of above 30% tested at room temperature and above 24% tested at 650 °C. Keywords: Additive manufacturing
Powder bed fusion melting
Inconel 718
Ductility
Elongation at break


Selective laser

1 Introduction Inconel 718 is a high temperature, high strength, corrosion-resistant nickel base superalloy that has been widely used in aerospace and energy industries [1]. The microstructure consist of a c matrix, and is preferentially strengthened by precipitation of c′′-Ni3Nb phase [2]. Other phases, such as brittle c′, d, Laves, and r phases, will form if the material is exposed to high temperatures for extended periods of time [2]. The formation of these phases is what effectively limits the service temperature of the alloy. Inconel 718 has proven to be a suitable material for laser powder bed fusion (LPBF) additive manufacturing, with excellent material properties and high relative density [1]. Heat treatment for cast and wrought Inconel 718 has been thoroughly researched and standardized, but the recommended heat treatment for LPBF Inconel 718 in e.g. ASTM F3301-18 refers to SAE AMS2774E, which is developed for conventionally manufactured Inconel 718, and does not take into account the unique properties of LPBF materials. The microstructure of LPBF Inconel 718 is typically fine © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 98–105, 2020. https://doi.org/10.1007/978-981-15-2341-0_13

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grained compared to its cast and wrought counterparts [3], with traces of microsegregation, MC carbides and Laves-phase as a result of the LPBF process [4–8]. Several studies have been conducted to investigate the influence of different heat treatment procedures on the microstructure and mechanical properties of Inconel 718 processed by LPBF. Aydinöz et al. [4] investigated the effect of, amongst other, solution annealing (1000 °C/1 h, air cooling) and ageing (720 °C/8 h, furnace cool to 621 °C/2 h, 621 °C/8 h), and reported elongation at break of approximately 10% at room temperature. Chlebus et al. [5] conducted experiments with an identical ageing scheme, but with different annealing temperatures prior to ageing. The reported elongation at break for specimens built parallel to the build direction and solution annealed at 1100 °C is 19 ± 2% at room temperature. Hovig et al. [2] reports elongation at break of 5 ± 2% at room temperature for specimens manufactured parallel to the build direction, solution annealed at 980 °C/1 h, and aged following the same scheme as Aydinöz et al. and Chlebus et al. Hot isostatic pressing (HIP) at 1160 °C prior to ageing increased the elongation at break up to 9 ± 4%. Schneider et al. [9] compared the mechanical properties of 10 different heat treatment variations, with reported elongation at break ranging from 15.44 ± 2.00% to 34.34 ± 1.52% depending on the heat treatment condition. Amongst the aged conditions, stress relief at 1066 °C/1.5 h, solution treatment at 954 °C/1 h, followed by ageing at 720 °C/8 h and 620°C/10 h, produced the highest elongation at break with a reported value of 21.96 ± 0.37%. Common for all the mentioned studies are yield strengths upwards of 1000 MPa, and in some cases exceeding 1300 MPa. When conducting tensile tests at elevated temperatures the yield strength, UTS, and elongation at break is reduced when comparted to testing at room temperature. Trosch et al. [3] compared the tensile properties of cast, forged and additively manufactured Inconel 718 tested at room temperature, 450° and 650 °C. The material was heat treated with solution treatment at 980°/1 h followed by ageing at 720 °C/8 h and 620 ° C/8 h. The elongation at break dropped from 20.4% at room temperature to 14.2% at 650 °C for the LPBF specimens built in parallel to the build direction. The elongation at break of LPBF specimens were reported to drop further than that of forged and casted samples at 650 °C compared to room temperature, which is attributed to the presence of d-phase within the grains. Zhang et al. [10] conducted a study where three different levels of d-phase was present in the material. The material was tested at 950 °C and the material condition without d-phase display the highest elongation at break. As the d-phase content is increased the elongation at break is significantly reduced. By increasing the level of dphase from 0% to 3.79%, the elongation at break is reduced by 20%. If the level of dphase is further increased from 3.79% to 8.21%, the elongation at break is reduced by an additional 20%. The yield strength and UTS was not as greatly affected by the alteration of d-phase content. This study focuses on understanding the mechanisms that influence the elongation at break, in order to increase the ductility while maintaining acceptable strength.

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2 Experimental Method Twenty tensile specimens were manufactured out of Inconel 718 powder using an EOS GmbH EOSINT M280. The processing parameters are denoted as In718 Performance 2.1 by the vendor, and argon shielding gas was used with a Grid Nozzle type 2200 5501. The specimen geometry is shown in Fig. 1, with the specimen orientation with respect to the build direction indicated.

Fig. 1. Specimen geometry. All dimensions in mm, except for roughness (lm).

The chemical composition of the powder feedstock as given by the material vendor is shown in Table 1. Table 1. Chemical composition of the Inconel 718 powder feedstock as supplied by the material vendor. Ni Cr Nb Mo Ti Al Co Cu C Fe wt–% 50–55 17–21 4.75–5.5 2.8–3.3 0.65–1.15 0.2–0.8 < j¼1 skj  xji dkj ¼ h i1 > : Pnu skj  xji P p i¼1

k¼1 k ¼ 2; 3;


; M

2

d11 d12 6 d21 d22 6 D¼6 .. 4 . dp1 dp2




..

.




d1q d2q .. . dpq

j ¼ 1; 2;


; N

ð2Þ

3 7 7 7 5

ð3Þ

The smaller the distance from dkj , the greater the possibility of judging the object in class j state according to the kth sensor’s information. Hence the definition: mkj ¼ Normalization:

PQ j¼1

1 dkj

ð4Þ

mkj ¼ 1 2

m11 m12 6 m21 m22 6 M¼6 .. 4 . mP1 mP2




..

.




m1Q m2Q .. . mPQ

3

2

3 M1 7 6 M2 7 7 6 . 7 7¼4 . 5 5 . Mp

  Mk ¼ MK1; MK2;


; MkQ;

ð5Þ

ð6Þ

So it can be used as the reliability value of the kth sensor for state recognition. On this basis, the theory is optimized and improved. Extracting fault signals from multisource signals is the first condition for lift fault prediction and diagnosis. The framework of fault recognition chooses fault characteristic parameters, and establishes basic trust allocation function. The combination rules are used to calculate the fault. Finally, the fault is predicted and judged according to the fusion results. In order to prevent multi-source evidence information from being influenced by subjective factors, which lead to large differences in forms, high conflict and poor real-time, it has a negative impact on the realization of the fusion process and the rationality of the results. Using the above data characteristic value with high degree of objectivity, strong real-time, easy to implement and analyze and adjust, the evidence information generated can be automatically calculated according to multi-source information data.

4 Fusion of Neural Network, DS and Sensor Data The vibration signal of lift will change with the change of its running state. Therefore, its time domain index will also change. There are many time domain indexes of vibration signal, such as kurtosis index, peak value, pulse index, square root amplitude, waveform index, effective value, mean value, margin index, etc. [10]. For different

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types of fault signals, different time-domain indicators show different advantages. For example, kurtosis index, impulse index and margin index are more suitable for impulse type faults. At the beginning of the fault, these indexes will change significantly, but with the gradual aggravation of the fault, the sensitivity of sending indicators will gradually decline, and the stability is poor, which is only applicable to early faults; the peak value is the signal at one time. The maximum value in the section is sensitive to the fault of instantaneous impact and has a good diagnostic effect. Mean value reflects the average value of signal amplitude, which is relatively stable. Mean square root represents the average energy of the signal, and peak amplitude is better for the signal with slow time variation. According to the four different states of car trajectory, an identification framework is set up = {Normal, X-direction vibration signal, Y-direction vibration signal, wear signal}to determine whether there is conflict between evidences before evidence fusion. If there is conflict, BP neural network method is used to improve evidence combination for conflict evidence. In order to validate the effectiveness of the algorithm, vibration sensors and microphones are used to collect vibration and noise signals in four different states of the car. In the experiment, multiple sets of data are collected in each state, and multiple points are sampled in each group. Three of them are randomly selected as training of the neural network set, the remaining two groups as test sets. The specific sensor installation location is determined by the selected lift. The specific route and scheme of extraction of fault features are as follows [11]. a. Fault feature extraction of vibration signals According to the characteristics of non-stationarity of vibration signals, eight characteristic parameters of time-domain statistics (including RMS, peak, skew, kurtosis, peak index, margin index, impulse index and waveform index) and the energy characteristics of the first eight IMF components decomposed by EEMD are selected as the characteristic parameter space 1. b. Fault feature extraction of noise signal Using wavelet packet analysis, we can get the characteristics of low frequency and high frequency noise signal decomposition. In this paper, we use the wpdencmp function of the wavelet packet to de-noise the collected noise signal, and then decompose the threelayer wavelet packet with coif5 as the basis function of the wavelet packet to get eight frequency bands, and extract the energy characteristics of each frequency band as the characteristic parameter space 2. c. Construction of neural network model The construction of BP neural network is determined according to the characteristics of input and output data. For BP sub-network 1 and 2, the spatial dimension of feature parameters is 16, and for BP sub-network 3, the spatial dimension of feature parameters is 8. There are four classes of states to be identified. The BP algorithm is improved by using additional momentum and variable learning rate.

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d. Fusion diagnosis based on D-S evidence theory According to the normalized basic reliability distribution of the diagnosis results of the neural network, the fusion diagnosis results of the network output are obtained by using Dempster combination rules and decision method based on basic probability assignment. The information fusion fault diagnosis method based on neural network and D-S evidence theory can get correct diagnosis results. When the two methods are combined, especially when multi-sensor information fusion diagnosis is carried out, the diagnostic accuracy and uncertainty reach the extreme value, that is, multi-sensor information fusion can make full use of the redundancy and sum of sensors. Complementary information can effectively improve the reliability of fault diagnosis. A method of lift fault diagnosis based on DS data fusion and neural network as shown in Fig. 3 is adopted. Multi-sensor fusion is used to extract fault signals from multi-source signals. Then the initial reliability distribution of sensors is initialized according to the membership degree of data corresponding to each diagnostic category. The data collected by each sensor is taken as evidence body and combined. Rules are calculated and the final diagnosis results are obtained based on the fusion results. Thus, the uncertainty and inaccuracy of single sensor fault diagnosis can be overcome, and the accuracy of lift fault diagnosis and prediction can be improved.

Fig. 3. Neural network based flow chart of evidence theory diagnosis and prediction

Before evidence fusion, judge whether there is conflict or not between evidences. If there is conflict, BP neural network method is used to improve evidence combination of conflict evidence. Otherwise, it is fused according to Dempster combination rule. The fusion process is shown in Fig. 4. According to the normalized basic reliability distribution of the diagnosis results of the neural network, the fusion diagnosis results of the network output are obtained by using Dempster combination rules and decision method based on basic probability assignment.

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Fig. 4. Evidence-based fusion process

5 Conclusions This research can provide new theory and technology for lift early monitoring, and has positive significance for ensuring lift safety. It is also used for analysis and fault diagnosis of analog data and part of actual data. The comprehensive research and engineering application of monitorability design theory will help to improve the development of condition monitoring and fault diagnosis technology of lift mechanical system, improve the acquisition and identification of weak signals in fault diagnosis of lift mechanical system, how to adapt the threshold according to the characteristics of data flow is the next step of experimental research. Acknowledgements. The work is supported by Natural Science Research Major Project of higher education institution of Jiangsu Province (grant no. 17KJA460001).

References 1. Jiang, T., Liu, G.: Lift safety monitoring and early warning information platform based on internet of things. China J. Constr. Mach. 162–167 (2015) 2. Li, J., Li, L., Guo, X., Liu, L., Fang, J.: Design and application of service platform for emergency rescue and disposal of lift. J. Saf. Sci. Technol. 133–138 (2016)

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3. Lin, C., Wang, X., He, B., Li, Z.: Lift condition monitoring device based on current features. Autom. Inform. Eng. 6–10 (2013) 4. Wang, J., Zhang, L.: Lift operation monitoring system based on wireless sensor network. Sens. World, 31–34 (2012) 5. Jiang, X.: Research on vibration control of traction elevator. In: International Industrial Informatics and Computer Engineering Conference, pp. 2144–2147. Atlantis Press (2015) 6. Jiang, X.: Research on intelligent elevator control system. Adv. Materials Res. 605–607, 1802–1805 (2012) 7. GB T 31821-2015 Specifications for discard of the main parts of elevators 8. Han, C., Zhu, H., Duan, Z.: Multi-source Information Fusion, 2nd edn. Tsinghua University Press, Beijing (2010) 9. Han, D., Yang, Y., Han, C.: Advances in DS evidence theory and related discussions. Control Decis. 1–11 (2014) 10. Guo, L., Jiang, X.: Research on horizontal vibration of traction elevator. In: International Workshop of Advanced Manufacturing and Automation, pp 131–140. Springer, Singapore (2018) 11. Xu, J., Xu, L., Wang, H., Zheng, B., Tang, B.: Condition monitoring of elevators based on vibration analysis. J. Mech. Electr. Eng. 279–283 (2019)

Design of Permanent Magnet Damper for Elevator Xinyong Li1,2, Jian Wu1,2,3(&), Jianfeng Lu1,2, Peijun Jiao1,2, and Lanzhong Guo1,2 1

School of Mechanical Engineering, Changshu Institute of Technology, Changshu, China [email protected] 2 Jiangsu Elevator Intelligent Safety Key Construction Laboratory, Changshu, China 3 Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway

Abstract. With the continuous improvement of living standards, people have put forward higher requirements for the reliability and safety of elevators. An elevator damper based on the permanent magnetic field is designed, which uses the magnetic field motion to form an eddy current on the surface of the conductor (Cu), thereby decelerate the car in elevators, and the isotropic permanent magnet supports the weight of the car. In this paper, the effects of falling speed, magnetic field strength, magnetic field distribution on deceleration time and deceleration distance are analyzed, and the fact of magnet spring also studied. The results have shown the eddy current generated by the permanent magnetic field on the surface of the conductor can effectively achieve the deceleration of the elevator. The combination of a heterotrophic magnetic field and a discontinuous conductor can achieve the best deceleration time and distance. The deceleration time is proportional to the entry speed. The higher the speed, the stronger the eddy current field generated, the greater the impact, and the longer the deceleration time. After the speed is reduced to a minimum, the bottom permanent magnet spring can effectively support the weight of the entire car. Keywords: Elevator


Permanent magnet damper
Eddy current
Design

1 Introduction With the increase in the number of elevators and the years of use, people have become more and more demanding on the reliability of elevator safety systems. In general, the elevator safety system includes speed limiters, safety gears, rope grippers, and dampers. Once the elevator fails, the safety devices (speed limiters, safety gears, rope grippers) ensure the safety of the elevator car. If three safety devices are failed, the car will eventually fall, the damper installed in the ground is called the last “safety line” of the elevator and is one of the necessary safety devices for the elevator [1]. The damper currently used in elevators come in two main forms: energy storage and energy-consuming [2, 3]. Energy storage damper refers to spring damper, which is © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 169–175, 2020. https://doi.org/10.1007/978-981-15-2341-0_21

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dampened by the spring force of the spring. The spring can return to its original shape after the end of the movement, which is the most commonly used buffering method. Energy-consuming dampers generally refer to hydraulic dampers that are dampened by the pressure of hydraulic oil when subjected to an impact [4]. The permanent magnet reducer uses a permanent magnet to form an eddy current field on the surface of the conductor, and the two magnetic fields are coupled to each other to generate a force opposite to the motion, thereby achieving the purpose of deceleration. This feature is widely used in the fields of brakes, buffers and so on [5, 6]. In this paper, a magnetic eddy current damper is designed. The deceleration effect of different magnetic field combinations is analyzed, and the time and distance of deceleration at different speeds are analyzed. In the last, the magnet spring also is simulated.

2 Structure and Principle The structure of the elevator permanent magnet damper is shown in Fig. 1(a). The permanent magnet damper contains two parts: eddy current permanent magnet reducer on both sides, and the permanent magnet spring in the car bottom, the 3D model is shown in Fig. 1(b).

Fig. 1. The schematic of permanent magnet damper

As shown in Fig. 1, the permanent magnet damper contains a magnet and conductor. The permanent magnets are respectively installed on the inspection layer, the bottom of the car and the sidewall of the well. The conductors are mounted on the outer wall of the car. When the car suddenly falls, the speed of the car is increasing. The permanent magnet was placed upon the bottom. Under normal operating, the car speed is low, at that time, the eddy current field in the conductor is weak and the force is small. The elevator can be work normally. When a disaster occurs, the conductor falling with the car, and getting in touch with each other before reach the bottom. The conductor cuts the magnetic lines of the permanent magnet with high speed and induces an eddy current on the conductor surface. The two magnetic fields are coupled to each other to generate a force opposite to the direction of motion, forcing the car to

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slow down. The isotropic permanent magnet at the bottom provides support and eventually causes the car to stop.

3 The Design and Analysis of Magnet 3.1

The Design of Eddy Current Reducer

The permanent magnet damper includes permanent magnet and conduction. The permanent magnets are placed in the elevator pit, which can effectively reduce the impact on the surrounding environment. The conduction moves up and down with the elevator car. When the magnetizer receives the eddy current deceleration, the entire car is decelerated to a standstill. The eddy current relationship between the permanent magnet and conduction determines the effect of the damper. Eddy current reducer 3D model as shown in Fig. 4. The size of the permanent is 300  50  50 mm, the material is NdFeB, 10 pieces, arrange with the same size. The conduction is made with cu, size is 900  600  30 mm, 1 piece.

Fig. 2. The 3D model of eddy current reducer

3.2

Design of Permanent Magnet Spring

The permanent magnet spring is composed of a static ring, a moving ring, and an auxiliary port, and adopts a symmetrical structure, and a single group permanent magnet spring model is shown in Fig. 2. The small ring magnet is fixed to the nonmagnetic disc-shaped base, and the shaft is vertically fixed by the base of the nonmagnetic disc, which made with Al, and the permanent magnet is NdFeB which remanence Br = 1.17T. The spring 3D model as shown in Fig. 3.

Fig. 3. The 3D model of permanent magnet spring

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In order to verify the feasibility of the scheme, the permanent magnet buffer simulation device was designed and manufactured, and its main structural parameters: weight is 30 kg, the size of the car is 300  300  300 mm; the car line is 1200 mm.

4 Results and Discusses 4.1

Effect of Magnetic Arrangement

The arrangement of the permanent magnets directly affects the stop time and distance of the permanent magnet damper. The arrangement of a permanent magnet as shown in Fig. 4. It contains (a) Horizontal isotropic and vertical heterotrophic magnetic field; (b) Horizontal and vertical are heterotrophic magnetic field; (c) two pieces of Horizontal isotropic and changed to a heterotrophic magnetic field; (d) Horizontal heterotrophic and vertical isotropic magnetic field. The conductor arranged in Fig. 5. It contains 4 types: (a’) with one block; (b’) 4 pieces of blocks are arranged; (c’) several pieces of block; (d’) 8 pieces of block. Different combinations are recorded as a–a’, a–b’…

Fig. 4. Arrange of permanent magnet

Fig. 5. Arrange of conduct

Figure 6 shows the change in the curve of buffer time and buffer distance for 16 different combinations. From the figure, the permanent magnets and the conductors in different combinations can increase in different degrees as the distance of action increases. The max value is 1500 N in a–b’,but unstable. It can be seen from the overall picture that the overall rise of a–c’ and b–c’ is relatively flat and meets the requirements for use. Considering the processing and installation costs, a combination of b–c’ (each magnet isotropic, multiple vertical uniform distribution) is used here. The magnetic field distribution during motion is shown in Fig. 7.

Design of Permanent Magnet Damper for Elevator

Fig. 6. The magnet force VS place

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Fig. 7. The magnetic field of magnet and conductor

Effect of Falling Speed

The permanent magnet damper has different stopping times for different drop speeds as shown in Fig. 8. The deceleration trend of different initial speeds is basically the same. When the deceleration starts, the descending speed is faster. As the speed continues to decrease, the trend tends to be gentle. The larger the initial speed, the greater the acceleration that begins to decrease, and the longer it takes. Take out the time from Fig. 8 when the speed is reduced to 0 m/s, and draw the graph as shown in Fig. 9. It is found that the time it takes with the linear increase and decrease speed is basically in line with the linear transformation relationship.

Fig. 8. Different speed versus time

4.3

Fig. 9. Stopping time with the speed

Permanent Magnet Spring Support Effect

There are 9 pairs of isotropic permanent magnets at the bottom of the car to support the car and be stationary and suspended. 9 pairs of isotropic permanent magnets were simulated by finite element software, and the results are shown in the figure. The figure shows that the relationship between any pair of the same sex is a repulsive force whose magnitude is mainly related to the properties of the magnet itself and the distance between a pair of magnets. In order to select the most suitable magnetic field

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distribution and magnetic field size, the distance between the magnetic field size and the coupled magnetic field is simulated and the results are shown in Fig. 10.

Fig. 10. The magnetic field of magnet spring

Fig. 11. The spring force VS distance

Figure 11 shows the expulsive force generated by five sets of magnets with different diameters with increasing spacing. The repulsive force of any area with the same magnetic field decreases with the increase of the spacing, and its trend is an exponential decline, the initial decline is more obvious, and the later stage tends to be stable. When the distance reaches a certain value, the repulsive force disappears. Increasing the contact area is the best way to increase the repulsive force. It can be seen from the figure that when the coupling area reaches 50 mm, the maximum repulsive force generated when the spacing is 5 mm reaches 1800 N (180 kg), which is much larger than the 30 kg requirement. In the specific use of the elevator, it can have a certain assembly error, set to 20 mm, according to the above figure, when the contact area is 40 mm, it can meet the use requirements.

5 Conclusion (1) A permanent magnet elevator damper is designed, which is decoupled by coupling the side eddy current field with the permanent magnet, and the suspension of the elevator car is realized by the permanent magnet spring installed at the bottom. (2) The eddy current reducer has been optimized, and various combinations have been designed. The eddy currents generated by different combinations are compared. The best solution for the permanent magnets and the array of conductors is preferred. (3) The stopping time of the eddy current buffer at different decreasing speeds is analyzed. The falling speed of the car is non-linear. The faster the speed, the more obvious the deceleration, and the longer the deceleration time of the speed. The deceleration rest time is substantially linear with the initial speed. (4) The permanent magnet spring installed at the bottom of the car is analyzed, and the permanent magnets of different diameters are simulated and analyzed. The supporting force of the permanent magnet spring decreases nonlinearly with the increase of the spacing. The larger the magnetic field strength, the more the supporting force.

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Acknowledgments. The work is supported by Jiangsu Government Scholarship for Overseas Studies, Open Project of Jiangsu Elevator Intelligent Safety Key Construction Laboratory (JSKLESS201703), and The Doctoral Science Foundation of Changshu Institute of Technology (No. KYZ2015054Z).

References 1. Husmann, J.: Elevator car frame vibration damping device. Google Patents (2005) 2. Uchida, T., Sato, S., Nakagawa, T.: A proposal of control method for an elevator emergency stop device with a magnetic rheological fluid damper. IEEE Trans. Magn. 50(11), 1–4 (2014) 3. Qiong, Z.: Application of piston accumulator in oil buffer. Fluid Power Transm. Control 3, 25–28 (2006) 4. Yao, R., Zhu, C., Zhan, Y., et al.: Impact research on the oil buffer with air accumulator. Zhendong yu Chongji/J. Vib. Shock 25(1), 153–155 (2006) 5. Gulec, M., Aydin, M., Lindh, P., et al.: Investigation of braking torque characteristic for a double-stator single-rotor axial-flux permanent-magnet eddy-current brake. In: 2018 XIII International Conference on Electrical Machines (ICEM), Investigation of Braking Torque Characteristic for a Double-Stator Single-Rotor Axial-Flux Permanent-Magnet Eddy-Current Brake, pp. 793–797. IEEE (2018) 6. Chen, Q., Tan, Y., Li, G., et al.: Design of double-sided linear permanent magnet eddy current braking system. Prog. Electromagnet. Res. 61, 1–73 (2017)

Design and Simulation of Hydraulic Braking System for Loader Based on AMESim Junjun Liu1,2(&), Lanzhong Guo1,2, Jiaxin Ma1,2, Yang Ge1,2, Jian Wu1,2, and Zhanrong Ma3 1

2

Changshu Institute of Technology, Changshu, China [email protected] Jiangsu Key Laboratory for Elevator Intelligent Safety, Changshu, China 3 China University of Geosciences, Beijing, China

Abstract. The loader develops towards the direction of heavy haul and high speed intellectualization, and puts forward higher requirements for its braking system. The power braking system is widely used in the field of engineering equipment due to its superior braking performance and reliability. Based on the analysis of the working principle of the hydraulic braking system, this paper establishes the mathematical model of the hydraulic braking system, and establishes the simulation model of the hydraulic braking system using the AMESim simulation platform, and carries on the simulation analysis to it. The results show that the pressure rise time is 0.1 s, the pressure drop time is 0.1 s when the friction disc contacts with the brake disc, and the brake pressure rise can be divided into three stages when the brake disc is unloaded; the accumulator can perform about 4 brakes until the next filling, and the piston reaches the limit position within 0.1 s. 0.8 mm.. Keywords: Loader Simulation


Hydraulic braking system
Mathematical modeling


1 Introduction Construction machinery is an important part of the equipment industry. It is the necessary mechanical equipment for earthwork construction, pavement construction and maintenance, mobile crane loading and unloading operations and comprehensive mechanized construction projects required by various construction projects. Loader is a kind of earthwork construction machinery widely used in highway, railway, building, hydropower, port, mine and other construction projects. With the development of China’s industry, loader is developing in the direction of heavy load, high speed, automation and so on. It puts forward higher and higher requirements for the controllability, reliability, stability, fast response and control accuracy of its brake system. The function of the braking system is to exert resistance on the machine so that it can slow down or stop. Brake system plays an important role in ensuring personal safety and mechanical safety. Wheeled machinery, in particular, sometimes needs to operate on the highway, while people and cars on the highway often need to slow down and stop, or even emergency parking. Therefore, it is required that the machinery has © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 176–184, 2020. https://doi.org/10.1007/978-981-15-2341-0_22

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good braking performance, otherwise, it may cause major accidents. In addition, according to the requirements of operation and terrain, braking in long downhill and parking on slope or long-term parking on site also require wheel braking or parking braking devices with good mechanical performance. Hydraulic braking systems and other power braking systems are widely used in construction machinery [2, 3] because of their superior braking performance and reliability. Because of its high reliability and good stability, full hydraulic brake system has been widely used in engineering equipment. Full hydraulic braking system has the following characteristics: pure hydraulic braking system does not use gas as medium, so its structure has been greatly simplified compared with gas-hydraulic braking system, which is convenient for maintenance and maintenance; the braking force and moment generated are larger and easier to achieve electronic control; and because the full hydraulic braking system is fully closed, oil is not easy to leak. With less pollution to the environment; using accumulator to store energy, emergency braking after power cut-off can be realized; the volume and quantity of components used should be reduced relatively, which is beneficial to the arrangement of the original parts of the system in space; the braking force and the force acting on the pedal are linear, which is conducive to the normal operation of the driver; the system uses hydraulic oil as working medium, because of hydraulic oil. Compressibility is very small, so it can transfer large braking force in a very short time, fast and reliable action. However, the price of the full hydraulic brake system is more expensive than that of the gas-hydraulic brake system, so the sales of the full hydraulic brake system in the domestic market is not very good, but in terms of the overall trend, the brake system of construction machinery is developing towards the full hydraulic brake system [4, 5]. In order to prove the reliability and fast response of the hydraulic braking system, starting from the design of the hydraulic system itself, the mathematical model of the hydraulic braking system is established, and the simulation model of the hydraulic braking system is established by using AMESim simulation platform to study the problems of pressure rise and fall, time, piston stroke and time of the system.

2 Hydraulic Braking System Simulation The principle of hydraulic braking system is shown in Fig. 1. Its working principle is that the system pressure is maintained by accumulator, and each braking circuit is equipped with accumulator separately. When the oil pressure in the accumulator is lower than the set minimum working pressure of the system, the filling valve inputs the hydraulic oil from the brake pump into the accumulator. When the oil pressure in the storage gas reaches the set maximum working pressure of the system, the filling valve stops supplying oil to the brake system and turns to supplying oil to the next hydraulic system. When the brake pedal is depressed, the braking force and braking moment will be generated by the high-pressure oil flowing out of the accumulator and flowing through the brake valve and then to the brake. When the hydraulic oil in the parking brake flows back to the tank, the piston in the parking brake will stop when the friction plate is pressed on the brake disc under the force of the spring. Removing the parking brake is that the high-pressure oil stored in the

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accumulator in the parking brake circuit enters the parking brake through the parking brake valve, and pushes the piston backwards to compress the spring in the parking brake, thus separating the friction disc from the brake disc. As far as the shoe brake system is concerned, the piston of the parking brake cylinder is reset under the action of spring tension, and the pull rod prompting the parking brake is driven to make the shoe open and press tightly on the brake disc for braking. When the parking brake is released, the high-pressure oil in the system will flow into the parking brake tank from the accumulator and reverse-drive the piston to compress the bullet in the parking brake cylinder. Reed, relax the pull rod of the shoe-type parking brake, so that the brake shoe and the brake disc are separated [6].

Fig. 1. Principle of hydraulic wet brake system

3 Mathematical Model of Hydraulic Braking System 3.1

Simplification of Braking System

The structure and function of the front and rear axle circuit of the loader brake system are the same. When building a mathematical model, only one loop should be considered. The accumulator is filled by a pump through a filling valve. When the accumulator is filled with oil, the filling valve will stop supplying oil. Only when the accumulator’s oil pressure is low to its set value will the filling valve re-supply oil. Therefore, when simplifying the system, the influence of pump and filling valve on the system pipeline after accumulator can be neglected, and accumulator can be regarded as a device for direct function of the system. And because the pipeline of the system is very complex, we need to ignore the influence of the pipeline on the system. The simplified schematic diagram of the full hydraulic braking system is shown in Fig. 2.

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Fig. 2. Simplified hydraulic braking system

3.2

Mathematical Model of Simplified System

(1) Exit pressure of accumulator The accumulator is an air-bag hydraulic accumulator. The following assumptions are made in the accumulator modeling: (1) The accumulator has a fast filling and releasing process, and the change of gas pressure and volume is approximately adiabatic; (2) Comparing with gas, the compressibility of oil is neglected; (3) Oil is laminar flow in accumulator; (4) There is a connection between accumulator and system. Pipeline and connecting pipeline have great influence on accumulator body. When modeling, connecting pipeline is regarded as part of accumulator [7]. The force balance equation of accumulator is:
 1 dQs þ Ba Qs ðPs  Pa ÞAa ¼ ma Aa dt

ð1Þ

Derivation of time on both sides,


dPs dPa  dt dt




 1 d 2 Qs dQs ¼ 2 ma 2 þ Ba Aa dt dt

ð2Þ

Flow continuity equation of accumulator: Qs ¼ 

dVa dt

ð3Þ

From Boyle’s law of thermodynamics, the adiabatic equation is Pa Van ¼ n Pa0 Va0 ¼ const, Take the reciprocal of time on both sides: nPa

dVa dPa dPa nPa dVa ¼ Va or ¼ dt dt dt Va dt

Substitute (4) and (3) into (2),

ð4Þ

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 dPs 1 d 2 QS dQS nPa Qs ¼ 2 ma 2 þ Ba þ Aa dt dt dt Va Substitute Va ¼ Va0

 1n Pa0 Pa

ð5Þ

into (5),


1
 dPs 1 d 2 QS dQs nPa Pa n ¼ 2 ma 2 þ Ba Qs þ Aa dt dt dt dt Pa0

ð6Þ

Formula (6) is the relationship between the outlet pressure of accumulator and the outlet flow rate. (2) Flow rate of fluid in moving cylinder Regardless of the influence of the pipeline from accumulator to brake valve and brake valve to brake cylinder, it is assumed that the oil is incompressible and that the pipeline, components and their interfaces are fully sealed. Represents the flow of oil from the accumulator through the brake valve to the brake cylinder. sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2jPs  PL j QS ¼ Cd Ab1  signðPs  PL Þ q

ð7Þ

sign—Symbolic function, 8 < 1 signðaÞ ¼ 0 : 1

ða\0Þ ða ¼ 0Þ ða [ 0Þ

(3) Flow from Brake Cylinder to Fuel Tank Similarly, the flow equation from the brake cylinder to the oil tank is similar to that flowing into the brake cylinder. sffiffiffiffiffiffiffiffiffiffi 2j P L j Qs ¼ Cd Ab2  signðPL Þ q

ð8Þ

The brake is a sliding valve structure, the control valve orifice is a circular orifice, which is a non-circular valve orifice. The area gain of the valve orifice is non-linear, the flow area is an arcuate area, and the calculation formula of the arcuate area is [8]: " # rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
 2 d2 2y 2y y y 1 Ay ¼  cos 1  2  ð1  Þ  d d d d 4

ð9Þ

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Therefore, the flow area from P port to T port is: 8

1 > u þ v u v ¼ u þ u v u < c c k k k k k k 2 k k k  k  ð7Þ P P 2 1 P 3 P 2 > > : uc v k uk þ v c v k ¼ 2 vk þ v k uk k

k

k

k

Where uk ¼ xk  x and vk ¼ yk  y are the transformed coordinates. Then, the radius values for each point can be calculated as following: qk ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðxk  xc Þ2 þ ðyk  yc Þ2 :

ð8Þ

The circle centre ðxc ; yc Þ and radius values qk are calculated in the each iteration. Then the radius variation range of the n-sample for each particular location is estimated by the residual Dq ¼ qmax  qmin . Eventually, after all iterations were completed, the smallest estimated radius variation range Dqmin for the particular sample size n was defined. The maximum estimation error dmax due to the sample size n was calculated as ANN dmax ¼ DRANN  Dqmin , where DRANN ¼ RANN max  Rmin is the precise radius variation range estimated from 480 variables, which were simulated with the continuous virtual profile.

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Fig. 4. The five-point sample: p2 ; p3 ; . . .pn – the measured points; q1 ; q2 ; . . .q5 – the estimated radius variables; ref.lsc – reference least squares circle; r1ANN – a radius of the original reference circle; ðXc ; Yc Þ – a reference circle center based on 480 points; ðxc ; yc Þ – a new circle center based on 5 points.

5 Results and Discussion The simulation procedure described in Sect. 3 was applied with different sample sizes from 5 to 400 measuring points, and for 9 circle sections with nominal diameter from 40 mm to 500 mm. The final simulation results are tabulated in Table 1. Table 1. The maximum estimated error dmax due to the sample size n for various diameters Di Sample size n 5 15 D1 ¼ 40 mm* 11.2 7.5 D2 ¼ 80 mm 10.2 5.4 D3 ¼ 100 mm 7.1 4.9 D4 ¼ 150 mm 21.4 11.5 D5 ¼ 200 mm 15.1 4.4 D6 ¼ 250 mm 30.2 7.8 D7 ¼ 300 mm 22.2 8.7 D8 ¼ 400 mm 21.5 8.7 D9 ¼ 500 mm 24.3 10.6 *dmax is given in lm

30 5.0 5.0 4.0 9.1 2.0 2.6 5.9 4.1 7.8

60 4.3 4.1 3.1 7.3 0.9 1.9 3.7 1.8 7.8

93 3.1 2.5 2.5 6.6 0.7 1.1 2.1 1.6 5.0

150 2.8 1.9 2.0 6.6 0.3 0.7 1.4 1.1 3.2

200 2.5 1.6 1.3 4.5 0.2 0.7 1.3 0.7 3.2

300 1.3 1.1 0.7 2.1 0.1 0.4 1.0 0.4 1.9

400 0.7 0.5 0.4 0.8 0.1 0.0 0.8 0.2 0.8

The graphical interpretation of the results (see Fig. 5a) shows that relation between the maximum estimated error dmax and the sample size n has nonlinear, asymptotic behavior. This behavior appears relatively predictable. However, the relation between the maximum estimated error and the diameter size for a given sample size does not follow a clear trend, when a five point-sample is used (see Fig. 5b). The maximum estimated error for different diameters varies between 7.1 µm and 30.2 µm. In our tests, the maximum estimated error is up to 6.6 µm for the ninety three points sample size, and up to 2.1 lm for three hundred measuring points.

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(a) The maximum estimated error vs sample size for diameters D1 , D2 ,...D9

(b) The maximum estimated error vs diameter size for samples of 5, 93 and 300 points

Fig. 5. The relationship of the maximum estimated error with the sample size and the diameter size of circle profiles

6 Conclusion According to the simulation results, the error due to the sample size can be a significant contributor to the measurement uncertainty and thus it must be considered in the measuring strategy for CMM. The simulation procedure presented in this paper is a new algorithm for estimating the maximum error due to number of the measuring points. As shown with the test pieces, the diameter size is not the main factor for defining the sample strategy. The presented ANN approach can be adapted to profile forms generated by any machine operations. The approximated nondeterministic profile can be further used as the continuous function for other simulations regarding the sample strategy, alignment, filtration methods and measuring uncertainty estimation.

References 1. De Chiffre, L.: Geometrical Metrology and Machine Testing. DTU Mech. Eng. (2015) 2. Summerhays, K.D.: Optimizing discrete point sample patterns and measurement data analysis on internal cylindrical surfaces with systematic form deviations. Precis. Eng. 26(1), 105–121 (2002) 3. Changcai, C.: Research on the uncertainties from different form error evaluation methods by CMM sampling. Int. J. Adv. Manuf. Technol. 43(1), 136–145 (2009) 4. Moroni, G.: Coordinate measuring machine measurement planning. Springer, London (2010) 5. Desta, M.T.: Characterization of general systematic form errors for circular features. Int. J. Mach. Tools Manuf 43(11), 1069–1078 (2003) 6. Qimi, J.: A roundness evaluation algorithm with reduced fitting uncertainty of CMM measurement data. J. Manuf. Syst. 25(3), 184–195 (2006)

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7. Cho, N.: Roundness modeling of machined parts for tolerance analysis. Precis. Eng. 25(1), 35–47 (2001) 8. Ruffa, S.: Assessing measurement uncertainty in CMM measurements: comparison of different approaches. Int. J. Metrol. Qual. Eng. 4, 163–168 (2013) 9. Papananias, M.: A novel method based on Bayesian regularized artificial neural networks for measurement uncertainty evaluation. In: EUSPEN Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, Nottingham, UK, pp. 97–98. EUSPEN (2016) 10. Zhang, Y.F.: A neural network approach to determining optimal inspection sampling size for CMM. Comput. Integr. Manuf. Syst. 9, 161–169 (1996) 11. Grossberg, S.T.: Studies of the Mind and Brain. Reidel Press, Drodrecht (1982) 12. Wang, K.: Applied computational intelligence in intelligent manufacturing systems. In: International Series on Natural and Artificial Intelligence, vol. 2, 2nd edn. Advanced Knowledge International, Adelaide (2007) 13. Jones, W.: Back-propagation: a generalized delta learning rule. Byte 12(11), 155–162 (1987) 14. Beale, M.H.: Neural Network Toolbox, User guide (2017) 15. Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431–441 (1963)

Digital Modeling and Algorithms for Series Topological Mechanisms Based on POC Set Lixin Lu1, Hehui Tang1, Guiqin Li1(&), and Peter Mitrouchev2 1

Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China [email protected] 2 University Grenoble Alpes, G-SCOP, 38031 Grenoble, France

Abstract. The algorithm and software implementation of POC set automatic analysis for serial mechanism based on position and orientation characteristic equations are studied. Firstly, a digitized matrix which can describe the complete topological structure of the serial mechanism is proposed. The POC set of the serial mechanism is represented by two-dimensional binary matrix, which not only describes the dimension of the motion output, but also gives the relationship between the output characteristic azimuth and the axis of the motion pair. Based on the digital description method, the suitability calculation is proposed on the basis of binary “and”, “or” operation. The automatic analysis algorithm of POC set of computer automatic analysis is realized, and then the automatic calculation and analysis of POC set of serial mechanism is realized. Finally, the feasibility of the algorithm is verified by digital modeling analysis. Keywords: Series mechanism


POC set
Digital matrix
Algorithm

1 Introduction According to the topology design theory and decoupling-reducing design method of PM based on the orientation characteristic (POC) equations, the complex positional relationship of the parallel topology mechanism is solved. Computer-aided analysis can be traced back to the analysis of planar parallel mechanisms. In 1963, Dobrjanshyj [1] and Freudenstein [2] first proposed graph theory-based analysis theory, and analyzed the automatic configuration of planar mechanisms; Olson [3] and Belfiore [4] respectively focused on the planar mechanism kinematic chain. Automated mapping has done some related research; Saura [5] studied an automatic configuration of planar structures including low and high pairs; and Mrutyunjay [6] based on digital configuration, on the plane kinematic chain The program automatic algorithm is studied; Li [7, 8] is based on the Assur rod group theory and uses it as the basic element of the mechanism, thus creating a mechanism topology matrix-rod group adjacency matrix, which is used to describe the Assur rod group. Plane body. Computer-aided analysis of spatial organization topology is not much research at home and Han Yali [9] proposed a method for the synthesis of parallel mechanism configuration using VB, and used it to synthesize the three-degree-of-freedom parallel mechanism; Liao [10] proposed The symbolic representation method of the parallel mechanism expresses each mechanism © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 442–450, 2020. https://doi.org/10.1007/978-981-15-2341-0_55

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of the mechanism as a symbol polynomial form, and the algebraic operation function between the symbol polynomials, but this method is complicated, which is not conducive to computer reading and recognition. In this paper, according to the symbolic habits related to institutional science, a digital matrix is proposed to represent the order of f motion pairs in the mechanism and the positional relationship between them. At the same time, a 2 * 6 matrix is established to represent the mechanism. The POC set [11], the first row and the second row respectively represent the number of moving elements and the number of rotating elements of the mechanism; and then the automatic generation algorithm of the mechanism POC set is established to realize the automatic analysis of the organization POC set. The automatic analysis of the series mechanism POC set is realized by programming, and the correctness of the program is verified by an example.

2 Digital Modeling of Series Mechanism Topologies 2.1

Sports Subtype and Scale Constraint

The most common motion pairs in the series mechanism are the mobile pair (P pair) and the rotating pair (R pair). The rest, such as the cylindrical pair (C pair), the spiral pair (H pair), and the ball pair (S pair), can be regarded as the combination of the P pair and the R pair. For example, the S pair can be regarded as an R pair whose three axes intersect at the same point, and the C pair can be regarded as two R pairs and a P pairs of the same axis. Since the C pair, the S pair, etc. can be regarded as consisting of the R pair and the P pair, the series mechanism can be regarded as consisting of only the R and P pairs of a single degree of freedom. The P pairs are sequentially stored in the character string, as shown in Table 1. The positional relationship between the motion pairs can be divided into six basic scale constraint types, namely: parallel Coaxial, coplanar, spatial co-point and general position. To facilitate programming, digital representation can be followed by the following rules: parallel is 1, vertical is 2, coaxial is 3, coplanar is 4, and space is 5; The general position is recorded as 0. Thus, the type, arrangement, and orientation of the motion pair can be represented by an ordered topology matrix L, namely: 2

J11 6 Ni1 6 6
L¼6 6
6 4
Nfi

N1i J22





















3
N1f

7 7

7 7

7 7

5
Jff

ð1Þ

Jii—Diagonal element, indicating the type of motion pair (R/P vice), Jii= R or P; f—Number of institutional movements; Nij—Orientation relationship between the i, j movement pairs

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L. Lu et al. Table 1. Digital modeling of topological structure of serial mechanism Institutional topology

Institutional topology

SOC fP1 ==R2 Institutional topology ==R3 ==R4 g String PRRR String 2 3 Azimuth relation matrix Azimuth relation matrix P 1 1 1 61 R 1 17 6 7 41 1 R 15 1 1 1 R Institutional topology

2.2

SOC fP1  R2 R3 ==R4 g PRRR 2 3 P 0 0 0 60 R 5 0 7 6 7 40 5 R 1 5 0 0 1 R

A Digital Matrix Description of Series Mechanism POC Set

The defined POC set matrix must contain not only the end output motion dimension information, but also the direction or reference of the motion output axis. On the basis of the ordered description matrix of the mechanism topology, the organization POC set matrix adopts a digitization with a row number of 2, a column number f (the number of motion pairs), and a bitmap-ordered topology matrix L. structure. " P¼

t1 t2 r1 r2

. . . tf . . . rf

# ð2Þ

ti—Moving element output; ri—Rotate element output; The description rules for the two-dimensional matrix P are as follows: (1) The elements in the first and second rows represent independent movement and independent rotation output, respectively; (2) The non-zero elements in the i-th (i = 1, 2,…, 6) column represent the direction of the motion and rotational motion: (1) When ti = 1, it represents the i-th motion pair (ri = 0). The axis or vertical i-th motion pair (ri = 1) axis has an independent moving output; When ti = 2, there are two independent moving outputs in the plane of the vertical i-th moving auxiliary axis. (3) The rotation or moving element in any direction is described only once, and the sum of the number of moving sub-elements and rotating sub-elements is equal to the degree of freedom of the series mechanism; (4) When there are both the rotating pair and the moving pair in the series mechanism, the direction of motion of the rotating pair is preferentially used to indicate the direction of motion;

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3 Planar Substring and Its POC Set A plane substring is a sub-series mechanism in which two or more motion pairs are connected in series. Therefore, the 10 kinds of plane substrings which are commonly used are stored in the computer, so that when the mechanism POC set is automatically analyzed, it can be identified and called. The common motion subtypes, orientation relationship matrix C and POC set matrix of 10 plane substrings are shown in Table 2. Table 2. Planar series sub-mechanisms Number Sports subtype Topological matrix LC   R||R G21 R 1 LC21 ¼ 1 R   R⊥P G22 R 2 LC22 ¼ 2 P   P⊥R G23 P 2 LC23 ¼ 2 R 2 3 G31 R//R//R R 1 1 LC31 ¼ 4 1 R 1 5 1 1 R 2 3 R//R⊥P G32 R 1 2 4 LC32 ¼ 1 R 2 5 2 2 P 2 3 P⊥R//R G33 P 2 2 LC33 ¼ 4 2 R 1 5 2 1 R

POC set matrix " # 1 0 PC21 ¼ 1 0 " # 1 0 PC22 ¼ 1 0 " # 0 1 PC23 ¼ 0 1 " 2 0 PC31 ¼ 1 0 " 2 0 PC32 ¼ 1 0 " 0 2 PC33 ¼ 0 1

0 0 0 0 0 0

#

#

#

(1) For a single-degree-of-freedom motion pair, the POC set matrix structure is 2  1; (2) For a series mechanism with a degree of freedom less than 6, the POC set matrix structure is 2  f (f is the number of motion pairs); (3) The first motion pair of the plane substring is not necessarily the first motion pair of the series mechanism, and may be the second, third or fourth motion pair of the series mechanism. The rules for judging various possible situations are as follows: ① The two-degree-of-freedom plane substring corresponds to the numbers G21, G22and G23 in Table 2, which is R//R, R⊥P or P⊥R. If their first motion pair is also the first motion pair of the series mechanism, the POC set matrix is the same as Table 2; if their first motion pair is the second motion pair of the series mechanism, The elements of the rotating pair and the moving pair in the POC set matrix are shifted to the right by one bit, the first column is complemented by 0; if their first motion pair is the third motion pair of the series mechanism, the rotating pair and the moving in the POC set matrix The secondary element is shifted to the right by two digits, while the first

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and second columns are complemented by 0; if their first motion pair is the fourth motion pair of the series mechanism, the elements of the rotating pair and the moving pair in the POC set matrix are to the right. Move three places, and add 0 to the first, second, and third columns. As shown in Fig. 1, in the series mechanism, R4//R5 is a plane substring, and the first rotating pair R4 is the fourth motion pair of the series mechanism, and the POC set matrix of the plane substring R4//R5 is shown. 

PR==R

0 ¼ 0

0 0

0 0

1 1

0 0 0 0

 ð3Þ

t4 = 1—One-dimensional movement perpendicular to the R4 axis; r4 = 1—One-dimensional rotation around the R4 axis;

Fig. 1. R//R//R-R//R series mechanism

② The three-degree-of-freedom plane substring corresponds to G31–G33 in Table 2, that is, R//R//R, R//R⊥P or P⊥R//R. If their first motion pair is also the first motion pair of the series mechanism, the POC set matrix is complemented by 0 in the last 3 columns of the matrix of Table 2; if their first motion pair is the tandem mechanism In the case of two motion pairs, the elements of the rotating pair and the moving pair in the POC set matrix are shifted to the right by one bit, and the first and fifth and sixth columns are complemented by 0; if their first motion pair is the third motion pair of the series mechanism When the elements of the rotating pair and the moving pair in the POC set matrix are shifted to the right by two bits, the first two columns and the sixth column are filled with zeros. As shown in Fig. 2, the series mechanism includes a plane substring of R3//R4//R5, and the first rotating pair R3 is the third motion pair of the series mechanism, and the rotating pair and the moving in the POC set matrix The secondary element is shifted to the right by two. The POC set matrix of the plane substring R3//R4//R5 is 

PR==R==R

0 ¼ 0

0 0

2 0 1 0

0 0

0 0



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t3 = 2—There is a two-dimensional movement perpendicular to the direction of the R3 axis; r3 = 1—One-dimensional rotation around the R3 axis;

Fig. 2. R//R//R-R//R series mechanism

4 Automatic Generation of Series Mechanism POC Sets 4.1

Identification of Planar Substrings in Series Branches of Automatic Generation Algorithm Flow and Extraction of POC Set Matrix

The topology matrix L is split into the following sub-matrices according to the main diagonal direction. (1) Two degrees of freedom tandem branch L is a 2  2 matrix, so it can be split into 1 2  2 submatrix. (2) Three-degree-of-freedom series branch L is a 3  3 matrix, so it can be split into one 3  3 submatrix and two 2  2 submatrices. (3) Four degrees of freedom tandem branch L is a 4  4 matrix, so it can be split into two 3  3 sub-matrices and three 2  2 sub-matrices. (4) Five degrees of freedom tandem branch L is a 5  5 matrix, so it can be split into three 3  3 sub-matrices and four 2  2 sub-matrices. Now take the five-degree-of-freedom series mechanism as an example to illustrate how to extract and identify the planar substring in the series mechanism. The ordered topology matrix of the five-degree-of-freedom series mechanism is: 2

J11

6 6 a21 6 L¼6 6 a31 6 4 a41 a51

a12

a13

a14

a15

J22 a32

a23 J33

a24 a34

a25 a35

a42 a52

a43 a53

J44 a45 a54 J55

3 7 7 7 7 7 7 5

In the main diagonal direction, the Eq. (3) is decomposed into three third-order matrices and four second-order matrices, as shown by the dashed boxes in Fig. 3, which are defined as L31, L32, L33 and L21, L22, L23, respectively.

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Fig. 3. Planar substring extraction of series mechanism

The extracted sub-matrices are: 2

2 2 3 3 3 J11 a12 a13 J22 a23 a24 J33 a34 a35 6 6 6 7 7 7 6 6 7 7 7 ð1Þ L31 ¼ 6 4 a21 J22 a23 5; ð2Þ L32 ¼ 4 a32 J33 a34 5; ð3Þ L33 ¼ 4 a43 J44 a45 5; a31 a32 J33 a42 a43 J44 a53 a54 J55         J11 a12 J22 a23 J33 a34 J44 a45 ; ð5Þ L22 ¼ ; ð6Þ L23 ¼ ; ð7Þ L24 ¼ : ð4Þ L21 ¼ a21 J22 a32 J33 a43 J44 a54 J55

Substring identification and POC set matrix extraction: 2

R

2

3

1

6 7 ð1Þ L31 ¼ 4 2 P 2 5 ¼ LC34 ; 1 2 R " Then output the POC set matrix as: PC34 ¼ 

ð2Þ L24

R ¼ 1

2 1

0 0 0 0

# ; i¼1

 1 ¼ LC21 ; R "

Then output the POC set matrix as: PC21 ¼

1

0

#

; i¼4 1 0 Since L31 corresponds to i = 1, that is, the first motion pair of the substring is the first motion pair of the series mechanism, according to the rule defined in Sect. 3, the output POC complement matrix is: 

P31

2 ¼ 1

0 0 0 0

0 0

0 0

0 0





P24

0 ¼ 0

0 0

0 0

1 1

0 0 0 0



The bitwise OR operation means that the elements are added together; if the sum of the moving element or the rotating element is equal to 3, then ti = 3, ri = 3. The bitwise OR of this tandem mechanism is:

Digital Modeling and Algorithms for Series Topological Mechanisms



2 0 0 0 0 0 P ¼ P31 þ P24 ¼ 1 0 0 0 0 0   3 0 0 0 0 0 ¼ 1 0 0 1 0 0   3 0 0 0 0 0 The output is: P ¼ 1 0 0 1 0 0





0 þ 0

0 0

0 0

1 1

0 0

0 0

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5 Conclusion (1) Based on the theory of POC set, a digital description matrix of mechanism topology is proposed, which not only includes the type of motion pair and axis orientation of the constituent mechanism, but also facilitates the identification and analysis of the computer. (2) A POC set matrix of series mechanism is established. The POC set of the series mechanism is represented by a 2 * 6 matrix. The output motion information is complete, including not only the movement/rotation type and dimension of the relative motion output, but also the characterization. The axis orientation or reference of the output motion is clear, the geometric meaning is clear, and the computer calculation, storage, and query are convenient; (3) Our work Converts the end-of-chain POC set operations into algebraic additions of the same-dimensional matrix by extracting the branched planar sub-strings and spherical sub-strings in order;

References 1. Dobrjanskyj, L., Freudenstein, F.: Some applications of graph theory to the structural analysis of mechanisms 89(1), 153 (1967) 2. Sohn, W.J., Freudenstein, F.: An application of dual graphs to the automatic generation of the kinematic structures of mechanisms, 392–398 (1986) 3. Olson, S.T., Francis, A.M., Sheffer, R., et al.: Parallel mechanisms of high molecular weight kininogen action as a cofactor in kallikrein inactivation and prekallikrein activation reactions. Biochemistry 32(45), 12148 (1993) 4. Chakarov, D., Parushev, P.: Synthesis of parallel manipulators with linear drive modules. Mech. Mach. Theory 29(7), 917–932 (1994) 5. Dasgupta, B., Mruthyunjaya, T.S.: The Stewart platform manipulator: a review. Mech. Mach. Theory 35(1), 15–40 (2000) 6. Li, S., Dai, J.: Topological description of planar mechanism based on Assur rod group elements. J. Mech. Eng., 8–13 (2011) 7. Li, S., Dai, J.: The composition principle of the metamorphic mechanism based on the extended Assur rod group. J. Mech. Eng., 22–30 (2010) 8. Ding, H., Huang, Z.: Motion chain topology diagram and automatic generation of feature description based on loop characteristics. J. Mech. Eng., 40–43 (2007)

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9. Han, Y., Ma, L., Yang, T., et al.: Research on mechanism type of parallel robot based on VB programming. J. Agric. Mach., 139–142 (2007) 10. Liao, M., Liu, A., Shen, H., et al.: Symbol derivation method for azimuth feature set of parallel mechanism. J. Agric. Mach., 395–404 (2016) 11. Yang, T.: Robotic Mechanism Topology Design. Science Press (2012)

Optimization of Injection Molding for UAV Rotor Based on Taguchi Method Xiong Feng, Zhengqian Li, and Guiqin Li(&) Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, No. 99 Shangda Road, Shanghai 200072, China [email protected]

Abstract. The injection molding of fiber-reinforced UAV rotor blades is put forward in this paper. Plastic injection molding for fabricating plastic products with complex shape is one of the significant operations in industry for its high precision and low cost. The simulation of the rotor forming process is conducted by using Moldflow software. The excessive deformation in the process of TAGUCHI test is optimized to obtain the combination of process parameters. The experiment results show that the deformation of rotor blade decreases, which meets the requirements of UAV flight. The method has good feasibility for the production of fiber-reinforced rotor blades in large quantities. Keywords: UAV
Injection molding
Process optimization
Taguchi method

1 Introduction The rapid rise of multi-rotor UAV drives the development of drone-related industries. The drone rotor is the most delicate part, with the highest replacement rate [1]. The winding forming is one of the most common methods of making drone rotors. However, this method can’t produce high precision parts but some small batches. The cost is relatively high for some complicated structures. Though the compression molding method is also used to form fiber reinforced parts, this method is known for high cost and low efficiency. Plastic injection molding is one of the most important methods applied for forming plastic products in industry. The mechanical properties of materials don’t be destroyed by using the method. Moreover, this method is very efficient and suitable for mass production. Some domestic researchers have been also studied in the aspect of fixed wing forming. Wang Xiadan [2] optimized the fixed wings of carbon fiber-reinforced drones by using orthogonal experiments and BP neural network. Wang Xiadan, Li Linyang, etc. [3, 4] studied the optimal gate of the UAV fixed-wing injection molding process, which had obvious effects on fixed-wing molding. However, the blades of the multirotor UAV have not been received enough attention. In this paper, we performed numerical simulations of the drone rotor using Moldflow software. Many experiments were performed by utilizing the combination of process parameters based on four-level of L16 Taguchi.

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2 Construction of Injection Molding Systems 2.1

Process Analysis

The width and length of the drone rotor were 240 mm and 264 mm respectively. The nominal thickness of the rotor was 1.2 mm. And the maximum wall thickness was 5.18 mm while the minimum wall thickness was less than 0.5 mm. It was a typical thin and easy-warping part. The surface quality of the rotor was very high. Large deformation was not allowed during injection molding. The drone blade material was nylon 66 reinforced with carbon fibers and its properties were given in Table 1. Table 1. Material properties of PA66/30%CF Material properties Melt density/(g/cm3) Water absorption rate/% Melt temperature/°C

2.2

Value 1.236 C>E>

C 0.853 0.927 0.944 1.037 0.184 B>D

D 0.943 0.934 0.953 0.931 0.022

E 0.997 0.957 0.913 0.894 0.102

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5 Verification and Analysis The final optimal combination of process parameters A1B1C1E4F4 was verified by using Moldflow. The result was shown in Fig. 3. The maximum deformation was 0.517 mm. The deformations of rotor blades were relatively small, which met the requirement of 0.8 mm rotor deformation in production. Compared with the minimum value in 16 orthogonal experiments, the deformation value decreased by 25.7%. It proved that the injection molding had a great advantage in the process of fiber reinforced rotor forming. And the injection molding had certain quality assurance in manufacturing fiber reinforced rotor. Further analysis showed that the deformation caused by the shrinkage factor was mainly distributed in the negative direction of the Z-axis of the rotor, and the fiber orientation was positive. From the variation of the curve in Fig. 4, we knew that the deformation caused by shrinkage and fiber orientation was consistent with the total deformation. Therefore, it was judged that the warping deformation of the rotor was caused by two factors: plastic shrinkage and fiber orientation. Consequently, future research can focus on the deformation principle of shrinkage and fiber orientation.

Fig. 3. Deformation of the parts under optimal process

Fig. 4. Deformation comparison chart

6 Conclusion The injection molding method for UAV rotor blade molding is put forward to meet the manufacturing requirements. The results of the experiments show that the main factors affecting the deformation are uneven shrinkage and fiber orientation. The melt temperature has the greatest influence on the deformation of the rotor blade, followed by the injection time and holding pressure, and the effect of mold temperature and holding time is the smallest. The optimal molding process parameters of the rotor are as follows: melt temperature 300 °C, mold temperature 115 °C, injection time 0.5 s, holding pressure 110% of filling pressure, and holding time 8 s. Under the optimal parameters combination, the maximum warping deformation of the rotor blade is

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0.517 mm, which is 25.7% lower than the initial results of simulation. It is proved that injection molding is suitable for manufacturing UAV rotor blades.

References 1. Han, X., Liu, J., Wang, X., Lu, B., Yang, J., Liao, Y., Li, F.: Design and application of carbon fiber composites on general aviation aircraft. Dual Use Technol. Prod. (07), 8–11 (2015) 2. Wang, X.: Injection molding and process parameters optimization for carbon fiber reinforced unmanned aerial vehicle fixed wing. Xi’an University of Science and Thecnology (2018) 3. Yu, Y., Wang, X., Li, L., Lu, Y.: Optimization design for gate of UAV fixed-wing based on MPI. Plast. Sci. Technol. 45(12), 87–91 (2017) 4. Yu, Y., Wang, X.: Optimization of injection molding process for fixed wing of unmanned aerial vehicle based on BP neural network. Plast. Sci. Technol. 45(09), 74–78 (2017) 5. Amiruddin, H., Mahmood, W.M.F.W., Abdullah, S., Mansor, M.R.A., Mamat, R., Alias, A.: Application of Taguchi method in optimization of design parameter for turbocharger vaned diffuser. Ind. Lubr. Tribol. 69(3), 409–413 (2017) 6. Xu, C., Zhou, J.: Mold design and process optimization of automobile clip injection molding. Plastics (01), 92−96+101 (2019) 7. Anugraha, R.A., Wiraditya, M.Y., Iqbal, M., Darmawan, N.M.: Application of Taguchi method for optimization of parameter in improving soybean cracking process on dry process of tempeh production. In: IOP Conference Series: Materials Science and Engineering, vol. 528, no. 1 (2019)

Assembly Sequence Optimization Based on Improved PSO Algorithm Xiangyu Zhang1(&), Lilan Liu1, Xiang Wan1, Kesheng Wang2, and Qi Huang3 1

School of Mechanical Engineering and Automation, Shanghai University, Shanghai, China {shuxyz,lancy,wanxiang}@shu.edu.cn 2 Norwegian University of Science and Technology, Trondheim, Norway 3 Shanghai Baosight Software Corporation, Shanghai, China

Abstract. For the structural characteristics of products, the interference matrix is established according to the assembly direction, and the optimization of product sequence assembly planning is studied with the aim of maximizing the number and stability of parts assembled without interfering with the assembled parts and minimizing the number of changes in assembly direction. Aiming at the problem of low convergence speed and precision of basic PSO algorithm, a population initialization method based on Feigenbaum iteration is proposed, and a new inertia weight update function is designed to improve the basic PSO algorithm with reference to Sigmoid function. The performance of the proposed algorithm is verified by an assembly example. The results show that the improved PSO (IPSO) algorithm is effective and stable in solving assembly sequence optimization problems. Keywords: Assembly sequence optimization
Interference matrix Feigenbaum iteration
Improved PSO algorithm




1 Introduction Assembly is a crucial link in the process of product manufacturing, accounting for 20% of the total manufacturing cost and 50% of the total production cycle [1]. Assembly sequence planning (ASP) is to obtain the optimal assembly sequence of products under certain constraints. It is a typical NP-hard problem. Reasonable optimization of assembly sequence can not only reduce the accumulation time of parts and improve the production line balance, but also have important significance for reducing product cost and improving production efficiency. With the increasing complexity of products, the number of parts that need to be assembled increases, and the scale of solution will appear combination explosion [2]. Therefore, intelligent optimization algorithm has been widely used in solving assembly sequence optimization. Xie et al. [3] applied ant colony algorithm to solve ASP problem. Zeng et al. [4] proposed an improved ASP method of firefly algorithm, and constructed the objective function with the interference times of tool parts in assembly sequence as the evaluation index. Zhang [5] used immune algorithm to overcome the © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 457–465, 2020. https://doi.org/10.1007/978-981-15-2341-0_57

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premature convergence of particle swarm optimization (PSO) to solve the ASP problem; Somay’e [6] studied the solution space distribution of ASP problem, and proposed a jump-out local search (BLS) algorithm based on the near-optimal solution’s approximate uniform distribution in solution space. In the existing swarm intelligence algorithms, the diversity of the population is poor, and it depends strongly on the quality and size of the initial population. In this paper, the interference matrix is established under the condition that the geometric constraints of the product are satisfied. Combining with co-evolution theory, a population initialization method based on Feigenbaum iteration is proposed to improve the quality of the initial population. The existing particle swarm optimization algorithm is easy to fall into local optimum when solving ASP problem, and the convergence speed is not ideal. To solve this problem, a new inertia weight adjustment function is proposed based on “Sigmoid function”, which can improve the convergence speed while ensuring the convergence accuracy and quickly generate the optimal assembly sequence. The improved PSO (IPSO) algorithm is validated by an assembly example, and the experimental results are compared with the basic PSO algorithm. The remaining part of this paper is organized as follows. Section 2 establishes the mathematical model of assembly sequence optimization. Section 3 describes in detail the improvement process of PSO algorithm and its performance verification. Section 4 verifies the practicability and superiority of PSO algorithm through an example. The last part is the conclusion. Finally, the conclusions are given in Sect. 5.

2 Problem Statement 2.1

Constraints

The interference matrix is used to describe the geometric constraints of parts in all assembly directions, so the interference matrix can be expressed as: 2

I11k 6 I21k 6 IMk ¼ 6 .. 4 . In1k

I12k I22k .. . In2k

3


I1nk


I2nk 7 7 .. 7 .. . 5 .


Innk

ð1Þ

Where k 2 fx; y; zg represents the direction of assembly; Iijk is a binary variable, if the part pi interferes with the part pj in the k direction of assembly, Iijk ¼ 1, otherwise, Iijk ¼ 0. In particular, Iiik ¼ 0. The main function of interference matrix is to derive the next assembly part pi and its assembly direction. Assuming that m parts have been assembled, the temporary subassembly can be expressed as Xsub ¼ ½p1 ; p2 ;


pm , whether the part pi can be assembled smoothly depends on the value of Iik , which is calculated by formula (2):

Assembly Sequence Optimization Based on Improved PSO Algorithm

Iik ¼ Ii1k _ Ii2k _


_ Iimk

459

ð2Þ

In which, _ is Boolean operation “OR”. If Iik ¼ 0, the part does not interfere with all parts in the assembly direction; otherwise, the part interferes with at least one part in the assembly direction.     The connection matrix CM ¼ Cij nn and support matrix SM ¼ Sij nn are used to express the assembly stability relationship. Elements Cij and elements Sij represent the connection type and support relationship between pi and pj respectively. 8 < Cij = 2 Stable connection Cij = 1 Contact connection :  Cij = 0 Disconnection Sij = 1 pi can support pj stably Sij = 0 pi can not support pj stably

2.2

Fitness Function

Suppose that an assembly sequence is Xl ¼ ½xl1 ;


; xli ;


; xln  and pi þ 1 is a part to be assembled. Let the number of times the sequence satisfies geometric constraints, that is, the number of parts assembled without interfering with the assembled parts: f1 ¼

n X

PIik

ð3Þ

k 2 fx; y; zg

ð4Þ

i¼1

Where PIik can be expressed as:  PIik ¼

1; 0;

Iik ¼ 0; Iik ¼ 1;

If the assembly direction of the pl;i þ 1 and pl:i in the sequence is the same, then the assembly does not need to change the direction,in this case Ql:i ¼ 0. Otherwise, Ql:i ¼ 1 ,the assembly is represented as formula (5):  Ql:i ¼

0; dl;i þ 1 ¼ dl;i; 1; dl;i þ 1 6¼ dl;i:

ð5Þ

Set the number of times that the sequence needs to change direction to complete assembly as follows: f2 ¼

n X

Ql:i

ð6Þ

i¼1

In addition, the judging rules of whether the assembly sequence is stable are as follows: (1) Cij ¼ 2; j 2 ½1; i  1 ) stable; (2) Cij ¼ 0 ) unstable; (3) stable if Cij ¼

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0 or Cij ¼ 1 when Sij ¼ 1; j 2 ½1; i  1; (4) unstable if Cij ¼ 0 or Cij ¼ 1 when Sij ¼ 0. f3 is the number of unstable operations in all assembly operations. The objective function of the assembly sequence optimization problem studied in this paper is to minimize the number of assembly direction changes while maximizing the stability and the number of assembly under geometric constraints, that is: fitness ¼ minfk1 ðn  i  f1 Þ þ k2 f2 þ k3 f3 g l

ð7Þ

In which, k1 , k2 and k3 are the weights of three objective functions, ranging from 0 to 1, and k1 þ k2 þ k3 ¼ 1. To synthesize the importance of each factor, take k1 ¼ 0:35, k2 ¼ 0:25, k3 ¼ 0:4.

3 Algorithmic Design 3.1

Basic PSO Algorithms

Mathematically, the basic PSO algorithms can be expressed as follows:     vkidþ 1 ¼ xvkid þ c1 g1  pid  xkid þ c2 g2  pgd  xkid

ð8Þ

xkidþ 1 ¼ xkid þ vkidþ 1

ð9Þ

Where c1 and c2 are called acceleration coefficients or learning factors; g1 and g2 are random numbers between [0, 1], which are used to increase the randomness of search. x is used to balance the inertia weight of global search and local search. 3.2

Improved PSO Algorithm

In order to enhance the diversity and randomness of population, combining with the theory of co-evolution, chaos is introduced into evolutionary computation, which is a unique motion form of nonlinear dynamic system with randomness, ergodicity and regularity. This paper uses Feigenbaum iteration to construct chaotic sequence and initialize particle position and velocity, as shown in formula (10), the initial value of the xn can not take the fixed point of the chaotic iteration equation. xn þ 1¼f ðu;xn Þ ¼ uð1  xn Þn ¼ 0; 1; 2




ð10Þ

Inertial weight is used to control the search behavior of particles in the search space. In order to improve the accuracy of operation and reduce the possibility of PSO falling into local optimum, the method of adjusting inertia weight is improved. Study on “Sigmoid Function”: f ð vÞ ¼

1 1 þ eav

ð11Þ

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For the “Sigmoid function” in formula (11), at that time av\  10, f ðvÞ ¼ 0; on the contrary, at that time av [ 10, f ðvÞ ¼ 1. Based on the above analysis, a method of inertia weight adjustment is proposed, which is defined as formula (12): xðtÞ ¼ xmax 

xmax  xmin 1 1 þ e0:08ðt2tmax Þ

ð12Þ

Where xmax and xmin represents the maximum and minimum inertia weight, respectively, t represents the number of iterations. For the inertia weight adjustment method proposed by formula (12), in the case of xmax = 0:9, xmin = 0:4, in the initial stage of evolution, the inertia weight is close to 0.9, while in the end stage of evolution, the inertia weight is gradually close to 0.4. In the intermediate stage, the inertia weights calculated by formula (12) are in the range of (0.4, 0.9) at any time in the evolution process, which is very consistent with the conclusion that PSO has better optimization effect when the inertia weights in document [7] are in the range of [0.4, 0.95]. Figure 1 is a comparison of several inertia weight adjustment curves, in which x0 , x1 , x2 x3 and x4 represent the value of x decreasing linearly with the number of iterations, convex function decreasing, concave function decreasing, exponential function decreasing, and the proposed method decreasing. Compared with the four curves, it can be found that in the initial stage of particle evolution, the inertia weight curve keeps a larger value, which can avoid PSO falling into the local optimum and make it search in the whole space, which is conducive to obtaining more suitable seeds. In the latter stage of particle evolution, on the contrary, the convergence accuracy of the algorithm is guaranteed by keeping it in a small numerical range.

ω (t)

0.9

ω0

0.8

ω1

0.7

ω3

ω2 ω4

0.6 0.5 0.4

0

200

400

t

600

800

1000

Fig. 1. Several inertial weight curves

The acceleration factor is controlled by the way that c1 decreases linearly with time and c2 increases linearly: 

  c1 ðtÞ ¼ c1;min  c1;max ttmax þ c1;max c2 ðtÞ ¼ c2;max  c1;min t tmax þ c2;max

ð13Þ

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In order to verify the superiority of the improved PSO algorithm, the following two test functions are optimized by using basic PSO improved PSO algorithm. (1) Rosenbrock function f 1 ð xÞ ¼

nP 1 i¼1

 2 100 xi þ 1  x2i þ ð1  xi Þ2

2:048  xi  2:048

ð14Þ

(2) Griewangk function f2 ð xÞ ¼

n P i¼1

x2i 4000



n Q i¼1

cos

  xiffi p þ 1 600  xi  600 i

ð15Þ

The above two test functions all have global optimal solutions minð f Þ ¼ f ð0;


; 0Þ ¼ 0, and the more variables, the more difficult it is to converge when PSO is used to search and optimize. The parameters for standard PSO are: learning factor is 1.5, inertia weight is 0.8, variable dimension is 10, particle number is 30, maximum iteration number is 1000. In order to facilitate comparison, only the inertia weight of the improved PSO is set to formula (12), and the other parameters are the same as the standard PSO. The results of optimization are shown in Table 1. Figure 2 are the iterative process of optimization.

Fig. 2. Iterative process diagram for Rosenbrock function (top) and Griewangk (bottom) function optimization by PSO (left) and IPSO (right) Table 1. Test function optimization results Functions Basic PSO Improved PSO Rosenbrock 5.52E−3 1.21E−3 Griewangk 5.21E−7 9.80E−8

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4 Examples Verification 4.1

Experimental Settings

The practicability of the proposed algorithm is verified by an example of seat assembly. The assembly structure consisting of 12 parts are shown in the Fig. 3. The parameters of the algorithm are as follows: the learning factor is 1.5, the inertia weight is 0.8, the dimension of variables is 10, the number of particles is 30, and the maximum number of iterations is 200. The algorithm code in this paper is written in MATLAB R2014a, and the computer parameters of simulation operation are: 64-bit Windows 7 operating system Intel (R) Core (TM) i5-4460 CPU @ 3.20 Hz 8.00 GB memory.

Fig. 3. Assembly drawing of base

4.2

Running Results

Based on the same fitness function and the program running environment (population capacity is 30, 200 trials), the average fitness changes of the two algorithms are shown in Fig. 4 and Table 2.

12 IPSO algorithm PSO algorithm

10

fitness

8 6 4 2 0

0

50

100

150

Number of iteration

Fig. 4. Algorithmic contrast map

200

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Algorithms Improved PSO algorithm Basic PSO algorithm

Population number 30

Program running time/s 42

Average fitness value 1.30

30

105

1.41

It can be seen that the improved PSO algorithm tends to converge in the 68th generation, when the average fitness value is 1.3, at this time, the corresponding assembly sequence results as follows: p1 ! p11 ! p5 ! p10 ! p4 ! p6 ! p2 ! p8 ! p3 ! p7 ! p9 ! p12 While the basic PSO algorithm converges to the optimal solution 1.41 in the 110th generation. The improved PSO algorithm converges faster and has higher accuracy; and the basic PSO algorithm is easy to fall into local convergence. Therefore, the quality, performance and efficiency of the improved PSO algorithm are significantly improved compared with the basic PSO algorithm. The analysis shows that the above assembly sequence planning meets the assembly requirements and meets the needs of engineering applications.

5 Conclusions Aiming at the characteristics of ASP problem, this paper proposes an assembly sequence planning method based on improved PSO algorithm on the basis of basic PSO algorithm. Firstly, the mathematical model of assembly sequence planning is established. In order to improve the diversity and randomness of initial population of PSO algorithm, the Feigenbaum iteration is used to construct chaotic sequence and initialize the position and velocity of particles. Aiming at the low convergence efficiency of PSO algorithm, a new inertia weight updating method is proposed to improve the algorithm. It is applied to the assembly sequence planning of the machine base. The results show that the assembly sequence planning method based on the improved PSO algorithm is an effective method and achieves the purpose of improving the optimization ability of PSO algorithm. Acknowledgements. The work is supported by the Ministry of industry and information technology for the key project “The construction of professional CPS test and verification bed for the application of steel rolling process” (No. TC17085JH).

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References 1. Mrashid, M.F.F., Hutabarat, W., Tiwari, A.: A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches. Int. J. Adv. Manuf. Technol. 59(1–4), 335–349 (2012) 2. Wang, K., Wang, Y.: How AI affects the future predictive maintenance: a primer of deep learning. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds.) Advanced Manufacturing and Automation VII, IWAMA 2017. Lecture Notes in Electrical Engineering, vol. 451, pp. 1–9. Springer, Singapore (2018) 3. Xie, L., Fu, Y.L., Ma, Y.L.: Assembly sequence generation strategy based on ant colony algorithms. J. Harbin Univ. Technol. 38(2), 180–183 (2006) 4. Zeng, B., Li, M.F., Zhang, Y.: Assembly sequence planning based on improved firefly algorithms methods. Comput. Integr. Manuf. Syst. 20(4), 799–806 (2014) 5. Zhang, H.Y., Liu, H.J., Li, L.Y.: Research on a kind of assembly sequence planning based on immune algorithm and particle swarm optimization algorithm. Int. J. Adv. Manuf. Technol. 71(5), 795–808 (2014) 6. Ghandi, S., Masehian, E.: Breakout local search (BLS) method for solving the assembly sequence planning problem. Eng. Appl. Artif. Intell. 39(3), 245–266 (2015) 7. Wang, T., Li, Q.Q.: Parallel evolutionary algorithm based on spatial contraction. China Eng. Sci. 5(3), 57–61 (2003)

Influence of Laser Scan Speed on the Relative Density and Tensile Properties of 18Ni Maraging Steel Grade 300 Even Wilberg Hovig(&)

and Knut Sørby

Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway {even.w.hovig,knut.sorby}@ntnu.no

Abstract. Laser powder bed fusion (LPBF) enables tool makers to design tools with complex geometries and internal features. To exploit the possibilities provided by LPBF it is necessary to understand how the processing parameters influences the properties of the end-product. This study investigates the effect of laser scan speed on relative density and tensile properties of 18Ni300 in the asbuilt condition. The results show that there is a relatively wide processing window which gives satisfactory relative density and tensile properties. Furthermore, it was shown that the scan speed which produced the highest relative density in this study did not provide satisfactory tensile properties, indicating that processing parameters can not be established based on relative density measurements alone. Keywords: Laser powder bed fusion
Laser melting 18Ni300
Maraging steel
Tensile properties


Relative density


1 Introduction 18Ni maraging steel grade 300 (18Ni300) is a precipitation hardening tool steel with excellent mechanical properties in the aged state, which is easy to machine in the solution annealed state [1]. The high strength and hardness of the material makes it suited for tooling in applications such as aluminium casting, plastic injection molding, and extrusion applications [2, 3]. Tooling components are excellent candidates for laser powder bed fusion (LPBF) processing, since the geometry is often complex, and can benefit from possibilities enabled by LPBF, such as conformal cooling channels and vent slots for casting applications [4–6]. In order to efficiently process the material with LPBF it is necessary to identify processing parameters which results in high density microstructures and satisfactory mechanical properties. Several authors have investigated the effect of processing parameters on the density of 18Ni300 processed by LPBF [7–10], focusing on the effect of laser power, P, laser scan speed, v, hatch spacing, h, and layer thickness, t, on the relative density of the end-result. A commonly used parameter to relate the laser parameters to each other is volumetric energy density, Ed ¼ P=vht. The validity of using volumetric energy density to explain the response of LPBF materials to changes © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 466–472, 2020. https://doi.org/10.1007/978-981-15-2341-0_58

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in the processing parameters have been questioned however [7, 11]. Suzuki et al. suggests using Pv−1/2 to correlate laser parameters with material density [7]. This model does not account for hatch spacing, laser spot diameter, and layer thickness, however. It is not within the scope of this study to derive a model for accurate density prediction based on laser parameters, and as such only the effect of laser scan speed on the relative density and tensile properties of 18Ni300 will be investigated.

2 Method Twelve flat tensile specimens were prepared as blocks in a Concept Laser M2 Cusing (installed 2009) LPBF machine, and then machined to dog-bone specimens with a cross-section of 6  6 mm2, with a reduced section length of 32 mm, and radii of 6 mm. The tensile specimens were processed with a laser power of 180 W, hatch spacing of 105 lm, layer thickness of 30 lm, and scan velocities ranging from 600 mm/s to 725 mm/s with 25 mm/s increments. The scan strategy applied is the ‘island’ scan strategy by Concept Laser, with 5  5 mm2 islands with an angular shift of 45° and an XY shift of 1 mm. The tensile specimens were built in parallel to the build direction (Z-oriented). In addition to the tensile specimens, cubes with a volume of 10  10  10 mm3 were prepared for density analysis. The 18Ni300 powder feedstock was supplied by Sandvik Osprey. The chemical composition, as supplied by the material vendor, is listed in Table 1. Table 1. Chemical composition of 18Ni300 as supplied by Sandvik Osprey. Fe C Mn Si Cr Ni Mo Co Ti Al wt% Bal. 0.03 0.1 0.1 0.3 18.0 4.8 9.0 0.7 0.1

The relative density was determined by investigating the cross section of the cubes with optical microscopy and image manipulation software (similar to the process described by the current authors in a previous work [11]). The tensile specimens were tested in an MTS 809 Axial Test System with a 100 kN load cell at room temperature. The displacement rate was 1 mm/min for all specimens.

3 Results and Discussion Density Figures 1 and 2 shows contrast images of polished cross section of cubes processed at 600 mm/s and 725 mm/s respectively. In both images spherical pores are visible, and in Fig. 1 there are no signs of cracks or pores of large irregular shapes, as observed by other authors when the processing parameters are sub-optimal [7, 9]. Furthermore, in Fig. 1 the pores appear to be arbitrarily distributed in the cross section, while at the higher scan speed in Fig. 2 the pores appear to have a systematic pattern.

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Fig. 1. Contrast image of XY-cross section of a cube processed with v = 600 mm/s. The width and height of the cross-section is 10  10 mm2.

Fig. 2. Contrast image of XY cross-section of a cube processed with v = 725 mm/s. The width and height of the cross-section is 10  10 mm2.

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Figure 3 shows the measured relative density with respect to scan speed. As can be seen, the highest relative density is measured for the sample processed at 725 mm/s, and the lowest relative density is for the sample processed at 625 mm/s. The lowest measured relative density is above 99.9%, however, which indicates that there is a large processing window which results in satisfactory relative density. There appears to be a steady increase in relative density with the increase of scan speed, except for the scan speed of 625 mm/s, which should be considered an outlier. Based purely on the measured relative density, a scan speed of 725 mm/s appears to be favorable. It is interesting to note that the periodic pattern in the top right, and bottom right, corners of Fig. 2 appears to be along straight lines with a length of 5 mm at a 45° angle to the perimeter of the cube. This corresponds to the perimeter width, length, and angle of an ‘island’ in the scan strategy. During laser melting, the core of each island is scanned first, going from one corner to the opposite corner in a zig-zag pattern. Once the core is melted, the contour is scanned as a continuous line along the perimeter of the island. Based on the observed porosity in Fig. 2 it appears that as the scan speed increases, and thus the energy input is reduced, the material fails to completely melt and bond between the core and contour of the islands. A possible reason for this can be that the energy input is not sufficiently high to give a wide enough melt pool to form a dense material in this region. Within the islands the density appears to be higher in Fig. 2 however. In a future work experiments can be conducted to verify this, either by changing the hatch spacing between the contour and the core, or by modifying the laser power or scan speed in the contour scan. It appears that the higher scan speed results in a dense core, but porous perimeter, which leads to unsatisfactory tensile properties, as will be demonstrated in the next section.

99.97 99.96

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Fig. 3. Relative density as a function of scan speed.

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Tensile Properties The tensile properties of 18Ni300 with respect to scan speed is shown in Fig. 4. The tensile properties do not vary significantly as the scan speed increases from 600 mm/s to 700 mm/s, but at 725 mm/s the tensile properties drop. If the processing parameters are evaluated on the relative density alone, a scan speed of 725 mm/s appears to give the best results. For lower scan speeds, when the microstructure is decorated with arbitrarily spaced pores, the tensile properties are not significantly influenced by a small change in relative density. The porosity observed at the perimeters of the scanned islands significantly reduces the tensile properties, however. This is likely due to a concentration of stress in the porous region as the specimen is being loaded, leading to pre-mature failure.

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Fig. 4. Mechanical properties of 18Ni300 as a function of scan speed.

When the yield strength is plotted against the relative density in Fig. 5 there is no obvious correlation between relative density and tensile properties. If anything, it appears that the yield strength drops as the relative density increases.

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Fig. 5. Yield strength as a function of relative density for 18Ni300.

4 Conclusions This work investigates the effect of scan speed on the relative density and tensile properties of 18Ni300 processed by LPBF. The density results show that there is a wide processing window which results in relative density of above 99.9%. Furthermore, the tensile results indicate that an increase in relative density is not necessarily accompanied by an increase in tensile properties. On the contrary, the scan speed which resulted in the highest relative density was accompanied with significantly lower yield strength, ultimate tensile strength, and elongation at break. Both relative density and tensile properties were satisfactory for scan speeds between 600 mm/s and 700 mm/s with the laser parameters used in this study. Acknowledgements. The authors would like to thank SINTEF Industry, Oslo, Norway for performing the relative density analysis. This work is funded in part by the Norwegian Research Council through grant number 248243, and by the TROJAM project in the INTERREG A/ENI program.

References 1. Fortunato, A., Lulaj, A., Melkote, S., Liverani, E., Ascari, A., Umbrello, D.: Milling of maraging steel components produced by selective laser melting. Int. J. Adv. Manuf. Technol. 94(5–8), 1895–1902 (2017) 2. Pereira, M.F.V.T., Williams, M., Du Preez, W.B.: Application of laser additive manufacturing to produce dies for aluminium high pressure die-casting: general article. S. Afr. J. Ind. Eng. 23(2), 147–158 (2012)

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3. Ahn, D.-G.: Applications of laser assisted metal rapid tooling process to manufacture of molding & forming tools — state of the art. Int. J. Precis. Eng. Manuf. 12(5), 925–938 (2011) 4. Hovig, E.W., Brøtan, V., Sørby, K.: Additive manufacturing for enhanced cooling in moulds for casting. In: 6th International Workshop of Advanced Manufacturing and Automation. Atlantis Press (2016) 5. Brøtan, V., Berg, O.Å., Sørby, K.: Additive manufacturing for enhanced performance of molds. Procedia CIRP 54, 186–190 (2016) 6. Hovig, E.W., Sørby, K., Drønen, P.E.: Metal penetration in additively manufactured venting slots for low-pressure die casting. In: Wang, K., Wang, Y., Strandhagen, J.O., Yu, T. (eds.) Advanced Manufacturing and Automation VII, pp. 457–468. Springer, Singapore (2018) 7. Suzuki, A., Nishida, R., Takata, N., Kobashi, M., Kato, M.: Design of laser parameters for selectively laser melted maraging steel based on deposited energy density. Add. Manuf. 28, 160–168 (2019) 8. Bai, Y., Yang, Y., Wang, D., Zhang, M.: Influence mechanism of parameters process and mechanical properties evolution mechanism of maraging steel 300 by selective laser melting. Mater. Sci. Eng. A 703, 116–123 (2017) 9. Casalino, G., Campanelli, S.L., Contuzzi, N., Ludovico, A.D.: Experimental investigation and statistical optimisation of the selective laser melting process of a maraging steel. Opt. Laser Technol. 65, 151–158 (2015) 10. Kempen, K., Yasa, E., Thijs, L., Kruth, J.P., Van Humbeeck, J.: Microstructure and mechanical properties of Selective Laser Melted 18Ni-300 steel. Phys. Procedia 12, 255–263 (2011) 11. Hovig, E.W., Holm, H.D., Sørby, K.: Effect of processing parameters on the relative density of AlSi10Mg processed by laser powder bed fusion (2019)

Application of Automotive Rear Axle Assembly Shouzheng Liu1(&), Lilan Liu1, Xiang Wan1, Kesheng Wang2, and Fang Wu3 1

Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, China [email protected], {lancy,wanxiang}@shu.edu.cn 2 Norwegian University of Science and Technology, Trondheim, Norway 3 Huayu-Intelligent Equipment Technology Co., Ltd., Shanghai, China

Abstract. With the mutual integration of digitalization and information technology, in the assembly process of automotive rear axle, the connection between assembly process planning and on-site process implementation is gradually strengthened, the assembly process of automotive rear axle is increasingly automated and intelligent. Aiming at the practical problems existing in the assembly process of automotive rear axle, combining the lightweight processing technology of geometric model, heterogeneous data acquisition technology and digital twinning technology, this paper focuses on the planning and simulation of automotive rear axle assembly process in digital space, the interaction mechanism between digital space and physical space, and finally realizes the real-time monitoring of the assembly process of the rear axle and the assembly guidance for the workers, and put forward new ideas and methods for the closed-loop control of process planning and process execution. Keywords: Digital twin
Assembly process Feedback-based optimization


Real time monitoring


1 Introduction With the integration and application of new generation information technologies (such as Cloud Computing, Internet of Things, Big Data, Mobile Internet, Artificial Intelligence, etc.) and manufacturing, countries around the world have successively proposed their own manufacturing development strategies at the national level, representative examples are Industry 4.0, Industrial Internet, CPS-based manufacturing, Made in China 2025 and Internet + Manufacturing, Service Oriented Manufacturing or Service Manufacturing [1]. As a benchmark in the manufacturing industry, the automotive industry has a tremendous impact on economic development and social progress. Automobile assembly is the last stage of automobile manufacturing. The assembly process of automotive rear axle is an important part of the assembly process of the whole automobile. Therefore, the assembly quality of the rear axle directly affects the product quality of the automobile. In the assembly process of automotive rear axle, many assembly parts are involved, and the assembly process is complicated. In recent

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years, with the development of robot technology and new generation information technology, the use of robots instead of workers in some factories has reduced the labor intensity of workers, but the large amount of data in the factory has not been effectively utilized, real-time monitoring of assembly process and real-time adjustment of assembly tasks are not implemented. The concept of digital twin was first proposed by Professor Grieves in 2003 and has been rapidly developed in recent years. Digital twin is a multi-physical, multi-scale, multi-probability simulation process that uses historical data and real-time updated data from sensors to characterize and reflect the full life cycle of physical objects. The virtual assembly line is established by the 3D modeling method, and the bidirectional real mapping and real-time interaction between the real assembly line and the virtual assembly line are realized by digital twin technology, thereby realizing real-time monitoring of the assembly process. According to the current order, material, and machine running state, the multi-objective particle swarm optimization algorithm is used to predict the number of rear axles currently required to be assembled, so as to achieve optimal production. The key research content of this paper is to realize the realtime monitoring of the assembly process of automotive rear axle based on digital twin technology, and provide the basic content for the subsequent in-depth study of the rear axle assembly line.

2 System Overall Technical Framework Real-time simulation of assembly process of automobile rear axle assembly line based on digital twin technology mainly needs to break through three technical points, namely, lightweight processing of geometric model of rear axle assembly line, data acquisition of heterogeneous equipment during assembly execution, interaction between physical space and virtual space. The geometric model of the rear axle assembly line of the automobile has problems such as large number of geometric vertices, large number of patches, and a large number of hidden bodies. It is difficult to be used in the development of application systems, and it is necessary to lighten the geometric model. The real-time simulation of the virtual model to automotive rear axle assembly process is to realize the real-time driving of the data to the model. Therefore, the collection of heterogeneous data becomes the key technology. The most important point of this paper is to provide a method for real-time simulation of actual assembly line driven virtual models. Therefore, the interaction between physical space and virtual space is the most important technical point (Fig. 1).

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Fig. 1. System overall framework diagram

2.1

Lightweight Treatment of Automobile Rear Axle Assembly Line

In the design process of the rear axle assembly line of the automobile, the geometric model designed generally has higher resolution requirements, so the modeling software such as Solidworks and Catia is usually used, but at the same time, the geometric model has a large number of geometric vertices and patches. The problems require high computational performance and memory capacity, and there is a high delay in real-time simulation of the virtual model to the actual assembly line, affecting the user experience. Therefore, in actual development, it is necessary to lighten the geometric model of the rear axle assembly line, reduce the number of vertices and the number of patches of the model, improve the system realization effect, and improve the user experience. After the geometric model is lightly processed, the number of points and the number of faces can be significantly reduced, and the frame rate in the Unity 3D drive engine is also greatly improved. The tool software for the lightweight model selected in this paper is 3dmax, and the lightening process is as follows: Step 1: Determine Export the geometry model created in Catia V5 to the .stl format that 3dmax can import. Step 2: Import the geometric model of .stl format into 3dmax, analyze the imported model, determine whether there are redundant points, delete the redundant points if they exist, and then use the PolygonCruncher tool to reduce the surface of the model, complex models can be shelled.

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Step 3: Correct the reduced UV map according to the position of the UV map before the patch is reduced to ensure that the patch is consistent before and after the patch is reduced. Step 4: Finally, the model is exported in fbx format in 3dmax software. The export setting parameters include geometry parameter setting, scale factor parameter setting, fbx file format setting, animation setting, and lighting setting [2]. 2.2

Data Acquisition During Assembly Execution

With the application of electronic identification technology represented by RFID, electronic tags, barcodes, in the assembly shop, the induction and collection of workshop resource information is more convenient [3]. In the assembly process of automotive rear axle, the electronic identification technology is introduced, and the data of the assembly elements (assembly components, assembly aids, quality inspection equipment, etc.) are collected in real time, the collected data are used in digital twin technology has greatly changed the process execution process and data application method of the traditional automobile rear axle assembly shop, and the production, management and control methods of the assembly shop have been greatly improved. In the assembly process, because the structure of automotive rear axle is very complicated, the cost is extremely high and the accuracy of the key parts is difficult to be ensured by the robot alone. Therefore, the assembly process is completed by the workers and the robots (The workers mainly complete the pre-assembly of the parts and the quality inspection of the key parts, and the robots complete the fastening of the parts). In the station where the workers perform the assembly task, the workers first specify whether the required parts are in place, and the parts are labeled with a barcode containing the material information, thereby associating the parts with the barcode. After the required assembly parts are assembled to the specified position, the worker scans the barcode using the scan code gun. If the workpiece is assembled successfully, the fixture under the assembly will follow the line body to the next station, otherwise the fixture will be locked by the line body. Line body will not be released until the component is properly assembled. In the key components, infrared detection technology is also used to judge whether the components are accurately installed (Fig. 2). The robot workstation adopts data acquisition architecture based on OPC_UA for data acquisition. The OPC_UA architecture adopts a newer SOA architecture. The OPC_UA architecture supports multiple platforms such as Windows, Linux, and microcontrollers, supports information encryption and mutual access authentication, and security monitoring. It has good scalability and can be in XML or binary format. Advantages of passing data, communication protocols compatible with multiple communications [4]. The data of multiple robots is transmitted to the controller of the robot workstation, and the digital twin system communicates with the controller of the robot workstation through the OPC_UA architecture to drive the digital twin system.

Application of Automotive Rear Axle Assembly

Parts name

picture of real products

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Bridge barcode

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Upper arm barcode

Adjustment link

Adjust the link bar code

Brake caliper

Brake caliper barcode

……

…… Fig. 2. Schematic diagram of the rear axle to be assembled

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Interaction Mechanism Between Physical Space and Virtual Space

The interaction mechanism between the physical space and the virtual space of the rear axle assembly process, It’s core is to realize virtual manufacturing based on digital twin technology and real manufacturing synchronization based on manufacturing execution system [5]. Virtual manufacturing is a real-time simulation and analysis of the actual assembly process through the data-driven geometric model generated in the actual manufacturing process, and the actual assembly process is optimized through the analysis results to form an effective closed-loop assembly. The production order is generated according to the current assembly task, and the rear axle assembly line begins to assemble the rear axle. In the assembly process, the data acquisition tools deployed on site are used to collect data of assembly line elements such as line body, robot workstation, parts, fixtures, etc., and store the data in the database (such as MySQL). This model simulation platform uses the Unity 3D driver engine to obtain the corresponding real-time information by continuously retrieving the data in the database. Combined with the programming of Visual Studio, the singlecolumn data obtained is converted into JSON format, realizing accurate acquisition of real-time information, and real-time monitoring of the assembly process by real-time data-driven virtual assembly lines following the real-time movement of the actual assembly line. The simulation analysis system of the digital twin platform can simulate the assembly process according to the production plan and the rear axle assembly line model, and can feedback the simulation analysis results to the user, and the user can optimize the assembly line according to the simulation analysis result. In the actual rear axle assembly line, the assembly plan is optimized under the consideration of assembly materials, robot quantity, production cycle and other factors to achieve the optimal assembly plan [6]. In the algorithm for solving the optimal solution in the flexible assembly process of mechanical products, the multi-objective particle swarm optimization algorithm is more common. The dispatcher temporarily adjusts the current production plan according to the optimal production plan obtained by the multiobjective optimization algorithm. At the same time, the corresponding functional modules can complete the work of establishing and maintaining data information, working time statistics, and equipment load accounting, and provide basis for dynamic scheduling.

3 Framework Application Examples and Effects The real-time simulation system of automobile rear axle assembly process based on digital twinning technology studied in this paper has been effectively applied in the rear axle of E2XX, A2XX and other models of Shanghai Huayu-intelligent Equipment Technology Co., Ltd. Through the corresponding assembly line model and the lightweight processing of the product model, the collection of heterogeneous data, the interaction between the physical space and the virtual space, the assembly process planning and simulation of the rear bridge in the digital space is realized, and the simulation process can be released to wearable device to guide workers in the assembly

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of the rear axle. Secondly, through this process, the simulation process of the assembly process is associated with the process execution process in real time, real-time monitoring of the assembly process is realized, and the process execution process can be optimized through the simulation process, in particular, the production orders are optimized to form an effective closed-loop assembly.

4 Conclusions In this paper, the digital twin technology of the rear axle assembly line of the automobile is studied. The main achievement is to complete the virtual and real synchronous simulation of the assembly process of automotive rear axle, and provide a way to optimize the actual assembly process for the simulation analysis process. Through the lightweight processing of assembly line and product geometric model, the real-time collection of heterogeneous data in the workshop, and the research on the interaction mechanism between physical space and virtual space. The basic structure of the digital twin system of the rear axle assembly line of the automobile is constructed, and the real-time monitoring of the entire assembly process is completed, which lays a foundation for a more intelligent assembly line in the future. Acknowledgements. The authors would like to express appreciation to mentors in Shanghai University and Huayu-intelligent Equipment Technology Co., Ltd. for their valuable comments and other helps. Thanks for the pillar program supported by Shanghai Economic and Information Committee of China (No. 2018-GYHLW-02009).

References 1. Tao, F., Zhang, M., Cheng, J.: Digital twin workshop: a new paradigm for future workshop. Comput. Integr. Manuf. Syst. 23(01), 1–9 (2017) 2. Zhang, X.: Design and implementation of workshop management and control system based on digital twins. Zhengzhou University (2018) 3. Zhang, P.: Research of Digital Twin Based Assembly Process Planning and Simulation of General Aircraft Product. Hebei University of Science and Technology (2018) 4. Tao, F., Cheng, J., Cheng, Y.: SDMSim: a manufacturing service supply-demand matching simulator under cloud environment. Robot. Comput. Integr. Manuf. 45, 34–46 (2017) 5. Zhang, J., Gao, L., Qin, W.: Big-data-driven operation analysis and decision-making methodology in intelligent workshop. Comput. Integr. Manuf. Syst. 22(05), 1220–1228 (2016) 6. Armendia, M., Cugnon, F., Berglind, L., Ozturk, E., Gil, G., Selmi, J.: Evaluation of machine tool digital twin for machining operations in industrial environment. Procedia CIRP 82, 231– 236 (2019)

Improvement of Hot Air Drying on Quality of Xiaocaoba Gastrodia Elata in China Xiuying Tang1, Chao Tan2, Bin Cheng3(&), Xuemei Leng1, Xiangcai Feng1, and Yinhua Luo1 1

3

College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming, China 2 Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yuban, China College of Mechanics and Transportation, Southwest Forestry University, Kunming, China [email protected]

Abstract. As a perennial herb, gastrodia elata is distributed in most parts of China. Its tuber is a valuable Chinese herbal medicine, and controlling its dryness is the most critical factor to ensure the quality of storing gastrodia elata. This paper takes Gastrodia tuber drying as the research object, and attempts to improve the local processing method which is hot air drying by coal heated. Our team use electricity instead of coal as the energy for hot air drying and takes 50 ° C for drying under constant temperature. Experimental results show the appearance and the polysaccharide content of gastrodia elata are not improved, however, that the processing time has been reduced by one third, and the gastrodin content in boiled gastrodia elata is significantly higher compared to local drying method. Keywords: Hot air drying


Gastrodia elata
Gastrodin
Polysaccharide

1 Introduction Gastrodia elate is a perennial herb and well-known for treatment effect on dizziness, headache, migraine, infantile convulsion, limb spasm, wind-cold dampness, neurasthenia [1], and has been recorded for medicinal purposes in China more than 1000 years. Additional, many experiments have shown that it also has certain effects against oxidation, hypoglycemic, vascular headache and concussion sequela [2, 3]. In recent years, the market demand for gastrodia elata has gradually increased. However, wild gastrodia elata has been near extinction due to excessive exploitation. So artificial cultivation in imitation of the wild environment has emerged on a large scale. Zhaotong city is one of the main production area in China. The planting area of gastrodia elata increased from 3400 ha in 2012 to 5367 ha in 2017, and the output value increased from 300 million Yuan in 2011 to 3.97 billion Yuan in 2017 [4–6]. Due to the special physical geography and climate of XiaoCaoba, for example, the average altitude of 1710 m, the average annual sunshine of 927.3 h and the annual average temperature of

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9.8°, and 47% forest coverage. Gastrodia elate in XiaoCaoba is well-known by excellent quality [7]. Fresh gastrodia elata is difficult to store for a long time, and it is easy to corrupt, so dryness is the most critical factor to ensure the quality of storing gastrodia elata. Our team found that coal-powered hot air drying is a widely used method in Xiaocaoba by field research. However, this method is difficult to control and adjust the temperature, and heating is uneven in different positions in local drying room. If the temperature in drying room is too low, gastrodia elata is prone to decompose; If the temperature is too high, the surface of gastrodia elata will has a large number of folds and the effective components will be partially destroyed resulting in poor quality. In addition, this method needs a lot of manpower, large space, and result in sulfur residue easily exceeding the standard. There are many Chinese researches on the drying of gastrodia elata, and the drying methods mainly include Hot-air Drying, Wind Drying, Sunlight Drying, Oven Drying, Microwave Drying, Vacuum Drying, Vacuum Freezing Drying, Infrared Drying and other various combination techniques [8–12]. Yandao Liu, Changli Wang, Bin Tang et al. took 5 methods including Wind Drying, Sunlight Drying, Oven drying, Vacuum drying, Vacuum Freeze Drying for drying gastrodia elata. Experimental results showed that the best drying method of gastrodia elata was vacuum drying, and the drying temperature was 52–58 °C, followed by Vacuum Freeze Drying, Sunlight Drying [13]. Ji, Ning, Zhang et al. selected Sunlight Drying, Hot air drying, Microwave drying, Infrared drying, Hot-air and Microwave combined drying. Compare to the comprehensive analysis of the surface, content of active ingredients, production cost and other factors of gastrodia elata, hot air drying or hot air combined with microwave drying is the preferred method [14]. Therefore, this paper choose hot air drying to improve the quality of gastrodia elata. One reason is because this method is widely used in Xiaocaoba area, another is that electricity-powered hot air drying is clean, safe operation, and thoroughly solve these problems that include mildew easily, excessive sulphide which come from the smoke from burning coal. In addition, hot air drying by electricity can improve the effective components of gastrodia elata and apply to large-scale production, which has certain guiding significance for local gastrodia elata processing and improvement.

2 Materials and Methods 2.1

Raw Materials

Samples preparation of gastrodia elata were grown at Xiaocaoba, Zhaotong city, Yunnan Province, China. The plant grew in the mountain forest, and planted in imitation wild environment. On January 12, 2019, our research team collected gastrodia elata at 10 am. The topsoil was dug with tools first, when approaching gastrodia elata, samples collected directly by hand in order to avoid damage. A total of 46 kg of gastrodia elata was collected, and the retained soil in their surface was not cleaned after collection. All samples were divided into two parts: one part was dried by coal-powered hot air dying in local drying room, and another part brought back to Kunming on the same day to dried

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by electricity-powered hot air dryer in the laboratory. At 9:00 am on January 13, samples covered with soil were cleaned with tap water at the same time. 2.2

Main Equipment

The main equipments include local drying facilities and equipments, laboratory equipments and detection equipments. Local drying facilities and equipments: drying room, Auy220 electronic balance, coal stove, Mt-4612-c infrared thermometer gun (Pro’skit), shovel, turnover box, steamer, etc. The drying room (shown in Fig. 1) is 6 m long and 5 m wide, and the coal stove is 1.2 m long and 0.5 m wide. From the vertical plane, the drying room is divided into two layers: the first layer is a hot air room, and the second layer is for gastrodia elata.

Fig. 1. Local drying room

Fig. 2. Electric thermostatic hot air blower dryer

Laboratory equipment in Yunnan Agricultural University: electric thermostatic air blower dryer (model 101) as shown in Fig. 2, Auy220 electronic balance, induction cooker, steamer, knife, etc. Detection equipment and reagents: LC-20A liquid chromatograph, SPD-20A ultraviolet-visible detector, JA2003 electronic balance, Auy220 electronic balance, High-speed multifunctional grinder (JHF-150), p-hydroxybenzyl alcohol, heavy distilled water, gastrodin standard, ethyl acetate, ethanol, acetonitrile, gastrodin which number is 0807-200104,chromatographic grade methanol, ultra-pure water, etc. 2.3

Hot Air Drying

Hot air drying takes hot air as drying medium, exchange heat and moisture with dry products by convective circulation way, meanwhile, the surface become dry because water on the products surface diffuses into the main air stream, and this results in a water different between the inside and outside of the material and makes internal moisture movement to the surface, liquid outward expansion, and finally the main air discharge to achieve the purpose of drying [15].

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The Evaluation Index

(1) Appearance of Gastrodia elata. The appearance indicators of gastrodia elata include color, curl degree and texture. Colors differ in yellow, dark yellow, yellow transparent, milky white and etc. The curl degree fall into flat, little curls and big curls. The texture is divided into hard or loose status. (2) Dry base water content. The material without moisture content is usually called absolute dry material, and the ratio of the moisture content to the absolute dry material quality in the wet material is called the dry base water content (p) of the wet material. The calculation formula is as follows [16]: p¼

ð m  nÞ  100% n

Where m and n are respectively the initial weigh and dry weight. (3) Gastrodine. Gastrodine is one of the critical effective ingredients of gastrodia elata, according to the Chinese pharmacopoeia (2015 edition), gastrodine is measured by the high performance liquid phase chromatography method [17]. (4) Polysaccharide. Polysaccharide of gastrodia elata is another important effective ingredients of gastrodia elata. Anthrone-sulfuric acid method is one measured method [18]. The polysaccharide content of gastrodia elata was calculated as follows: w¼

CDf m

Where C is the concentration of glucose in polysaccharide solution (mg/ml), D is the dilution ratio of polysaccharide, m is the raw polysaccharide quality of gastrodia elata (g), f is the conversion factor, and w is the polysaccharide content of gastrodia elata (%). 2.5

The Technological Process

The local drying process includes: (1) classification, (2) boiled or non-boiled, (3) the first dry in about 60°, (4) the temperature dropping gradually to about 30°, (5) put samples in plastics bags for releasing the water naturally, (6) the second dry in about 60°, then drop to 30°, then put in bags, and repeatedly processing until dry samples. In the drying process, the data of weight record by every 4 h (except the time in bags). The laboratory drying process includes: (1) classification, (2) boiled or non-boiled, (3) drying in 50°, and the data of weight record by every 4 h.

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3 Results and Discussion 3.1

The Appearance

As can be seen from the Fig. 3, Fig. 3(a) is the dried sample in the laboratory, and of which No. L5 is the dried sample without boiled and No. A5 is the dried sample boiled. Figure 3(b) is the dried sample in the local drying room, and of which No. 1 is the dried sample without boiled and No. 7 is the dried sample after boiled. The color of the boiled samples of the two drying methods are all obviously lighter than that of the samples without boiled, and the color of the local drying method is lighter than that of the laboratory drying method, showing a yellow transparent. Samples without boiled is dark yellow. From the appearance of gastrodia elata, the curl degree is related to samples weight. The lighter the weight, the easier it is to curl. Looking at cross section, samples were free of hollow and mildew. The texture of all samples are hard, but the color inside is differ in different methods. For laboratory samples, the boiled section is yellow, while the not-boiled section is white in the middle seeing Fig. 3(c). However, for local drying samples, the boiled section is yellow transparent, while the not-boiled section is milky white seeing Fig. 3(d). In summary, although the differences in appearance of two methods are significant, they are all acceptable by market.

(a) sample in the laboratory

(c)sample section of boiled and not-boiled in the laboratory

(b)sample in the local drying room

(d)sample section of boiled and not-boiled in the local dying room

Fig. 3. (a) The appearance, curling degree, the texture of samples with boiled or not-boiled in two methods

3.2

Dry Base Moisture Content

In calculating results, comparing with the two methods, the process of water loss is different of different-weight gastrodia elata. Nonetheless, the general trend is fell sharply and then slowly. Taking D5 and XKD4 as examples, the weight of XKD4 dropped sharply from 0 to 48 h, then changes slowly until 190 h. D5 loses water rapidly at 0–35 h, slowly after 35 h and stabilizes after 133 h. Therefore, hot air drying by electricity is faster than hot air drying by coal.

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The range of dry base moisture content of samples is from 1.801 to 4.77. According to the calculated data, the dry base moisture content in the weight range of 80–110 g is the largest. Above or below this range, the dry base moisture content will decreases gradually. The dry base moisture content of samples without boiled is higher than that of boiled. From the comparison of similar weights boiled samples between two methods, the dry base moisture content of the samples dried by electric hot air was generally smaller than that of the samples dried by coal hot air, however, the data of the non-boiled samples dried by two methods were not clearly distinguished. Therefore, electric hot air drying is better than coal hot air drying dealing with boiled samples. 3.3

Gastrodin

An appropriate amount of gastrodin control substance was added to acetonitrile solution with a concentration of 3%, and then different concentrations solutions were injected into the HPLC to obtain the standard curve square Area = 17777.57082 * Amt + 8.0580846, R = 0.99999, and detailed steps referring to the Chinese pharmacopoeia. The experimental data are shown in the Table 1. Lab number means sample number in laboratory and local number means sample number in local drying method.

Table 1. Gastrodin content (%) of samples in two methods Lab number Boiled A5 A6 B5 B6 C5 C6 Non- L6 boiled L5 D5 D6 F5 F6

The initial weight 128.41 120.99 89.22 81.78 70.30 62.30 159.70 156.12 107.28 93.87 73.12 60.38

Dry weight 45.84 36.04 21.64 25.14 15.31 16.21 33.24 42.3 26.98 23.86 17.53 14.01

Gastrodin (%) 0.27 0.27 0.33 0.31 0.38 0.36 0.04 0.07 0.07 0.01 0.01 0.12

Local number XKD8 XKD7 XKD5 XKD4 XKD1 XKD2 XK2 XK1 XK4 XK6 XK8 XK7

The initial weight 161 154 112 108 82 74 154 144 130 105 80 75

Dry weight 55.3 50.2 32 22 15.3 20.2 45.5 40.4 34.3 18.2 17 18.2

Gastrodin (%) 0.51 0.44 0.18 0.19 0.11 0.11 0.08 0.13 0.07 0.02 0.03 0.01

Compared to the same weight gastrodia elata, the gastrodin in boiled gastrodia elata is significantly higher than that of the non-boiled, and as for different weight of gastrodia elata, the content of gastrodin with large weight is higher. The data compared between two drying methods of boiled gastrodia elata show that the gastrodin content in hot air drying by electricity are all higher than 0.20% (according to the Chinese pharmacopoeia, gastrodin content in gastrodia elata should not be less than 0.20%). However, except for samples XKD8 and XKD7, hot air drying by coal fails to meet the requirements. Through analysis of these two samples, the possible reason is that they

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have large volume and thickness. The temperature in local drying room is in 60–30°, which is conducive to rapid dehydration due to temperature difference, while the temperature of electric constant dryer is always around 50°, which is easy to cause uneven dehydration inside and so there are many surface folds. Therefore, fresh gastrodia elata with less than 130 g is better to be dried at a constant temperature of 50° by electric hot air drying, while those with more than this weight should be dried at a variable temperature. The specific process needs to be further studied. 3.4

Polysaccharide

Taking 1 mg/mL glucose standard solution 0, 1, 2, 3, 4, 5 and 6 mL put respectively in a 50 mL volumetric flask with constant volume. Then, taking 1 mL of each tube put respectively in a plugged tube and place them in an ice water bath, and add 4 ml 0.2% anthranone - sulfuric acid reagent. After that, they are put in a boiling water bath for 10 min and cooling it, place them in the dark for 10 min. The absorbance was measured at 620 nm, and the regression equations y = 30.65x + 0.6617, R2 = 0.9925 were obtained. Experimental data show that polysaccharide content of all samples are between 16.21% and 16.49%. In general, the polysaccharide content of the boiled samples is a bit higher than that of the non-boiled samples, however, the difference could be ignored. So there was no significant difference in the amount of polysaccharides between the two methods. The possible reason is that the drying temperature of both methods is less than 60°, and the loss of polysaccharides of gastrodia elata below this temperature is less.

4 Conclusions In this paper, constant hot air drying by electricity in 50° is compared to local variable temperature hot air drying by coal. Although the appearance and the polysaccharide content are not improved, the previous method could improve the content of gastrodine which meet the national standards. Therefore, it can be concluded from this experiment that the 50 °C electric hot air drying method is better than the local hot air drying method. The research group gives suggestions on local gastrodia elata drying: (1) All the gastrodia elata should be deal with boiled, the suggested temperature is 90 °C, and the boiled time is: 140–175 g for 15–20 min, 105–140 g for 10–15 min, and 70–105 g for 8–10 min. (2) The local drying room should be designed to a fully sealed, temperature adjustable electric hot air drying room. The drying temperature should be controlled at 50 °C below 130 g. For gastrodia elata over 130 g, backwater treatment is recommended, but the specific process needs to be further studied. Acknowledgements. This work was supported by the Opening Fund of Key Lab of Process Analysis and Control of Sichuan Universities of China (2017001).

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References 1. Su, S.: Herbal Classic of Materia Medica (annotated). Fujian Science and Technology Press, Fu Zhou (1988) 2. Yang, S., Lan, J., Xu, J.: Research progress of gastrodia elata. Chin. Tradit. Herbal Drugs 31 (1), 66–69 (2000) 3. Kong, X., Liu, T., Guan, J.: Effect of polysaccharide from the Gastrodia elata Blume on metabolism of free radicals in subacute aging model mice. J. Anhui Univ. Nat. Sci. Ed. 29 (2), 95–99 (2005) 4. Yunnan “zhaotong gastrodia elata” industry development becoming strong. http://yn. yunnan.cn/html/2018-04/20/content_5172277.htm. Accessed 21 June 2019 5. The investigation of the gastrodia elata industry by National gastrodia conference organizing committee. http://www.emushroom.net/news/201208/08/11700.html. Accessed 21 June 2019 6. Report on the development of gastrodia elata industry by Zhaotong people’s government. http://www.ztrd.gov.cn/article/201408/t20140821_1274_1.shtm,last. Accessed 21 June 2019 7. Xiaocaoba gastrodia elata in Zhaotong City-the world’s original factory. https://www. taodocs.com/p-50138411.html. Accessed 21 June 2019 8. Qin, J., Zhang, J., Zhou, H.: Influence on the content of gastrodin of different processing methods. J. Shanxi Univ. Sci. Technol. 23, 76–79 (2005) 9. Ning, Z., Mao, C., Lu, T., et al.: Effects of different processing methods on effective components and sulfur dioxide residue in Gastrodiae Rhizoma. China J. Chin. Materia Med. 39(15), 2814–2818 (2014) 10. Huang, X., Qi, C., Zhu, Y., et al.: Determination of Gastrodin and Gastrodigenin in fresh and different processing Gastrodia elata BI. J. ZhaoTong Univ. 39(5), 43–47 (2017) 11. Yong, W., Zhao, Y., Gu, Y.: Effect of different drying methods on quality of Rhizoma Gastrodiae. Chin. Trad. Pat. Med. 27(6), 673–676 (2005) 12. Tian, Z., Wang, J., Liu, J., et al.: Effects of different processing methods and steamed time on quality of Zhaotong Gastrodiae rhizoma. Southwest China J. Agric. Sci. 29(7), 1701–1706 (2016) 13. Liu, Y., Wang, C., Tang, B., et al.: Effect of different drying methods on the content of gastrodin in gastrodia elata. Mod. Trad. Chin. Med. 33(3), 108–109 (2013) 14. Ji, D., Ning, Z., Zhang, X.: Effects of different drying methods on quality of gastrodiae Rhizoma. China Jo. Chin. Materia Med. 41(14), 2587–2590 (2016) 15. Yu, M., Zhang, X., Mu, G., et al.: Research progress on the application of hot air drying technology in Chian. Agric. Sci. Technol. Equip. 8, 14–16 (2013) 16. Du, X.: Xinxing Shipin Ganzao Jishu Ji Yingyong. Chemistry Industry Press, Beijing (2018) 17. National Pharmacopoeia Commission: Chinese Pharmacopoeia, 2015th edn. China Medical Science and Technology Press, Beijing (2015) 18. Peng, Z.: Research overview of polysaccharide from gastrodia elata. J. Gansu Coll. TCM 25 (4), 49–51 (2008)

Installation Parameters Optimization of Hot Air Distributor During Centrifugal Spray Drying Yunfei Liu and Jingjing Xu(&) School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China [email protected], [email protected]

Abstract. The location of hot air distributor plays an important role in the centrifugal spray drying. Based on response surface optimization and numerical simulation, the overall desirability of fine powder ratio and outlet air moisture is used as the response value, and the installation angle of hot air distributor and the distance between atomizer and hot air distributor are optimized. A better drying effect was obtained by optimizing parameters of hot air distributor, which provides guidance for the industrial production of centrifugal spray drying. Keywords: Hot air distributor
Spray drying
Response surface optimization
Numerical simulation Nomenclature

Mi Mmax, Mmin d1 d2

indicator value. the maximum and minimum values of each indicator. he normalized value of ratio of fine powder. he normalized value of outlet air moisture.

1 Introduction Many parameters such as atomizer speed, the hot air inlet temperature and the inlet air volume affect the centrifugal spray drying effect, and many researchers have studied on it. Meanwhile, the hot air distributor, which can make hot air contact fully and uniformly with droplets sprayed from the atomizer, also has influence on the centrifugal spray drying effect like reducing or avoiding sticking or scorching. Li [1] showed that larger or smaller installation angle of the hot air distributor will cause serious sticking phenomenon, which is not conducive to the advantages of spray drying. Gao [2] proposed to improve the drying effect by improving the hot air distributor. Yang [3] showed that the tangential velocity provided by hot air can change dispersion of droplets. However, the above research didn’t give a specific numerical research. In this paper, the effect of the hot air distributor is fully considered, and installation angle and the distance of the hot air distributor are adjusted to change hot air tangential speed and © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 488–494, 2020. https://doi.org/10.1007/978-981-15-2341-0_61

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increase contact time between hot air and droplets. The overall desirability (OD) of fine powder ratio and outlet air moisture is defined as the goal to optimize the parameters based on central composite design and response surface methodology (CCD-RSM), and the optimal installation angle and the distance of the hot air distributor with better drying performance are obtained, which plays a guiding role in actual industrial production.

2 CFX Simulation and Verification According to the principle of fluid dynamics simulation, the model of industrialized centrifugal spray drying tower is simplified [4–6], and important parts such as hot air inlet, hot air distributor and atomizer are remained. The process verifies the validity of numerical simulation, and 3D model of the 5T/d spray drying tower is shown in Fig. 1 and the enlarged hot air distributor and atomizer is shown in Fig. 2.

air inlet

wall

air outlet particle outlet

Fig. 1. 3D of drying tower

θ

air distributor d atomizer(droplet inlet)

Fig. 2. Enlarged hot air distributor and atomizer

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1. Hot air inlet: The hot air, with 290 °C temperature and 2.173 kg/s flow rate calculated based on the heat balance and material balance [7, 8], is composed of 0.051 H2O, 0.201 CO2 and 0.748 N2 [9] and its turbulent flow intensity is 4.33%. 2. Feed liquid inlet: The feed liquid, with 30 °C temperature, 0.175 kg/s flow rate and 67% moisture content, are divided into droplets through the atomizer with 7780 r/min. Droplets obeying to Nukiyama-Tanasawa [10] with distribution parameter of 2.8953 and 0 enter the drying tower at the tangential speed of 85.267 m/s and the radial speed of 18.111 m/s. 3. Hot air outlet: The average static pressure generated by the induced draft fan is −300 Pa. 4. Particle outlet: The flow rate is 0. 5. Wall boundary: Considering that the wall surface of insulation layer has a small amount of heat loss, the wall surface is set to surface with a heat dissipation coefficient of 0.961 W/(m2
K). 6. The installation angle of the hot air distributor and the distance between the hot smoke dispenser and the atomizing disk are 0° and 250 mm, respectively. The numerical simulation results are shown in Table 1. Table 1. The comparison between numerical analysis and industrial measured data Unit Outlet air temperature °C Ratio of 20–150 lm % Product mean volume diameter lm Particle moisture %

Industrial data Simulation results Relative error/% 140 137.40 1.86  98 97.56 0.45 60–70 61.23 – 2 2.06 3.00

The simulation results such as temperature distribution, particle moisture, particle size distribution are in good agreement with the industrial data, indicating that the numerical simulation of centrifugal spray drying is reliable.

3 Optimization and Result Verification 3.1

CCD-RSM Parameters Optimization

In the CCD-RSM optimization [11, 12], the installation angle of the hot air distributor and the distance between the hot air distributor and the atomizer are regarded as variables, and the OD of fine powder ratio and outlet ait moisture is defined as the goal. The values of every variable and test design are shown in Table 2, and the different parameters results and the test design points are shown in Table 3.

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OD Calculation [13, 14]: The normalization of fine powder ratio and outlet air moisture are calculated through Eqs. (1) and (2) based on Hassan method, respectively. Then the OD of fine powder ratio and outlet air moisture is calculated through Eq. (3). d1 ¼ ðMmax  Mi Þ=ðMmax  Mmin Þ

ð1Þ

d2 ¼ ðMi  Mmin Þ=ðMmax  Mmin Þ

ð2Þ

OD ¼ ðd1  d2 Þ1=2

ð3Þ

Table 2. The factor and level of the CCD Factor

Label Level −1 0 1 Installation angle h/° A 0 5 10 Distance d/mm B 210 250 290

Table 3. Design test points and results Test 1 2 3 4 5 6 7 8 9 10 11 12 13

Distance 260 210 285 220 260 210 210 260 220 280 235 235 285

Installation angle Fine powder ratio Outlet air moisture OD 10 0.09785 0.05149 0.40813 10 0.09763 0.05560 0.00000 5 0.09791 0.04219 0.82540 5 0.09801 0.04212 0.96250 5 0.09794 0.04286 0.85266 5 0.09786 0.04105 0.78593 0 0.09796 0.04587 0.75959 0 0.09800 0.04849 0.68701 0 0.09794 0.04683 0.69848 10 0.09790 0.04840 0.59299 10 0.09771 0.05463 0.11636 0 0.09794 0.05148 0.48122 0 0.09797 0.05066 0.54840

The data in Table 3 (angle, distance and OD) is imported into the Design-Expert 10.0.7 software for multivariate binomial fitting. The result is Eq. (4): OD ¼ 2:5662  0:0128A  0:1265B þ 0:0010AB þ 0:00002A2  0:0163B2

ð4Þ

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The variance results are shown in Table 4. Table 4. Variance results Source Model A B AB A2 B2 Residual Cor total

Sum of squares df Mean square 0.874944 5 0.174989 0.154833 1 0.154833 0.470925 1 0.470925 0.170281 1 0.170281 0.001435 1 0.001435 0.44535 1 0.44535 0.095445 7 0.013635 0.97039 12

F value 12.83377 11.3555 34.53786 12.48849 0.105208 32.66218

p-value Prob > F 0.002056 Significant 0.011924 0.000614 0.009548 0.755145 0.000724

The results in Table 4 show that the model is significant, so the method can be used to optimize the process parameters of the centrifugal spray drying. The values of R2 and Radj2 are 0.9016 and 0.8314, respectively, indicating that the model has a good agreement with the simulation. The p values of B and B2 are extremely small, indicating that the angle has a significant influence on the results. A and A2 are not significant, indicating that the installation angle has a greater influence on the spray drying effect than the distance. The optimization result is that the installation angle is 5.741° and the distance is 219.758 mm. Considering the actual situation, the installation angle is 6° and the distance is 220 mm. 3.2

Verify Optimized Process Parameters

The results before and after optimization are compared as shown in Table 5.

Table 5. The results before and after optimization Unit Installation angle ° Distance mm Ratio of 20–150 lm % Product mean volume diameter lm Particle moisture %

Optimized 6 220 98.04 63.49 2.03

Unoptimized 0 250 97.56 61.23 2.06

The optimization results show that the optimized parameters can improve the drying effect of spray drying with lower particles moisture, larger ratio of 20–150 lm and lower product wear, showing that it has contributed to controlling product wear and ensuring particles moisture.

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Optimization Results

Fig. 3. Air temperature distribution before optimization

Fig. 4. Air temperature distribution after optimization

As can be seen in Figs. 3, 4 and 5, the hot air temperature distribution in the drying tower after optimization can provide a more uniform energy field for the droplets, which ensures that the droplets are evenly heated and obtains good roundness and moisture particles. It can be seen from the particle size distribution that the proportion of small particles decreases, indicating that lower proportion of fine powder is beneficial to reduce waste and increase production.

Fig. 5. Particle size distribution before and after optimization

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4 Conclusions 1. The influence of the installation position of the hot air distributor on the centrifugal spray drying effect was verified by the numerical simulation. The reasonable installation position of hot air distributor is of great significance to the centrifugal spray drying. 2. The overall desirability (OD) of fine powder ratio and outlet air moisture is defined as the goal to optimize installation angle and the distance of the hot air distributor based on CCD-RSM, and the optimal installation angle and the distance are 6° and 220 mm, respectively. 3. The optimization results show that reasonable position of the hot air distributor can improve the centrifugal spray drying effect and provide guidance for industrial drying.

References 1. Li, Q.: The improvement and optimization of centrifugal spray-drying tower. Dry. Technol. Equip. 9(5), 268–273 (2011) 2. Gao, Z.L.: Development and application of electric high speed centrifugal spray dryer. Chem. Eng. 26(3), 58–60 (1998) 3. Yang, S.J., Wei, Y.C., Woo, M.W., Wu, D.: Numerical simulation of mono-disperse droplet spray dryer under influence of swirling flow. CIESC J. 69(9), 3814–3824 (2018) 4. Xu, J.J., Wang, Z.T., Yuan, K.: Numerical simulation of CFX gas-liquid two-phase flow in spray drying tower for preparation of FCC catalyst. In: 2014 ANSYS China Technology Conference, pp. 21–23 (2014) 5. Huang, L.X., Kumar, K., Mujumdar, A.S.: Simulation of a spray dryer fitted with a rotary disk atomizer using a three-dimensional computational fluid dynamic model. Dry Technol. 22, 1489–1515 (2004) 6. Huang, L.X., Passos, M.L., Kumar, K.: A three-dimensional simulation of a spray dryer fitted with a rotary atomizer. Dry Technol. 23, 1859–1873 (2005) 7. Wang, B.H., Wang, X.Z.: Two heat balance methods in spray drying process. Dry. Technol. Equip. 9(2), 76–81 (2011) 8. Liu, G.W.: Spray Drying Technology, pp. 33–36. China Light Industry Press, Beijing (2001) 9. Xu, S.H.: Direct calculation method of flue gas physical properties. J. Suzhou Inst. Silk Text. E Technol. 19(3), 32–36 (1999) 10. González-Tello, P., Camacho, F., Vicaria, J.M.: A modified Nukiyama-Tanasawa distribution function and a Rosin-Rammler model for the particle-size-distribution analysis. Powder Technol. 186, 278–281 (2008) 11. Ma, Y.H., Lu, J.Y., Hu, Z.Z., Wei, S.H.: Preparation of 1-pentene/1-octene/1-dodecene terpolymer drag reducer by response surface method. CIESC J. 68, 2195–2203 (2017) 12. Wang, X.H., Xia, L.L., Hu, M., Song, Y.: Optimization of extraction process for compound Tongmai prescription by Box-Behnken response surface methodology combined with multiindex evaluation. Chin. J. Hosp. Pharm. 37, 712–716 (2017) 13. Wu, W., Cui, G.H., Lu, B.: Optimization of multiple evariables: application of central composite design and overall desirability. Chin. Pharm. J. 35, 532 (2000) 14. Hassan, E.E., Parish, R.C., Gallo, J.M.: Optimized formulation of magnetic chitosan microspheres containing the anticancer agent, oxantrazole. Pharm. Res. 9, 390–397 (1992)

Wear Mechanism of Curved-Surface Subsoiler Based on Discrete Element Method Jinguang Li1, Liangliang Zou1,2, Xuemei Liu1,2, and Jin Yuan1,2(&) 1

College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China [email protected] 2 Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment, Tai’an 271018, China

Abstract. The subsoiling technique in conservation tillage can effectively break the bottom plow layer and improve the tillage structure. But the wear problem of subsoiler is an important obstacle to the popularization of subsoiling. In this paper, the surface of the subsoiler is defined, and the subsoiling process of the curved-surface subsoiler with different speeds is simulate by discrete element method. By comparing with the actual subsoiler that wears heavily, the results shows that the main wear surfaces of the subsoiler are the upper surface and the front surface of the subsoiler tip, the front surface of the subsoilersurface and the front surface of the subsoiler handle; the wear degree of subsoilers has a positive correlation with the work speed, wherein subsoiler handle has the greatest correlated with the speed. Keywords: Subsoiling


Discrete element method
Simulation

1 Introduction Subsoiling technology is one of the basic contents of conservation tillage [1]. It can effectively break the plough pan and deepen the tillage layer, which is beneficial to the renewal of rain and oxygen in the soil [2]. However, there are lots of problems in subsoiling preparation, such as high resistance, serious wear of subsoilers, and high energy consumption, which hinder the development and popularization of subsoiling technology. Therefore, clarifying the wear mechanism of the subsoiler becomes the important conditions to solve these problems. But the relationship between soil and subsoilers is very complicated. It is difficult to analyze it by traditional test methods. With the development of modern science and technology, the discrete element method for analyzing the motion law and mechanical properties of complex granular systems is proposed [3]. It can effectively solve some complex dynamic problems in agricultural soil cultivation. That provide new ways for explore the relationship between soil particles and agricultural implements. In this paper, to explore the wear mechanism of the curved-surface subsoiler, the discrete element method is used to simulate the subsoiling process at three working speeds. Using the slicing function in the EDEM post-processing module, the main wear surface the subsoiler has found. The wear mechanism of the subsoiler is explored for © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 495–503, 2020. https://doi.org/10.1007/978-981-15-2341-0_62

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reducing wear, resistance and energy consumption. That provide the theoretical basis for optimum design of subsoiler.

2 Establishment of Discrete Element Simulation Model 2.1

Subsoiler Model

Accurate geometric models are the prerequisite for accurate simulation results. To ensure that the simulation is the same as the field test, the geometric parameters of the curved-surface subsoiler are obtained by reverse engineering modeling according to the ratio of 1:1. Firstly, the subsoiler is pre-treated by spraying the coloring penetrant, and then the three-dimensional scanning is performed to obtain the data point cloud. The data point cloud is repaired, merged, smoothed, and physically restore used by software such as Geomagic Studio and Design Modeler. Finally, export the geometric entity. The modeling process of subsoiler is shown in Fig. 1.

a. 3-D laser scanner

b. Date point cloud

c. Subsoiler model

Fig. 1. The modeling process of subsoiler

2.2

Soil Model

Establishing an accurate soil particle model is the basis for ensuring the validity of the simulation results [4, 5]. According to a large number of studies, the basic structure of soil particles is different, mainly divided into spherical block particles, core particles, flake particles and column particles. The four particle models are shown in Fig. 2.

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a. Block particle

b. Core particle

c. Flake particle

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d. Column particle

Fig. 2. Soil particle model

Soil compaction is an indicator to measure the internal strength of the soil. It has a great impact on the subsoiling operation. The uniform compactness between the simulation soil and the actual soil is one of the important conditions to ensure the simulation accurately. In order to simulate the real soil state as much as possible, six test points were selected at the test site, and the soil compactness at different depths is shown in Table 1.

Table 1. Soil compaction Test depths/mm Soil compaction/KPa Test point 1 Test point 2 Test point 3 Test point 4 Test point 5 Test point 6 Average value

50 10 8 7 11 9 11 9.4

100 52.9 60 58 80 67 62 63.6

150 89 95 97 102 121 99 103.6

200 162 152 156 176 164 183 163.8

250 192 189 178 168 190 179 184.6

300 332 341 314 324 350 310 318.9

Existing soil-related studies have shown that there is bond force between soil particles, which has a direct impact on soil compaction. Therefore, there should also be adhesion between the simulated soil particles [6]. The particles were regarded as viscous bodies, and the Bonding model in the Hertz-Mindlin contact model is used as the final particle contact model [7]. After bonding, Bond bonds are formed to provide the bonding force. In this paper, two particle factories are set up to produce two layers of soil. The bottom layer is the plow-bottom layer and the surface layer is the tillage layer. The particle formation time is 5 s, and a total of 620,000 particles are formed. After 1 s deposition, the particles are bonded at 6 s to form bonds. The soil model is shown in Fig. 3.

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a. Two layers of the soil

b. Bonds Fig. 3. Soil model

2.3

Subsoiling Model

Discrete element simulation parameters include material parameters and contact parameters. These parameters are quoted from the literature [8, 9]. Through the relevant literatures mentioned in the paper, the repeated simulation debugging of the parameter combination is carried out, and the final simulation parameter s are determined as shown in Table 2. Table 2. Basic parameters of the discrete element model Parameter Density of soil particles q1/(kg/m3) Poisson’s ratio of soil particles v1 Shear modulus of soil particles G1/Pa Density of subsoiler q2/(kg/m3) Poisson’s ratio of subsoiler v2 Shear modulus of subsoiler G2/Pa Coefficient of restitution between the soil and soil e1 Coefficient of rolling friction between the soil and soil e2 Coefficient of static friction between the soil and soil e3 Coefficient of restitution between the soil and subsoiler f1 Coefficient of rolling friction between the soil and subsoiler f2 Coefficient of static friction between the soil and subsoiler f3

Numerical value 1346 0.4 1 * 106 7830 0.35 7.27 * 1010 0.2 0.3 0.4 0.3 0.05 0.5

Enter the corresponding simulation parameters in the software and import the threedimensional model of the subsoiler into the discrete element software. The tillage depth is set to 300 mm, which is in line with the actual subsoiling operation. Finally, simulate the subsoiling process at three working speeds of 0.6 m/s, 1.0 m/s and 1.5 m/s. The subsoiling discrete element model is established as shown in Fig. 4.

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Fig. 4. Subsoiling model

3 Results and Analysis of Discrete Element Simulations 3.1

Analysis of the Wear Surface of the Subsoiler

In order to facilitate the study of soil contact in subsoiler operation, as shown in Fig. 5 surfaces of the subsoiler are marked.

Fig. 5. The marked figure of curved-surface subsoiler

3.1.1 The Wear Surface of Subsoiler Tip Taking 10 mm after the subsoiler tip as the starting point of slicing, a slice with thickness of 400 mm is established. The contact area of subsoiler tip is shown in Fig. 6. Figure 6 shows that the upper surface and front surface of the subsoiler tip are in close contact with the soil, which is the main wear surface. The lower surface and the back surface of the subsoiler tip is the non-wear surface. The main reason for this phenomenon is that the soil in the upper part of the subsoiler tip is uplifted. The working object of subsoiler tip is plow-bottom layer, which is difficult to be deformed.

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a. 0.6 m/s

b. 1.0 m/s

c. 1.5 m/s

Fig. 6. Contact areas of subsoiler tip

The backfilling of the soil is not timely, so a confined space will be formed below the subsoiler tip. It can be seen that the upper surface and front surface of the subsoiler tip are worn severely, and the lower surface and the rear surface are worn lightly. It is observed in Fig. 6 that the contact range between soil and subsoiler tip at a working speed of 1.5 m/s is larger than that at 1.0 m/s and 0.6 m/s, therefore, the higher the working speed, the more serious wears of subsoiler tips. 3.1.2 The Wear Surface of Subsoiler-Surface Slicing the simulation model in the vertical direction. The slice center was 100 mm behind the subsoiler tip and the slice thickness was 30 mm. The contact area of subsoiler-surface is shown in Fig. 7.

a. 0.6 m/s

b. 1.0 m/s

c. 1.5 m/s

Fig. 7. Contact areas of subsoiler-surface

As shown in Fig. 7, the soil is in close contact with the front surface of the subsoiler-surface, and the back surface has a gap with the soil, Therefore, the front surface of the subsoiler-surface is the wear surface. It can be seen that the gap formed between soil with subsoiler is the largest at the working speed of 1.5 m/s. And at 0.6 m/s, the gap formed is small. The main reason for the gap formed is the special geometric shape and the working angle of the subsoiler, that causes the asymmetry of the soil bulge around the subsoiler after the operation, which is more obvious with the increase of the speed.

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3.1.3 The Wear Surface of Subsoiler Handle The slice of subsoiler handle is centered at 300 mm above the subsoiler tip and the thickness is 30 mm. Contact areas of subsoiler handle is shown in Fig. 8.

a. 0.6m/s

b. 1.0m/s

c. 1.5m/s

Fig. 8. Contact areas of subsoiler handle

Because of the squeezing and shearing action of the subsoiler handle, the soil moves to both sides. The soil contact area is formed with both sides of the subsoiler. With the speed increases, the front of the subsoiler handle is in closer contact with the soil. It is regarded as the main wear surface. At the same time, the gap between soil and back surface of subsoiler handle increases, the friction surface decreases, and the wear of subsoiler decreases. This is due to the special curved shape of the subsoiler. The higher the speed, the more the soil in front of the shovel is lifted, and the soil movement is more severe. The soil in contact with the subsoiler handle is the tillage layer soil, the density is smaller than that of the plough bottom layer, soil movement is more intense. This increases the soil movement difference between the sides of the subsoiler handle. Ultimately, there is a difference in wear on both sides. 3.2

Verification of the Subsoiler Wear

Figure 9 shows the wear of the curved-surface subsoiler after long-term subsoiling operation. Figure 9a shows the back surface of subsoiler, and Fig. 9b shows the front surface of subsoiler. It can be seen from the graph that the upper surface of the subsoiler tip has been worn seriously during operation, and the edge of the subsoiler tip has been completely worn and deformed. After a long period of operation, the front face of the subsoilersurface has also been worn seriously and rubbed to a smooth surface. That is not conducive to soil debonding and drag reduction. The lower surface and the back surface of the subsoiler tip, the back surface of the subsoiler handle have less contact with the soil and basically no wear. In addition, the

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a.Back surface

b.Front surface

Fig. 9. Wear condition of the curved-surface subsoiler

paint sprayed on the subsoiler has not been rubbed off by the soil. The wear state of the curved-surface subsoiler is consistent with the results of the discrete element analysis in Sect. 3.1 of this paper, which verifies the authenticity of the results of the discrete element simulation analysis.

4 Conclusions In order to explore the wear mechanism of curved-surface subsoiler, the various surfaces of the subsoiler is defined in this paper, the subsoiling process is simulated through discrete element simulation software. Combined the actual subsoiler that has a long-working, the wear surface is analyzed, and draws the following conclusions. (1) The upper surface and the front surface of the subsoiler tip, the front surface subsoiler-surface and the front surface of the subsoiler handle are the friction surface of the curved-surface subsoiler. These surfaces are the main wear parts of the subsoiler, and that can be treated emphatically when optimizing the loss reduction (2) The wear of curved-surface subsoiler is related to working speed. The greater the speed, the closer the subsoiler contacts with the soil, and the greater the force, the more the wear. Among them, the wear of the subsoiler handle has the greatest correlation with the change of speeds. Acknowledgements. This work was supported by National Key R&D Program of China (2017YFD0701103-3) and Key research and development plan of Shandong Province (2018GNC112017), (2017GNC12108).

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References 1. Zhiqiong, W., Weixin, W., et al.: Development status of subsoiling technology under conservation tillage conditions at home and abroad. Agric. Res. 6, 256–257 (2016) 2. Baumhardt, R.L., Jones, O.R.: Residue management and tillage effects on soil-water storage and grain yield of dryland wheat and sorghum for a clay loam in Texas. Soil Tillage Res. 68 (02), 71–82 (2002) 3. Cundall, P.A., Strack, O.D.L.: A discrete numerical model for granular assembles. Geotechnique 29(1), 47–65 (1979) 4. Sadek, M.A., Tekeste, M., Naderi, M., Calibration of soil compaction behavior using discrete element method (DEM). In: 2017 ASABE Annual International Meeting, Spokane, WA, USA (2017) 5. Yuxiang, H., Chengguang, H., Mengzhao, Y., et al.: Discrete element simulation and experiment on disturbance behavior of subsoiling. J. Agric. Mach. 47(07), 80–88 (2016) 6. Hang, C., Gao, X., Yuan, M., et al.: Discrete element simulations and tests of soil disturbance as affected by the tine spacing of subsoiler. Biosyst. Eng. 168, 73–82 (2018) 7. Potyondy, D.O., Cundall, P.A.: A bonded particle model for rock. Int. J. Rock Mech. Min. Sci. 41(8), 1329–1364 (2004) 8. Tamás, K., Jóri, I.J., Mouazen, A.M.: Modelling soil–sweep interaction with discrete element method. Soil Tillage Res. 134, 223–231 (2013) 9. Liu, X., Du, S., Yuan, J., Yang, L., et al.: Analysis and test on selective harvesting mechanical end-effector of white asparagus. J. Agric. Mach. 49(04), 110–120 (2018)

Development Status of Balanced Technology of Battery Management System of Electric Vehicle Xiupeng Yan(&), Jianjun Nie, Zongzheng Ma, and Haishu Ma School of Mechatronics Engineering, Zhongyuan University of Technology, Zhengzhou, China [email protected]

Abstract. In order to solve the problem of lithium battery inconsistency and improve the service life and utilization rate of lithium batteries, it is always the focus of research in this field to use equalization technology to improve the above situation. Through reviewing the research progress of lithium ion battery equalization technology at home and abroad in recent years, two main types of equalization methods, namely active equalization and passive equalization, are introduced, and the characteristics of different methods of equalization techniques are introduced from the equalization circuit structure and the equalization strategy. The challenges facing the current equalization technology are identified, and the future research direction is presented. Keywords: Lithium batteries The equilibrium strategies


Balancing techniques
Active equilibrium


1 Introduction Compared with other batteries, lithium-ion batteries have the advantages of small accumulation, light weight, high single cell voltage, high specific energy, long cycle life, no memory effect, low self-discharge rate and no pollution [1, 2]. However, lithium battery has inconsistent battery performance caused by long-term use, which may cause overcharge, overdischarge, overheat and overcurrent of the battery, which may cause irreparable damage to the battery. In severe cases, it may even cause battery explosion and spontaneous combustion [3]. In order to give full play to the excellent characteristics of lithium-ion batteries, many people at home and abroad use battery management systems (BMS) to improve battery utilization and life cycle, and solve the problem of inconsistent battery performance through the balanced management technology in BMS. This paper mainly summarizes the development and characteristics of equalization technology in battery management systems at home and abroad in recent years. It analyzes the advantages and disadvantages of different equalization techniques from the topology structure and equalization strategy of equalization circuit, and proposes the future of lithium ion battery equalization technology. The direction of research and the key technologies that need to be addressed. © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 504–510, 2020. https://doi.org/10.1007/978-981-15-2341-0_63

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2 Equalization Circuit Structure 2.1

Passive Equilibrium

Passive equalization uses resistance in parallel with the single cell. When the energy of the cell is higher than other cells, the excess energy is consumed by the resistor during the charging process. Single and multiple cells can be realized simultaneously. Balanced, it is also called energy consumption. At present, passive equalization control used in the market is divided into software solutions and hardware solutions. The software solution is to set the highest voltage, the lowest voltage, and the threshold between the high and low voltages of the single battery. If the threshold is exceeded, the equalization is started; the hardware scheme is con-trolled based on the comparison between the voltage value detected by the single chip microcomputer and the special chip and the set value. Whether it is balanced [4, 5]. In the passive equalization circuit, the simplest and typical circuit is a switched resistor circuit. As shown in Fig. 1, each single cell in the circuit is connected with a resistor and a switch in parallel. The circuit has a simple structure, is convenient to control, and is inexpensive, but is prone to generate excessive heat. In a special case, a certain heat dissipation mechanism needs to be set, and the energy utilization rate is low, and is now used for equalization during charging. In addition to this, there are analog shunt circuits and fixed shunt resistor structures. 2.2

Active Equilibrium

Active equalization is an equalization method applied to charge and discharge. It is to shift the energy of a battery with a high voltage to a battery with a low voltage, to improve the inconsistency of each single cell, and there is no energy loss in the whole process, so it can be called non-energy. type. At present, common methods for active equalization circuits include energy storage inductance method, bypass equalization method, switched capacitor method, and DC-DC converter method (transformer method) (Fig. 2).

Fig. 1. Power battery double-layer equalization control circuit schematic

Fig. 2. Principle of modular multilevel energy storage system

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Energy Storage Inductance Method The method is based on an inductive device, that is, energy transfer between the monomer and the monomer can be realized, energy transfer between the single cell and the battery pack can be realized, the control is easy, the structure is simple, the implementation is convenient, and the circuit energy The loss is low, and the disadvantage is that the inductance balancing efficiency is low when a plurality of single cells are simultaneously equalized. Junfeng [6] and others use an inductive equalization structure. An equalization module consists of a storage inductor, a MOSFET HE and a Schottky diode achieve a redundant single cell with high terminal voltage during charging. The energy is transferred to other batteries, the circuit structure is simple, the cost is low, and it is easy to control, and is suitable for battery applications with many series connections. Switched Capacitance Method The switched capacitor method is similar to the stored energy inductor method. The capacitor is used as a carrier for energy transfer between any two cells in the battery pack. The circuit contains a switch matrix controlled by a single chip system. The method has the advantages of fast charging and discharging speed, long cycle life and large working temperature range, but too many control switches increase the difficulty of control, and the switch occupies a part of energy consumption. Shengshuang [7] based on the traditional CUK circuit to improve, proposed a battery pack equalization system using array selection switches, capacitors, and inductors. The high-voltage single cell and the low-voltage single cell form a balanced pair. The switch can realize voltage equalization between adjacent single cells, complete energy conversion, balance across batteries, high efficiency, short time consumption, and can be balanced in both charged and non-charged states. The module circuit used for equalization is shown in Fig. 3. DC-DC Converter Method The converter is used as the equalization circuit of the key device, the equalization speed is fast, the multi-cell battery can be charged at the same time, and the whole process has no energy loss. It is the main control method of current popularization and research, but compared with the methods such as capacitance and inductance. In this way, the cost is high, and since there are multiple transformers or coils, the circuit control is complicated. According to the different circuit topology, it can be divided into two types: centralized and distributed equalization. (1) Centralized equilibrium The centralized equalization circuit transfers the energy obtained from the battery pack to the low-voltage battery by using a multi-stage coil or a multi-output transformer, and can be divided into one-way equalization and two-way equalization according to the flow direction of the current. Zhiguo [8] proposed a new two-layer energy transfer control strategy based on the buck-boost converter equalization module circuit. The structure principle is that all the batteries are divided into multiple groups, and the group is balanced and controlled. Separate equalization controls are also implemented within the group. The 6  2

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circuit model is taken as an example, and the 3  2 circuit model is configured to effectively solve the disadvantage that the battery balance efficiency is not high. (2) Distributed The characteristic of the distributed equalization circuit structure is that each single cell has an independent proprietary equalization module, which has high charging flexibility, modularity and good expandability, but has many control signals, high cost, many control elements, and complicated circuits. Widely used circuit structures include Buck-Boost circuits and Cuk circuits. Kaitao [9] and other modular multi-level DC converters and super capacitors form an energy storage system. As shown in Fig. 4, the phase-shifted bidirectional DC converter is made up of multiple sub-modules. When the arm is turned on, the super capacitor starts to charge and discharge. The SOC of the supercapacitor of the independently operated submodule is determined by the duty ratio of the submodule. The control strategy combining the supercapacitor and the droop control idea is based on the level of its own energy. Adjust the average operating current to achieve equalization. The structure has high redundancy and system stability, which greatly reduces the SOC inconsistency of the battery pack.

Fig. 3. Equalization module circuit structure

Fig. 4. Principle of modular multilevel energy storage system

3 Balance Criteria At present, the equalization variables used in the equalization control strategy mainly include terminal voltage, SOC and battery capacity. The equalization methods used are maximum value method, average value method and fuzzy control method. 3.1

Equilibrium Variable

Terminal Voltage The control variable based on the terminal voltage of the single cell is the most widely used and intuitive means at present, and is easy to control and has high precision. By setting the cut-off voltage to the single cell, the process or over-discharge of the cell can be effectively prevented in the process of charging and discharging, and the continuity of the terminal voltage of the cell is also ensured. However, as the number of cycles of battery use increases, the internal resistance increases, the capacity becomes smaller,

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and the influence of the opposite terminal voltage is relatively large, and such a method cannot be applied in a parallel circuit. Ruihua [10] of Tongji University and others used the terminal voltage as the equilibrium variable, and applied the integrated equalization circuit topology to the active equalization strategy of the series battery, avoiding overcharging or overdischarging of the battery and improving the equalization speed. Remaining Power The equilibrium strategy with SOC as the equilibrium variable is the most researched and the future development direction. Le and Jianlong [1, 2, 11, 12] proposed a balanced strategy aiming at the SOC consistency of single cells. The battery SOC cannot be directly measured by the battery. It can only use the relevant monitoring circuit to detect the remaining power calculated by the parameters such as the current, voltage and temperature of the battery. When the difference in the remaining capacity of the battery pack exceeds the theoretical value, the battery is charged. Start balancing at the beginning. At present, the SOC estimation methods include open circuit voltage method, ampere-time integration method, neural network method, Kalman filter method, etc., but the accurate measurement of SOC still needs to be improved, and the SOC cannot accurately estimate the battery overcharge and undercharge. On the other hand, when the number of battery cycles increases, the effects of polarization effects and aging of the battery gradually deepen, and the internal resistance of the battery becomes large, which hinders accurate measurement of the battery SOC. Battery Capacity Accurate measurement of battery capacity is the main reason that restricts its wide application. As with the measurement of SOC, as the number of cycles of battery charge and discharge increases, the factors such as aging, polarization effect, electrolyte concentration and temperature of the battery deepen. The accuracy of battery capacity measurement is difficult to guarantee, and offline estimation of battery capacity is currently the most common method. 3.2

Equilibrium Strategy Method

The maximum value method and the minimum value method have a combined maximum value method, which usually detects the highest voltage and the lowest voltage of the single cell, and the difference exceeds the set threshold value, and the equalization starts, and the energy of the high voltage single cell is low. The voltage battery is transferred and cycled several times until the pressure difference between all the cells is less than the set value, and the equalization is ended, which is regarded as achieving the consistency of the battery. However, if the consistency of the battery pack is extremely poor, the equalization speed and equalization efficiency are extremely poor, and it is highly likely to cause logic confusion. The average comparison method is also called the adjacent battery comparison method, which compares the average voltages of all the single cells and the battery packs respectively, and transfers the energy of the cells higher than the battery cells of the battery pack to the adjacent low-voltage single cells, compare multiple times multiple times until all batteries are consistent. Longdistance battery energy transfer efficiency is extremely low, so this method is only applicable to two adjacent single cells.

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The fuzzy control method is a relatively complicated control method, and it is also the digital development direction of battery online control equalization. Taking the battery consistency parameter as the input variable, the equilibrium non-linear characteristic model of the battery is established by the correlation algorithm, and the output of the controller is used. Control the equalization voltage and current. Time and other parameters. Thi Thu Ngoc Nguyen [13] and others combined the neural network with fuzzy logic control, which is both learnable and adaptive. It can use the online measurement data to find the optimal control point and equalize the current between the batteries.

4 Conclusion Through the understanding of battery equalization technology at home and abroad in recent years, it can be found that the research in the field of active equalization technology has deepened and achieved good results. Regardless of the method and theoretical basis, the objectives are the same, aiming to improve the equilibrium speed of the battery, improve the balance accuracy and efficiency of the battery, reduce the cost of the equalization circuit, and simplify the structure. However, there are still problems in the formulation of the topological structure and equalization rules of the equalization circuit, and it is necessary to continuously improve and strengthen the research. (1) Active equalization technology consumes less energy, and even some topo-logical structures do not consume energy theoretically. Therefore, in the era of re-source shortage, active equalization technology is still a hot topic in the future, and has a wide range of development prospects and use value. (2) The topological structure of the equalization circuit is diverse, and there are still many problems. However, there are still many problems. For example, the energy transfer between two single cells that are far away needs to set a specific circuit structure, otherwise the equilibrium speed is slow and takes a long time. Balanced efficiency and so on. At present, the existing equalization circuit cannot have both cost, structure simplicity, efficiency, service life and performance. (3) With the development of computer technology, the trend of digitization is deepened, and SOC as the best target for digital online measurement will be the focus of research. Improving the acquisition accuracy of battery SOC is a difficult point in this field. (4) The research on composite topological structure and control strategy is gradually increasing. By adopting modular equalization control of battery charge and discharge, the battery is improved in various aspects such as equalization speed and efficiency, so the composite equalization technology is also Future re-search directions.

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References 1. Dongjin, Y., Janan, L.: Review of lithium ion battery and its online testing technologies. Chin. J. Power Sources 42(09), 1402–1403+1419 (2018) 2. Mingli, L., Bingtao, Q.: Research and design of battery management system for pure electric vehicle. Process. Autom. Instrum. 39(09), 21–24 (2018) 3. Jun, T., Cuijun, T.: Safety test and evaluation method of lithium ion battery. Energy Storage Sci. Technol. 7(06), 1128–1134 (2018) 4. Guopeng, T., et al.: Research progress of power battery equalization. Chin. J. Power Sources 39(10), 2312–2315 (2015) 5. Ying, P., et al.: Research status of equalization technology for series batteries. Electron. Meas. Technol. 38(08), 21–24+49 (2015) 6. Junfeng, H., et al.: Research of charging equalization circuit and equilibrium strategy for Liion battery series. Chin. J. Power Sources 40(12), 2439–2443 (2016) 7. Shenshuang, Y., Xiangzhong, Q.: Design of equalization control for battery of electric vehicle. Electron. Des. Eng. 25(22), 154–157+161 (2017) 8. Zhiguo, A., et al.: Design of energy transfer equalization control for electric vehicle power battery. Comput. Simul. 34(05), 147–150+252 (2017) 9. Kaitao, B., et al.: Distributed energy balancing control strategy for energy storage system based on modular multilevel. Trans. China Electrotech. Soc. 33(16), 3811–3821 (2018) 10. Ruihua, L., et al.: Voltage equalization optimization strategy for LiFePO4 series-connected battery packs based on Buck-Boost converter. Electr. Eng. 19(03), 1–7 (2018) 11. Le, Q., et al.: Research on control strategy of energy storage system based on SOC. Coal Technol. 37(06), 247–249 (2018) 12. Jianlong, H., et al.: Research on equalization strategy of energy storage battery strings based on SOC. Renew. Energy Resour. 35(12), 1828–1834 (2017) 13. Yoo, H.G., et al.: Neuro-fuzzy controller for battery equalisation in serially connected lithium battery pack. IET Power Electron. 8(3), 458–466 (2015)

Application Analysis of Contourlet Transform in Image Denoising of Flue-Cured Tobacco Leaves Li Zhang1, Haohan Zhang1(&), Hongbin Liu2, Sen Wang2, and Xiaoyu Liu3 1

Faculty of Mechanical and Electrical Engineering, Yunnan Open University, Kunming, China [email protected] 2 Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China [email protected] 3 School of Culture Tourism and International Exchange, Yunnan Open University, Kunming, China [email protected]

Abstract. Image denoising is one of the most basic and important tasks in image processing when computer is used for quality inspection of flue-cured tobacco leaves. The Contourlet transform has the advantages of multiresolution, anisotropy, and sparsity. Wavelet denoising, median filter, mean filter, gaussian filter and wiener filter are used to conduct comparative experiments on tobacco leaf images so as to verify the denoising effect of Contourlet transform. It is showed that the image denoising method based on the Contourlet transform has the advantages of high signal-to-noise ratio and good visual effect when applied to tobacco image denoising, which is effective and feasible for image denoising of flue-cured tobacco. Keywords: Tobacco leaves
Image denoising
Contourlet transform
Tower directional filter bank

1 Introduction After the fresh tobacco leaves are picked from the field and sorted according to different maturity and shape characteristics, their subsequent baking quality can be improved [1]. Therefore, the tobacco leaf sorting technology based on computer vision is applied. There will be noise when acquiring the image of tobacco leaves due to the dust or strains on their surface, which will have impact on their identification in severe cases. Therefore, the image of the tobacco leaf needs to be denoised before the feature extraction. The image denoising can be divided into two kinds: spatial domain denoising and transform domain denoising based on the actual characteristics of image and the spectral distribution of noise. Among them, the spatial domain denoising mainly includes mean filter, Gaussian filter and median filter, while the transform domain © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 511–516, 2020. https://doi.org/10.1007/978-981-15-2341-0_64

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denoising mainly includes wavelet denoising, multi-scale analysis methods, etc. Wavelet transform is widely used in various denoising processes [2, 3] due to its multiresolution and time-domain characteristics. However, it has the drawback of isotropic, mainly showed in the weaker effect in image denoising to express its directionality. In order to overcome its limitations, Do and Martin proposed Contourlet transform in 2002 [4], which not only contains the advantages of wavelet transform, but also has more directionality to the target when performing image denoising. In 2005, Cunha [5] improved it. The improved method makes good use of the geometric structure of the image to satisfy the anisotropic scale relation of the curve, and provides a fast and structured method to decompose the sampled signal. After that, the denoising algorithm based on Contourlet transform is gradually applied to the field of agricultural product image processing [6]. In view of the above, this paper proposes a tobacco image denoising algorithm based on Contourlet transform. By decomposing the image of the noisy tobacco leaf through PDFB, and estimating the speckle noise variance of the sub-bands in each high-frequency direction and the local mean of the transform coefficient modulus, the multi-scale shrinkage thresholding is used to determine the Contourlet coefficient, and denoise the image of the tobacco leaves for the better effect of image denoising.

2 General Model of Image Denoising Denoising are generally represented by the following model: y ¼ xþr  e

ð1Þ

Among them, x is the ideal signal, y, the observed noisy signal e, the noise and r, the noise variance. The purpose of denoising is to recover the original signal x from the noisy signal y. For the image of N  N pixel size, its original image is set as: fi;j ¼ 1; 2; . . .; n ; n  N

ð2Þ

fi;j is the grayscale at the point ði; jÞ. Then the image with noise can be expressed as: gi;j ¼ fi;j þ ei;j ; i; j ¼ 1; 2; . . .; n; n  N

ð3Þ

Among them, gi;j is the grayscale at the point ði; jÞ where the noise is superimposed on the original image ei;j , the noise at the point ði; jÞ, which i; j is the row and column coordinates of the corresponding pixel.

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3 Image Denoising Based on Contourlet Transform Contourlet transform is a multi - scale geometric analysis method with multi - resolution, multi - direction and anisotropy. Compared with wavelet transform, the coefficient energy of the images obtained after Contourlet transform is more concentrated in different directions and scales, and the effect of image denoising is better [7]. In the image denoising of Contourlet transform, the decomposition layer J is firstly determined, then the low frequency coefficient a0 and high frequency coefficient d0 ; d1 ; . . .; dJ1 are respectively obtained by Laplace transform and directional filter bank. Then the new Contourlet coefficient dbt , t ¼ 0; 1; . . .; J  1 is obtained by setting the thresholding to address coefficient. The transposition is made in the last, which can gain the estimated signal fbi;j of the original fi;j . Thus, the denoised image after the tobacco leaf treatment db0 ; db1 ; . . .; dd J1 and the Contourlet inversion a0 is obtained. The choice of thresholding and its functions is the key to the denoising algorithm. In the Contourlet-based denoising, the hard thresholding denoising algorithm can better preserve the local and edge details of the image, but after reconstruction, the image may have hair-like visual interference. While soft thresholding has smoother denoising effect, but it is easy to blur the edge details. First, the hard thresholding function is used for denoising, as shown in Eq. (4). dbt ¼



dt ; when jdt j  d ; t ¼ 0; 1; . . .; J 0; others

ð4Þ

In the equation, dt is the high frequency coefficient, dbt , the processed low frequency coefficient, and d, shrinkage thresholding. The denoising effect is directly related to the assignment of d. The larger d selected, the more noise will be eliminated, but the high-frequency information in the image will also be lost. The smaller d selected, the more image information can be retained, but so the noise can. In view of this situation, Donoho proposed the shrinkage-thresholding algorithm in 1994 [8, 9]. pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi d ¼ r 2 lnðN0 Þ

ð5Þ

Although this method has certain basis for the selection of shrinkage thresholding, it can only obtain the upper limit of the optimal thresholding, not the optimal thresholding. In different scale subbands, the proportion of image and noise information is also different. And the higher the scale, the more obvious the trend. Therefore, one selection method based on multi-scale decomposition denoising thresholding is proposed [10]. Tl ¼ r

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðlJ Þ=2 2 lnðN0 Þ
2

ð6Þ

In the equation, r is the size of noise; N0 , the total number of image pixels; l, the scale level; and J, the number of decomposition layers. Through the above formula, the selection of thresholding can be changed according to the different scale of coefficient, thus achieving its adaptive selection.

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4 Analysis and Comparison of Simulation Results In order to verify the de-noising effect of Contourlet transform de-noising algorithm, pepper and salt noise, which accounts for 10% of all the pixels of the current images, was added to test images in the experiment. Six methods, namely mean filter, gaussian filter, wiener filter, median filter, wavelet de-noising and Contourlet transform, were used to conduct de-noising experiments and comparisons on tobacco images. The first four are all denoised by filtering windows with a size of 3  3 pixels; wavelet transform adopted global denoising based on db3 wavelet filter; and Contourlet transform, “9–7” tower decomposition and “pkva” directional filter bank. The denoising effects of tobacco image are shown in Fig. 1 respectively, and PSNR in Table 1. PSNR represents the denoising effect of the algorithm, and the higher its value is, the better denoising effect would be.

Fig. 1. Tobacco images after denoising

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In Fig. 1, it is obvious that most of the noise in the image after mean filter has been removed, but the details become blurred. Median filter, gaussian filter, wiener filter and wavelet denoising methods have similar denoising effects, and better texture details are preserved. Contourlet-based denoising method not only removes all noise, but also performs better than other methods in preserving texture details of tobacco image, which also proves the effectiveness of this method in denoising agricultural products. Table 1. PSNR of different image denoising algorithms Mean filter Channel 1 34.00 Channel 2 33.96 Channel 3 33.16

Gaussian filter 43.47 43.43 42.54

Median filter 40.15 39.85 40.23

Wiener filter 40.12 39.87 40.62

Wavelet denoising 40.37 40.21 40.45

Contourlet transform 46.11 46.41 44.50

It can be concluded from Table 1 that the PSNR by using Contourlet transform for denoising is the highest, followed by gaussian filter. The denoising effect of median filter, wiener filter and wavelet is similar, slightly lower than that of gaussian filter. While the effect of mean filter is relatively low.

5 Conclusions To better realize the image denoising of fresh tobacco, this paper proposes an image denoising algorithm based on Contourlet transform. Compared with other denoising methods, it is showed that Contourlet transform thresholding denoising is an algorithm more suitable for denoising tobacco images.

References 1. Xu, F., Zhang, F., Du, B., et al.: Effects of different fresh leaves classification on tobacco leaf quality and benefits. J. Anhui Agric. Sci. 41(25), 10429–10432 (2013) 2. Donoho, L.: De-noising by soft threshholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995) 3. Yang, F., Tian, Y., Yang, L., et al.: Agricultural product image denoising algorithm based on hybrid wavelet transfor. Trans. Chin. Soc. Agric. Eng. 27(3), 172–178 (2011) 4. Do, M.N., Vetterli, M.: Contourlets: a directional multi resolution image representation. In: IEEE International Conference on Image Processing, Rochester, NY, pp. 357–360 (2002) 5. Cunha, L., Zhou, J.P., Do, M.N.: The nonsubsampled contourlet transform theory design and applications. IEEE Trans. Image Process. 15(10), 3089–3101 (2005) 6. Song, H., He, D., Han, T.: Contourlet transform as an effective method for agricultural product image denoising. Trans. Chin. Soc. Agric. Eng. 28(8), 287–292 (2012) 7. Dai, W., Yu, S., Sun, S.: Image de-noising algorithm using adaptive threshold based on Contourlet transform. Acta Electron. Sinica 35(10), 1939–1943 (2007)

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8. Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation via wavelet shrinkage. Biometrika 81, 425–455 (1994) 9. Donoho, D.L.: Denoising by soft-thresholding. IEEE Trans. Inf. Theory 3, 613–627 (1995) 10. Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000)

Monte Carlo Simulation of Nanoparticle Coagulation in a Turbulent Planar Impinging Jet Flow Hongmei Liu1,2(&), Weigang Xu1,2, Faqi Zhou1,2, Lin Liu1,2, Jiaming Deng1, Shuhao Ban1, and Xuedong Liu1,2 1

School of Mechanical Engineering, Changzhou University, Changzhou, China [email protected] 2 Key Laboratory of Green Process Equipment in Jiangsu, Changzhou, China

Abstract. A Monte Carlo method coupled with large eddy simulation is employed to study nanoparticle evolution in a confined impinging jet. The transient and discrete particle distributions and the time-averaged particle number density distributions can be obtained. The results show that the coherent structure evolution has large effect on the particle dispersion pattern and the particle diameter distributions. Keywords: Nanoparticle
Coagulation simulation
Monte carlo method


Impinging jet
Large eddy

1 Introduction In many scientific and industrial applications, the phenomenon of nanoparticle dynamics in turbulent flows is of great interest. For example, particulate matter evolution in ground vehicles [1], nanoparticle synthesis in reactive flows [2] and soot formation in engine combustion chambers [3]. In these areas, the impingement of nanoparticles on a solid surface occurs in many processes. The impinging jet is a full-fledged technique to study the deposition of nanoparticles onto a solid surface [4]. Furthermore, the particle diameters in the mentioned applications are usually nanoscale, and the complicated coherent structures in the stagnation region and wall flow region usually lead to non-determined particle distributions in the impinging jet, which results in difficulties in fully understanding the particle dynamic behaviors in those phenomena [5]. Among the numerical methods in solving the general dynamic equation of nanoparticles, the Monte Carlo (MC) method [6, 7] is more and more preferred by people because of its stochastic nature by using simulated particles to imitate the dynamic behaviors and movement trajectories of particles. In the present study, the MC method is coupled with the large eddy simulation (LES) to study nanoparticle evolution in a confined impinging jet. The remaining part of this paper is organized as follows. Section 2 introduces the governing equation of the nanoparticle in turbulent flows. Section 3 details the © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 517–522, 2020. https://doi.org/10.1007/978-981-15-2341-0_65

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algorithmic implementation of the coupled LES-MC method. Section 4 presents the results and discussions. Finally, the conclusions are given in Sect. 5.

2 Numerical Methodology 2.1

Governing Equations for Gas Phase

The transport of the continuous fluid phase is governed by the well-known NavierStokes (N-S) equations. In LES, the turbulent flows are decomposed into two parts of large- and small-scale structures: the large eddies are directly computed on a Eulerian grid, while the small eddies are modelled. In LES, the filtered N–S Equation is written as, @q @ðqui Þ þ ¼0 @t @xi

ð1Þ

@ðqui Þ @ðqui uj Þ @p @ @ ui @sij þ ¼ þ ðl Þ  @t @xj @xi @xj @xj @xj

ð2Þ

where ui is the velocity, p is the pressure, q is the density and l is the viscosity. sij refers to the subgrid scale (SGS) stress tensor. 2.2

Governing Equations for Particle Phase

The dispersed particle phase is described by a Lagrangian Monte Carlo method, and the governing equation of the position and velocity of a particle is given by the following equations: dxp;i ¼ up;i dt

ð3Þ

dup;i 3 q ¼ cD ðui up;i Þj~ u! up j þ f s 4 qp dp dt

ð4Þ

where xp;i is the position, up;i is the velocity of the particles, ui is the velocity of the continuous gas phase and dp is the diameter of the dispersed particles. The first term in the right hand side of Eq. (4) denotes the drag force that the carrier flow imposes on the particles. The second term in the right hand side represents the contributions from forces other than drag force.

3 Algorithmic Implementation A brief outline of the algorithm of the LES-MC method is given as follows [8]: (a) Initialization.

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Choose a time-step, Dt for the gas phase flow. Solving the gas flow fields. Updating the spatial position and velocity field of particles. Choose a time-step, dt for the nanoparticles. Start M (i.e., M ¼ Dt=dt) Monte Carlo loops. Treatment of coagulation process by MC method [8, 9]. The properties of simulated particles are updated. If the current MC loop number, R does not reach the predetermined MC loop number, M, then start a new MC loop. Otherwise, quit the MC loop for the nanoparticles. (j) If the calculation time, t reaches tstop, output the results of two-phase flow fields.

(b) (c) (d) (e) (f) (g) (h) (i)

The flowchart of the LES-MC algorithm is shown in Fig. 1.

Fig. 1. Flowchart of the LES-MC algorithm [8].

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4 Results and Discussion 4.1

Configuration and Model Description

Figure 2 shows a planar impinging jet flow configuration which is used in the present study. The width of the nozzle D is 25 mm and the Reynolds number, Re = DU0/m, is 30000 [5]. The nozzle-to-plate distance H is 2D. The computational grid is comprised of 400  800. The fluid in this study is air at a temperature of T = 300 K. The nanoparticles are injected with a diameter of 5 nm. In this size regime, free molecule regime coagulation rate [10] is used.

Fig. 2. A sketch map of a planar impinging jet flow [5].

4.2

Evolution of Coherent Structures

Figure 3 shows the transient evolution of the vorticity in z-direction. It can be seen that vorticity is generated at the interface where the jet and environment gas mix together. The core of the vortex firstly moves downward and then split to the direction of far away from the y-axis because of the effect of impinging plate. It can be seen that at time t = 0.02 s, a series of vortexes appear near the bottom wall because of the impingement effect between the flow and the wall.

(a) t=0.0005s

(b) t=0.010s

(c) t=0.0015s

(d) t=0.020s

Fig. 3. Contours of vorticity in the evolution of vortex

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Evolution of Nanoparticles

The transient states and dispersion characteristics of the particle field by using the discrete simulated particles are shown in Fig. 4. The transient distributions of the particles are affected by the vortex structure of the fluid flows and the transient distribution of the particles is quite similar to the distribution of vorticity shown in Fig. 3. It can also be seen that the diameters of particles increase along the stream-wise direction which illustrated that the coagulation process happens over time.

(a) t=0.0005s

(b) t=0.010s

(c) t=0.0015s

(d) t=0.020s

Fig. 4. Transient particle field distribution coloured by the diameter of particles

The time-averaged normalized particle number density (N/N0, where N0 is the particle number density at the jet inlet) distribution at time t = 0.020 s is shown in Fig. 5. It can be seen that the particle number density becomes smaller along the stream-wise direction which has complete opposite changes with the particle diameter. The results reveal that near the inlet of the jet, the particle number density shows dramatic decrease and the decrease tendency slows down in the wall flow region. This is because the coagulation rate will become slower with the decrease of particle number density.

Fig. 5. Time-averaged normalized particle number density distribution at time t = 0.020 s

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5 Conclusions A coupled LES-MC method is used to study the transient evolution of nanoparticles in a turbulent planar impinging jet flow. The results show that the coherent structure evolution has a great effect on the transient particle dispersion pattern and the particle diameter distributions, and the coagulation rate will change accordingly along the stream-wise direction because of the decrease of particle number density. Acknowledgements. The work is supported by the Sinopec Corp. major research project (Grant No. 417002-2). The research work is based on the MC method developed by H.M. Liu during her PhD study at the Department of Mechanical Engineering in the Hong Kong Polytechnic University.

References 1. Chan, T.L., Liu, Y.H., Chan, C.K.: Direct quadrature method of moments for the exhaust particle formation and evolution in the wake of the studied ground vehicle. J. Aerosol Sci. 41(6), 553–568 (2010) 2. Yu, M., Lin, J., Chan, T.: Numerical simulation of nanoparticle synthesis in diffusion flame reactor. Powder Technol. 181, 9–20 (2008) 3. Rodrigues, P., Franzelli, B., Vicquelin, R., Gicquel, O., Darabiha, N.: Coupling an LES approach and a soot sectional model for the study of sooting turbulent non-premixed flames. Combust. Flame 190, 477–499 (2018) 4. van de Ven, T.G.M., Kelemen, S.J.: Characterizing polymers with an impinging jet. J. Colloid Interface Sci. 181, 118–123 (1996) 5. Yu, M., Lin, J., Xiong, H.: Quadrature method of moments for nanoparticle coagulation and diffusion in the planar impinging jet flow. Chin. J. Chem. Eng. 15(6), 828–836 (2007) 6. Liu, H.M., Chan, T.L.: Differentially weighted operator splitting Monte Carlo method for simulating complex aerosol dynamic processes. Particuology 36, 114–126 (2018) 7. Liu, H.M., Chan, T.L.: Two-component aerosol dynamic simulation using differentially weighted operator splitting Monte Carlo method. Appl. Math. Model. 62, 237–253 (2018) 8. Liu, H.M., Chan, T.L.: A coupled LES-Monte Carlo method for simulating aerosol dynamics in a turbulent planar jet. Int. J. Numer. Methods Heat Fluid Flow (2019). https:// doi.org/10.1108/hff-11-2018-0657 9. Zhao, H., Kruis, F.E., Zheng, C.: A differentially weighted Monte Carlo method for twocomponent coagulation. J. Comput. Phys. 229(19), 6931–6945 (2010) 10. Zhou, K., He, Z., Xiao, M., Zhang, Z.: Parallel Monte Carlo simulation of aerosol dynamics. Adv. Mech. Eng. 6, 1–11 (2014)

Structural Damage Detection of Elevator Steel Plate Using GNARX Model Jiaxin Ma1,2(&) and Yan Dou1,2 1

School of Mechanical Engineering, Changshu Institute of Technology, Changshu, China [email protected] 2 Jiangsu Key Laboratory for Elevator Intelligent Safety, Changshu Institute of Technology, Changshu, China

Abstract. For elevator steel plate, the tiny crake is difficult to find out. However, the crake growth may cause fatality. Thus, it’s crucial to detect structural damage of elevator steel plate. With the expression of GNARX model deduced, the modified Mahalanobis distance least square (MMDLS) is proposed for parameter estimation. Then, the structure pruning algorithm based on parameters’ rate of standard deviation (SPRSD) is proposed for structure identification. With experimental data, GNARX model is applied to structural damage detection for elevator steel plate. The results show that the effect of structural damage detection of GNARX model is better than those of AR, ARX, GNAR models, which indicates the superiority of GNARX model applied to structural damage detection of elevator steel plate. Keywords: Structural damage detection
GNARX model estimation
Structure identification
Elevator steel plate


Parameter

1 Introduction As closely related to people’s daily life, elevator safety receives more and more attention. However, for elevator steel plate, the initial and tiny crack is hard to be discovered. Yet, crack growth may cause serious failure, sometimes even leading to fatal disaster. Hence, it is critical to detect, locate, and estimate the extent of the structural damage. Generally, structural damage detection can be categorized as local-damage detection and global-damage detection [1]. The local-damage detection techniques [2–4] refer to dye penetration, magnetic powder, eddy current, radial, ultrasound, strain, etc. The main advantage is that there is no need to develop specific model or obtain baseline data of undamaged structure. Thus, local-damage detection is very effective for small and regular structures. However, for large and complex structures in invisible or closed environments, it is very difficult and time-consuming to complete an inspection of the whole structure using local-damage detection methods. To overcome the limitation of local-damage detection, the vibration-based structural damage detection as a globaldamage detection technique is proposed [5, 6].

© Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 523–532, 2020. https://doi.org/10.1007/978-981-15-2341-0_66

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Vibration-based methods mostly consist of two categories: finite element model updating methods [7] and statistical time series model methods [8]. However, most finite element model updating methods and statistical time series model methods of available literatures require developing the baseline models of undamaged structures. This will greatly limit the application, e.g., structural damage detection of in-service equipment. Thus, to overcome the limitation, a novel approach is proposed to detect, locate, and estimate the extent of the structural damage for in-service equipment based on a linear and nonlinear auto-regressive model with exogenous input (GNARX model) [9]. The remaining part of this paper is organized as follows. Section 2 introduces the expression of GNARX model. Section 3 details the parameter estimation and structure identification of GNARX model. In Sect. 4, the methodology of structural damage detection for elevator steel plate is proposed and verified. Finally, the conclusions are provided in Sect. 5.

2 Expression of GNARX Model According to the modeling strategy of time series analysis, the general linear and nonlinear auto-regressive model (GNAR) takes a zero mean white noise {at} as input to the system [10]. When one of the exogenous inputs {ut} is known, the GNAR model is converted into the GNARX model with a single exogenous input. If the system has two exogenous inputs, ut and vt, the abbreviation of GNANX model with double inputs can be written as GNARX (p; su, sv; nw,1, nw,2, …, nw,p; nu,1, nu,2, …, nu,p; nv,1, nv,2, …, nv,p), which is expressed as follows: xt;i;1 ¼ fwt1 ;


; wtnw;i ; utsu ;


; utsu nu:i þ 1 ; vtsv ;


; vtsv nv:i þ 1 g wt ¼

nw;1 þX nu;1 þ nv;1

ð1Þ

hði1 Þxt;1;1 ði1 Þ

i1 ¼1

þ

nw;1 þX nu;1 þ nv;1 nw;2 þX nu;2 þ nv;2 i1 ¼1

þ


þ

nw;1 þX nu;1 þ nv;1






i1 ¼1

¼

nu;1 þ nv;1 p nw;1 þX X j¼1

hði1 ; i2 Þxt;2;1 ði1 Þxt;i;1 ði2 Þ

i2 ¼1

i1 ¼1

nw;p þX nu;p þ nv;p

hði1 ;


; ip Þ

ip ¼1






nw;j þX nu;j þ nv;j ij ¼1

p Y

ð2Þ xt;p;1 ðik Þ

k¼1

hði1 ;


; ij Þ

j Y

xt;j;1 ðik Þ þ at

k¼1

where xt,i (i = 1, 2, …, p) is the ith-order term; xt,i,j (j = 1, 2, …, i) is the jth-order transitional term in the derivation process of xt,i; xt,i,1(j) is the jth element of vector xt, i,1; wt-i is the observation at time t − i; ut-su−i is the exogenous input ut at time t − su − i; vt-sv−i is the exogenous input vt at time t − sv − i; at-i is the white noise at time t − i, i = 1, 2, …, n; su and sv are the input delay of ut and vt, respectively; h(i1), h(i1, i2),… are the model parameters; p is the model order; nw,j (j = 1, 2, …, p) is the

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memory step of the jth-order term of output {wt}; nu,j and nv,j (j = 1, 2, …, p) are the memory step of the jth-order term of input {ut} and input {vt}, respectively. Similarly, Eq. (2) can also be generalized into multi-input systems, which need not be repeated here.

3 Identification of GNARX Model 3.1

Parameter Estimation of GNARX Model

The modified Mahalanobis distance least square (MMDLS) is proposed and applied to GNARX model parameter estimation. It is better than least square (LS) method as MMDLS takes total sample’s secondary moment property into account while LS only considers sample mean. Using the GNARX model with double inputs indicated in Eq. (2) as example, the FFRLS algorithm for the parameter estimation of GNARX is deduced as follows: xt;i;1 ¼  fwt1 ;


; wtnw;i ; utsu ;


; utsu nu:i þ 1 ; vtsv ;


; v tsv nv:i þ 1 g xt;i;1 ð1Þfxt;i;1 ð1Þg; xt;i;1 ð2Þfxt;i;1 ð1Þ; xt;i;1 ð2Þg;


; xt;i;2 ¼ xt;i;1 ðmi;1 Þxt;i;1 .. .   xt;i;i1 ð1Þfxt;i;i1 ð1Þg; xt;i;i1 ð2Þfxt;i;i1 ð1Þ; xt;i;i1 ð2Þg;


; xt;i;i ¼ xt;i;i1 ðmi;i1 Þxt;i;i1

ð3Þ

where mi;j ¼ Cnjw;i þ nu;i þ nv;i þ j1 (j = 1, 2, …, i). xt;p ¼ xt;p;p

ð4Þ

xt ¼ fxt;1 ; xt;2 ;


; xt;p g

ð5Þ

X ¼ fxTt ; xTt þ 1 ;


; xTt þ k gT w ¼ fwt ; wt þ 1 ;


; wt þ k g

ð6Þ

Accordingly,

Thus, the equation for least square estimation is given as follows: ^h ¼ ðXT XÞ1 XT w

ð7Þ

The model residual is given as follows: e ¼ ½e1 ; e2 ;


; en T ¼ w  X
^ h0

ð8Þ

where ^h0 is calculated with Eq. (7). Thus, ei is the model residual of ith sample. Covariance matrix of e is given as Ce.

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eðiÞ ¼ ½ei;1 ; ei;2 ;


; ei;n T ¼ wðiÞ  XðiÞ
^ h0 k h i 1X eðiÞ eTðiÞ Ce ¼ E ðe  E½eÞðe  E½eÞT ¼ k i¼1

ð9Þ ð10Þ

However, the covariance matrix Ce may be singular matrix. M ¼ diagðCe ; lÞ þ


þ diagðCe ; 0Þ þ


þ diagðCe ; lÞ

ð11Þ

where diag(Ce, 0) is the leading diagonal of Ce and diag(Ce, l) is the lth diagonal. Thus, GNARX model parameter estimation with MMDLS is given as follows: ^hi ¼ ðXT M1 XðiÞ Þ1 XT M1 w ðiÞ ðiÞ

ð12Þ

where ^hi is the estimated parameters of ith sample. 3.2

Structure Identification of GNARX Model

The structure pruning algorithm based on parameters’ rate of standard deviation (SPRSD) is proposed and applied to structure identification of GNARX model. The flow chart of the SPRSD is shown in Fig. 1, and the concrete steps are shown as follows:

Start The data are divided into K groups. The large enough model is confirmed.

Parameter estimation of K groups The joint AIC value is calculated Parameters’ rate of standard deviation is calculated The term with biggest rate of standard deviation is deleted

One term remains

No

Yes The model structure with the smallest AIC value is obtained as the optimal model structure The end

Fig. 1. Schematic overview of key monitoring components of hydropower plant

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Step 1: The data are divided into K groups. Step 2: The initial model with high enough order and long enough memory steps is confirmed. Step 3: Repeat. Step 3:1: Parameter estimation for K groups. Step 3:2: The joint AIC value is calculated as Eq. (13). Step 3:3: Parameter’s rate of standard deviation is calculated. The term with biggest rate of standard deviation is deleted. Step 3:4: When one term remains, turn to step 4. Otherwise, turn to step 3.1. Step 4: The model structure with the smallest AIC value is obtained as the optimal model structure. Step 5: Finally, the algorithm is terminated. With consideration of modeling residuals, forecasting error, and model complexity, the joint Akaike Information Criterion (AIC) is proposed. It can be calculated as follows: AIC ¼ lnð

K K Nm 1 X Nf 1 X   r2m;i þ r2 Þ þ 2R=N K i¼1 N N K i¼1 f ;i

ð13Þ

where r2m;i is the ith sample’s variance of the modeling residuals; r2f ;i the ith sample’s variance of forecasting error; R is the number of model parameters; N is the sequence length; Nm is the modeling sequence length; Nf is the forecasting sequence length.

4 Structural Damage Detection of Elevator Steel Plate GNARX model is applied to steel plate structural damage detection. With parameter estimation and structure identification, suitable model is developed and model parameters are taken as feature vector. With k-nearest neighbors (KNN) algorithm, the steel plates with crake and without crake are identified. 4.1

KNN Algorithm

The main idea of KNN is to assign new examples to be classified to the class to which the majority of its k nearest neighbors belongs. Euclidean distance expressed as follows can be used to determine the similarity between samples. sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n X dða; bÞ ¼ ðai  bi Þ2

ð14Þ

i¼1

where a = (a1, a2, …, an) and b = (b1, b2, …, bn) are two sample data; n is the sample length.

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To evaluate the accuracy of classification, the indicator is presented as follows: n P

Acuuracy ¼

i¼1

dðyi ; ci Þ n

ð15Þ

where yi and ci denote the true category label and the obtained cluster label, respectively; d is a function that equals 1 if yi = ci and equals 0 otherwise. 4.2

Data Acquisition

Two steel plates with the same size (300 mm  100 mm  3 mm) are prepared. One is undamaged and the other one has a through crack (50 mm  1 mm). The crack position is shown as Fig. 2. Four PK151 resonant sensors are set in four positions on the steel plate (shown as Fig. 2). The signals are collected by SENSOR HIGHWAY acoustic emission acquisition instrument. The experimental facility is shown as Fig. 3. The sampling frequency is 1 MHz and sampling number is 8192. The threshold value is 26 db. Five groups of acoustic emission data are collected for each steel plate. Typically, one group of data for undamaged steel plate is shown as Fig. 4 (a) and one group of data for damaged steel plate with a crack is shown as Fig. 4(b).

Fig. 2. Dimension of the damaged steel plate and the location of four measuring points

Fig. 3. The sketch map of experimental facility and the layout of sensors

sampling #1 measure point

0.2 0 -0.2 0

2000 4000 6000 8000

2000 4000 6000 8000 sampling #4 measure point

0.2 0 -0.2 0

2000 4000 6000 8000

sampling

0 -0.2 0

2000 4000 6000 8000 sampling #1 measure point

0.2 0 -0.2 0

2000 4000 6000 8000

sampling

(a) undamaged steel plate

sound pressure (µBar)

0

#2 measure point

0.2

sound pressure (µBar)

2000 4000 6000 8000

0 -0.2

sound pressure (µBar)

0

#3 measure point

0.2

sound pressure (µBar)

0 -0.2

sound pressure (µBar)

#2 measure point

0.2

sound pressure (µBar)

sound pressure (µBar)

sound pressure (µBar)

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#3 measure point

0.2 0 -0.2 0

2000 4000 6000 8000 sampling #4 measure point

0.2 0 -0.2 0

2000 4000 6000 8000

sampling

sampling

(b) damaged steel plate

Fig. 4. One group of acoustic emission data for each steel plate

4.3

Result and Discussion

For each of totally 10 groups of data, the first 1024 data and the last 3072 data are deleted. Finally, the middle 4096 data are applied to GNARX model. Each 256 data are taken as one sample (the first 200 data are used for modeling and the left 56 data are used for forecasting). Thus, both of undamaged and damaged steel plates are divided into 80 samples, from which the random 50 samples are used for training and the remained 30 samples are used for test. The upper model takes #2 measure point as input and #3 measure point as output. The bottom model takes #1 measure point as input and #4 measure point as output. The distance between input and output is 160 mm. As shear wave velocity in steel plate is 3000 m/s, the time delay is about 5.3  10−5 s and the input delay is taken as 53. For structure identification, GNARX(3;53;10,4,2;8,2,2) is taken as the initial model. With SPRSD, the upper and bottom model structures of undamaged and damage steel plate are shown as follows: The upper model structure of undamaged steel plate: xt;1 : wt1 ; wt3 ; wt4 ; wt5 ; wt7 ; wt8 ; wt10 ; utsu 3 xt;2 : w2t3 ; wt3 utsu 1 ; utsu 1 utsu 2 xt;3 : wt1 u2tsu 1

ð16Þ

The upper model structure of damaged steel plate: xt;1 :wt1 ; wt3 ; wt4 ; wt5 ; wt7 ; wt8 ; wt10 ; utsu 1 ; utsu 2 ; utsu 3 xt;2 :wt2 wt3 ; w2t3 ; wt1 utsu 1 ; wt2 utsu 1 ; wt3 utsu 1 ; wt3 utsu 2 ; wt4 utsu 2 ; u2tsu 2 xt;3 :w3t1 ; wt1 u2tsu 1

ð17Þ

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The bottom model structure of undamaged steel plate: xt;1 : wt1 ; wt2 ; wt3 ; wt4 ; wt6 ; wt8 ; utsu 3 xt;2 : w2t2 xt;3 : w3t1

ð18Þ

The bottom model structure of damaged steel plate: xt;1 : wt1 ; wt2 ; wt3 ; wt4 ; wt6 ; wt8 ; wt10 ; utsu 2 ; utsu 3 ; utsu 5 xt;2 : w2t1 ; w2t2 ; u2tsu 1 ; wt1 utsu 2 xt;3 : w3t1 ; w2t1 utsu 1 ; wt1 u2tsu 1 ; u3tsu 1

ð19Þ

To make the feature vectors exactly exhibit the nonlinear characteristics and the vector dimension of undamaged and damaged steel plate data stay the same, the final model structures are shown as follows: The final upper model structure: xt;1 :wt1 ; wt3 ; wt4 ; wt5 ; wt7 ; wt8 ; wt10 ; utsu 1 ; utsu 2 ; utsu 3 xt;2 :wt2 wt3 ; w2t3 ; wt1 utsu 1 ; wt2 utsu 1 ; wt3 utsu 1 ; wt3 utsu 2 ; wt4 utsu 2 ; utsu 1 utsu 2 ; u2tsu 2

ð20Þ

xt;3 :w3t1 ; wt1 u2tsu 1 The final bottom model structure: xt;1 : wt1 ; wt2 ; wt3 ; wt4 ; wt6 ; wt8 ; wt10 ; utsu 2 ; utsu 3 ; utsu 5 xt;2 : w2t1 ; w2t2 ; u2tsu 1 ; wt1 utsu 2 xt;3 : w3t1 ; w2t1 utsu 1 ; wt1 u2tsu 1 ; u3tsu 1

ð21Þ

With parameter estimation, the model parameters are taken as the feature vectors and KNN algorithm is applied. The results are listed in Tables 1, and 2. For comparison, the results of AR, ARX, different order GNAR, and different order GNARX models are also listed in Tables 1, and 2. From the above, the following can be obtained: (1) As steel plate structural damage detection, the classification accuracy of GNARX models are higher than those of AR, ARX, and GNAR models. This indicates that GNARX model is suitable for structural damage detection. (2) The classification accuracy of GNARX model with SPRSD is the highest. This indicates the effectiveness of SPRSD for GNARX model structure identification. (3) The classification accuracy of the bottom model is obviously higher than that of the upper model. This indicates that model closer to damaged location embodies more damage information.

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Table 1. The KNN classification accuracy of different models applied to the upper data of undamaged and damaged steel plate Model Maximum accuracy* Mean accuracy** AR(6) 66.67% 61.11% GNAR(2;6,1) 65.00% 61.11% GNAR(3;8,4,1) 70.00% 65.74% ARX(8,3,53) 68.33% 62.78% GNARX(2;53;7,3;1,2) 73.33% 67.22% GNARX(3;53;8,4,1;1,2,1) 75.00% 68.89% GNARX with SPRSD 75.00% 70.19% * is the maximum KNN classification accuracy of different k values (k = 3, 5, 7, …, 19). ** is the mean KNN classification accuracy of different k values (k = 3, 5, 7, …, 19).

Table 2. The KNN classification accuracy of different models applied to the bottom data of undamaged and damaged steel plate Model AR(8) GNAR(2;6,2) GNAR(3;8,1,1) ARX(10,3,53) GNARX(2;53;10,2;2,2) GNARX(3;53;10,2,0;5,2,1) GNARX with SPRSD

Maximum accuracy* 73.33% 68.33% 75.00% 70.00% 85.00% 93.33% 95.00%

Mean accuracy** 68.89% 62.59% 71.67% 67.04% 83.15% 86.11% 91.48%

5 Conclusions The expression of GNARX model is deduced. On the basis of the structure characteristics of GNARX model, a novel approach of parameter estimation (MMDLS) and structure identification (SPRSD) for GNARX model is proposed. With the experimental data, structural damage of elevator steel plate is detected by time series models, among which the effect of GNARX model is obviously better than those of other models. In this paper, simple structure steel plates’ damage detection is used for research. For elevator car, multiple GNARX models can be developed for subsections of elevator car. Then, parameter matrix can be obtained, of which the change state can reflect the structural damage’s the location and extent of elevator car. However, this needs further study.

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References 1. Ghiasi, R., Fathnejat, H., Torkzadeh, P.: A three-stage damage detection method for largescale space structures using forward substructuring approach and enhanced bat optimization algorithm. Eng. Comput. 35, 1–18 (2019) 2. Janapati, V., Kopsaftopoulos, F., Li, F., et al.: Damage detection sensitivity characterization of acousto-ultrasound-based structural health monitoring techniques. Struct. Health Monit. 15(2), 143–161 (2016) 3. Souridi, P., Chrysafi, A.P., Athanasopoulos, N., et al.: Simple digital image processing applied to thermographic data for the detection of cracks via eddy current thermography. Infrared Phys. Technol. 98, 174–186 (2019) 4. Tabatabaeipour, M., Hettler, J., Delrue, S., et al.: Non-destructive ultrasonic examination of root defects in friction stir welded butt-joints. NDT E Int. 80, 23–34 (2016) 5. Santos, A., Santos, R., Silva, M., et al.: A global expectation–maximization approach based on memetic algorithm for vibration-based structural damage detection. IEEE Trans. Instrum. Meas. 66(4), 661–670 (2017) 6. Loh, C.H., Chan, C.K., Chen, S.F., et al.: Vibration-based damage assessment of steel structure using global and local response measurements. Earthq. Eng. Struct. Dyn. 45(5), 699–718 (2016) 7. Vahidi, M., Vahdani, S., Rahimian, M., et al.: Evolutionary-base finite element model updating and damage detection using modal testing results. Struct. Eng. Mech. 70(3), 339– 350 (2019) 8. Vamvoudakis-Stefanou, K.J., Sakellariou, J.S., Fassois, S.D.: Vibration-based damage detection for a population of nominally identical structures: Unsupervised Multiple Model (MM) statistical time series type methods. Mech. Syst. Signal Process. 111, 149–171 (2018) 9. Ma, J., Xu, F., Huang, K., et al.: Improvement on the linear and nonlinear auto-regressive model for predicting the NOx emission of diesel engine. Neurocomputing 207, 150–164 (2016) 10. Huang, R., Xu, F., Chen, R.: General expression for linear and nonlinear time series models. Front. Mech. Eng. China 4(1), 15–24 (2009)

Production Management

The Innovative Development and Application of New Energy Vehicles Industry from the Perspective of Game Theory Jianhua Wang1,2(&) and Junwei Ma2(&) 1

2

Evergrande School of Management, Wuhan University of Science and Technology, Hongshan District, Wuhan, China [email protected] School of Economics and Management, Changshu Institute of Technology, No. 99, 3rd South Ring Road, Changshu, China [email protected]

Abstract. Since the advent of new energy vehicles, it has attracted much attention from many parties, but the market performance has not been very competitive. Therefore, in order to make more consumers accept new energy vehicles, so as to improve the state energy consumption structure, how to effectively promote new energy vehicles has become an urgent problem to be solved by the government. This paper believes that the marketing of new energy vehicles is a typical game process. By constructing the game model among the supply and demand side (automobile manufacturer and consumer), the supply side (the automobile manufacturer and competitor), the government and the enterprise (government and enterprise), the game optimal solution of the participants of the new energy vehicle market is analyzed. Finally, this paper proposes that consumers should actively improve their product needs, the auto manufacturers should focus on improving the profit of the products, and the government should use different support policies at different stages. Keywords: New energy vehicles promotion
Game theory


Innovative development
Market

1 Introduction Under the impact of the 2008 world financial crisis, oil prices rose, the global car industry sales fell, and auto makers began to reflect on the car structure and production technology upgrading. At the same time, because of the enhancement of public crisis awareness of the traditional energy exhaustion, more and more pollution to the modern society such as haze, acid rain and so on, the voice of the ecological environment protection and sustainable development is becoming intense. Under the multifaceted situation, energy conservation and new energy vehicles have developed rapidly under the intensive technology innovation support led by government and began to gradually enter the public view. China is the world’s fastest-growing auto market, with more than 23.6 million vehicles sold in 2016. By 2020, China is projected to have around 300 million automobiles, which would surpass the current U.S. fleet of 265 million. Some © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 535–544, 2020. https://doi.org/10.1007/978-981-15-2341-0_67

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consumers are willing to consume for environmental protection because of the deterioration of their living environment, but many other consumers also have many concerns about new energy vehicles, such as price performance, safety and practicality. As a result, all car manufacturers have intensified their technological research and joined the market competition successively. In addition, there are different demands between automobile manufacturers and the government. Car manufacturers pursue profit maximization because it needs a lot of cost investment for car manufacturers to shift from traditional energy vehicles to new energy vehicles. The government hopes to bring more social value and implement the concept of sustainable development, so it need to publicize and advocate new energy vehicles and guide market demand. However, because of the high cost of new energy vehicles, the market price has remained high. Due to the price, technology and supporting facilities, consumers who are willing to buy the new energy vehicles are still small even if the government has introduced the relevant preferential subsidy policies. As a product that breaks through the energy restriction, the new energy vehicle is conducive to changing the energy consumption structure of the country, and its marketing promotion is particularly important.

2 Literature Review The game theory was founded in 1944 by John von Neumann and Oskar Morgenstern. In the later study, foreign scholars (Harsanyi [1], Nash, Selen) continued to enrich the theoretical framework and contributed to the important achievements of the transformation of the sea and the Nash equilibrium and so on. In the study of new energy vehicles marketing promotion, the foreign scholar Scott believes that the government’s subsidy policy has a positive effect on the enterprise, that is, the government subsidy policy has a stimulating effect on the R & D investment of the enterprise [2]. Chinese scholars generally believe that the development of new energy vehicles is in line with the national conditions of our country. It can deal with the impact of the international financial crisis while solving the energy environment problems. At the same time, it is the important breakthrough point of the industrial upgrading and the establishment of strategic new industries [3]. Shen [4], Zhang [5], Xu [6], Luo [7] and so on, respectively from the product, publicity and marketing strategy, marketing mode and policy support, pointed out the current problems in the development of new energy vehicles in China, and put forward solutions and suggestions. With the background of the development of new energy vehicles in Japan, Jin [8] put forward the optimization strategy for the promotion of the new energy vehicle in China by studying the specific practices and the factors that restrict the development in new energy vehicle promotion in Japan. In combination study of market promotion and game theory, Wang and Miu had obtained a more reasonable government subsidy mode through the game model between government subsidies and enterprises [9]. Wang and Wang used the game evolution model to study the income matrix of the government and related enterprises in the process of technology research and development of new energy vehicles and put forward a proposal to promote the technology development of new energy vehicle by analysing the optimal solution [10].

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It can be seen that many scholars have proposed solutions to the promotion of new energy vehicles from the aspects of products, prices, channels and promotions. On the basis of this, this paper tries to establish a game model for the promotion of new energy vehicle. From three perspectives of supply-demand side, supply side and government enterprise, this paper analyzed the strategic combination of game players and sought equilibrium strategy, so as to provide countermeasures and suggestions for the market promotion of new energy vehicle.

3 Case Analysis and Propositions New energy vehicles, as new products of technological innovation, have received favorable reviews from all walks of life but its market performance is not good. How to effectively promote its market performance has become the focus of attention of all participants. In the process of market promotion of new energy vehicles, there are three main roles: supplier side, demand side and government. They held different positions and sought different benefits in the development of new energy vehicles. 3.1

Model Assumptions and Definitions

Since there are three main participants in the game of the new energy vehicle market promotion, in order to make a more detailed game analysis, this paper will build the model from three aspects and do the following basic assumptions. Hypothesis 1: in the game model of supply-demand side and demand side, automobile manufacturers and automobile consumers are the main participants. Government policy is considered to be the influencing factor. In the game model of supply side, the automobile manufacturers and their competitors are the main participants, and the government policy is considered to be the influencing factor. In the game model of government and business, government and automobile manufacturers are the main participants. Hypothesis 2: all the participants in the market promotion game of new energy vehicles are rational. Automobile manufacturers aim at the best of their own interests, and consumers pursue their own purchase demand. The government takes healthy and orderly development of new energy vehicles as the utility maximization. Hypothesis 3: the demand for automobile products is limited, and the development of new energy vehicle market is at the initial stage, and the market share is relatively low. It is a fast response production mode with no inventory. Consumers’ consumption demand is elastic, which is driven by price fluctuations and not necessarily considers environmental benefits. Hypothesis 4: all participants in the market promotion game of new energy vehicle understand each other’s characteristics, the set of action strategies that can be chosen and the utility of them. Hypothesis 5: assuming that all the games are static, that is, the actions of all participants in the game take place at the same time. There is no first action and a post action.

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3.2

Model Analysis and Recommendations

3.2.1

Game Model Analysis of Supply Side and Demand Side in New Energy Vehicle Market Under the influence of government policies, this paper constructs a game model of supply and demand for manufacturers and consumers in new energy vehicle market, and makes the following definitions. The utility of a consumer who bought a car is UC, and a car manufacturer promotes a new energy car with a profit of Un; a car manufacturer promotes a traditional car with a profit of U0; the cost of a car manufacturer which promotes a new energy vehicle to the market is Cn; the cost of a car manufacturer which promotes a traditional vehicle to the market is Co; The policy subsidy for consumers who buy new energy vehicles is Sc and the policy subsidy for new energy vehicle manufacturers is Sm. According to the basic assumption, under the influence of government policies the game model of new energy vehicle manufacturers and consumers is shown in Table 1. Table 1. Payoff matrix of vehicle manufacturers and customers Consumers

Manufacturers Promotion of new energy vehicles Promotion of traditional vehicles (UC, U0) Consumption (Sc+UC, Sm+Un) No consumption (0, -Cn) (0, -Co)

Based on the consumer’s perspective, consumers have multiple decision choices when considering automobile products, and. The income obtained is Sc + UC or UC when they choose to consume. At this time, under the influence of government policy, the income Sc + UC of buying new energy vehicles is obviously greater than the benefit UC from the purchase of traditional vehicles. Consumers can also choose not to spend money on vehicles with earning zero. Based on manufacturer’s point of view, the automobile manufacturer also has multiple decision choices in car market. Its utility is Sm + Un or -Cn when manufacturer choose to promote the new energy vehicle. The utility is not necessarily positive. This depends on consumer decision. If the consumer’s dominant strategy is to consume, the manufacturer’ utility is Sm + Un. If the consumer chooses not to consume, the manufacturer needs to bear a certain cost which is -Cn. Owing to no inventory factors in previous assumption, so here the cost consists of R & D, technology, patents and other aspects. Automobile manufacturers can also choose to popularize traditional vehicles, and their utility is Uo or -Co. In the same way, the utility is not necessarily positive, and it depends on consumer decision. If the consumer’s dominant strategy is consumption, the utility Uo is obviously greater than -Co. If the consumer chooses not to consume, the manufacturer needs to bear a certain cost which is -Co. Automobile manufacturers and consumers, who are both rational, are certain to consider the best decision of the other party when making their best action.

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To sum up, the utility is assigned and compared in order to find out the optimal solutions under different circumstances in the game in new energy vehicle. Because of the influence of government policy, it can be easily found out that Sc + UC is greater than UC or 0, so that only when consumers have vehicles consumption demand each other’s best decisions are considered at this time. 1. If Sc + UC is greater than UC and Sm + Un is greater than Uo, the equilibrium solution is (consumption, popularizing new energy vehicles). 2. If Sc + UC is larger than UC and Sm + Un is less than Uo, the equilibrium solution is (consumption, popularizing traditional cars). Conclusion: when the profits of new energy vehicles are greater than those of traditional vehicles, the cost factors of new energy vehicles and consumers’ demand for new energy vehicles have little effect on the optimal results. under these circumstances, vehicles manufacturers are more inclined to promote new energy vehicles. As new energy vehicles have certain environmental benefits, consumers will be more willing to buy new energy vehicles when their environmental awareness is enhanced. 3.2.2

Game Model Analysis of Supply Side in New Energy Vehicle Market In the case of government policy intervention, this paper constructs game model of the automobile manufacturers and their competitors in new energy vehicle market, and makes the following definitions: The profit of vehicle manufacturers to promote new energy vehicles is Un According to the basic assumption, the game models of new energy vehicle manufacturers and their competitors under the influence of government policies are shown in Table 2. Table 2. Payoff matrix of vehicle manufacturers and their competitors Competitors

Promotion of new energy vehicles Promotion of traditional vehicles

Manufacturers Promotion of new energy vehicles (Un, Un)

Promotion of traditional vehicles (Un, 0)

(0, Un)

(0, 0)

Based on the competitors’ point of view, the competitors have two options. They can choose to promote new energy vehicles and obtain the profit Un, or they can choose to promote the traditional vehicles and obtain the profit 0. The profit 0 here refers to no enjoyment of government’s policies welfare in promoting traditional cars, which is no more utility than conventional gains.

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Based on manufacturers’ point of view, the manufacturers also have two options when choosing the product. They can choose to promote new energy vehicles and obtain the profit Un, or they can choose to promote the traditional vehicles and obtain the profit zero. As rational-economic man, the manufacturers and their competitors will consider the best decision of the other party when making their best action. In summary, because Un is greater than zero, the equilibrium solution is to (promote new energy vehicles, promote new energy vehicles). Conclusion: when the profit of the new energy vehicle industry is higher than that of the traditional vehicle, the manufacturer and the competitor will both choose to promote new energy vehicle. However, as new energy vehicles are new products, it is faced with a great challenge when entering the market. In absence of knowing each other’s action, as a rational person, the manufacturer will choose to promote the traditional vehicles in order to avoid risks. 3.2.3

Game Model Analysis Between Government and Enterprise in New Energy Vehicle Market In the case that the revenue of the government to maintain the order of the automobile market is greater than the maintenance cost, this paper constructs the game model between the automobile manufacturer and the government in the new energy vehicle market, and makes the following definitions: The profit of vehicle manufacturer to promote new energy vehicles is Un. Government gains Ug from the order maintenance of the vehicle market. The cost of vehicle manufacturers to promote new energy vehicles is Cn. Government takes Cg for the order maintenance of the vehicle market. The profit of vehicle manufacturer to promote traditional vehicles is Uo. When the government maintains the market order, the cost of vehicle manufacturer promoting traditional vehicles is Co. According to the basic assumption, the game models between new energy vehicle manufacturers and government are shown in Table 3.

Table 3. Payoff matrix of vehicle manufacturers and government Government

Market maintenance No market maintenance

Manufacturers Promotion of new energy vehicles (Ug-Cg, Un-Cn) (Un, Un-Cn)

Promotion of traditional vehicles (Uo-Ug, -C0) (0, 0)

From the perspective of government, government has two choices in this game. First, it can choose to maintain the order of vehicle market to ensure the healthy and orderly development. At this time, the revenue of the government is Ug-Cg or Uo-Ug. The specific revenue needs to be determined according to the dominant strategy of the other side in the game.

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Government can also choose not to maintain the order of the vehicle market in this competition and let it develop naturally. At that time, the government’s revenue is Un or 0. Un refers to the benefits brought by the increasing market share of new energy vehicles in vehicle market. 0 means that there will be no more revenue and more cost when manufacturers promote traditional vehicles under the circumstance of no government’s order maintain in vehicle market. Based on vehicle manufacturer’s point of view, vehicle manufacturer still has two choices when choosing the vehicle. It can choose to promote the new energy vehicle, whose utility is Un-Cn. The utility is not necessarily positive, which needs to be determined according to the government’s decision. If the government’s dominant decision is to maintain the vehicle market, the earning of the vehicle manufacturer is Un-Cn. And if government does not maintain the market, the earning of the vehicle manufacturer to promote new energy vehicles is still Un-Cn. Vehicle manufacturer can also choose to promote traditional vehicle, and the profit is -C0 or zero. In the same way, the specific utility needs to be determined by the government’s dominant decision. If government chooses to maintain market order, the vehicle manufacturer needs to face a certain penalty -Co. If government does not maintain market order, the vehicle manufacturer which promotes traditional vehicle gains zero and the zero here means that vehicle manufacturer does not need to bear the payment used to maintain the market and has no more utility. As rational economic man, the vehicle manufacturer and government must consider the best decision of the other party first when they making the best action. To sum up, we compare the utilities in order to find out the optimal solution for the new energy vehicle promotion under different circumstances. 3. If Un-Cn is greater than zero and Uo-Ug is less than zero, the equilibrium solution is (not to maintain the market, to promote new energy vehicles). 4. If Un-Cn and Uo-Ug are both greater than zero, the equilibrium solution is (not to maintain the market, to promote new energy vehicles). 5. If Un-Cn is less than- Co and Ug is greater than Uo, the equilibrium solution is (not to maintain the market, to promote traditional vehicles). 6. If Un-Cn is less than - Co and Ug is less than Uo, the equilibrium solution is to (to maintain the market, to promote new energy vehicles). 7. If Cois less than Un-Cn and less than zero, and Ug is greater than Uo, the equilibrium solution is (not to maintain the market, to promote traditional vehicle). 8. If Co is less than zero and less than Un-Cn and Ug is less than Uo, this situation is special. When the decision of vehicle manufacturer is to promote new energy vehicles, the optimal decision of government is not to maintain market order. When the decision of vehicle manufacturers is to promote traditional vehicles, government’s best decision is to maintain market order. When the government’s decision is to maintain market order, the best decision of vehicle manufacturer is to promote new energy vehicles. When the government’s decision is not to maintain market order, the best decision of vehicle manufacturer is to popularize traditional vehicles. Since it is a static game, there is neither Nash equilibrium and nor optimal solution. Conclusion: when the profits of new energy vehicles are not as high as those of traditional vehicles, the vehicle manufacturers are more willing to focus their

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production on the original products. But government has different demands compared to vehicle manufacturers. Government hopes that the market consumption can be selfadjusted and upgraded. Both vehicle manufacturers and customers can turn to new energy vehicles, which can be realized when the profits of new energy vehicles are better than traditional vehicles, but in the current market, it needs to cut down the cost further in order to make sure that profits of new energy vehicles are as good as these of traditional vehicle. Therefore, government should maintain order in the vehicle market so as to ensure the smooth promotion of new energy vehicles.

4 Conclusions As a whole, new energy vehicles are not mature enough and have not been widely accepted by consumers. The profit of promoting the new energy vehicles may not be able to achieve the profit from the promotion of traditional vehicles. As a rational economic man, vehicle manufacturer will not take the social responsibility and promote new energy vehicles on his own for the sake of environment. The market demand of new energy vehicles is unknown, and no one can undertake the loss which manufacturers will face in promoting new energy vehicles. At this time, government’s intervention is needed to guide the healthy and orderly development of the new energy vehicle industry. Firstly, we should make macro control means play a role in the development of new energy vehicle market, and let the policy be effectively landed. Secondly, government needs to guide consumer demand, so that vehicle consumers can accept and purchase new energy vehicles. First, consumers should pay close attention to the good policy of the new energy vehicle market. They should seize the opportunity to enjoy the welfare subsidies. At the same time, whether they need convenience, safety or other attributes, they should clear their own demand for the product itself, and must communicate effectively with vehicle manufacturers through a reasonable path. To ensure that the manufactured car products can meet their own needs. In addition, consumers should improve the awareness of environment, and should not let the vehicle manufacturers do these things alone. When the supply and demand are turned to new energy vehicles, market development of new energy vehicles can enter a virtuous cycle and jointly improve the energy consumption structure. Second, when promoting new energy vehicles, vehicle manufacturers should further reduce the production cost and channel management cost. Manufacturers should increase the investment of technology, break through the limitation of poor battery endurance, make the battery endurance of new energy vehicles greatly improved, and reduce the production cost of new energy vehicles by mass production or cooperation with other manufacturers. Vehicle manufacturers must catch hold of Internet plus era. Production or distribution should be flat as possible in order to respond quickly, save marketing costs and enhance overall competitiveness. Except for product quality, the service cannot be ignored. As a new product, the new energy vehicle has a certain resistance to it. It needs to do a good job of after-sales service for new energy vehicles, and to invest in the construction of the supporting facilities in the area covered by the market including increasing the input of charging pile and equipment maintenance in

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order to eliminate consumer concerns. Try to avoid competing with competitors in the price war, which is not helpful to the healthy and long-term development of new energy vehicle industry. Manufacturers should set up their own brand with correct positioning and pay close attention to the consumer’s product demand in order to meet consumers’ need while get the product upgrade and development. Third, the government should play a different role in the different stages in promoting new energy vehicle. At the beginning, government should guide the consumption by propaganda and education of consumption consciousness in the field of new energy, and introduce the subsidy policy of vehicle purchasing in order to reduce the consumer’s using cost. Government can also adjust the fuel tax policy to raise the cost of using traditional vehicles. At the same time, through the technology subsidy policy, the automobile manufacturers should be encouraged to carry out technological innovation, cultivate professional talents, strengthen the protection of intellectual property rights, and establish a national technical standard system based on the national conditions of China, so as to meet the needs of new energy vehicles production. Meanwhile, we should maintain the market order of new energy vehicles, and resolutely avoid some bad manufacturers’ cheating on tax fraud, and try not to let “bad money drives out good money” appear. In the middle period of the development of new energy vehicle market, government should focus on the construction of service projects which large manufacturers can’t accomplish by themselves, such as public charging piles, maintenance facilities, and purchase new energy vehicles in the public transport field. Through various efforts, government assists manufacturers and other forces to promote the market promotion of new energy vehicles. Acknowledgement. This research was financially sponsored by fund of six talents peak in Jiangsu province (Grant No. JY-001), Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (Grant NO. 2017ZDIXM004). We would like to thank anonymous references for their insightful comments and suggestions which lead to the significant improvement and better presentation of the paper.

References 1. Harsanyi, J.C.: Games with incomplete information played by “Bayesian” players, the basic model. Manag. Sci. 14(3), 159–182 (1997) 2. Scott, J.T.: Firm versus industry variability in R&D intensity. NBER Chapters, pp. 233–248 (1984) 3. Sun, L.W.: Development status and countermeasures of new energy vehicles in China. China Sci. Technol. Inf. 7(7), 135 (2012) 4. Shen, L.: Introduction strategy of new energy vehicle market. Shanghai Mot. 1(1), 37–40 (2009) 5. Zhang, F., Bao, X.J.: Problems and countermeasures of new energy vehicle market promotion in China. Price Theory Pract. 5(5), 85–86 (2011) 6. Xu, C.X.: New energy vehicles need a new marketing model. China’s Strat. Emerg. Ind. 14(21), 54–55 (2014) 7. Luo, J.: Research on marketing strategy in new energy vehicle. J. Qigihar Inst. Eng. 5(2), 52–54 (2011)

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8. Jin, Y.H.: Reference to Japan’s marketing strategy in new energy vehicle for China. Northeast Asia Forum 21(3), 105–112 (2012) 9. Wang, H.X., Miao, X.M.: Game research on R&D subsidies of new energy vehicles in China. Soft Sci. 27(6), 29–32 (2013) 10. Wang, R., Wang, Z.L.: Evolutionary game analysis of R&D process of new energy vehicle. J. Daqing Norm. Univ. 36(3), 61–66 (2016)

Survey and Planning of High-Payload Human-Robot Collaboration: Multi-modal Communication Based on Sensor Fusion Gabor Sziebig(&) Department of Production Technology, SINTEF Manufacturing, Trondheim, Norway [email protected]

Abstract. Human-Robot Collaboration (HRC) has gained increased attention with the widespread commissioning and usage of collaborative robots. However, recent studies show that the fenceless collaborative robots are not as harmless as they look like. In addition, collaborative robots usually have a very limited payload (up to 12 kg), which is not satisfactory for most of the industrial applications. To use high-payload industrial robots in HRC, today’s safety systems has only one option, limiting speeds of robot motion execution and redundant systems for supervision of forces. The reduction of execution speed, reduces efficiency, which limits more widespread of automation. To overcome this limitation, in this paper, we propose novel sensor fusion of different safety related sensors and combine these in a way that they ensure safety, while the human operator can focus on task execution and communicate with the system in a natural way. Different communication channels are explored (multi-modal) and demonstration scenarios are presented. Keywords: Human-Robot collaboration communication
Sensor fusion


Industrial robot
Multi-modal

1 Introduction Automation is a tool to increase productivity, while decreasing the amount of human involvement in production. Humans, after introduction of automation are either used solely in hard to automate processes or very high-skilled processes. Interfaces toward automated industrial equipment (like an industrial robot) is usually a screen or a keyboard, where the interaction is regulated and feels unnatural. The only way how we can build trust and connection with industrial robots is a way, where we think about them as co-workers and can communicate with them in a natural way without buttons, script language, etc. In order to increase acceptance among co-workers, the communication channels need to become fenceless. As soon as a co-worker sees a machine behind fences (which is typical today in case of industrial robots) associates danger with the given machine and results in resistance toward acceptance. In this paper first the state-of-the-art results (Sect. 2) from the perspective of Human-Robot Collaboration (HRC), cognition in HRC and safety in HRC will be © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 545–551, 2020. https://doi.org/10.1007/978-981-15-2341-0_68

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presented. This will be followed by proposal of different cooperation scenarios (Sect. 3) and discussion of such scenarios (Sect. 4). Concluding remarks are Sect. 5.

2 Related Research One of the main concerns in the realization of Human-Robot Collaboration systems is the safety of the human. Robots used for automation in production are usually industrial robotic arms (i.e. rigid) with load capacity ranging from 1 to 1000 kg. Lightweight robots grant human safety through the use of a power and force limitation approach. For lightweight robots with a load capacity of about 20 kg, various tactile sensors have been developed which ensure controlled collision with humans. Tactile sensors which have been proposed for the development of “Robot Skin” and Robot cell flooring [1, 2] ensure detection of stationary and moving objects. Robots with higher load capacity are dangerous for humans if left functional at full speed in their presence [3] since power and force limitation cannot be applied. In such a context, industrial practice establishes the robot working region, enclosing it by steel barriers or with certified safety sensors. These barriers or doors, once opened, result in complete halt in the routine, avoiding the equipment of robots with perception system to make them aware of their surroundings. Eventually, various technologies and safety regulations were developed to overcome these limitations. Technologies ranging from tactile sensors to 3D sensors [4] have been developed as well as a set of safety norms for collaborative tasks in robotic cell. Various modes for collaboration with humans have also been developed which invokes different levels of safety and security levels. Once the robot is set in initial levels of collaboration where it will only share the workspace with the human, it reduces its speed as the human comes near and completely stops once the human can collide with the robot (speed and separation monitoring) [5]. In general, most of the vision algorithms and the proposed safety functions [6, 7] have taken safety procedures with respect to a specific task into account without considering the variance of the required shared tasks between human and robot. Useful information that a human can convey to a robot are, e.g., gestures [8, 9], verbal commands and physical interaction with digital devices such as buttons and touch screens. The computer vision community has made impressive recent developments concerning detection and recognition of human actions and behaviour (see e.g. [10–14]). Speech recognition and the interpretation of commands for devices is also an up and coming development that moves towards the consumer market (e.g. Amazon Alexa and Google Home). Physical interaction is the current standard in the interaction with robotic equipment. Buttons and interfaces, such as smart-phones, tablets and other touchscreens, are well developed and used abundantly. However, these come with the drawback of introducing an intrusive device in the working area of the human. This also holds for current state of the art augmented or virtual reality glasses (Microsoft HoloLens, Meta 2 AR headset). While most of these devices are meant for the consumer market and research is mostly demonstrated in non-industrial environments, slow integration towards industry is expected.

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When robots are used in unstructured environments, it is necessary to combine multiple sensory input to satisfy safety levels for human-robot collaboration. In addition, sensor fusion is needed to serve for path planning, force control and fault diagnostics in these cases. The choice of the appropriate sensor(s) is depending on the task and the given environment, where the human-robot collaboration is taking place. In the specific case for unstructured environment, where also humans are in the working space of the robots, range and proximity sensors are the most important. In addition: touch, vision or sound sensors could also be needed. More details could be found in [15].

3 Scenarios In this section, after a short introduction of the proposed architecture, scenarios will be described, which will highlight the novelty of sensor-fusion based Human-Robot Collaboration. System Overview The Intelligent Factory Space (IFS) concept represents a framework for interaction between a human and an automated system (e.g. industrial robot, mobile robot or CNC machine). Multiple layers build up the IFS, which are representing services for the humans in a modular way. The IFS concept was previously developed by the author [16] and here is only a very short overview is given, in order to give an understanding for the following scenarios and cases. The IFS architecture is composed from three layers, as illustrated in Fig. 1. The layers are ordered in a hierarchical manner, mirroring the necessary autonomy and requirements for the given layer. The combination of these layers forms the Intelligent Factory Space. The IFS layers offer specific services, in order to increase comfort and collaboration for the human operators, interacting with the automated machines. To establish connection between the IFS and humans, a physical interface is proposed, which can also be seen in Fig. 2. This physical interface is called POLE and placed in the lowest layer (single element). The available functions and services are also shown in Fig. 2. Overall Scenario The industrial robot is loading/unloading goods from pallet and places the heavy boxes to a transport conveyor. The operator is delivering the pallets with using a jack. The emptied pallets are also removed by the operator. An overview of the scenario can be seen in Fig. 3. In a standby case, the operator is outside the theoretical work-zone of the industrial robot and the Pole system projects a green circle around the work-zone of the industrial robot, signaling that everything is fine. Cases The operator enters the work-zone of the industrial robot in order to carry out its work. As soon as the operator enters the work-zone the Pole system detects and notifies the worker, that he/she is recognized with projecting a green circle around the worker, as seen in Fig. 4.

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Fig. 1. General architecture of the IFS

Fig. 2. Functions and services of POLE

The closer the worker goes to the industrial robot; the circle’s colour will turn toward red as a warning for the operator, that the situation is not comfortable for the human neither for the industrial robot, see Fig. 5. The Factory management cloud learns the standard behaviour of a worker for the typical execution of the tasks that are happening in the safety critical zones. If there is a difference, either from worker or from robot side, the Pole system can warn the operator and take countermeasure actions on robot task execution also in order to prevent any unwanted event. The Pole system adapts to the task’s natural execution and limits or modifies the industrial robot’s path for maximum safety. As the Pole system is designed to provide two-way communication and behaviour learning we can also detect if a worker begins to be tired and can adapt even to the capabilities/mood of the given worker who will interact with the industrial robot.

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Fig. 3. Scenario overview

Fig. 4. Worker detected in work-zone of industrial robot

If there was any additional equipment used to carry out the task and for some reason this remains in the work-zone of the industrial robot, the equipment is highlighted similar way a human being and the operator is warned about the situation.

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Fig. 5. Operator is in close proximity of the industrial robot, red circle is signalling for unnecessary proximity to the industrial robot

4 Discussion State-of-the-art in communication between human and a robot is typically limited to displaying information on a screen for industrial settings and speech for research settings. Industrial environments are noisy which limits the possibilities for more intuitive communication. In order to overcome this challenge, a multi-modal communication solution is proposed. Such solution can help representing information for different senses, which was originally assigned to. For multi-modal communication, sensory data fusion is necessary. This ensures that the environment, the robot is placed, will be monitored continuously and feedback from the robot can be communicated back to the human in a natural way. When the human could actually “touch” the robot, this will be even more crucial. To select the appropriate control parameters for such cooperation, this could be done in agreement with the human. Also, this would allow the robot and human sharing the responsibility when sharing the workspace. The Intelligent Factory Space is such an environment, where human-robot collaboration combined with multi-modal communication could be achieved.

5 Conclusion In this paper Human-Robot Collaboration scenarios has been introduced. The scenarios are based on the Intelligent Factory Space concept and describes the use of the IFS. It can stated that such scenarios are not possible in today’s safety standards, especially when we would like to use high-payload industrial robots and not so-called collaborative robots. Today’s system are “stupid” proof and need changing, with the scenarios

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detailed here, this could be the first toward fair responsibility sharing between human co-worker and industrial robot. Acknowledgements. The work reported in this paper was supported by the centre for research based innovation SFI Manufacturing in Norway, and is partially funded by the Research Council of Norway under contract number 237900.

References 1. Youssefi, S., Denei, S., Mastrogiovanni, F.: A real-time data acquisition and processing framework for large-scale robot skin. Robot. Auton. Syst. 68, 86–103 (2015) 2. IFF: Tactile sensor system, 15 February 2017. http://www.iff.fraunhofer.de/content/dam/iff/ en/documents/publications/tactile-sensor-systems-fraunhofer-iff.pdf 3. Haddadin, S.: Injury evaluation of human-robot impacts. In: IEEE International Conference on Robotics and Automation ICRA 2008 (2008) 4. https://www.pilz.com/en-INT/eshop/00106002207042/SafetyEYE-Safe-camera-system 5. Szabo, S., Shackleford, W., Norcross, R., Marvel, J.: A testbed for evaluation of speed and separation monitoring in a human robot collaborative environment. NIST Interagency/Internal Report (NISTIR) – 7851 (2012) 6. Saenz, J., Vogel, C., Penzlin, F., Elkmann, N.: Safeguarding collaborative mobile manipulators - evaluation of the VALERI workspace monitoring system. Procedia Manuf. 11, 47–54 (2017) 7. Baranyi, P., Solvang, B., Hashimoto, H., Korondi, P.: 3D Internet for cognitive infocommunication. In: 10th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2009, pp. 229–243 (2009) 8. Gleeson, B., MacLean, K., Haddadi, A., Croft, E., Alcazar, J.: Gestures for industry Intuitive human-robot communication from human observation. In: 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Tokyo, pp. 349–356 (2013) 9. Liu, H., Wang, L.: Gesture recognition for human-robot collaboration: a review. Int. J. Ind. Ergon. 68, 355–367 (2017) 10. Cao Z., Simon T., Wei S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR (2017) 11. Simon T., Joo H., Matthews, I., Sheikh, Y.: Hand keypoint detection in single images using multiview bootstrapping. In: CVPR (2017) 12. Vincze, D., Kovács, S., Gácsi, M., Korondi, P., Miklósi, A., Baranyi, P.: A novel application of the 3D VirCA environment: modeling a standard ethological test of dog-human interactions. Acta Polytech. Hung. 9(1), 107–120 (2012) 13. Herath, S., Harandi, M., Porikli, F.: Going deeper into action recognition: a survey. Image Vis. Comput. 60, 4–21 (2017) 14. Baranyi, P., Nagy, I., Korondi, B., Hashimoto, H.: General guiding model for mobile robots and its complexity reduced neuro-fuzzy approximation. In: Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No. 00CH37063), San Antonio, TX, USA, vol. 2, pp. 1029–1032 (2000) 15. Shu, B., Sziebig, G., Pieskä, S.: Human-robot collaboration: task sharing through virtual reality. In: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, pp. 6040–6044 (2018) 16. Reimann, J., Sziebig, G.: The intelligent factory space – a concept for observing, learning and communicating in the digitalized factory. IEEE Access 7, 70891–70900 (2019)

Research on Data Encapsulation Model for Memory Management Lixin Lu1, Weixing Zhao1, Guiqin Li1(&), and Peter Mitrouchev2 1

Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China [email protected] 2 University Grenoble Alpes, G-SCOP, 38031 Grenoble, France

Abstract. A data encapsulation model for memory management based on CAN-bus is proposed in this paper. The data encapsulation model includes the information about function, function attribute code, sampling frequency, time, sensor number, etc. It divides data packet into fixed size storage blocks to avoid the problem caused by data coverage when the functional code is same. Besides, the model is stored in SRAM to solve the problem of memory lifetime caused by Nor Flash. The memory management consists of memory pool and memory management table. Through memory management, each packet corresponds to a unique address. The efficient and fast allocation of memory resources is realized. Finely, a program flow for general real-time detection system’s data storage and reading is presented. The memory management method is universal, easy to expand, and has been successfully applied to the function detection device of massage chair. The utilization of memory is consistent with the actual situation. The data storage is more reliable and the retrieval is more convenient. Keywords: CAN-bus


Data storage
Detection

1 Introduction One of the important links in the modern industrial manufacturing process is detection. The detection of industrial product is the evaluation of the product itself and the results can be used as the basis for the improvement of the manufacturing process. The present problem, however, is that the host computer of the real-time detection system always keeps connection with the lower computer, to get the state of the lower computer and execute tasks, such as database reading, writing, etc. It is impossible to analyze the detection data of the lower computer in time. Both Peng and Zhang have developed a real-time detection system, but the data encapsulation model of each sensor are not packaged [1, 2]. Yang proposed a dynamic Scratch-pad memory management with data pipelining, which can effectively improve embedded systems’ performance [3]. Stilkerich, on the other hand, proposed a cooperative memory management method, but it is not suitable for the general industrial production environment [4]. Sun develops a fault detection device based on neural network model. It’s detection function is well classified [5].

© Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 552–559, 2020. https://doi.org/10.1007/978-981-15-2341-0_69

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Detection data is generally stored in Flash memory or RAM [6]. Flash memory can be erased and programed without removing the memory chip, which is a common way of data storage, but the number of erasure allowed per memory unit is limited. The Nor Flash can be used 100000 times. If the Flash is abnormal, the data written will go wrong. However, data refresh is frequent to a real-time detection system. In order to avoid the rapid loss of Flash, one method is to reduce the refresh frequency of each storage sector, thereby reducing the number of writes per unit and improving the utilization of Flash memory capacity [7]. Another method is to store the data in RAM when the storage requirement is small and there is no need for power-off storage. RAM is divided into dynamic RAM (DRAM) and static RAM (SRAM). DRAM uses capacitance storage and must be refreshed once every once in a while. SRAM is a kind of memory with static access. This paper introduces the method of memory management by storing data in SRAM. To solve the problem, this paper proposes a data storage model for memory management, which can realize the dynamic storage of data. The rest of this paper is organized as follows: Sect. 2 illustrates the data encapsulation model. Memory management method is presented in Sect. 3. Finally, the experiment and conclusions are given in Sects. 4 and 5.

2 Data Storage Modeling The detection data is required to be well packed. For embedded real-time detection system based on CAN-bus, the time and frequency of each sensor are variable and the data is stored in the lower computer in advance. When the data of a certain function is to be used, the master controller of the lower computer sends an instruction to the slave controller through the CAN-bus. The data packets of each sensor are retrieved from the controller in the function and function attribute code. Therefore, each packet contains the code of this function, the frequency and time of data acquisition and so on. The data encapsulation model for each detection packet is building. The form of encapsulation is shown in Table 1. The data start bit is 0xF0. The function and attributes are saved later. Bytes 4–6 are reserved bits. Byte 7 holds the number and number of detection sensors. If one or several of them is 1, the corresponding sensor value is stored, otherwise it is not stored. Time and sampling frequency of data are stored in bytes 8 and 9, respectively. The value of the sensor is then stored, and the end mark is 0xF1. The bytes of data stored for the function is as follows: ns number of sensors f the sampling frequency t the acquisition time A the number of bytes per sensor value (usually two bytes, that is, a = 2) N constant (the fixed length of function code, attribute and so on) Nd ¼ A
ns
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Number of bytes Address 1 0 1 1 1 2 3 3–5 1 6 1 7 1 8 N-8 9–N 1 N+1

The storage model is given in Fig. 1. The memory is divided into fixed-size blocks (such as 10 KB), according to the size of the packet. The detection data for each function takes up a block. The codes of the function and function attribute are stored in the fixed storage location. Each time a packet is retrieved, it is only required to add 10 KB to the previous position. In order to improve memory utilization, the storage block size should be similar to the maximum packet size.

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Fig. 1. Fixed-size storage model

In addition to the automatic detection mode, the detection system also includes the manual detection mode. When a function is detected manually, there may be a problem with the same functional code storing the data again. In this fixed-size blocks model, the following data will not be affected as a result of the different stored data length.

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3 Memory Management and Dynamic Storage The detection data is stored in SRAM, which is a kind of memory with static access function and can save the data without refreshing the circuit. Memory management is mainly used to manage the allocation of memory resources in the running process of MCU, to achieve rapid allocation and to recover memory in due course. The memory management consists of two parts: memory pool and memory management table. The memory pool is divided into n blocks, the corresponding memory management table size is also n. When the pointer calls the function to allocate memory, firstly, the number of the required memory blocks is calculated according to the required memory size. If the continuous m-blocks memory is not occupied (i.e. the value in the memory management table is 0), the memory management table memory corresponding to the continuous m-blocks memory is set. The memory of the segment is marked to be occupied (Fig. 2).

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Fig. 2. Memory management elements

The memory allocation method is shown in Fig. 3. When collecting and storing a function data, the lower computer first retrieves the function list according to the function and attribute code. If there is a same function, the new data over-writes it. Otherwise, computer will add a new column in the array. Then the required memory space is calculated. After the allocation is successful, the first address of the memory is stored in the corresponding column of the functional address array. And the functional code and related information are stored in the later memory. Function_list

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Based on the storage model and allocation method, the program flow of real-time detection system data storage and reading is shown in Fig. 4. After initializing the function list and data address table, the data storage or data reading is judged according to the instructions received by CAN-bus.

Initialization Function List

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Fig. 4. The program flow of data storage and reading

When data is stored, the instruction contains information such as function, attribute code, sampling frequency, time, sensor number, etc. Retrieving the list of function, the lower computer navigates to the first unused array line, and calculates the required

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storage space. Then the memory management table is traversed to allocate the required memory space, the storage function and attribute code are stored into the function array. The information related to the function is stored from the allocated memory header address. According to the acquisition frequency, the data is collected and filtered, and the filtered data is packaged and stored based on the data encapsulation model. When data is reading, the instruction contains only the function and attribute code that need to be read. The lower computer retrieves the list of functions until the function and attribute code match the instruction. Then it will read the description information in the packet and calculate the packet size. The packet is divided into 8 bytes of CAN data frames, loaded into can mailbox in turn. Finally the read state is fed back.

4 Application Example The data encapsulation model for memory management has been successfully applied to the function detection equipment of massage chair, as showed in Fig. 5. By sensing the pressure applied by the massage chair to the detection dummy, the pressure signal is collected. The performance of massage chair is judged by the amplitude and frequency of the signal.

Fig. 5. Massage chair detection system

In this device, the original massage chair feedback data received by the main control board is stored in the array. Figure 6(a) shows an abnormal frame which lacks end mark. In addition, there are other abnormal frames (not listed one by one). An algorithm for filtering invalid frames is proposed to solve this problem. The effective feedback frame is shown in Fig. 6(b). It can be seen that the byte length and value of the stored data are normal, and the number of bytes from F0 to F1 is the same as the number of bytes in the packet. The utilization of memory is consistent with the actual situation. The data storage is more reliable and the retrieval is more convenient.

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(a) Abnormal frame

(b) Normal frame Fig. 6. Massage chair detection system

5 Conclusions In this paper, the storage method for detection data in real-time detection system is studied. The reliable storage and retrieval of data can be realized by the encapsulation model. The data packet is stored in SRAM. On the one hand, it can solve the problem of storage life; On the other hand, because of the small memory, it is only suitable for small and medium-sized detection system. The memory management is convenient to store and read data, but it also wastes some resources, which is needed to further study. The experimental result gives a support of the correctness and the practicability of the data storage model. In a word, the data encapsulation model for memory management can be widely used in industrial measurement and control and other fields.

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References 1. Peng, D., Zhang, H., Weng, J., Li, H., Xia, F.: Research and design of embedded data acquisition and monitoring system based on PowerPC and CAN bus. In: Proceedings of the 8th World Congress on Intelligent Control and Automation, pp. 4147–4151. IEEE (2010) 2. Zhang, X., Zhang, J.: Design of embedded monitoring system for large-scale grain granary. In: 11th International Symposium on Computational Intelligence and Design, pp. 145–148. IEEE (2018) 3. Yang, Y.: Dynamic scratch-pad memory management with data pipelining for embedded systems. In: International Conference on Computational Science and Engineering, pp. 358– 365. IEEE (2009) 4. Stilkerich, I., Taffner, P., Erhardt, C.: Team up: cooperative memory management in embedded systems. In: International Conference on Compilers, Architecture and Synthesis for Embedded Systems. IEEE (2014) 5. Sun, L., Wang, D.: The development of fault detection system based on LabVIEW. In: 5th International Conference on Electrical and Electronics Engineering, pp. 157–161. IEEE (2018) 6. Zhang, H., Kang, W.: Design of the data acquisition system based on STM32. Procedia Comput. Sci. 17, 222–228 (2013) 7. Wei, P., Yue, L., Liu, Z., Xiang, X.: Flash memory management based on predicted data expiry-time in embedded real-time systems. In: ACM 2008 Symposium on Applied Computing, pp. 1477–1481 (2008)

Research on Task Scheduling Design of Multi-task System in Massage Chair Function Detection Lixin Lu1, Leibing Lv1, Guiqin Li1(&), and Peter Mitrouchev2(&) 1

Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China [email protected] 2 University Grenoble Alpes, G-SCOP, 38031 Grenoble, France [email protected]

Abstract. The lC/OS-II system has the advantages of interrupt service, nesting support, multi-tasking, etc., and has been successfully applied to the massage chair function detecting device. This paper proposes a massage chair function detection task scheduling design based on lC/OS-II real-time kernel by using the synchronization and communication between tasks rationally according to the task scheduling principle. It can transfer data and achieve massage chair function detection control. The semaphores are used to achieve the synchronization between related user tasks. The message mailbox mechanism and the whole process variable provided by lC/OS-II are used to realize the communication between related user tasks. We can use ECANTOOLS to send and receive data and read related feedback reports to verify whether the massage chair function detection logic task scheduling which we design can meet the requirements. Keywords: lC/OS-II
Task scheduling
Message mailbox mechanism
Task synchronization
Task communication
Massage chair function detection

1 Introduction In recent years, with the steady development of the economy, massage chairs as a new health care and daily necessities have become more and more popular in the market. Therefore, the efficient production and testing of massage chairs are particularly important. Hiyamizu et al. [1] proposed a massage chair function detection technology based on a human sensory sensor, but with certain uncertainty. Nowadays, the detection of the massage chair is carried out by lifting the humanoid inspection tool. Zoican et al. [2] proposed the application of task scheduling in embedded, Song [3] proposed lC/ OS-II based real-time operating system design and implementation and Quammen et al. [4] proposed more the application of the task system on the robot, there is a close relationship between the three. Task scheduling is an important part of the operating system. For real-time operating systems, task scheduling directly affects its real-time © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 560–566, 2020. https://doi.org/10.1007/978-981-15-2341-0_70

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performance, while in the embedded system, it can be processed simultaneously multiple tasks by using the operating system. In this paper, the task scheduling design based on lC/OS-II operating system is combined with the three. It can control the liftable humanoid inspection tool and realize the detection of the functions of the massage chair.

2 The Principle of Task Scheduling The massage chair function detection is based on the lC/OS system framework. The execution of the application depends on the scheduling of the user tasks by the lC/OSII real-time kernel. The task scheduling strategy is completely controlled by the application. If the application needs to perform task A at the next moment, task A must be made the highest priority task in the ready list.

3 The Logical Design of Task Scheduling for Massage Chair Function Detection One each user task of the application is independent, but in order to complete a certain task, it is necessary to maintain a certain relationship between multiple user tasks to form a whole. In the massage chair detection, the synchronization and communication between tasks are required. The completion of one task requires the execution result of another task, and this kind of constraint cooperation between tasks is called the synchronization between tasks. The message mechanism provided by lC/OS-II can be used to synchronize between tasks, as shown in Fig. 1, semaphores are used to synchronize task and ISR, task and task.

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Fig. 1. Achieve task synchronization by semaphores

As shown in Fig. 1, it uses a “key” flag to indicate the semaphore. This flag indicates the occurrence of an event. The semaphores used to synchronize the task need to be initialized to 0. This does not indicate a mutually exclusive relationship.

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The mailbox message mechanism provided by lC/OS-II sets the corresponding task list waiting for the message for each mailbox. The task waiting for the message will be suspended because the mailbox is empty until the mailbox receives the message or waits for the message mailbox to time out and will enter the ready state, as shown in Fig. 2, it can achieve the communication of ISR and task, task and task through the message mailbox, mailbox message waiting timeout can be set to infinite waiting according to design requirements [5].

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Fig. 2. Achieve task communication through message mailbox

In the function detection of the massage chair, the message mailbox mechanism provided by lC/OS-II and the whole process variable are used to realize the communication between related user tasks, as shown in Fig. 3, it is the scheduling design of the function of the massage chair function detection logic control based on the synchronization and communication between tasks. 11

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Fig. 3. Massage chair function detection control task scheduling logic based on the synchronization and communication between tasks

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(1) The user task Task_Detect_Process gets the CPU usage rights and is in the running state. (2) The user task Task_Detect_Process makes the UART send function to send a frame control instruction to the object under test, enables the UART to receive the interrupt, waits for the message mailbox Mbox_UART_fb to be empty, and defines the wait timeout. (3) The user task Task_Detect_Process is suspended because the mailbox is empty and enters the blocking state. At this point, the UART receives an interrupt trigger, causing the CPU enters the ISR. (4) The UART interrupt service stores the received data in bytes and counts it. (5) After the CPU completes the ISR, repeat steps (4) and (5) until the number of received data bytes reaches the limit. At this time, the UART receiving interrupt is closed, and the first array address *UART_Rev_Data which is stored in the UART data is placed in the mailbox. Mbox_UART_Rev. (6) The user task Task_UART_DataVer waits for the mailbox Mbox_UART_Rev event to occur, the task enters the ready state, and the task immediately enters the running state because it has the highest priority in the ready list. (7) The user task Task_UART_DataVer performs double redundancy check processing on the data pointed to by the pointer to obtain a frame of valid UART feedback information, and puts a pointer to the information into the mailbox Mbox_UART_fb. (8) The user task Task_Detect_Process waits for the message mailbox to be empty, and immediately enters the ready state, and the task is the task with the highest priority among the ready tasks, so the CPU usage right is obtained. (9) The user task Task_Detect_Process identifies and compares the UART feedback data stored at the address pointed to by the pointer. (10) If the UART feedback data is correct, the user task Task_Detect_Process makes the CAN1 send function to send a function execution instruction to the corresponding lower-level module of the function. (11) The function execution instruction is completed, the feedback function executes the result, triggers the CAN1 reception interrupt, and the CPU enters the ISR of CAN1. (12) The CPU executes the ISR of CAN1, receives the CAN message, and places a pointer to the message into the message mailbox Mbox_CAN1_Rev. (13) The user task Task_CAN1_fb_Handing enters the ready state by waiting for the mailbox event to occur, and becomes the ready state task with the highest priority and enters the running state. (14) The user task Task_CAN1_fb_Handing processes the CAN1 feedback and marks the result. Regardless of the outcome, the entire variable is assigned. (15) The user task Task_Detect_Process receives the global variable, ends the wait, and enters the ready state. Because the priority is highest in the ready list, it enters the running state. (16) The user task Task_Detect_Process continues to detect other functions. The massage chair massages the corresponding airbag on the liftable humanoid inspection tool. The airbags feedback massage function report. We can achieve one-by-

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one detection of the function of the massage chair by controlling the humanoid inspection tool and reading the feedback reports. In theory, this logic task scheduling can meet the requirements.

4 The Verification of Massage Chair Function Detection Logic Task Scheduling According to the real-time event channel implemented on the CAN bus proposed by Kaiser et al. [6], we perform short-frame test of bus communication, and send a MAC address request to each node module by means of the CAN analyzer. If the receiver feeds back its MAC address, then it indicates that the node has normal short frame communication on the bus.

Fig. 4. CAN bus short frame communication test

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As shown in Fig. 4, a MAC address request is sent to the 0#–8# node module, and the first byte of the feedback frame is the MAC address of the node. After testing, the contents of the eight feedback information are correct. After that, the ECANTOOLS detection tool sends the function execution instruction to the corresponding lower computer module according to the CAN protocol. For example, here we control the arms of the liftable humanoid inspection tool and the massage of massage chair arms. As shown in Fig. 5, the command is sent and the corresponding feedback is obtained, and the feedback data is correct, and the arms retract as shown in Fig. 6. What’s more, the massage function feedback reports of the arms are as shown in Table 1.

Fig. 5. CAN protocol test

Fig. 6. Arm retraction

Table 1. The feedback report of massage chair arms function Right arm airbag qualified

Gear position

Left arm airbag qualified

Gear position

Right arm front peak 615.25 Right arm front peak 1689.8

Peak in the right arm 736.8 Peak in the right arm 1539.2

Peak after the right arm 1034 Peak after the right arm 841.2

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It is known from the above that the task scheduling design of this detection system for massage chair function detection meets the requirements.

5 Conclusions In this paper, the task scheduling design of the detection system in the massage chair detection is studied. The task scheduling logic design of the massage chair function detection control is carried out to realize the maximum guarantee for the real-time performance of the system under the controllable process and make massage chair detection more efficient. This program can be widely used in industrial testing and other fields.

References 1. Hiyamizu, K., Fujiwara, Y., Genno, H., et al.: Development of human sensory sensor and application to massaging chairs. In: Proceedings of 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No. 03EX694), Kobe, Japan, vol. 1, pp. 140–144 (2003) 2. Zoican, S., Zoican, R., Galatchi, D.: Improved load balancing and scheduling performance in embedded systems with task migration. In: International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services, pp. 354–357. IEEE (2015) 3. Song, X., Chen, L.: The design and realization of vehicle real-time operating system based on UC/OS-II. In: 6th International Conference on Networked Computing, Gyeongju, Korea (South), pp. 1–4 (2010) 4. Quammen, D.J., Kountouris, V.G., Stephanou, H.E., Tabak, D.: Multitasking system for robotics source. In: Proceedings of the 1989 American Control Conference, 21–23 June 1989, pp. 2743–2748 (1989) 5. Labrosse, J.J.: Embedded Real-Time Operating System lC/OS-II. Beijing Aerospace University Press, Beijing (2003) 6. Kaiser, J., Brudna, C., Mitidieri, C.: Implementing real-time event channels on CAN-bus. In: Proceedings of IEEE International Workshop on Factory Communication Systems, Vienna, pp. 247–256 (2004)

A Stochastic Closed-Loop Supply Chain Network Optimization Problem Considering Flexible Network Capacity Hao Yu(&), Wei Deng Solvang, and Xu Sun Department of Industrial Engineering, Faculty of Engineering Science and Technology, UiT The Arctic University of Norway, Narvik, Norway {hao.yu,wei.d.solvang,xu.sun}@uit.no

Abstract. Nowadays, due to the concern of environmental challenges, global warming and climate change, companies across the globe have increasingly focused on the sustainable operations and management of their supply chains. Closed-loop supply chain (CLSC) is a new concept and practice, which combines both traditional forward supply chain and reverse logistics in order to simultaneously maximize the utilization of resource and minimize the generation of waste. In this paper, a stochastic CLSC network optimization problem with capacity flexibility is investigated. The proposed optimization model is able to appropriately handle the uncertainties from different sources, and the network configuration and decisions are adjusted by the capacity flexibility under different scenarios. The sample average approximation (SAA) method is used to solve the stochastic optimization problem. The model is validated by a numerical experiment and the result has revealed that the quality and consistency of the decision-making can be dramatically improved by modelling the capacity flexibility. Keywords: Closed-loop supply chain
Network design Stochastic optimization
Sample average approximation


Location problem


1 Introduction In today’s global market, the competition is not only between different individual enterprises but also largely between different supply chains. The effectiveness and efficiency in handling material flow, information flow and capital flow within a supply chain will determine the profitability and success of a company. Supply Chain Management (SCM) aims, through decision-makings at both strategic level and operational level, at properly managing different players and flows within a supply chain in order to maximize the total supply chain surplus or profit [1]. Network design is one of the most essential strategic decisions in SCM, which formulates the configuration of a supply chain through facility selection and determines the operational strategies. Traditionally, the design of a supply chain only focuses on the forward direction from raw material supplier towards end customer. However, due to the concern of environmental challenges, global warming and climate change from the whole society, increasing attention has been paid to the value and resource recovery © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 567–576, 2020. https://doi.org/10.1007/978-981-15-2341-0_71

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through reverse logistics activities [2, 3]. Closed-loop supply chain (CLSC) is a new concept and practice, which combines both traditional forward supply chain and reverse logistics in order to simultaneously maximize the utilization of resources and minimize the generation of wastes. Compared with the traditional supply chain network design, the planning of a CLSC is more complicated due to the involvement of more players. Furthermore, reverse logistics involves more uncertainties compared to the forward supply chain [3], and this needs to be appropriately treated in a CLSC network design problem. Due to the aforementioned complexity of the CLSC network design problem, significant efforts have been given in order to develop advanced optimization models and algorithm for a better decision-making. Yi et al. [4] developed a mixed integer linear program for minimizing the total cost of a retailer oriented CLSC for the recovery of construction machinery. The model was solved by an enhanced genetic algorithm and was validated through a case study in China. Özceylan et al. [5] proposed a linear program for maximizing the total profit generation of an automotive CLSC. The model was solved by CPLEX solver and was validated by a real world case study in Turkey. Taking into account of the recovery options, Amin et al. [6] investigated a tire manufacturing CLSC network optimization problem, which was validated by a real world case study in Canada. In addition to the economic incentives from incorporating reverse logistics activities, some research works considered the overall environmental performance of a CLSC. Hasanov et al. [7] formulated a mathematical model for the optimization of a CLSC network design problem considering remanufacturing options. The model aims at minimizing the total cost and emission cost of greenhouse gas (GHG) through optimal decision-making on both production planning and inventory management. Taleizadeh et al. [8] investigated a bi-objective optimization model for CLSC network design considering the balance between total cost and total CO2 emission. A fuzzy Torabi-Hassini (TH) method was used to solve the multi-objective optimization problem. Due to the complexity of the proposed mathematical models, significant computational efforts may be required to solve the optimization problems of CLSC network design. Therefore, several research works have been conducted to develop improved algorithm. Soleimani and Kannan [9] developed a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) for improving the computational efficiency of a multi-period and multi-level CLSC network optimization model. Chen et al. [10] investigated a location-allocation problem for the CLSC network design of cartridge recycling, which was solved by an enhanced two-stage GA. Hajipour et al. [11] formulated a non-linear mixed integer program for maximizing the profit generation in CLSC network design. Two metaheuristics: PSO and greedy randomized adaptive search procedure (GRASP) were employed to solve the proposed mathematical model. The treatment of uncertainty within the life cycle of a CLSC is another focus of the recent modeling efforts. Zhen et al. [12] proposed a two-stage stochastic optimization model for optimizing the decision-making of facility location and capacity allocation in a CLSC, and an enhanced Tabu search algorithm was developed to solve the model. Jeihoonian et al. [13] formulated a two-stage stochastic model for CLSC network design considering uncertain quality. Mohammed et al. [14] proposed a stochastic

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optimization model for minimizing the total cost of CLSC network design. The model was further incorporated with different carbon policies in order to test their effectiveness in carbon reduction. In this paper, we developed a new two-stage stochastic mixed integer program for CLSC network optimization. Compared with the existing models, the main difference is the capacity flexibility is taken into account in order to improve the stability and consistency of the objective values under different scenarios. In addition, the sample average approximation (SAA) method is used to test the performance of the proposed mathematical model.

2 Mathematical Model In this paper, we considered a network optimization problem of a single-product multiechelon CLSC. As shown in Fig. 1, the forward supply chain consists of manufacturer, wholesaler/distributor and customer. The reverse logistics activities are performed at collection center, disposal center and recycling center. The material flows between different facilities are given in Fig. 1.

Fig. 1. Network structure of a CLSC.

In this paper, the flexible network capacity is taken into account and is formulated in the mathematical model. The capacity limitation in a traditional facility location model may lead to unstable objective values and sub-optimal decisions under a stochastic environment [3, 15]. For example, because of the rigid capacity constraint, one more facility may be opened for dealing with a small increase on the customer demand in some scenarios, which results in unreasonable decisions and inefficient use of capacity opened. Due to this reason, the flexible network capacity is formulated as a penalty in the objective function in order to solve the problem and generate reasonable decisions. In practice, the inclusion of the flexible network capacity is a more realistic representation of the decision-making problem of CLSC network design, which enables different interpretations under different conditions, i.e., increase of facility capacity, outsourcing options, hire of temporary or seasonal workers, or even loss of sales. In addition, the uncertainty related to the customer demand in the forward supply

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chain and the rate of waste generation and the quality level in the reverse logistics are taken into consideration and are formulated as stochastic parameters. Min cost ¼ð

M X

F m im þ

m¼1

þ

W X w¼1

S X

C X

M X

Us

c¼1

þ

V X v¼1

M X W X m¼1 w¼1

þ

C X R X c¼1 r¼1

þ

M X m¼1

þ

D X

Pm

Asvc þ

c¼1

d¼1

r¼1

Pr

v¼1

Fr ir Þ

c¼1

!

Asrm

w¼1 v¼1

!

W X w¼1

!

Cwv Aswv þ

Ccd Ascd þ

Ov Asv þ

þ

Pw

M X m¼1

Asmw

Ascr

W X V X

c¼1 d¼1

V X

R X

C X

C X D X

Ccr Ascr þ

C X

þ

r¼1

Cmw Asmw þ

Pum Apsm þ Pd

R X

R X r¼1

Apsm

m¼1

Pc

Fc ic þ

c¼1

s¼1

þ

C X

Fw iw þ

V X v¼1

V X C X v¼1 c¼1

R X M X r¼1 m¼1

Cvc Asvc

ð1Þ

!

Crm Asrm

Orv Arvs

Ascd

Subject to: Dsv 

W X w¼1

#s Dsv 

Apsm þ

R X r¼1

M X m¼1 V X v¼1

C X c¼1

R X

V X v¼1

Asvc þ Arvs ; 8s; v

Asrm ¼ a

Asmw ¼

Asvc ¼

qs b

Aswv þ Asv ; 8s; v

r¼1

V X v¼1

W X w¼1

Aswv ; 8s; w

Ascr þ

Asvc ¼

Asmw ; 8s; m

D X d¼1

R X r¼1

Ascd ; 8s; c

Ascr ; 8s; c

ð2Þ

ð3Þ

ð4Þ

ð5Þ

ð6Þ

ð7Þ

A Stochastic Closed-Loop Supply Chain Network Optimization Problem

c

C X c¼1

Ascr ¼

R X

Apsm þ

m¼1

m¼1

Asrm ; 8s; r

Asrm  Capm im ; 8s; m

r¼1

M X

M X

Asmw  Capw iw ; 8s; w

V X v¼1

ð8Þ

ð9Þ

ð10Þ

Asvc  Capc ic ; 8s; c

ð11Þ

Ascr  Capr ir ; 8s; r

ð12Þ

Ascd  Capd ; 8s; d

ð13Þ

C X c¼1 C X c¼1

571

V X

Asv  Uo; 8s

ð14Þ

Arvs  Uro; 8s

ð15Þ

v¼1 V X v¼1

Objective function (1) minimizes the total cost that is comprised of fixed facility cost, processing cost, transportation cost, purchasing cost, flexible network capacity cost and disposal cost. Besides, the model includes 14 constraints. Constraints (2) and (3) require the CLSC system should be capable to deal with the customer demands in both forward and reverse directions. Constraints (4) and (5) specify the relationship between the input and output amount in the forward channels. Constraints (6)–(8) balance the material flows in the reverse logistics. Constraints (9)–(13) are capacity requirements of respective facilities. Constraints (14) and (15) give the upper limits of the flexible network capacity in both forward and reverse logistics. Besides, the decision variables fulfill their respective binary and non-negative requirements.

3 Algorithm Equation (16) defines a generic form of a two-stage stochastic optimization problem, which has the same structure as a CLSC network optimization problem. The first stagedecisions should be robust to withstand the change of the external environment under which the system is operated, and the second-stage decisions should be flexible to

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Fig. 2. Algorithmic procedures of the SAA.

adapt those changes and can be easily altered in order to maximize the system performance. Solving a stochastic programming model is a complex optimization problem that may require large computational efforts. In this paper, a sample average approximation (SAA) method is employed in order to obtain the optimal objective value of a large stochastic optimization problem with a great number of scenarios.  min  f ðx; yÞ :¼ CT x þ EP ½Uðx; nðyÞÞ x; y 2 H

ð16Þ

A Stochastic Closed-Loop Supply Chain Network Optimization Problem

( ) Q X 1 min T q ~f ðx; yÞ :¼ C x þ Uðx; nðy ÞÞ x; y 2 H Q Q q¼1

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ð17Þ

With the SAA, the optimal objective value is approximated by solving a set of randomly generated small problems repeatedly instead of solving the original problem directly, as shown in Eq. (17). In such a way, the computational efforts required is manageable. Figure 2 illustrates the algorithmic procedures of the SAA method. For more details of the solution method, the research works given by Verweij et al. [16] and Kleywegt et al. [17] can be referred.

4 Experiment and Discussion In order to illustrate the application of the proposed model for CLSC network optimization, this section presents a computational experiment based on a set of randomly generated parameters. The stochastic parameters are generated from uniform distribution of respective parameter intervals. Besides, we investigated the performance of three different sample sizes: 10, 30 and 50, respectively. All the optimizations were performed with Lingo 18.0 solver. The results are presented in Figs. 3 and 4.

Fig. 3. CV of the total cost, facility operating cost, transportation cost and flexible network capacity cost.

First, the in-sample stability is tested with coefficient of variation (CV) that is obtained using CV ¼ r=l . Figure 3 illustrates the CVs of the total cost as well as different cost components. With the increase of sample size, the CVs of all relevant cost

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components reduce, which reveals an improvement on the in-sample stability. When the sample size increases from 30 to 50, the improvement on the in-sample stability of the total cost is negligible. In addition, compared with the CVs of other cost components, the CV of flexible network capacity cost is extremely high. This can be explained that the flexible network capacity can be used as an adjustment factor for mitigating the negative impact on the first-stage network decisions and the objective values from uncertainty. In such a way, the unsatisfied demand in some scenarios can be fulfilled by the flexible network capacity, i.e., outsourcing, instead of opening new facilities, which may dramatically reduce the in-sample stability and result in a low capacity utilization.

Fig. 4. Percentage of the optimality gap and standard deviation.

Then, the quality of the SAA solutions are tested with the reference example. As shown in Fig. 4, the optimality gap reduces significantly with the increase of sample size. Compared with 10 scenarios, the optimality gap is decreased by 90.6% when 50 scenarios are used. However, in this case, the combined standard deviation will be increased by 2.51%. Thus, the solution quality of the stochastic optimization problem can be improved drastically with the increase of the sample size. It is noteworthy that the selection of the sample size is based upon a trade-off analysis between quality of solution and computational efforts required.

5 Conclusions In this paper, a novel two-stage mixed integer programming model is formulated for the network optimization of a single-product multi-echelon CLSC. The model aims at minimizing the total cost for opening and operating the CLSC through optimal decision-makings on both facility locations and transportation strategies. Compared with the existing optimization models, this model takes the flexible network capacity into account and thus formulates a penalty in the objective function. In order to solve the proposed model, the SAA method is used. The result of the computational experiment has shown that the inclusion of the flexible network capacity can significantly improve the in-sample stability, and the increase on sample size will improve the quality of solution of a large stochastic optimization problem.

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For further improvement of the current research, two suggestions are given. First, the environmental impact and policies, i.e., different carbon policies or strategies [3, 14], may be formulated in the CLSC network optimization problem under an uncertain environment. Second, different alternatives may be tested in order to increase the network flexibility [18]. Notations Set and Parameters m w v c d r s Fm , Fw , Fc , Fr Pm , Pw , Pc , Pd , Pr Us Pum Ov , Orv Dsv #s a qs b, c Capm , Capw , Capc , Capd , Capv Uo, Uro Variables im ,iw , ic ,ir Asmw , Aswv , Asvc , Ascr , Ascd , Asrm Apsm Asv , Arvs

Index of manufacturer, m = 1, …, M Index of wholesaler, w = 1, …, W Index of customer, v = 1, …, V Index of collection center, c = 1, …, C Index of disposal center, d = 1, …, D Index of recycling center, r = 1, …, R Index of scenario, s = 1, …, S Fixed opening cost of respective plants Unit processing cost at respective plants Probability of occurence Purchasing cost of materials Flexible network capacity cost in both forward and reverse logistics Customer demand from respective locations Conversion rate to used products Materials required for assembling one product Quality level Conversion fraction at respective plants Capacity of respective plants Upper limits on flexible network capacity in both forward and reverse logistics Binary decision variables for the location decision on respective candidates Amount of products transported on respective links Amount of materials purchased Amount of flexible network capacity used in both forward and reverse logistics

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References 1. Chopra, S., Meindl, P.: Supply chain management: strategy, planning, and operation (2016) 2. Yu, H., Solvang, W.D.: A general reverse logistics network design model for product reuse and recycling with environmental considerations. Int. J. Adv. Manuf. Technol. 87(9–12), 2693–2711 (2016) 3. Yu, H., Solvang, W.D.: A carbon-constrained stochastic optimization model with augmented multi-criteria scenario-based risk-averse solution for reverse logistics network design under uncertainty. J. Clean. Prod. 164, 1248–1267 (2017) 4. Yi, P., Huang, M., Guo, L., Shi, T.: A retailer oriented closed-loop supply chain network design for end of life construction machinery remanufacturing. J. Clean. Prod. 124, 191–203 (2016) 5. Özceylan, E., Demirel, N., Çetinkaya, C., Demirel, E.: A closed-loop supply chain network design for automotive industry in Turkey. Comput. Ind. Eng. 113, 727–745 (2017) 6. Amin, S.H., Zhang, G., Akhtar, P.: Effects of uncertainty on a tire closed-loop supply chain network. Expert Syst. Appl. 73, 82–91 (2017) 7. Hasanov, P., Jaber, M., Tahirov, N.: Four-level closed loop supply chain with remanufacturing. Appl. Math. Model. 66, 141–155 (2019) 8. Taleizadeh, A.A., Haghighi, F., Niaki, S.T.A.: Modeling and solving a sustainable closed loop supply chain problem with pricing decisions and discounts on returned products. J. Clean. Prod. 207, 163–181 (2019) 9. Soleimani, H., Kannan, G.: A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Appl. Math. Model. 39(14), 3990–4012 (2015) 10. Chen, Y., Chan, F., Chung, S.: An integrated closed-loop supply chain model with location allocation problem and product recycling decisions. Int. J. Prod. Res. 53(10), 3120–3140 (2015) 11. Hajipour, V., Tavana, M., Di Caprio, D., Akhgar, M., Jabbari, Y.: An optimization model for traceable closed-loop supply chain networks. Appl. Math. Model. 71, 673–699 (2019) 12. Zhen, L., Sun, Q., Wang, K., Zhang, X.: Facility location and scale optimisation in closedloop supply chain. Int. J. Prod. Res. 57, 7567–7585 (2019) 13. Jeihoonian, M., Zanjani, M.K., Gendreau, M.: Closed-loop supply chain network design under uncertain quality status: case of durable products. Int. J. Prod. Econ. 183, 470–486 (2017) 14. Mohammed, F., Selim, S.Z., Hassan, A., Syed, M.N.: Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transp. Res. Part D: Transp. Environ. 51, 146–172 (2017) 15. King, A.J., Wallace, S.W.: Modeling with Stochastic Programming. Springer, New York (2012). https://doi.org/10.1007/978-0-387-87817-1 16. Verweij, B., Ahmed, S., Kleywegt, A.J., Nemhauser, G., Shapiro, A.: The sample average approximation method applied to stochastic routing problems: a computational study. Comput. Optim. Appl. 24(2–3), 289–333 (2003) 17. Kleywegt, A.J., Shapiro, A., Homem-de-Mello, T.: The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2), 479–502 (2002) 18. Yu, H., Solvang, W.D.: Incorporating flexible capacity in the planning of a multi-product multi-echelon sustainable reverse logistics network under uncertainty. J. Clean. Prod. 198, 285–303 (2018)

Solving the Location Problem of Printers in a University Campus Using p-Median Location Model and AnyLogic Simulation Xu Sun, Hao Yu(&), and Wei Deng Solvang Department of Industrial Engineering, Faculty of Engineering Science and Technology, UiT The Arctic University of Norway, Narvik, Norway {xu.sun,hao.yu,wei.d.solvang}@uit.no

Abstract. The location decision on service facilities is of significant importance in determining the accessibility of the service provided. Due to this reason, it has been extensively focused over the past decades by both researchers and practitioners. This paper investigates a novel two-phase hybrid method combining both optimization model and agent-based simulation in order to solve the location problem of printers at a building of UiT The Arctic University of Norway, Narvik campus. In the first phase, the p-median location problem is employed to select the optimal locations of printers from a number of pre-determined candidate points so that the total travel distance by both employees and students can be minimized. In the second phase, both the original and the optimal location plans of printers are tested, validated and visualized with the help of AnyLogic simulation package. The result of the case study shows, however, the mathematically optimized solution may not yield a better performance under a realistic environment due to the simplification made and incapability to deal with the randomness. This has revealed that AnyLogic simulation can be used as a powerful tool to validate and visualize the result obtained from an optimization model and to make suggestions on the improvement. Keywords: Location problem
Service facility model
Simulation
AnyLogic


Optimization
p-median

1 Introduction The location problem of printers in a university campus is to select the optimal locations from a set of pre-determined candidates so that the accessibility and satisfaction of users (students and employees in this case) can be improved. Considering the nature of this problem, it is a service location and network design problem that has already been extensively focused on and investigated by both researchers and practitioners for more than half a century. In management science, the basic idea of this problem is to locate a number offacilities and, meanwhile, to allocate customer demand to different facilities [1]. Over the years, several methods, i.e., mathematical optimization model, analytical hierarchy process (AHP), geographical information system (GIS), etc., have been developed and used to support the decision-making of the location problem and network design of service facility. © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 577–584, 2020. https://doi.org/10.1007/978-981-15-2341-0_72

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Based on a previous research given by Yu et al. [2], this paper presents an improved hybrid method for the location problem of printers in a university campus in Norway. The rest of the paper is structured as follows. Section 2 presents the methodological development. Section 3 illustrates the application of the proposed method with a real world case study at UiT The Arctic University of Norway, Narvik campus. The result and discussion are given in Sect. 4. Finally, Sect. 5 concludes the paper.

2 Method In order to support the decision-making on service facility location problems, a novel two-phase hybrid method combining both optimization model and agent-based simulation is developed. Figure 1 illustrates a general framework of the method. First, based on the problem identified, a mathematical optimization model is formulated to make the optimal decisions on both facility location and demand allocation. After that, the result is validated and is visualized in a realistic simulation environment. If the result fulfills the performance required, it will be suggested and be visualized to the decision-makers. Otherwise, all the previous steps need to be re-visited in order to identify the problem of optimization.

Fig. 1. Framework of the method.

In this paper, the p-median model is employed to make the optimal decisions of the locations of printers. Then, AnyLogic is applied to create a realistic simulation environment and to validate the optimal result obtained.

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p-median Location Problem

The p-median location problem was first put forward by Hakimi [3]. Since then, it has been extensively investigated and the research focus has been given to both the development of efficient computational algorithms and the application in real-world network design problems. Wheeler [4] investigated a location problem of patrol areas with the help of p-median model in order to improve both traveling distance to calls and workload equality. Combining a p-median problem with a novel part assignment procedure, Won and Logendran [5] studied the balanced cell formulation in a cellular production process. Incorporating with the environmental evaluation, Pamučar et al. [6] formulated a green p-median model for the optimization of city logistics terminals. Taleshian and Fathali [7] proposed a fuzzy p-median location problem in order to properly manage the uncertainty in decision-making. Adler et al. [8] investigated a phub median problem for the network and hub design of air transport in order to deal with the demand expansion in African aviation market. Yu and Solvang [9] employed both maximal covering model and p-median model to improve the post office relocation decisions in a city in Norway. The purpose of the p-median model is, through the optimal location-allocation decisions, to minimize the total travel distance in the service network. A mathematical formulation of the p-median location problem is given in Eqs. (1–5) [9]. Herein, I and J are the sets of customers and candidate locations for service facility, respectively. The demand from customer i is represented by qi , and dij is the distance between i and j. Variable xj determines if a facility is opened or not, and variable uij determines if the demand from i is served by j. Finally, p specifies the number of service facilities in the system. Min

X

qi dij uij

ð1Þ

i2I

S.t. X

uij ¼ 1; 8i 2 I

ð2Þ

j2J

uij  xj ; 8i 2 I; j 2 J X xj ¼ p

ð3Þ ð4Þ

j2J

uij ; xj 2 f0; 1g; 8i 2 I; j 2 J

ð5Þ

Objective function (1) minimizes the total travel distance to satisfy all the customer demands. Constraint (2) assigns each customer to one service facility. Constraint (3) requires a customer can only be allocated to an opened facility. Constraint (4) is the requirement on the number of service facilities. Constraint (5) is the binary requirement of decision variables.

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AnyLogic Simulation

AnyLogic is a professional simulation software package with a graphical interface, which can be used to create a realistic virtual environment for large and complex systems with different types of behavior (discrete, continuous and hybrid) [10, 11]. AnyLogic is a powerful and flexible tool that enables the modeling of three main features in a simulation: discrete event, agent-based system and system dynamics, namely, which can be combined in order to create a more accurate representation of a complex process or system in the real world. AnyLogic equips a wide range of built-in modules and database that can easily and quickly be used to create the simulation of a complex system in a great number of industries and service sectors, i.e., manufacturing, logistics and supply chain, networks, dynamic systems, business processes, healthcare, customer behavior, and transportation, etc. Furthermore, for obtaining the analysis and implication from the simulation, AnyLogic has a set of analytical and optimization tools that can be accessed from the modeling environment directly [12]. Except from the standard resources, AnyLogic also enables users to build a highly customized simulation based on the features of the systems modeled. In this regards, Yang et al. [10] investigated the passenger flow at the entrance of a subway station with agent-based pedestrian library in AnyLogic in order to optimize the number of ticket windows opened in peak and off-peak periods. In order to understand the influence of the adoption of electric vehicles on pedestrian traffic safety, Karaaslan et al. [13] built up and studied an agent-based simulation of a real intersection with AnyLogic. Kim et al. [14] used AnyLogic to optimize the location-allocation problem of a biomass supply chain.

3 Case Study Combining with both p-median location model and agent-based simulation in AnyLogic, we present a case study of the location problem of printers at the third floor of the main building at UiT The Arctic University of Norway, Narvik campus. The objective is to locate five printers in order to minimize the total travel distance. The optimization process and result with p-median model have been given by Yu et al. [2]. In order to simplify the problem, several assumptions are made. 1. Each room is considered as a unique customer demand point and a set of candidate locations is pre-determined. 2. The users are divided into three groups: academic employees, laboratory employees and students. The demand for printing service from different types of users are by no means identical. 3. The demand is aggregated at the center point of each room. 4. The demand is associated with three influencing factors: type of user, printing frequency and number of user, respectively. Besides, it is also adjusted by the sensitivity to distance from different types of users. 5. The distances between the customer locations and the candidate locations of printers are approximated by the Manhattan distance.

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Figure 2 illustrates the layout of the studied area. The original location plan (red squares) and the optimal location plan (green squares) are also given in the figure.

Fig. 2. The building layout and the printer locations in the original plan and the optimized plan.

In this paper, both the original and the optimal location plans of printers are simulated in AnyLogic in order to validate the optimization and visualize the result under a realistic environment. In this modeling and simulation environment, there are many independent objects/individuals (students and employees), so an agent-based approach is used. Compared with the original optimization procedures, several realistic assumption are made as follows in order to have a better representation of the real problem and generate a more reliable analysis. 1. In order to maintain the consistency, the customer demand estimated by Yu et al. [2] is used in the simulation for determining the generation of agents (number and frequency). 2. Instead of aggregating all the customers in the center point of each room, the customer demand can be generated at a random location within the room in the simulation. 3. Instead of using the Manhattan distance to calculate the distances between the customer locations and the candidate locations of printers, the real routes are defined in the simulation. 4. Compared the optimization environment from Yu et al. [2] with the current layout of the building, significant changes of the layout of the area served by the left most printer in Fig. 2 are observed. Thus, this part is not taken into account in the simulation and only the four-printer scenario is tested.

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Fig. 3. Agent-based mobile process diagram in AnyLogic.

The flow chart of agent movement is illustrated in Fig. 3. The sources (students and employees) are randomly generated from respective classrooms or offices. They will go to the printing area via the real routes defined, use the printers and then move to the exit. In this case, the goal is to calculate the movement distance of all the agents within the studied period. In order to simplify the calculation and reduce the simulation time, we only considered the movement distance in one direction: from the offices or classrooms to the printers. The distance in the reverse direction (back to the rooms) is assumed to be the same as that in the forward direction, so it will not influence the result of the comparison between different location plans. In addition, the movement speed of all the agents is set to 1 m/s, so the movement distance is directly proportional to the time consumed in the movement.

4 Result and Discussion Table 1 illustrates the comparison between the performance evaluation conducted by both optimization method and AnyLogic simulation. It is interesting to observe that, with different approaches, controversial results may be obtained. In this case, the result of the optimization by p-median model, which suggests the total travel distance may be reduced by 10% through relocating the printers, is, however, not supported by the simulation result that shows the original location plan has a better performance. Table 1. Performance evaluation of the optimal location plan and the original location plan in both optimization and simulation environments Performance evaluation

Optimization Simulation 1 month 3 month 5 month Reduction of total travel distance 10% −8.06% −7.08% −7.36%

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As the procedures given in Fig. 1, the mathematical modeling, assumptions and solution code were carefully re-visited in order to identify the problems of the optimization method. In this case study, the main problems of the use of p-median location model are the two assumptions made in order to simply the optimization. 1. First, the aggregation of customer demand at the center point neglects the randomness of the real demand generation, which may have a critical impact on the objective value and decision-making especially in a small-scale problem. 2. The most critical problem is the Manhattan distance used in the optimization. The distances between two points is one of the most important influencing factors in the p-median location problem, However, as shown in Fig. 4, Manhattan distance cannot always give a realistic representation of the actual distance traveled and thus may lead to an improper result.

Fig. 4. Illustration of the difference between the Manhattan distance (blue) and the actual distance (green).

In order to solve the aforementioned problems, a stochastic p-median model may be formulated so that the randomness of customer generation can be accounted. Moreover, a more realistic distance calculation should be used.

5 Conclusions In this paper, the location problem of printers in a university campus in Norway is investigated using a two-phase hybrid method combining both optimization method and AnyLogic simulation. First, the p-median location model was used to optimize the location plan of printers, and then the result was tested in AnyLogic simulation. The simulation result has revealed the problems related to the assumptions and simplifications of the original optimization. The research has proved the effectiveness of AnyLogic simulation in the validation and visualization of the result of optimization. Future research may be conducted in order to address the problems identified in the case study. Furthermore, with the help of AnyLogic simulation, not only the movement

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of agents can be estimated but also comprehensive analysis can be conducted to analyze the overall performance of the printers, i.e., usage of different printers, queueing time, etc.

References 1. Abareshi, M., Zaferanieh, M.: A bi-level capacitated p-median facility location problem with the most likely allocation solution. Transp. Res. Part B Methodol. 123, 1–20 (2019) 2. Yu, H., Solvang, W.D., Yang, J.G.: Improving accessibility and efficiency of service facility through location-based approach: a case study at Narvik University College. Adv. Mater. Res. 1039, 593–602 (2014) 3. Hakimi, S.L.: Optimum distribution of switching centers in a communication network and some related graph theoretic problems. Oper. Res. 13(3), 462–475 (1965) 4. Wheeler, A.P.: Creating optimal patrol areas using the p-median model. Polic.: Int. J. 42(3), 318–333 (2019) 5. Won, Y., Logendran, R.: Effective two-phase p-median approach for the balanced cell formation in the design of cellular manufacturing system. Int. J. Prod. Res. 53(9), 2730–2750 (2015) 6. Pamučar, D., Vasin, L., Atanasković, P., Miličić, M.: Planning the city logistics terminal location by applying the green-median model and type-2 neurofuzzy network. Comput. Intell. Neurosci. 2016 (2016). http://downloads.hindawi.com/journals/cin/2016/6972818.pdf. Article ID: 6792818 7. Taleshian, F., Fathali, J.: A Mathematical model for fuzzy-median problem with fuzzy weights and variables. Adv. Oper. Res. 2016 (2016). Article ID: 7590492 8. Adler, N., Njoya, E.T., Volta, N.: The multi-airline p-hub median problem applied to the African aviation market. Transp. Res. Part A Policy Pract. 107, 187–202 (2018) 9. Yu, H., Solvang, W.D.: A comparison of two location models in optimizing the decisionmaking on the relocation problem of post offices at Narvik, Norway. In: Proceeding of IEEE International Conference on Industrial Engineering and Engineering Management, Thailand, pp. 814–818 (2018) 10. Yang, Y., Li, J., Zhao, Q.: Study on passenger flow simulation in urban subway station based on anylogic. J. Softw. 9(1), 140–146 (2014) 11. Borshchev, A., Karpov, Y., Kharitonov, V.: Distributed simulation of hybrid systems with AnyLogic and HLA. Future Gener. Comput. Syst. 18(6), 829–839 (2002) 12. Karpov, Y.G., Ivanovski, R.I., Voropai, N.I., Popov, D.B.: Hierarchical modeling of electric power system expansion by anylogic simulation software. In: Proceeding of IEEE Power Tech Conference, Russia, pp. 1–5 (2015) 13. Karaaslan, E., Noori, M., Lee, J., Wang, L., Tatari, O., Abdel-Aty, M.: Modeling the effect of electric vehicle adoption on pedestrian traffic safety: an agent-based approach. Transp. Res. Part C Emerg. Technol. 93, 198–210 (2018) 14. Kim, S., Kim, S., Kiniry, J.R.: Two-phase simulation-based location-allocation optimization of biomass storage distribution. Simul. Model. Pract. Theory 86, 155–168 (2018)

Intelligent Workshop Digital Twin Virtual Reality Fusion and Application Qiang Miao1(&), Wei Zou2, Lilan Liu1, Xiang Wan1, and Pengfei Wu1 1

Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, China [email protected] 2 Aerospace Systems Engineering Shanghai, Shanghai, China [email protected]

Abstract. With the intelligent development of the enterprise workshop, the amount of monitoring data of the workshop equipment doubles, and the characteristics of industrial data such as high speed, multi-source heterogeneity and variability are presented, and it is difficult to meet the real-time monitoring and health management of the workshop under dynamic and variable environment. In view of the above problems, based on the intelligent workshop-based equipment, combined with the application of digital twin key technology, multisource data acquisition and data fusion modeling development and application of the workshop production line, the realization of the workshop based on multiple feedback source data in the digital The real situation of physical entities is presented in the world. According to the true reflection of the multi-source data fusion digital world, it is possible to comprehensively supervise the various operating parameters and indicators of the product, and realize the system health management of the intelligent workshop. Keywords: Digital hybrid Intelligent workshop


Virtual and real fusion
Data collection


1 Introduction With the rapid development of industrial technology and the new generation of information technology, equipments in various fields such as intelligent workshops and industrial manufacturing have been upgraded, such as industrial robots, 3D printers, and machining centers, and the integration and intelligence of typical equipment have been continuously improved [1]. Along with the formation of information space, a large number of sensors are needed to collect and collect various information of complex equipment, and collect various information required for monitoring, connecting, and interacting with each other in real time, so as to realize the intelligence of the workshop [2]. However, at present, the physical world and the information world of the workshop are isolated from each other, and the data in the middle cannot be transmitted or integrated, which leads to the inability to realize interaction and

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integration in the virtual and real space, and the healthy quality management of the entire workshop cannot be realized [3]. Digital twin is the best way to achieve smart factory upgrades. Workshop manufacturing data has large data features such as large-scale, multi-source heterogeneous, multi-temporal scale, and multi-dimensional. Through the deep integration of digital twinning and workshop management system, the multi-source data of the whole workshop can be obtained, and the data correlation fusion modeling can be carried out to realize the digitization of the workshop. More importantly, the introduction of digital twin can make the traditional workshop Health management is more open and expansive. At present, the research and application of digital fusion modeling based on digital twinning in China is still immature and lacks experience in implementation [4, 5]. This focus is based on the health monitoring of the whole life cycle of the workshop. It mainly uses the application of multi-source data acquisition and digital integration to realize the monitoring of the health status of the workshop under actual operation, and does not fully apply all the technologies of digital twin key point. Through the intelligent data mapping integration and digital fusion modeling technology of the heterogeneous equipment data in the workshop, the function of digital health technology in the health monitoring of the workshop is realized, and the basis for the subsequent in-depth development of the digitalization of the workshop is provided.

2 System Framework The digital twinning system of the intelligent workshop introduced in this paper is mainly about several modules of multi-source data acquisition, data denoising modeling, data fusion modeling and data analysis results. The overall framework of the system is shown in Fig. 1.

Fig. 1. System overall scheme design drawing

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The main functions of the digital twin modules in the intelligent workshop are as follows. (1) Multi-source data acquisition module: The lowest-level information building module of the intelligent workshop is also the most basic module for building digital twins. For the data collection of multi-source heterogeneous equipment in the workshop, the system adopts PLC, wireless AP and various sensor converters, etc. Through the construction of the hardware network of the underlying equipment, the robot movement information, the processing status of the processing equipment, and the logistics equipment are obtained and the real-time information. At the same time, the remaining sensor devices are used to obtain the state information such as the temperature and speed of the actual processing equipment. Since the data collected by different sensors often appear in different formats, the multi-source heterogeneity data of the workshop is generated. (2) Data pre-processing module: The massive data collected by the multi-source acquisition module of the workshop will often generate some false information due to the sudden events of the workshop or the error of data collection, which will result in inaccuracy of the data. Before the information, the Web-Service is used to cluster the massive data of the workshop, and the error and inaccurate information are denoised and filtered to obtain the representative information of the workshop data. (3) Data fusion processing module: For the multi-source data information collected by the workshop, after denoising and filtering the false information of the workshop, an improved BP neural network fusion algorithm is adopted to integrate redundant and complementary information according to certain rules. Processing conflicting data yields an accurate judgment of the target.

3 System Structure Function Design and Implementation 3.1

Multi-source Data Collection Based on Intelligent Workshop

The data collection of multi-heterogeneous devices mainly relies on the centralized network architecture design. Through the wireless intelligent gateway AP technology, the network communication of various types of devices will be communicated, and the basic conditions for the interconnection and intercommunication of data information of heterogeneous devices will be realized. In the process of workshop equipment processing, the OPC general protocol is used to obtain real-time data such as field device operation and status through the underlying integrated heterogeneous equipment network technology; and the data information of each PLC is classified and monitored through multi-channel setting, which can be accurate and intuitive. Get and locate the real-time information of the equipment during operation, and keep abreast of the realtime operation and processing status of each equipment. For the entire workshop acquisition system, as shown in Fig. 2, for the entire automation workshop equipment, a number of channels are opened, each channel is provided with a main device, operating parameters, dynamic data, switch information,

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logistics information for specific equipment The workshop data is collected, and the real-time data and information of the first part of the line are finally obtained through the design and integration of the PLC network technology. The other channels are also arranged in such a way that the orderly management of the corresponding data is completed, and the real-time data of the workshop is effectively managed and monitored.

Fig. 2. Multi-source data acquisition diagram

3.2

Intelligent Workshop Data Fusion Model Based on Digital Twins

3.2.1 Intelligent Shop Floor Data Filtering Based on Digital Twins In the actual production workshop, the data of each production equipment, processing workpieces, material inventory, etc. will change according to the time, and as the production requirements of the smart workshop, it is necessary to accurately grasp this information in real time, which will inevitably bring the workshop. Massive Data. Moreover, due to the influence of the external environment and the mistakes of the data collection methods, the massive data of the workshop is usually mixed with some erroneous, redundant and uncertain data. The data filtering method based on WebService is an optimization method for massive data filtering. Filter and selectively store massive data according to requirements, avoid data redundancy, make data access more efficient, and realize data sharing between different application systems. In terms of business logic encapsulation, a REST (Representational State Transfer) distributed architecture-style Web service encapsulates the business process interface of data. Establish a set of friendly API functions, describe the terminal through URL, and implement CURD (addition, deletion, and change) of resources by common HTTP operations. This series of APIs can adapt to different platforms and have the advantages of cross-platform and cross-language. In terms of data format: Select JSON (JavaScript Object Notation) lightweight data format, JSON uses a key-value pair structure text format, easy for people to read and write, but also easy to machine parsing and generation, is an ideal data exchange language.

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According to the data collected by the intelligent workshop, it is mainly processed in the Web system. It is divided into two steps: grouping and clustering. Through the above two parts, the data filtering and cleaning work is completed: (1) Data grouping Through the category of the equipment in the process, the data information of the product in the processing process is grouped into categories, and when grouping, the data is converted into JSON format, and the data is stored and queried in the form of Key-Value key-value pairs. At the time of grouping, it takes a lot of effort to transform the results of specific data. Compared with the algorithm, the simplicity is not enough. However, for the cleaning and filtering of the underlying data, the processing of specific device data will greatly improve the device data. Accuracy and real-time, no redundant device data is grouped. (2) Data clustering After the data group is completed, the data is already in JSON format and has been stored in the database, but the data cannot be filtered and clustered, and data query and utilization cannot be performed. The role of data clustering is to leave a filtering channel in the grouped data set, that is, the API interface, and the interface reservation matching mechanism. When the upper computer calls the interface at the upper layer, it passes the filtered matching data through the reserved interface channel. The data of the workshop equipment is processed by grouping and clustering, and the data has been filtered and cleaned to the group according to the equipment category, and the reserved interface is convenient for querying. The results of the query part are as follows (Fig. 3).

Fig. 3. Denoising query processing result

3.2.2

Intelligent Workshop Data Fusion Modeling Based on Digital Twins The quantity of workshop equipment information, product information and material information in the production process is huge. After being collected and filtered, it is distributed in many nodes and cannot be classified and grouped autonomously. In order to improve the efficiency of the data collection in the intelligent workshop of the physical network, and to realize the data grouping according to the product independently, an intelligent data fusion GAPSOBP (BP Neural Data Fusion algorithm optimized by Genetic algorithm and Particle swarm) based on genetic algorithm and particle swarm optimization algorithm is proposed. The GAPSOBP algorithm compares the nodes of the wireless sensor into neurons in the BP neural network, extracts

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the sensory data collected by the wireless sensor network through the neural network, and combines the collected sensor data with the clustering route. When applying the GAPSOBP algorithm, it is first necessary to determine the structure of the BP neural network according to the topology map of the wireless transmission network. The wireless sensor network forms clusters according to the LEACH algorithm, and each cluster is regarded as a BP neural network structure. The number of nodes in the cluster is the number of input layers of the BP neural network, and the number of output layers, that is, the number of cluster heads is 1. The number of hidden layers is determined according to formula 1, and then the number of optimal hidden layers is determined by trial and error. L=

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðm þ nÞ þ a ð0\a \10Þ

ð1Þ

Where: n is the number of input layers, and m is the number of output layers The workflow of the GAPSOBP algorithm mainly consists of three phases: (1) Particle initialization: Firstly, the BP neural network structure needs to be determined. Then the initial weight and threshold of the BP neural network are passed to the particle swarm algorithm to become the primary population. The particle swarm algorithm encodes the neural network weights and thresholds, and initializes the candidate solutions and particles speed. (2) Solving the optimal BP neural network parameters: The particle swarm optimization algorithm calculates the individual extremum and the group extremum according to the actual output of the BP neural network and the error fitness function of the expected output, and updates the particle optimization speed and position of the PSO algorithm. In the optimization process of the particle swarm, the crossover and mutation operations of the genetic algorithm are added, and then the fitness value is recalculated to determine whether the termination condition is satisfied. If the condition is satisfied, the optimization result is transmitted to the BP neural network for network training, otherwise the iterative update particle speed is continued. And position until the algorithm reaches the termination condition. (3) Training BP neural network: BP neural network uses the optimization results of genetic particle swarm optimization algorithm to train the network, update the weight and threshold, until the network parameters are determined, and then the data fusion processing of the wireless sensor network can be performed. Through the above three stages of operation, the grouped data is regarded as a node, and the specific rules and methods are used to fuse the processing according to the nodes of the specific object, and the representative information of the specific device object is obtained, and the processing process of the algorithm is completed.

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4 Effect Application Examples and Effect Analysis The digital twinning system designed in this paper has been successfully applied to the production workshop of Shanghai Intelligent Manufacturing and Robot Key Laboratory, which satisfies the need for unified collection, management and operation of workshop information, and solves the unified centralized data management of heterogeneous equipment in the workshop. Provide a digital data foundation for intelligent management of the shop floor. The system obtains the representative information of the equipment through data collection, filtering and fusion processing, and displays the running data and real-time status of the entire workshop production line, as shown in Fig. 4, that is, the information collected on the workshop production line is displayed in a fusion manner, and the collection is performed. Real-time information, such as robot motion data and working status, is collected. Through the filtering and denoising and fusion display processing of the collected multi-source data, the accurate representative information of the robot arm can be obtained, and the working state information, the grabbing object, and the six-joint rotation angle information shown in the figure are displayed together.

Fig. 4. Fusion display of robotic arms

As shown in Fig. 5, real-time information such as machine motion data and working status of the line movement is collected. Through the filtering and denoising and fusion display processing of the collected multi-source data, the accurate representative information of the machine tool can be obtained, and the industrial information and temperature information shown in the figure are displayed together.

Fig. 5. Fusion machine tool display

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5 Conclusions This paper is based on the co-author’s “Research on the Virtual Reality Synchronization of Workshop Digital Twin” and carries out in-depth research on data fusion. This time, based on the health monitoring of the whole life cycle of the workshop, the application of multi-source data acquisition and digital fusion modeling is used to monitor the health status of the workshop under actual operation, and the digital health is not fully applied. Technology and key points. Through the intelligent data mapping integration and digital fusion modeling technology of the heterogeneous equipment data in the workshop, the function of digital health technology in the health monitoring of the workshop is realized, and the basis for the subsequent in-depth development of the digitalization of the workshop is provided. Acknowledgements. The authors would like to express appreciation to mentors in Shanghai University for their valuable comments and other helps. Thanks for the pillar program supported by Shanghai Economic and Information Committee of China (No. 2018-GYHLW-02020).

References 1. Liu, D., Guo, K., Wang, B., Peng, Y.: Summary and prospect of digital hygiene technology. Chin. J. Sci. Instrum. 39(11), 1–10 (2018) 2. Guo, D., Bao, J., Shi, G., Zhang, Q., Sun, X., Weng, H.: Modeling of aerospace structural parts manufacturing workshop based on digital twinning. J. Donghua Univ. (Nat. Sci. Ed.) 44 (04), 578–585 + 607 (2018) 3. Chen, Z., Ding, X., Tang, J., Liu, Y.: Exploration of production control model of aircraft assembly workshop based on digital hybrid. Aeronaut. Manufact. Technol. 61(12), 46– 50 + 58 (2018) 4. Tao, F., Liu, W., Liu, J., Liu, X., Liu, Q., Qu, T., Hu, T., Zhang, Z., Xiang, F., Xu, W., Wang, J., Zhang, Y., Liu, Z., Li, H., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Yan, F., He, L., Yi, W., Min, C.H.: Digital hygiene and its application exploration. Comput. Integr. Manufact. Syst. 24(01), 1–18 (2018) 5. Jiakai, G.: Digital twins: the best bond to connect the manufacturing physics world and digital virtual world. Softw. Integr. Circuits 09, 4 (2018) 6. Wu, P., Qi, M., Gao, L., Zou, W., Miao, Q., Liu, L.: Research on the virtual reality synchronization of workshop digital twin. In: 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, pp. 875–879 (2019)

Harvesting Path Planning of Selective Harvesting Robot for White Asparagus Ping Zhang1, Jin Yuan2(&), Xuemei Liu3, and Yang Li3 1

3

College of Information Science and Engineering, Shandong Agricultural University, Tai’an, China 2 College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an, China [email protected] Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment, Tai’an, China

Abstract. In order to optimize the harvesting path of harvesting robot for white asparagus, a global path planning algorithm based on multi-fork tree is designed according to the location distribution of the harvesting point and the placed collecting box point, the optimal harvesting path with the shortest harvesting distance is obtained. On this basis, a path planning algorithm based on sequential harvesting of white asparagus is proposed, which effectively increases the speed of path planning when the distance of harvesting path is not much different from the optimal path. The simulation results show that both algorithms can effectively improve the harvesting efficiency of white asparagus. With the global path planning algorithm, the moving distance of end-effector can be saved by 42.83% on average when the number of white asparagus is different. By adopting the path planning algorithm based on sequential harvesting, the average distance of end-effector motion can be saved by 37.2%, and the realtime performance is very good. The path planning of white asparagus harvesting process has a great impact on improving the harvesting efficiency of white asparagus. Keywords: Harvesting path harvesting
End-effector


Multi-fork tree
Global path
Sequential

1 Introduction At present, China is the country with the largest production and export of white asparagus, but its complicated harvesting process is the bottleneck of the development of white asparagus industry. And the manual harvesting method is widely used at home and abroad, the domestic white asparagus harvesting machinery has not been reported [1, 2]. Based on the visual positioning and harvesting system of the selective harvesting robot of white asparagus in the laboratory, this paper studied the harvesting path planning of the end-effector, which is of great significance to improve the harvesting efficiency of white asparagus [3]. Firstly, the image of the harvesting area is acquired by the machine vision system, and the white asparagus in the current area is identified and located by the image © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 593–599, 2020. https://doi.org/10.1007/978-981-15-2341-0_74

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processing technology to obtain the position coordinates of the asparagus tip, according to the location coordinates of white asparagus, the end-effector runs to the harvest point on the X-axis and Y-axis screws [4–6]. After the white asparagus is clamped and cut, the end-effector moves upwards by the Z-axis screw to bring the white asparagus out of the ground, and the end-effector moves to the collecting box point to place the white asparagus, a harvest is completed. The above process is repeated until the last white asparagus is harvested in the current harvesting area, and the robot enters the next area for harvesting [7–9]. Because the white asparagus is usually irregularly distributed on the ridge surface, in order to improve the harvesting efficiency, the harvest path planning is necessary and the optimal path is the shortest path taken by the end-effector from the starting point of harvesting to the completion of all white asparagus in the current region. In this paper, the global path planning algorithm with the shortest distance and the path planning algorithm based on sequential harvesting are proposed, and the simulation analysis of the algorithm shows that the designed algorithm can effectively improve the harvesting efficiency of white asparagus.

2 Global Path Planning Algorithm 2.1

Harvesting Path Planning

The schematic diagram of the harvesting path is shown in Fig. 1, there are collecting box point on both sides of the harvesting robot, as shown in the yellow area in Fig. 1, in order to ensure the reliable and damage-free placement of the asparagus, the harvested asparagus needs to be placed in the middle of the collecting box. For white asparagus harvesting sequence and the location of the place have many choices, so there are (N!*2N) paths for the end-effector to choose from. The global feasible path planning algorithm is as follows: (1) The end-effector randomly selects the first target from the initial point O, assuming white asparagus A, and the specific location of collecting box point after harvesting is related to the location of the next asparagus to be collected. (2) Find the remaining target points (except the harvested A) and randomly select any of them, assuming the target point B. Find out the mirror positions of collecting box point on both sides of B, which are B′ and B′′, and the intersection of the line connecting A and B′, B′′ with the middle of collecting box point is A1, A2 respectively, then the end-effector runs to position A1 or A2 to place asparagus A. (3) According to step (2), to complete harvesting of other target points until finishing the last target point in the current area. (4) After harvesting the last target point, it is placed horizontally. Assuming that the last white asparagus is C, it is placed at C1 or C2. (5) Record the moving path and calculate the moving distance of end-effector. The red line in Fig. 1 shows a feasible path, but it may not be the optimal path. In order to find the shortest path, the distance of all feasible paths should be calculated.

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Fig. 1. Schematic diagram of white asparagus harvesting path by global planning algorithm

2.2

Global Optimal Path Planning Algorithm

Take the three white asparagus A, B and C in the harvesting area as an example, a path planning decision tree as shown in Fig. 2 is constructed [10]. By traversing 48 paths, one path with the smallest distance is selected as the optimal harvesting path.

Fig. 2. Path planning decision tree

The coordinates of each point are shown in Fig. 1, the boundary point of the harvesting area is Mðxmax ; ymax Þ, The vertical distance from the position of the collecting box point to the boundary of the collecting box point is d. In Fig. 2, the harvesting path marked with the red line in the decision tree is O ! A ! A1 ! B ! B1 ! C ! C1 . Then calculate the distance the end effector moves.

3 Path Planning Algorithm Based on Sequential Harvesting According to the above method of establishing a path planning decision tree to traverse all feasible paths, although the shortest path can be found, as the number of white asparagus in the harvesting area increases, it takes a long time to determine the optimal

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harvesting path, and before the current target point is placed, the specific location of the collecting box point should be calculated according to the location of the next target point, the end-effector waits for the harvesting time to be much longer than the harvesting time, and the placement process requires the x-axis and the y-axis screw to move at the same time, the control of the end-effector is complicated. In view of the above characteristics, in the case where the number of white asparagus is relatively large, the method of harvesting in the order may be adopted, the harvest path planning algorithm of the end-effector is as follows: (1) After obtaining the position coordinates of each target point in the harvesting area, all the target points are sorted according to the order of the value of the ordinate y from small to large. (2) The end-effector starts from the initial point O and selects the target point with the smallest y coordinate after sorting (assuming target point A) for collection. (3) After harvesting, select the collecting box point close to the target point to place. (4) Find the target point of the remaining (except for the target point A that has been harvested), calculate the distance between point A and other target points, and select the one closest to point A, if the distance between the target point and A is less than L set previously, the target point is preferentially harvested, otherwise the target points sorted in step (1) are sequentially collected. (5) Repeat steps (3) and (4) until the target points of the current area are all harvested. (6) Record the moving path and calculate the distance the end-effector moves.

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Fig. 3. Schematic diagram of white asparagus harvesting path based on sequential algorithm

Take the three white asparagus A, B, and C in the harvesting area as an example. The harvest path is shown in Fig. 3. First, all the target points are sorted according to the order of the value of the ordinate y from small to large. If the initial planned path after sorting is A ! C ! B, and calculate the distance between A and B, C, which are d1, d2 respectively, if d1\L\d2, the harvesting path is adjusted to A ! B ! C, the size of L is set according to the actual situation. Then calculate the distance the end effector moves.

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4 Simulation Analysis of Path Planning Algorithm 4.1

Simulation Analysis of Global Optimal Path Planning Algorithm

According to Fig. 1, the vertical distance between the target point and the position of the collecting box point is d 5 cm, the coordinates of the three white asparagus A, B, and C are (30, 40), (45, 58), and (60, 77) respectively, the coordinates of O and M are (0, 0), (80, 100), calculate the distance the end effector moves by the above algorithm, the shortest distance among the 48 harvesting paths can be 258 cm. The number of white asparagus in the harvesting area is generally no more than five. In the process of path optimization, for every case where the number of asparagus in the harvesting area is different, photos of 5 groups of harvesting areas are randomly selected to obtain position coordinates for path optimization, and the results are shown in Table 1.

Table 1. Simulation data of global optimal path planning algorithm

2 3 4 5 Average value – Number of white asparagus

Shortest path distance (cm) 172 212 286 372 –

Longest path distance (cm) 335 471 634 777 –

Maximum efficiency improvement (%) 48.66 54.99 54.89 52.12 52.67

Average efficiency improvement (%) 36.90 47.89 46.53 39.98 42.83

By analyzing the data in the Table 1, white asparagus harvesting area number 2, 3, 4 and 5 respectively, each case respectively using five different groups of area harvested, the group with the highest efficiency is selected among the 5 groups, after adopting the path optimization algorithm, the shortest path distance saves 48.66%, 54.99%, 54.89% and 52.12% respectively compared with the longest path distance, with an average efficiency improvement of 42.83%. It can be seen that the path optimization algorithm can effectively improve the harvesting efficiency of the end effector. 4.2

Simulation Analysis of Path Planning Algorithm Based on Sequential Harvesting

In the process of path optimization, in order to verify the effectiveness of the algorithm, the same five groups of harvesting areas as the above simulation experiment are selected for the different numbers of asparagus in the harvesting area. According to Fig. 3, the initial point coordinate is O(0,0), the vertical distance between the target point and the position of the collecting box point is d 5 cm, and the distance of L is 20 cm, that is, on the basis of sequential harvesting, the harvesting path is slightly adjusted according to the distance L. The coordinates of the three white asparagus A, B, and C are (30, 40), (45, 58), and (60, 77) respectively. According to the path optimization algorithm, the moving distance of the end effector is 267 cm.

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Table 2. Simulation data of path optimization algorithm based on sequential acquisition

2 3 4 5 Average value – Number of white asparagus

Shortest path distance (cm) 180 228 321 402 –

Longest path distance (cm) 229 303 390 501 –

Maximum efficiency improvement (%) 46.27 51.59 46.85 44.78 47.37

Average efficiency improvement (%) 31.94 41.85 40.27 34.75 37.20

The results is shown in Table 2, take the number of 3 white asparagus as an example, the minimum and maximum values of the movement distance of the end effector are 228 cm and 303 cm respectively in five different harvesting areas, which is because the position of white asparagus in the ridge is unevenly distributed and 5 groups of harvesting areas is randomly selected. Compared with the longest path distance of the global optimal path planning algorithm, the maximum moving efficiency of the end effector of each group is 51.59%, and the average value of improving efficiency is 41.85%. In the four cases, the maximum efficiency is 46.27%, 51.59%, 46.85% and 44.78% respectively, and the average of the maximum efficiency is 47.37%. Considering the five different harvesting areas, the average of the efficiency in the four cases is 31.94%, 41.85%, 40.27%, 34.75% respectively, the average efficiency increases by 37.20%. Therefore, the path planning algorithm based on sequential harvesting can effectively improve the harvesting efficiency of end-effector. 4.3

Comparison Between Global Optimal Path Planning Algorithm and Path Planning Algorithm Based on Sequential Harvesting

By analyzing and comparing the average of the maximum improvement efficiency in four different cases and the average of the improvement efficiency of the five different collection areas in the four cases, the path planning algorithm based on sequential recovery is compared with the global optimal path planning algorithm, it is about 5% lower. When the number of white asparagus in a harvesting area is more than 5, the path planning algorithm based on sequential harvesting is obviously superior to the global optimal path planning algorithm. And through practical test, when the number of asparagus is 6, according to the global optimal path planning algorithm, a total of 46,080 paths need to be traversed, the time required to determine the optimal path of harvesting is greater than 180 s. When the number of asparagus is 7, there are a total of 645,120 paths that need to be traversed, data is out of memory in the case of simulation with a normal computer. When the number of white asparagus is 6, 7, 8 and 9, the simulation time of harvesting sequence is much less than 1 s by using the sequential harvesting path planning algorithm.

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5 Conclusion In this paper, the harvesting path of the harvesting robot of white asparagus is planned, and the global optimal path planning algorithm and the path planning algorithm based on sequential harvesting are proposed. According to the global optimal path planning algorithm, the path with the smallest running distance of the end-effector can be obtained. The path planning algorithm based on sequential harvesting is adopted, the harvesting sequence of target points can be obtained faster and the operation of the end effector is simple when the white asparagus is placed at the collecting box point. Simulation results show that the algorithm is effective. Both methods can effectively improve the harvesting efficiency, especially in the case of a large number of asparagus, the improved path planning algorithm based on sequential harvesting has a good realtime performance and can better meet the actual needs. Acknowledgements. This work is supported by National Natural Science Foundation of China (51675317), Key R&D plan of Shandong Province (2017GNC12110) and National Key R&D Program of China (2017YFD0701103-3).

References 1. Lu, B.: Development status and development trend of asparagus industry in China. Shanghai Vegetables 12(4), 3–4 (2018) 2. Chen, D., Zhang, Q., Wang, S., et al.: Current status and future solutions for asparagus mechanical harvesting. J. China Agric. Univ. 21(4), 113–120 (2016) 3. Dong, F., Heinemann, W., Kasper, R.: Development of a row guidance system for an autonomous robot for white asparagus harvesting. Comput. Electron. Agric. 79(2), 216–225 (2011) 4. Li, Q., Hu, T., Wu, C., et al.: Review of end-effectors in fruit and vegetable harvesting robot. Trans. Chin. Soc. Agric. Mach. 39(3), 175–179 (2008) 5. Yuan, J., Du, S., Liu, X.: A clip-cut white asparagus harvesting device and harvesting method. ZL201610887545.8 6. Zhang, S., Ai, Y., Zhang, B., Sun, X., Zhang, M., Zhang, T., Song, G.: Path recognition and control of cigarette warehouse robot based on machine vision. Shandong Agric. Sci. 51(03), 128–134 (2019) 7. Wang, X.: Studies on information acquisition and path planning of greenhouse tomato harvesting robot with selective harvesting operation. JiangSu University (2012) 8. Chen, J., Wang, H., Jiang, H., et al.: Design of end-effector for kiwifruit harvesting robot. Trans. Chin. Soc. Agric. Mach. 43(10), 151–154 (2012) 9. Liu, X., Du, S., Yuan, J., Li, Y., Zou, L.: Analysis and experiment on the operation of the end actuator of the white asparagus selective harvester. Trans. Chin. Soc. Agric. Mach. 49 (4), 110–120 (2018) 10. Yuan, Y., Zhang, X., Hu, X.: Algorithm for optimization of apple harvesting path and simulation. Trans. CSAE 25(4), 141–144 (2009)

Optimization of Technological Processes at Production Sites Based on Digital Modeling Pavel Drobintsev(&), Nikita Voinov(&), Lina Kotlyarova(&), Ivan Selin(&), and Olga Aleksandrova(&) Institute of Computer Science and Technology, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia {drob,voinov}@ics2.ecd.spbstu.ru

Abstract. Optimized technological routes at production sites provide more balanced and effective organization of the whole manufacturing process. The approach described in this paper is based on digital modeling and allows to automatically obtain correct and verified technological routes. It includes formalization based on modular technology, optimization, simulation of a product implementation and analysis of simulation results. Keywords: Optimization of technological routes
Digital modeling Automation of technological processes
Simulation




1 Introduction The main value of modern production is information, the amounts of which have become too large for a human to process effectively. Technologies are changing faster than enterprises manage to integrate them; the level of automation is constantly growing. However, it is not enough only to provide modern equipment for a factory, it is necessary to ensure the efficiency of its work. This can be achieved by an adequate analysis of the incoming information and its subsequent processing. At the modern mechanical engineering site, the main work on the manufacturing of products is carried out on equipment with computer numerical control, therefore the optimization of the technological process often comes down to the optimization of the program code for these machines. At the same time, the work on the analysis and processing of information is not always fully automated due to the need to operatively adapt to the production environment, especially for small enterprises with small-scale production. In this area, it is necessary to quickly create production plans that can change depending on the state of the process equipment and manufactured products, and the implementation of plans should be effectively automated. Usually, the tasks of operational planning and automated production management are carried out by the manufacturing execution systems (MES) [1]. They occupy an intermediate place in the hierarchy of enterprise management systems between the level of information collection from equipment in workshops done by supervisory control

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and data acquisition (SCADA) systems [2] and the level of operations over a large amount of administrative, financial and accounting information done by enterprise resource planning (ERP) [3] systems. Nowadays on the Russian market there are three most popular largest solutions: PHOBOS system [4], YSB.Enterprise.Mes system and PolyPlan system [5]. PHOBOS is traditionally used in large and medium-sized mechanical engineering enterprises. YSB.Enterprise.Mes originated from the woodworking industry and focuses on the sector of medium and small enterprises. The PolyPlan system has a smaller set of MES functions, but is positioned as an operational scheduling system for automated and flexible manufacturing in engineering. The developed solution given in this work is designed to solve a narrower class of problems - to simplify the technological preparation of production for small-scale mechanical engineering site, which can be based on the introduction of operative digital modeling and analysis of the technological process of the production site in order to optimize and generate programs for managing and monitoring the production process.

2 Representation of an Initial Technological Route as an MSC Diagram Formalization of a detail uses modular technology, which implies an effective adaptation of the technological process to the product. The choice of surface and compound modules (SMs and CMs) of a detail as objects of classification allows resolving the contradiction between continuous change of products and the desire for consistency in technological equipment. Since the detail is represented by a set of SMs and CMs, the technological processes of details manufacturing are built by assembling them from the modules of technological processes. In this case, the task of the technologist is to provide each SM and CM with standard modules of technological equipment. The process of the formalization is carried out by the technologist on the basis of its drawing. He should highlight the modules to be processed in the drawing and provide the description of each with a variety of design and technological parameters, such as geometry, dimensional accuracy, surface hardness, processing method, necessary equipment, cutting tools, cutting modes etc. For these purposes, the automated workplace of the technologist (AWT) is used, which is shown in Fig. 1.

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Fig. 1. Using AWT to select a cutting tool and its parameters

The technologist obtains the necessary parameters from the drawing, reference catalogues or other documentation. For a number of parameters, ranges of possible values are specified. In addition to the surface modules, the modules for the technological process of manufactured detail, the modules for equipment and gear, the modules for instrumental adjustment and the modules for measuring instruments are described in a similar way. Using modular formalization in AWT, the construction of technological blocks, modules to be processed in which use the same tool for the processing, and technological groups, which divide processing blocks into phases, is automated. As a result, technological routes (TRs) for manufacturing of a detail are formed from technological groups. All this information is recorded in a specialized database. Info about each technological route contains a list of surface modules with specified values of parameters. Information of each surface module contains a detailed description of the manufacturing operations necessary for its processing with symbolic parameters. By creating queries to the database, a route with symbolic parameters, on the basis of which a specific detailed route will be created, is automatically formed. In the approach presented here we use the MSC language [6] for the encoding of the technological route. MSC is a standardized language for describing behaviors using message exchange diagrams between parallel-functioning objects (CNC, robots). The diagram example is shown in Fig. 2.

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Fig. 2. Example MSC diagram of a technological route

The following messages are used in the diagram: 1. Messages about the preparation of the next detail for processing and verification of its suitability to the requirements of the route. 2. Messages checking the requirements for the necessary machinery and the availability of the machine. 3. Requests about processing tools and their working modes. 4. Requests about mounting fixtures. 5. Messages requesting a set of cutting tools from the storage to the CNC. 6. Messages about their retrieval on a pallet from the storage.

3 Optimization of a Technological Route Technological route with symbolic parameters can be converted to a specific one by replacing the character variables with their values. In a case when the range for a value is specified, it is necessary to check its boundaries for the out-of-range error, which is implemented using a symbolic verifier [7]. In the process of proving the correctness of a route, it is possible to check various constraints on the sequence of surface modules within the route formulated by means of the first order logic. The contradictions found in the process of proof can be corrected by imposing additional restrictions on the stated sequences in the route or on the ranges of the parameter values. For the correct technological route with the help of the formulas stored in the database, the technologist can estimate the time and cost of its processing. The fragment of the set of such formulas is shown in Fig. 3.

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Fig. 3. Formulas for turning time calculations

The relative estimate of the route shown in Fig. 4 can be obtained as the sum of the estimates of each operation on each individual surface module that make up the route, which is sufficient for ranking alternative solutions on the choice of parameters of the route. To obtain absolute values, it suffices to use the multiplicative and additive correction factors obtained on the basis of statistical estimates of the technological processes of a particular production.

Fig. 4. A technological route consisting of 4 operations performed on 4 machines

The correct route can be optimized. By changing the parameters of the route within the allowable ranges and re-calculating the indicators of processing time and cost, the technologist can get a solution that meets the limitations of the management on a particular job or get the Pareto-optimal solution [8]. However, it should be noted that the mentioned optimization is valid provided that the production by the route is carried out without taking into account the current state and restrictions on the resources of the production site. Obtaining more realistic estimates is possible with the help of simulation modeling of the distribution of resources for the routes simultaneously performed at the production site. Theoretical basis for the modeling approach including formalization of the structure of a technological process and technological matrix is described in details in previous work of the authors [9]. Shown below in Sect. 4 is a software module used for graphical representation.

4 Digital Modeling and Simulation The digital model of the production site simulates the implementation of the production of different batches of details by different specified technological routes. The site model is built on the basis of information on the resources of the production site (CNC

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machines, transport robots, warehouses, staff etc.) which include amounts of time for their usages. The size of the batch of details is also associated with the route. The model uses the method of dynamic priorities to simulate the workload of resources of the production site and determine the duration of the realization of the technological process for orders. The result of simulation modeling is a schedule for the implementation of the technological process shown in Fig. 5, which provides an estimate of the time to manufacture a batch of details in accordance with a specific route along with an estimate of the lead time for all routes shown in Fig. 6. A set of estimates of the time of execution of the route can be analyzed for the fulfillment of certain criteria and restrictions characterizing the conditions of the order.

Fig. 5. Example of the production schedule chart

Fig. 6. Time and cost estimations example of two technological routes

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In this regard, the following tasks can be solved: 1. Estimation of the minimal amount of additional resources that need to be allocated so that the total time for the implementation of the route is not more than the specified value T0. 2. Redistribution of processing tools between individual operations in order to minimize the total implementation time of the route (optimal transfer of resources from non-critical operations to critical ones). 3. The use of time reserves T0 - T arising when the calculated time T of the implementation of the route is less than the specified value T0 in order to further improve the process. In the process of implementing a specific work schedule (in a certain sense, optimal), various unforeseen failures are possible: machine breakage, shortage of components, unforeseen delays in performing individual operations, etc. Therefore, the management system should continuously monitor the entire process and should have a mode for operative changing of the schedule for the implementation of the remaining work in the new environment in order to optimize it. Thus, it turns out that it is necessary to correct the process of implementing the set of necessary operations in real time taking into account the set requirements for optimization and the formulated criteria of optimality. In addition, when forming the structure of the management system, it is necessary to take into account the possibility of multi-criteria formulation of optimization problems, when several particular indicators of the quality of the production site are set [8]. In this case, the task of ensuring the work of the production site in some Paretooptimal mode can be set. Usually it is advisable here to use some physically justified form of the convolution of the vector optimality criterion and proceed to optimization by the corresponding generalized criterion. When solving problems of managing the work of a production site with a hierarchical structure, it is necessary to take into account the organization of interactions of processes at different hierarchical levels, both among themselves and with the main control center. For this reason, it is advisable to refer to the principles of networkcentric management and methods of coordination in hierarchical systems. It is also advisable to use the methods of hierarchical construction of Pareto sets at various technological levels. The analysis of the results of modeling a set of technological routes consists in solving a multi-criteria task of selecting implementation options for a technological process of the IoT system. It is assumed that the direct solution of the original multidimensional problem with a set of difficultly computable criteria is either impossible or impractical because of the limitations determined by the requirement of execution time and consumed resources balance. The main difficulties are connected with the high dimension of the vector of tunable (selectable) parameters of the IoT system and with a large number of partial optimality criteria. The approach used is based on the application of well-known system analysis procedures to the specific subject area under consideration [10].

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5 Conclusions The final result of the work is the creation of a software system for the automation of the preparation and control of the technological processes of the mechanical engineering production site. Currently, a working prototype of the system has been implemented, on which the following properties have been tested: 1. Ability to quickly adapt to specific production conditions: equipment, resources and orders. 2. Optimization of the characteristics of specific production processes in accordance with the selected set of criteria for its success, carried out on-line. 3. Efficiency assessment of the execution time and cost of the order, which is very important for the small-scale production manager with the flow of orders for small batches of different products that require different technological routes to be performed. The platform provides a significant increase in the productivity of the technologist at the technological preparation phase of production. Total preparation time decreases to approximately 1 day per order. Acknowledgements. The work was financially supported by the Ministry of Science and Higher Education of the Russian Federation in the framework of the Federal Targeted Program for Research and Development in Priority Areas of Advancement of the Russian Scientific and Technological Complex for 2014–2020 (14.584.21.0022, ID RFMEFI58417X0022).

References 1. Miklosey, B.: The basics of MES. Assembly 62(3) (2019) 2. Ford, D.: SCADA is dead: Rethink your approach to automation. In: 91st Annual Water Environment Federation Technical Exhibition and Conference, WEFTEC 2018, pp. 2781– 2785 (2019) 3. Potts, B.: ERP implementation: define what ‘best practice’ means. Plant Eng. 73(3), 10 (2019) 4. “PHOBOS” MES-system. http://www.fobos-mes.ru/fobos-system/fobos-MES-system.html. Accessed 12 Aug 2019 5. “PolyPlan” MES-system. http://polyplan.ru/index.htm. Accessed 12 Aug 2019 6. Recommendation ITUT Z. 120. Message Sequence Chart (MSC), 11/2000 7. Drobintsev, P., Kotlyarov, V., Letichevsky, A., Selin, I.A.: Industrial software verification and testing technology. In: CEUR Workshop Proceedings, vol. 1989, pp. 221–229 (2017) 8. Voinov, N., Chernorutsky, I., Drobintsev, P., Kotlyarov, V.: An approach to net-centric control automation of technological processes within industrial IoT systems. Adv. Manufact. 5(4), 388–393 (2017) 9. Kotlyarov, V., Chernorutsky, I., Drobintsev, P., Voinov, N., Tolstoles, A.: Structural modelling and automation of technological processes within net-centric industrial workshop based on network methods of planning. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds.) Advanced Manufacturing and Automation VIII. IWAMA 2018. Lecture Notes in Electrical Engineering, vol. 484, pp. 475–488 (2019) 10. Chernorutsky, I., Drobintsev, P., Kotlyarov, V., Voinov, N.: A new approach to generation and analysis of gradient methods based on relaxation function. In: 19th IEEE UKSim-AMSS International Conference on Modelling and Simulation, UKSim 2017, pp. 83–88 (2018)

Smart Maintenance in Asset Management – Application with Deep Learning Harald Rødseth1(&), Ragnhild J. Eleftheriadis2, Zhe Li3, and Jingyue Li3 1

Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway [email protected] 2 Product and Production Development, SINTEF Manufacturing AS, Vestre Toten, Norway [email protected] 3 Department of Computer Science, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway {zhel,jingyue.li}@ntnu.no

Abstract. With the onset the digitalization and Industry 4.0, the maintenance function and asset management in a company is forming towards Smart Maintenance. An essential application in smart maintenance is to improve the maintenance planning function with better criticality assessment. With the aid from artificial intelligence it is considered that maintenance planning will provide better and faster decision making in maintenance management. The aim of this article is to develop smart maintenance planning based on principles both from asset management and machine learning. The result demonstrates a use case of criticality assessment for maintenance planning and comprise computation of anomaly degree (AD) as well as calculation of profit loss indicator (PLI). The risk matrix in the criticality assessment is then constructed by both AD and PLI and will then aid the maintenance planner in better and faster decision making. It is concluded that more industrial use cases should be conducted representing different industry branches. Keywords: Smart maintenance


Anomaly detection
Asset management

1 Introduction The mission of asset management can be comprehended as the ability to operate the physical asset in the company through the whole life cycle ensuring suitable return of investment and meeting the defined service and security standards [1]. Further, it is also stated in ISO 55000 that asset management will realize value from the asset in the organization where asset is a thing or item that has potential or actual value for the company [2]. The role of the maintenance function in asset management has been further detailed in EN 16646 standard for physical asset management and considers the relationship between operating and maintaining the asset is [3]. In particular it is © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 608–615, 2020. https://doi.org/10.1007/978-981-15-2341-0_76

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recommended in this standard that dedicated key performance indicator (KPI) can be applied in physical asset management. A proposed KPI that improves the integrated planning process between the maintenance and the production function in asset management is denoted as profit loss indicator (PLI). This indicator evaluates the different types of losses in production from an economic point of view. Also, PLI has been tested in different industry branches such as the petroleum industry [4] and manufacturing industry [5]. With the onset of digitalization in industry enabled by breakthrough innovations from Industry 4.0 changes the maintenance capability in the company. The shift is from a “off-line” maintenance function where data is collected and analyzed manually, towards a digital maintenance [6] and is often denoted as smart maintenance [7, 8]. Artificial intelligent (AI) and machine learning which is a central part of smart maintenance is considered as a fundamental way to process intelligent data. Yet, there is a difference between traditional machine learning and data driven artificial intelligence [9]. The difference lies in the performance of feature extractions, in manufacturing often mentioned as machine learning or Advanced Manufacturing. In this article anomaly detection for smart maintenance will be investigated more in details. Application of AI is also relevant in order to improve the plant uptime. Anomaly in mechanical systems usually cause equipment to breakdown with serious safety and economic impact. For this reason, computer-based anomaly detection systems with high efficiency are imperative to improve the accuracy and reliability of anomaly detection, and prevent unanticipated accidents [10]. From a smart maintenance perspective, the result of a more digitalized asset management should also include that maintenance is planned with insight from the individual equipment in combination of the system perspective of the asset [6]. This need is further supported with empirical studies that points out the necessity for criticality assessment when increasing the productivity through smart maintenance planning [8]. In fact, maintenance planning is regarded to be unlikely to achieve optimum maintenance planning without a sound criticality assessment of the physical asset such as the machines [8]. Smart maintenance has also been denoted with other terms such as deep digital maintenance (DDM) [11] where application of PLI is of relevance. In DDM it still remains to investigate in appropriate scenarios for the planning capabilities in smart maintenance that includes anomaly detection and criticality assessment. The aim of this article is to develop an approach for decision support in smart maintenance planning based on principles both from asset management and machine learning-based anomaly detection and criticality assessment. The future structure of this article is as follows: Sect. 2 presents relevant literature in smart maintenance whereas Sect. 3 demonstrates an essential application in smart maintenance planning where criticality assessment is conducted based on anomaly detection and PLI calculation. Finally, Sect. 4 discuss the results with concluding remarks.

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2 Smart Maintenance in Asset Management 2.1

The Trend Towards Smart Maintenance

To succeed with a successful asset management strategy it has been considered that it is vital to include PLI as an output for the strategy [12]. This has also been included in maintenance planning in the concept deep digital maintenance (DDM) [11]. In DDM it has also been demonstrated maintenance planning for one component. It remains to evaluate several work orders in maintenance planning in DDM. In asset management the machine learning method such as deep learning has gained popularity [13] where e.g. diagnostics of health states of power transformers has been applied [14]. In smart asset management a three-steps approach is proposed [13]: 1. Data gathering from observational data to evaluate the component condition and defining threshold rules. 2. Analysis of historical data to identify patterns that support in predictions of future failures. 3. Leverage the component condition with the defect of the failure. This step will also evaluate the economic perspective in the analysis. Also smart maintenance is outlined as a key element in the Industry 4.0 roadmap for Germany [15]. In this strategic roadmap, smart maintenance is considered to improve the competitive advantage for the maintenance function in the company and is an “enabler” itself for successful Industry 4.0 implementation where maintenance data is shared between manufacturer, operator, and industry service. Furthermore, smart maintenance has also other important characteristics: • A common “language” of maintenance processes defined in EN 17007 [16]. • Maintenance technology support with e.g., artificial intelligence (AI) [7]. In smart maintenance this has been addressed with the need for artificial intelligence (AI) [7]. Despite that maintenance work supported by AI still has barriers to overcome, it is considered to be an effort worth taking. With support from deep learning, we can create knowledge of extracted features in an end-to-end process [9]. For instance, neural networks make the smart data to predict what will happen and take proactive actions based on improved pattern. To succeed with smart maintenance in asset management, the emphasizes of specific plans for maintenance over a long span of time is expected to ensure the greatest value of equipment over its life cycle [7]. In smart maintenance it is also stressed that it is vital to have established criticality assessment in maintenance planning [6, 8]. In particular it is concluded that data-driven machine criticality assessment is essential for achieving smart maintenance planning. 2.2

Smart Maintenance Framework

To ensure value creation in smart maintenance it is necessary to devise a sound framework in smart maintenance. Figure 1 illustrates our proposed framework and is

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inspired from [17] and [18]. The starting point in this framework is the data source and includes external data such as inventory data of spare parts from suppliers. In addition, product data is from the equipment such as condition monitoring data, as well as enterprise data from computerized maintenance management system (CMMS). All the raw data sources are then aggregated in multiple formats in a data cloud.

Fig. 1. Value creation with data in smart maintenance framework inspired from [17] and [18].

The raw data is further applied as for smart data analytics including both predictive and prescriptive analytics. In the maintenance field, the predictive analytics will comprise e.g. forecasts of the technical condition of the machine. To ensure value creation of the physical asset it is also important to include prescriptive analytics that supports in recommended actions in maintenance planning. This will include anomaly detection to evaluate the probability of future machine breakdowns. In addition, it is also necessary to evaluate the consequences of the machine failure. In DDM, the PLI seems promising for this purpose [11]. To assess the criticality of the machine in maintenance planning, both the probability and the consequence can be combined in a risk matrix. The result of smart data is deeper insight in the business, where e.g., the plant capacity has increased as well as deeper insights of the partners where e.g., spare part supply is improved.

3 Smart Maintenance Planning with Criticality Assessment As shown in Fig. 1 and explained above, criticality assessment [8] could be an essential element of prescriptive analysis in our smart maintenance framework. In overall a criticality assessment should evaluate both the probability and consequences for each failure of the equipment. We hereby use a demonstration use case to explain our proposal of criticality assessment with three steps: structured approach (1) PLI estimation; (2) the anomaly degree calculation; (3) the criticality assessment. So far, few

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companies have collected data that can be used for PLI estimation and anomaly detection, we could not get both data from the same machine. The data we present in the case study come from two different machines. However, for the demonstration purpose and for explaining our ideas, we believe it is applicable to merge the data to explain our idea by assuming that the data come from the same machine. Step 1: PLI estimation The calculation of profit loss indicator is applied based on earlier case study from both [11] and [5]. The case study considers the malfunction of an oil cooler in a machine center. The malfunction was first observed when the machine cantered produced scrappage. A quality audit meeting evaluated economic loss of this scrappage. In addition, maintenance personnel conducted inspection on the machine center and found that the cause of this situation was due to malfunction of the oil cooler. This oil cooler was replaced, and the machine center had in total 6 days with downtime. In addition to scrappage it was also evaluated that the machine had lost revenue due to the downtime. Table 1 summarizes the different type of losses that occurred due to this situation of the malfunction of machine center.

Table 1. Expected PLI of malfunction of a machine center based on both [11] and [5]. Situation Damaged part (Scrappage) Quality audit meeting Maintenance labor costs New oil cooler Loss of internal machine revenue Sum

Type of loss Quality loss Quality loss Availability loss Availability loss Availability loss

PLI value/sNOK 120 000 3 500 21 570 47 480 129 600 322 150

When the consequences for the failure has been estimated, the next step is to calculate the anomaly degree (AI) for the physical asset and the industrial equipment’s. Step 2: Anomaly degree (AD) Calculation Figure 2 shows the obtained anomaly degree (AD) of one machine. An increasing AD will indicate an increasing probability of equipment failure. When maintenance planning is conducted, updated information about the anomaly degree for each equipment should be collected and analyzed. Likewise the calculation of PLI, the data used for calculating AD is also from an actual industry equipment. However, the data is not from a machine center and represents another industry branch. The primary datasets include failure records and measurement data from the monitoring system. The target is to obtain the anomaly degree of the equipment by using machine learning based analysis approaches. In the experiment, we labelled both failure and normal records. Thus, the obtained anomaly degree can describe the difference between the target observation and normal samples.

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During the experiment to calculate AD, we adjusted the measurement data collected in different scales to a common scale. Then, we applied standard normalization to preprocess the raw data. The applied machine learning model is constructed through a fully connected deep neural network with seven hidden layers. SoftMax is used as the activation function of the final output layer. Leaky Relu is applied as the activation functions of the hidden layers. The number of nodes in hidden layers of the constructed deep neural network is 64, 32, 32, 16, 16, 8, 2, respectively, to train the neural network smoothly. We selected Adam and categorical cross-entropy as the optimizer and loss function during the training process. Results in Fig. 2 demonstrates the obtained anomaly degree of the machine from the analysis using deep neural network, which represents the degradation of the machine’s health state along the time. Step 3: Criticality assessment When both the probability and the consequences have been evaluated for a future malfunction of a machine, the criticality assessment can be performed in a risk matrix. Figure 3 illustrates a proposed risk matrix in smart maintenance that supports planning of preventive work orders. In the consequence category, the PLI is established for the physical asset and classified as “medium, high” in. The probability category is evaluated with AD. By trending AD in the risk matrix it is possible to evaluate when a preventive maintenance work order should be executed and the possible costs and consequences. The color code is following a traffic-light logic; if the equipment is located in green zone, no further actions are necessary. If the equipment is in a yellow zone, it is an early warning where maintenance actions should be executed. If the equipment is in the red zone, it is an alarm where immediate maintenance actions should be executed. In addition to the color-code system each field in the matrix is marked with a number indicating a priority number. The criticality is of the machine has a yellow code in the start but will have a red color code if no maintenance actions are performed. When the maintenance planner has several machines that are being criticality assessed, it will be possible to prioritize which machine that should be maintained first.

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Fig. 3. Risk matrix as criticality assessment for equipment

4 Discussion and Concluding Remarks This article has demonstrated application of criticality assessment, which can be an essential element of our proposed smart maintenance framework, with application of both PLI as well as anomaly degree calculation. The benefit of this system is that the maintenance planner will have a “digital advisor” for evaluating the anomaly that can enable a faster and better decision-making process in maintenance planning. It is expected that the deep learning method with deep neural network will be further investigated and developed due to it’s promising results in AD. Also, with the aid from PLI calculations, it is possible to improve the evaluation of the consequences of e.g., machine breakdown. In a risk matrix it is then possible to establish a work priority system where some equipment with anomaly should be prioritized before others. For example, if there are future work orders both categorized in yellow sector and red sector, it would recommend to prioritize the work in the red sector. There are also some challenges with the criticality assessment that should be addressed in future research in contribution to theory of criticality assessment. First, it is of importance to improve the accuracy of both anomaly degree calculation as well as calculation of PLI. Second, it will be of importance to evaluate sound criteria for each category in the risk matrix. Yet, this seems to also be a challenge in existing risk matrices. A more practical aspect that needs to be investigated is to evaluate how the digital approach of the risk matrix will interfere with existing criticality assessment and still not reduce the performance of the physical asset. Although a use cases have been applied with data from different industry branches to demonstrate the criticality assessment, further research will also require a coherent demonstration in several industry sectors, including both manufacturing industry as well as the process industry. Acknowledgements. The authors wish to thank for valuable input from both the research project CPS-plant (grant number: 267750), as well as the research project CIRCit – Circular Economy Integration in the Nordic Industry for Enhanced Sustainability and Competitiveness (grant number: 83144).

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References 1. Schneider, J., et al.: Asset management techniques. Int. J. Electr. Power Energy Syst. 28(9), 643–654 (2006) 2. ISO: ISO 55000 Asset management - Overview principles and terminology. Switzerland (2014) 3. CEN, EN 16646: Maintenance - Maintenance within physical asset management (2014) 4. Rødseth, H., et al.: Increased profit and technical condition through new KPIs in maintenance management. In: Koskinen, K.T., et al. (Eds.) Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015), pp. 505–511. Springer, Cham (2016) 5. Rødseth, H., Schjølberg, P.: Data-driven predictive maintenance for green manufacturing. In: Advanced Manufacturing and Automation VI, pp. 36–41. Atlantis Press (2016) 6. Bokrantz, J., et al.: Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. Int. J. Prod. Econ. 191, 154–169 (2017) 7. Yokoyama, A.: Innovative changes for maintenance of railway by using ICT-to achieve “smart maintenance”. Procedia CIRP 8, 24–29 (2015) 8. Gopalakrishnan, M., et al.: Machine criticality assessment for productivity improvement: smart maintenance decision support. Int. J. Prod. Perform. Manage. 68(5), 858–878 (2019) 9. Wang, J., et al.: Deep learning for smart manufacturing: methods and applications. J. Manufact. Syst. 48, 144–156 (2018) 10. Li, Z., et al.: A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment. Int. J. Adv. Manufact. Technol. 103(1), 499–510 (2019) 11. Rødseth, H., Schjølberg, P., Marhaug, A.: Deep digital maintenance. Adv. Manufact. 5(4), 299–310 (2017) 12. Rødseth, H., Eleftheradis, R.: Successful asset management strategy implementation of cyber-physical systems. In: WCEAM 2108 (2019) 13. Khuntia, S.R., Rueda, J., Meijden, M.: Smart Asset Management for Electric Utilities: Big Data and Future (2017) 14. Tamilselvan, P., Wang, P.: Failure diagnosis using deep belief learning based health state classification. Reliab. Eng. Syst. Saf. 115, 124–135 (2013) 15. DIN, German Standardization Roadmap - Industry 4.0, in Version 3, Berlin (2018) 16. CEN, EN 17007: Maintenance process and associated indicators (2017) 17. Porter, M.E., Heppelmann, J.E.: How smart, connected products are transforming companies. Harvard Bus. Rev. 93(10), 96–114 (2015) 18. Schlegel, P., Briele, K., Schmitt, R.H.: Autonomous data-driven quality control in selflearning production systems. In: Proceedings of the 8th Congress of the German Academic Association for Production Technology (WGP), Aachen, 19–20 November 2018, pp. 679– 689 (2019)

Maintenance Advisor Using SecondaryUncertainty-Varying Type-2 Fuzzy Logic System for Offshore Power Systems Haitao Sang(&) College of Information Engineering, Lingnan Normal University, Zhanjiang, People’s Republic of China [email protected]

Abstract. Recently, Condition-based maintenance is a popular method to minimize the cost of maintenance failures in power systems. In order to effectively overcome the uncertainty of operational variables and information in offshore substations, a Type-2 fuzzy logic approach is proposed in this paper. The maintenance advisor optimize the maintenance schedules with multiobjective evolutionary algorithm, considering only major system variables. During operation, the offshore substation will experience continuing ageing and shifts in control, weather and load factors, measurement and all other equipments with uncertainties. More importantly, the advisor estimates the changes of reliability indices by Type-2 fuzzy logic and sends the changes back to the maintenance optimizer. At the same time, the maintenance advisor will also report to the maintenance optimizer any drastic deterioration of load-point reliability within each substation. The data analysis results shows this approach avoids complex inference process, it significantly reduces the computational complexity and rule base than conventional Type-1 fuzzy logic. Keywords: Adaptive maintenance advisor
System maintenance optimizer Offshore substation
Multi-objective evolutionary algorithm
Type-2 fuzzy logic




1 Introduction Proper condition-based maintenance schedules are very desirable to extend component lifetime in energy system. However, some uncertainties are associated with component reliability in power systems due to lack of upgrading of data [1, 2]. Offshore systems are often remotely located and the acquisition of data is more difficult than onshore systems. Hence more powerful tools are needed to deal with those uncertainties for continuous monitoring [3]. Fuzzy sets theory was proposed by Zadeh [4] to resemble human reasoning under uncertainties by using approximate information [5] to generate proper decision. Some attempt using type-1 fuzzy logic has also been carried out to handle uncertainties related to component reliability in power-system maintenance problems [6]. Fuzzy Markov model was employed to describe transition rates [7]. Zadeh further proposed the alternative type-2 fuzzy logic in order to handle the uncertainties in type-1 © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 616–624, 2020. https://doi.org/10.1007/978-981-15-2341-0_77

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membership functions [8]. In power-system applications, planned and unplanned operational variations occur continually, which can degrade the quality of maintenance scheduling of power systems. Such degradations can even be more pronounced for offshore power systems. The unplanned operational variations occurring in offshore substations are represented in by an independent set of fuzzy memberships for ensuring the quality of maintenance scheduling [9]. In this paper, type-2 fuzzy logic learning and analysis system is linked to a hidden Markov model and the type-2 fuzzy hidden Markov model analysis is proposed to analyze the reliability indices of the offshore power system. The remaining part of this paper is organized as follows. Section 2 presents the reliability models in system maintenance optimizer. Section 3 describes the secondaryuncertainty-varying type-2 fuzzy logic system for modeling operational variations and uncertainties of key components. Section 4 presents the relative impacts of various operational variations and uncertainties on the system reliability and maintenance schedules. This section also illustrates the advantages of type-2 against type-1 fuzzy logic. Section 5 concludes the paper.

2 Reliability Models in System Maintenance Optimizer Figure 1 outlines the type-2 fuzzy hidden Markov model for individual offshore power equipment. A regular Markov model has been used to provide a quantitative connection between maintenance and reliability [10]. Di, i = 1, 2, … N. D1 denotes the “as good as new” state, D2, D3,…, Dn are the states with different levels of deteriorations, and Df is the failed state. The transition rates among different states form the matrix K.

Fig. 1. Type-2 fuzzy hidden Markov model

DKðtÞ ¼ fT2 ðCðtÞÞ

ð1Þ

where C(t) represents the different operational variations and uncertainties for individual component in the time interval t. fT2 represents the mapping function of fuzzy logic system from the input C(t) to output DK(t). Given DKðtÞ, the reliability indices, such as MTTF and failure probability, which are obtained directly from the Markov model can be updated according to the day-to-day operation conditions.

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The configuration of a power system directly affects the load-point reliability. Minimum cut set method is believed to be particularly well suited to the reliability analysis of power systems [10]. By definition, a minimum cut set is systematic and hence easily implementable on a computer. It is a unique and necessary combination of component failures which cause system failure. From a reliability point of view, all the component failures in a minimum cut set can be viewed as connected in parallel, while all the minimum cut set associated with one event can be viewed as connected in series. Therefore, a system can be converted into a reliability block diagram based on its minimum cut sets and then be evaluated easily following the rules used for the simple configurations (series or parallel).

3 Intelligent Maintenance Advisor with Type-2 Fuzzy Logic System In a fuzzy logic system, the overall effects of uncertainties on reliability are captured by developing a rule-base expert system based on the available data. The rules are chained together by a reasoning process known as inference engine. The methods to propagate the uncertainties among the rules are essential for a inference engine, and are accomplished by the experts who are well acquainted with the characteristics of the operation in power systems. The inputs and outputs in a fuzzy logic system are combined through “IF-THEN” rules given by experts using the fuzzy inference engine to get the fuzzified output. In this work, a simpler way to implement the type-2 fuzzy logic is proposed, namely secondary-uncertainty-varying type-2 fuzzy logic system. The secondary uncertainty is captured by initializing a group of primary membership functions. As shown in Fig. 2, at a specific value x ¼ x0 ðx 2 XÞ, the membership functions take on the values wherever the vertical line intersects them. As a result, there are a range of membership values at x = x′, each of which is given by one specific membership function.

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Figure 3 is a schematic diagram the secondary-uncertainty-varying type-2 fuzzy logic system used in the intelligent maintenance advisor. As can be seen in Fig. 3, the implementation of this fuzzy logic system has two steps, (i) choice of primary membership functions, and (ii) mapping the primary input to output. For example, if the input (primary variable) is “short” time from previous maintenance, and the previous maintenance is “minor maintenance”, the choice of primary membership function is chosen firstly, and then sent to the fuzzifier 1. After that, the inference 1 will map this primary input to a output based on rules 1. Finally, a defuzzified output can be generated. If there are more operational variations to be considered, their influences can be incorporated by more fuzzy inputs and modification of fuzzy inference engine.

Fig. 3. Schematic diagram of secondary-uncertainty-varying Type-2 fuzzy logic system

4 Results and Discussion 4.1

Case Study and Parameters

The adaptive maintenance advisor first obtains the initial maintenance plan from the system maintenance optimizer. The method is presented in detail by application to a ring bus configuration and used to evaluate the effects of offshore stations in an analysis of the IEEE-reliability test system. The basic failure data of the transformer without any maintenance in the 1st maintenance interval. Different priorities are assigned to each load point to reflect the importance of the load they transfer. In this work, load point 2 has priority 1 because it transfers the load back to the medium-size system it connects, while load point 1 has the lower priority because it provides the load to personal customers. 4.2

Advantage of Secondary-Uncertainty-Varying Type-2 Fuzzy Logic in Reducing Computational Complexity

Take the fuzzy logic system designed for the transformer as the example to show the ease to implement of this proposed type-2 fuzzy logic system. In type-2 fuzzy logic

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system, age, load and time from previous maintenance are the input of transformer, each input has three membership functions. In addition, three additional uncertainties are superimposed on three inputs respectively, which are represented by three fuzzy sets. If type-1 fuzzy system is used to express uncertainty, it needs 729 rules, while type-2 fuzzy system needs 87 rules. Therefore, in dealing with uncertainty, type-2 fuzzy logic is superior to type-1 Fuzzy Logic in computational complexity. 4.3

Impacts of Operational Variations and Uncertainties on Optimization of Maintenance Schedules

The simulation studies have been conducted to cover different aspects of the operational variations and uncertainties. We assume that the same type of components experience the same operational conditions. The impact of each operational variation on the optimization of maintenance schedule as well as system reliability is investigated individually by assuming the rest operational variations remain static. (a) Impacts of age: the age of the transformer is modeled as the operational variation, and the component conditions at every age are the uncertainty, as shown in Fig. 4. The Pareto front in Fig. 5 gives a holistic view of optimal solutions considering different operational conditions. The maintenance schedule S provides the ENS of 1.36  104 MWh/y and failure cost of $2.5  105 with the operational cost of $2.13  105. However, when considering the continuing aging, the same reliability provided by solutions S can only be guaranteed by another solution A1 with higher operational cost of $3.39  105, as shown in Fig. 6. Furthermore, including uncertainty of age requires A2 to be chosen in order to provide the same reliability with lower operational cost than A1. The variations of ENS of both load points are plotted in Fig. 6(a) & (b), illustrating the effects of different maintenance schedules. As stated before, all of the three maintenance schedules S, A1, and A2 provide the same reliability. With initial operational conditions, maintenance schedule S continuously reduces the ENS. The ENS is more significantly reduced by maintenance schedules A1 and A2 from the beginning in order to counteract the increase of ENS caused by the aging of components from the 12th interval. However, the schedule A2 is less effective in reducing the ENS compared to A1. This is because that the good component conditions as shown in Fig. 4 slow up the increase of ENS caused by component aging, and require less efforts from the maintenance to provide the satisfactory reliability. The different Pareto fronts and variations of ENS indicate that type-2 fuzzy expert system successfully captures the impacts of component aging and uncertainty. In addition, compare Fig. 6(a) to (b), it can be seen that load point 2 suffers less ENS than load point 1. This is because that operations and maintenance are planned in order to firstly ensure the reliability of load point 2 which is more critical and assigned with higher priority.

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(b) Impacts of load: The load factor is modeled as an operational variation (Fig. 7), and there is no uncertainty associated with load. Pareto fronts before and after considering the varying load are shown in Fig. 8, showing the impacts of load on the optimization of maintenance schedules. It can be seen that the fuzzy expert system correctly relates higher load factor to higher ENS.

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Load Factor (%)

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(c) Impacts of maintenance information: The time from previous maintenance for the transformers is given in Fig. 9. The Pareto fronts are plotted to show the impacts of different operational conditions on the scheduling of maintenance in Fig. 10. As can be seen in Fig. 10, the longer time from previous maintenance in intervals 4, 8, and 19 makes the schedule C1 be chosen rather than S to provide the ENS of 1.36  105 MWh/y. Figure 11 shows the variations of ENS due to performing the schedules S and C1. As expected, the fuzzy system correctly relates the higher ENS with longer time lapse from previous maintenance and the lower ENS with shorter lapse from previous maintenance.

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5 Conclusions This paper proposes an approach for implementing a system-optimized maintenance plan on each offshore substation, and for estimating the change of load-point reliability due to operational variations and uncertainties of its key components. The maintenance advisor will report any drastic deterioration of load-point reliability within the substation, and requires the maintenance optimizer to re-optimize the substation’s maintenance activities for meeting its desired reliability during operation. Type-2 fuzzy logic is demonstrated to be superior to type-1 fuzzy logic for modeling operational variations and uncertainties arising from aging, load factor and time from previous maintenance. The operational variations and uncertainties for the transformer are shown to have a significant impact on the maintenance scheduling as well as load-point reliability. Acknowledgements. This work is supported by the Competitive Allocation of Special Funds for Science and Technology Innovation Strategy in Guangdong Province of China (NO. 2018A06001)

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References 1. Wang, K., Wang, Y.: How AI affects the future predictive maintenance: a primer of deep learning. In: IWAMA 2017. Lecture Notes in Electrical Engineering, vol. 451 (2018) 2. Mo, H., Sansavini, G., Xie, M.: Performance-based maintenance of gas turbines for reliable control of degraded power systems. Mech. Syst. Sig. Process. 103, 398–412 (2018) 3. Dai, Z., Zhang, T., Liu, X., et al.: Research on smart substation protection system reliability for condition-based maintenance. Power Syst. Prot. Control 44(16), 14–21 (2016) 4. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965) 5. Endrenyi, J.: Reliability Modeling in Electric Power Systems. Wiley, New York (1978) 6. Mohanta, D.K., Sadhu, P.K., Chakrabarti, R.: Fuzzy Markov model for determination of fuzzy state probabilities of generating units including the effect of maintenance scheduling. IEEE Trans. Power Syst. 20(4), 2117–2124 (2005) 7. Tanrioven, M., et al.: A new approach to real-time reliability analysis of transmission system using fuzzy Markov model. Int. J. Electr. Power Energy Syst. 26(10), 821–832 (2004) 8. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning I. Inf. Sci. 8(3), 199–249 (1975) 9. Pająk, M.: Modelling of the operation and maintenance tasks of a complex power industry system in the fuzzy technical states space. In: International Scientific Conference on Electric Power Engineering. IEEE (2017) 10. Yang, F., Kwan, C.M., Chang, C.S.: Multi-objective evolutionary optimization of substation maintenance using decision-varying Markov model. IEEE Trans. Power Syst. 23(3), 1328– 1335 (2008)

Determine Reducing Sugar Content in Potatoes Using Hyperspectral Combined with VISSA Algorithm Wei Jiang, Ming Li, and Yao Liu(&) School of Information Engineering, Lingnan Normal University, Zhanjiang, China [email protected]

Abstract. In order to explore nondestructive and rapid detection of reducing sugar in potatoes, hyperspectral imaging technology was applied for quantitatively analyze reducing sugar in potatoes. A quantitative analysis model of reducing sugar in potatoes was constructed by partial least squares method. Sacitzky-Golay (SG) smoothing filter, standard normal variable transformation (SNV), first derivative (FD), multivariate scattering correction (MSC) and other optimization models were used. Variable Iterative Space Shrinkage algorithm (VISSA) is proposed for feature wavelength selection, and compared with competitive adaptive weighting algorithm (CARS). A total of 229 samples were prepared, and the SXYP method was used to divide the samples. 181 samples were selected as the correction set and the remaining 48 samples as the verification set. The results showed that, the model of reducing sugar content in potato spectrum pretreated by SG + SNV was the best, and the partial least squares regression model (VISSA-PLS) based on VISSA algorithm to select characteristic variables had good prediction ability. The determination coefficient of model validation set was 0.8144, and the root mean square error of validation set was 0.0238. It was concluded that the model has good predictive performance after optimization and achieves rapid and nondestructive detection of reducing sugar in potatoes. Keywords: Hyperspectral


Potato
Reducing sugar
Wavelength selection

1 Introduction Reducing sugar content in potatoes is an important factor to determine the processing quality of potato chips [1]. Because reducing sugar reacts with alpha-amino acids of nitrogen compounds in the frying process, the surface color of potato chips becomes brown and unpopular with consumers [2]. In order to further improve potato breeding and deep processing technology, accurate and rapid determination of reducing sugar content in potatoes is of great significance. Although the traditional method for detecting reducing sugar content in potatoes has high accuracy, it is difficult to popularize in the analysis and detection of a large number of samples because of its complicated operation, strong destructiveness and high cost. Rapid and non-destructive intelligent detection of reducing sugar content in potatoes has important application prospects [3]. © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 625–632, 2020. https://doi.org/10.1007/978-981-15-2341-0_78

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Hyperspectral imaging technology is a new non-destructive detection technology. Because of its fast and nondestructive advantages, hyperspectral imaging technology has been widely studied in the field of food quality detection. At present, many scholars at home and abroad have established hyperspectral models for potato quality detection, including potassium [4], water [5], dry matter [6, 7], starch [8], protein [9, 10] and sugar [11, 12]. Angel and others [13–16] have studied the hyperspectral qualitative detection methods for potato internal and external damage. These studies laid the groundwork for the hyperspectral detection of potato quality. However, due to the large amount of redundant information in the spectrum, which seriously affects the speed and accuracy of modeling, and the strong correlation between bands, not all wavelengths can provide useful information. Previous studies have shown that optimizing the wavelength variables of hyperspectral detection, eliminating irrelevant or non-linear variables, reducing redundant information in the spectrum, simplifying the model, improving the prediction ability and robustness of the model [17]. In order to further explore the optimal method of hyperspectral characteristic variables of potatoes, an optimized prediction model of reducing sugar content was established. In this paper, VISSA algorithm is proposed for selection characteristic variables, and compared with CARS algorithm. Partial Least Squares (PLS) model is established and validated by validation set. The results of two selection methods in predicting reducing sugar content in potatoes are compared comprehensively, and the results suitable for reducing sugar content in potatoes are obtained. The optimal variable selection method for quantitative analysis of reducing sugar content in potatoes was obtained, which provided theoretical basis for the development of portable hyperspectral intelligent detection device for potato quality.

2 Materials and Methods 2.1

Sample Preparation and Determination

The fresh potatoes used in the experiment were purchased from Keshan 885 potatoes in the agricultural market of Xiangfang District, Harbin. Before the experiment, the potato surface was cleaned and the obvious surface defects were removed. A total of 229 samples were used for image acquisition. Then the content of reducing sugar in potato was determined by 3,5-dinitrosalicylic acid colorimetry. SPXY method was used to divide the samples into 3:1. 181 samples were selected as calibration set and 48 samples were selected as validation sample set. The calibration set sample is used to build the model, and the validation set sample is used to test the prediction performance of the model. Table 1 shows the distribution statistics of reducing sugar content in potatoes from the calibration set and the validation set samples. The values of reducing sugar content in 181 calibration sets ranged from 0.09 to 1.32, which basically covered the distribution range of reducing sugar value of potatoes and was representative.

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The hyperspectral reflectance image acquisition system produced by HeadWall Company of the United States was used in the experiment. The system consists of image acquisition unit, light source and sample conveying platform. The image acquisition unit includes image spectrometer, CCD camera and lens, and the light source is 150 W adjustable power halogen lamp. The spectral resolution is 1.29 nm and the spatial resolution is 0.15 mm. The schematic diagram of the potato reflectance hyperspectral detection system is shown in Fig. 1.

Table 1. Statistics of reducing sugar content distribution in potato Samples set Numble Minimum Maximum Mean Calibration set 181 0.09 1.32 0.816 Validation set 48 0.12 1.28 0.936

1. Hyperspectral camera 2. Bracket 3. light source 4. Loading stage 5. Light box 6. Collector 7.Computer Fig. 1. Schematic diagram of potato reflectance hyperspectral detection system

In the experiment, in order to reduce the interference of light source on image caused by temperature change, every 10 sample images are collected, and the full white and full black calibration images are collected once. According to formula (1), the corrected hyperspectral images are obtained [12, 17]. I¼

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Variable Iterative Space Shrinkage Algorithms

VISSA algorithm is a variable selection optimization strategy based on MPA framework proposed by Deng et al. The basic idea of the algorithm is to make full use of the statistical information obtained by MPA, select the best combination of variables with the best prediction ability, and cross-validate as the object function of model selection [18]. The flow chart of VISSA algorithm is shown in Fig. 2. In this study, VISSA algorithm is used to select characteristic wavelength, X is the extracted 181 * 203 spectral matrix, Y is the corresponding concentration matrix size 181 * 1.

Fig. 2. Flow chart of VISSA algorithm

2.4

Running Environment and Software Code

The hardware information used in the experiment is as follows: InterCore Dual Core (i7-8550) 1.99G, memory 8G, hard disk 500G. Software: Windows 7 is used as the operating system, and the software The Unscramle X 10.3 and MATLAB 2013 are used for spectral preprocessing, wavelength filtering and modeling.

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3 Results and Analysis 3.1

Spectral Analysis and Pretreatment

The original reflectance spectra of potato samples in the region of interest (ROI) range of 400–1000 nm were collected by diffuse reflectance method. As shown in Fig. 3(a), the sampling interval was 3 nm and each spectrum contained 203 bands. Reduced sugars are sugars containing reductive groups (e.g. aldehydes or ketones) in their molecular structure, and their constituent elements are C, H and O. In Fig. 3, there are obvious absorption peaks near 960 nm (Dotted line position), mainly the triple frequency absorption of O-H group, because the internal components of potatoes contain water [19]. Due to the rough skin of potato and stray light in the environment, there are large scattering and baseline drift in the spectral region. Therefore, pretreatment is needed before further spectral analysis. In this paper, Sacitzky-Golay (SG) smoothing, standardization, maximum normalization, multivariate scattering correction (MSC), first derivative (FD), standard normal variable transformation (SNV) and SG + MSC, SG + FD, SG + SNV are used to pretreat the spectra respectively, and PLS models are established. The original spectra and pretreatments are compared in turn. According to the principle of maximum determination coefficient and minimum root mean square error, the SG + SNV method is determined to have the best prediction effect, which can improve the prediction ability of the calibration model. Figure 3(b) is a spectral image preprocessed by SG + SNV. The trend of the curve is similar to that of the original spectrum.

Fig. 3. Spectra curves of potatoes

3.2

Selection of Characteristic Wavelength

In order to simplify the model, after eliminating spectral noise by SG + SNV pretreatment, variable space iterative shrinkage algorithm (VISSA) was proposed to selection the spectral bands of potatoes, and compared with CARS algorithm. According to five-fold cross validation, the maximum number of latent variables was set to ten, and the variables were selected. The position information of the wavelength selected by the two methods on the potato spectral data set is shown in

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Fig. 4(a)–(b). VISSA method screened out 32 optimal variables related to reducing sugar, and CARS selected 30 characteristic variables. Compared to the full spectra results, the number of variables decreased by 84% and 85%, respectively. Obviously, the wavelength points near 430–440 nm, 520–530 nm, 750 nm and 970 nm are chosen for both methods. The fourth and fifth frequency absorption peaks of O-H bond and C-H bond corresponding to the chemical structure of reducing sugar near 750 nm are also important wavelength points for establishing quantitative models of sugar content commonly used in literatures. This also proves that CARS and VISSA are very effective feature wavelength selection algorithms in potato sample system. However, if we compare the details, we can find that the CARS algorithm also chooses some wavelength points, such as the wavelength points near 810 and 830 nm, which is why the prediction result of the CARS algorithm is slightly worse than that of VISSA.

(a) VISSA

(b)CARS

Fig. 4. The wavelength selected by different methods on the potato. (a) VISSA; (b) CARS.

3.3

Establishment of Prediction Model

The characteristic variables selected by VISSA and CARS algorithm were used as input variables of PLS model, and the reducing sugar content of potato was used as dependent variables to establish PLSR regression model. In order to better analyze the selection effect of feature variables, the full-band data are also used for modeling and comparison. The results are shown in Table 2. The R2c values of PLS models based on CARS and VISSA are higher than those of the whole spectrum, and RMSECV values are lower than those of the whole band, which shows that the combination of these two variable selection methods with PLS model has a good correction effect. The prediction accuracy of VISSA-PLS model is the highest, with the highest R2p value of 0.8144 and the smallest RMSEP value of 0.0228, and the number of variables selected by VISSA is 32, which is far less than the number of variables in the whole band 203. The results showed that VISSA-PLS model could predict the reducing sugar content of potato quickly. Figure 5 is the prediction result of VISSA-PLS. It can be seen that the samples are evenly distributed around the regression line (y = x), which indicates that the

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significance level is high and the model established has good predictability. Therefore, VISSA-PLS was selected as the prediction model of reducing sugar content in potatoes. Table 2. Predicting results of by different selection methods Methods

N

nLVs Calibration set RMSECV R2 c

Validation set RMSEP R2 p

Full-PLS 203 9 0.0461 0.7858 0.0275 0.7729 VISSA-PLS 32 4 0.0392 0.8255 0.0238 0.8144 CARS-PLS 30 8 0.0424 0.8027 0.0257 0.7992 Note: Calibration samples n = 181, Validation samples n = 48. Partial least squares (PLS), Root mean square error (RMSE), N: number of variables, nLVs: number of latent variables.

Fig. 5. Reducing sugar content predicted results of VISSA-PLS model

4 Conclusions In this paper, hyperspectral imaging technology was used to detect the reducing sugar content of potatoes rapidly and nondestructively. The average spectral data of samples (400–1000 nm) were obtained. A new VISSA algorithm was used to extract characteristic wavelengths representing effective spectral information. After VISSA extraction, 32 wavelength points were mostly concentrated between 420–440 nm and 520– 530 nm, which accorded with the characteristics of spectral curves. The PLSR regression model of reducing sugar content was established with 32 characteristic wavelengths as input variables. The results were better than those of the whole band. The RMSECV was 0.0392 and the RMSEP was 0.0238. The results showed that the calibration model VISSA-PLS based on hyperspectral images had a higher value. Prediction accuracy. Acknowledgements. The work is supported by Science and Technology Innovation Strategy fund Project of Guangdong Province (Grant no. 2018A03017), Zhanjiang Science and Technology Project (Grant no. 2017B01143) and Special Innovation Projects of Universities in Guangdong Province (Grant no. 2018KTSCX130).

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References 1. Cui, H., Shi, G., An, J.: Comparison study on the testing method of the content of reduced sugar in potato. J. Anhui Agri. Sci. 34(19), 4821–4823 (2006) 2. Horvat, Š., Roščić, M., Horvat, J.: Synthesis of hexose-related imidazolidinones: novel glycation products in the Maillard reaction. Glycoconjugate J. 16(8), 391–398 (1999) 3. Yang, B., Zhang, X., Zhao, F., et al.: Suitability evaluation of different potato cultivars for processing products. Trans. Chin. Soc. Agric. Eng. 31(20), 301–308 (2015) 4. Liu, C., Gao, H., Li, A., et al.: Near-infrared model establishment for testing potato tubers potassium content. Chin. Potato J. 2, 65–68 (2011) 5. Song, J., Wu, C.: Simultaneous detection of quality nutrients of potatoes based on hyperspectral imaging technique. J. Henan Univ. Technol. 37(1), 60–67 (2016) 6. Helgerud, T., Wold, J.P., Pedersen, M.B., et al.: Towards on-line prediction of dry matter content in whole unpeeled potatoes using near-infrared spectroscopy. Talanta 143, 138–144 (2015) 7. Chen, Z., Feng, H., Yin, S., et al.: Assessment of potato dry matter concentration using VISSWIR spectroscopy. J. Heilongjiang Bayi Agric. Univ. 30(2), 47–51 (2018) 8. Jiang, W., Fang, J., Wang, S., et al.: Detection of starch content in potato based on hyperspectral imaging technique. Int. J. Sig. Process. Image Process. Pattern Recogn. 8(12), 49–58 (2015) 9. Chen, M., Chen, Y., Zhang, Y., et al.: Determination of soluble protein in potato by attenvated total reflection mid-infrared spectroscopy. J. Chin. Cereals Oils Assoc. 33(12), 118–125 (2018) 10. López, A., Arazuri, S., Jarén, C., et al.: Crude protein content determination of potatoes by NIRS technology. Procedia Technol. 8, 488–492 (2013) 11. Ahmed, R., Daniel, G., Lu, R.: Evaluation of sugar content of potatoes using hyperspectral imaging. Food Bioprocess Technol. 8(5), 995–1010 (2015) 12. Jiang, W., Fang, J., Wang, S., et al.: Hyperspectral determination of reducing sugar in potatoes based on CARS. Int. J. Hybrid Inf. Technol. 9(9), 35–44 (2016) 13. Dacal-Nieto, A., Formella, A., Carrión, P., Vazquez-Fernandez, E., et al.: Common scab detection on potatoes using an infrared hyperspectral imaging system. Image Anal. Process. 6979, 303–312 (2011) 14. Ainara, L., Janos, C.K., Mohammad, G., et al.: Non-destructive detection of blackspot in potatoes. by Vis-NIR and SWIR hyperspectral imaging. Food Control 70, 229–241 (2016) 15. Huang, T., Li, X., Jin, R., et al.: Non-destructive detection research for hollow Heart of potato based on semi-transmission hyperspectral imaging and SVM. Spectrosc. Spectral Anal. 35(1), 198–202 (2015) 16. Wang, C., Li, X., Wu, Z., et al.: Machine vision detecting potato mechanical damage based on manifold learning algorithm. Trans. Chin. Soc. Agric. Eng. 30(1), 245–252 (2014) 17. Zheng, J., Zhou, Z., Zhong, M., et al.: Chestnut browning detected with near-infrared spectroscopy and a random-frog algorithm. J. Zhejiang A & F Univ. 33(2), 322–329 (2016) 18. Deng, B., Yun, Y., Liang, Y., et al.: A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling. Analyst 139, 4836–4845 (2014) 19. Xu, Y., Wang, X., Yin, X., et al.: Visualization spatial assessment of potato dry matter. J. Agric. Mach. 49(2), 339 (2018)

Game Theory in the Fashion Industry: How Can H&M Use Game Theory to Determine Their Marketing Strategy? Chloe Luo and Yi Wang(&) Plymouth Business School, University of Plymouth, Drake Circus, Plymouth, Devon PL4 8AA, UK [email protected], [email protected]

Abstract. The fashion industry is a very competitive industry and it is hard to stay unique and stand out. It is hard to ensure a large consumer base and a high profit margin but each company is differentiated by brand and what their marketing strategy is. Many retailers like H&M are struggling to stand out and maintain a unique brand whilst satisfying their consumer base. Game theory allows a company to determine the best strategy in situations where you are faced with competing strategies. This paper demonstrates how game theory is applied in the interest of all participating businesses to conform rather than compete. Keywords: Game theory analysis


Retail management
Fashion industry
Critical

1 Introduction The Game theory is a framework of theory to conceive social situations amongst competing players. Game theory is like the science of strategy that deals with real life situations. The key pioneers are John von Neumann and Josh Nash who were both mathematicians [1]. The ‘prisoner’s dilemma’ is a prime example of game theory. The example explains how two criminals can either betray one another to gain freedom while increasing the others prison sentence, or maintain silent so both sentences can be reduce but if they betray each other, then their sentence stays the same [2]. Game theory is often used in business and most commonly known within an oligopoly, this means that companies settle on a similar pricing structure that is agreed on by majority of the businesses, or offer a lower price in competition with other businesses. On the other hand however, once a company decides to not conform and take a competitive advantage, all the other companies are typically either at a loss or follow in line [3]. H&M is a company that is right in the middle of that as their business has multiple competitors regarding product, price and structure. At the moment H&M are struggling to find the best marketing strategy for them in order to help them meet their goals as there are too many competing businesses in the same industry. H&M [4] identify themselves as a sustainable fashion source which helps them stand out and influence consumers to purchase their product, however, more and more fashion © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 633–638, 2020. https://doi.org/10.1007/978-981-15-2341-0_79

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companies are going down the sustainable route so what else can they do to maintain a large consumer base as well as make a profit. By having a strong marketing strategy it should help them achieve both a large consumer base and high profits. The structure of this paper is as follows: Sect. 2 presents an overview of the game theory concept. Section 3 presents the application of game theory in the industry, Sect. 4 identifies the limitation of game theory as an application and, lastly, Sect. 5 concludes the paper.

2 Literature Review Game theory started out as an attempt to find solutions for duopoly, oligopoly and bilateral monopoly problems. Within all these situations, a solution was difficult to come to as the interests and strategies of the organisations or individuals were conflicting. Hence why game theory was used in attempts to come to various equilibrium solutions which is based on rational behaviour of the participants involved. Companies are now increasingly utilising game theory to assist them in making high risk/high reward decisions in highly competitive markets. Game theory has been around for a long time and proven an ability to provide ideal strategic choices in multiple different situations, companies and industries. This theory is a very useful tool for predicting what may happen between a group of firms interacting, where the actions of a single firm can directly affect the payoff of other firms. Each participating player is a decision maker in the game of business [5]. So, when making a choice or choosing a strategy, all players, also known as firms, must take in consideration the potential choices and payoffs caused by other firms. This understanding that is quantified through payoff calculations allows a company to form their best strategy. A properly formed game can assist many businesses by reducing business risk. This can be done by yielding valuable insights into competitors and improving internal alignments around decision making which maximises strategic utility [6]. Not only is game theory used to gain valuable insight into competition but it can assist majorly when trying to make strategic decisions in relation to any business function. For example, game theory is excellent for situations like auctions, product decisions, bargaining and supply chain decisions etc. By applying game theory to all these functions that are used in the day to day running of a business it can help the company make the most strategic choices as you will be able to see all the outcomes. Peleckis [7] discusses how using game theory is effective in determining equilibrium within a market. It is used during risk analysis to determine optimal price strategy, number of customers and market share etc., to reduct business risk.

3 Game Theory in the Fashion Industry Experience In relation to marketing in the fashion industry, Game theory can allow companies to see what type of marketing will benefit them the most [8]. Researchers debated whether it is possible to apply this theory to solve problems regarding marketing, especially predicting competitive behaviour [9]. The discussion on whether

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game theory can be used in marketing then extended to other possibilities. Nash equilibrium is also a very important concept to consider when referring to game theory as it refers to a stable state in a game where no player can gain an advantage by changing their strategy, though this theory is typically used in economics. The fashion industry already uses game theory but they use it to determine how customers will buy their clothes. The fashion industry is hard for both buyers and sellers. Buyers are constantly trying to find good deals on clothes that suit them. Sellers on the other hand want to move as much inventory as possible at the best price possible. The solution to both worlds is sales. Sales move high inventory for sellers and buyers get reasonably prices clothing [10]. This can be seen as using game theory as they’ve found the best solution for both players. If sellers kept prices high all the time, buyers wouldn’t buy meaning that inventory will be low. If sellers kept prices low then inventory movement would increase, however, sellers wouldn’t be making the most out of their products. So, by having sales every now and then, buyers maintain happy and as do sellers. Game theory is also used in this industry to protect designs and explain fashion trends. For example, every season or phase there is always a trend that every retailer sells until the next trend passes by. How this works is that the fashion industry is in an oligopolistic competition with each other. Each firm’s product is typically unique to their own brand but they all want to maximise profit so they compete by creating products on trend until equilibrium is achieved. Game theory shows the outcomes and how people will benefit if the designs are copied. If copying the exact design gives the copier a high incentive to copy then the designers are more likely to legally protect their designs [11]. If the fashion industry already use game theory to determine how customer will buy their clothes then why not see if game theory can determine their marketing. A good marketing strategy plays a very important role in a market where competitors are targeting the same consumers with either identical or very similar products. Companies competing in this market have to choose from two main marketing strategies: product discounting or advertisement expenditure. As expenditure on advertisement can help the brand and differentiate the product, amplify the consumers perception about the product and make sure that consumers are aware of the products advantages. However, product discounting implies that the product is sold at a cheaper rate and assist the brand in increasing the customer base so more people buy it. Both strategies have benefits and limitations and by using game theory, it should assist the firm in finding a balance between the two strategies for optimum payoffs. H&M [4] have the difficulty of finding a suitable strategy as their products are of a reasonable price as well as have excellent expenditure on their advertisement, hence why game theory should be able to help them decide what route is best for them.

4 Critical Analysis of Game Theory There are many limitations to using game theory when making marketing decisions. Many marketers are against using game theory as they believe it’s not useful. They believe that because game theory is very practical it doesn’t take in any consideration of managerial insights regarding competition and co-operative decision making

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behaviour [12]. There are many criticisms regarding the usefulness of using game theory in making marketing decisions, for example, in marketing the best choice can’t be chosen due to how good the price is but instead chosen with irrational motives like the emotional connection that the consumer may have [13]. In relation to H&M a marketing decision that may seem logical might not be the best suited because H&Ms consumers may not be able to relate or understand the marketing strategy. While game theory is useful in indicating the outcomes of many different strategic choices, it may be unlikely for it to yield precise solutions in regards to marketing issues. This theory requires choosing the appropriate set of variables or available options, the set of potential outcomes and the objectives assumed by the firms involved. Due to the uncertainty presented in all of the different measure it means that the precision of the choice is limited [14]. One of the main issues with using game theory as a marketing tool is that game theory analyses behaviour of rational participants, with predictable decisions and easily explained deviations if any. Marketing’s main reason for existence is to control consumer behaviour, which is typically irrational and can be affected by multiple and usually unidentifiable factors, like feelings and desires which cannot be predicted. Game theory also doesn’t consider the marketing departments role in making sure that the brands image is created and protected. Due to the uncertainty of the public opinion, it means that a decision that may seem the most rational or logical could be the worst approach in terms of publicity [13]. This means that a marketing idea that may be ideal, may not work as it doesn’t work with H&Ms branding which can cause many issues with consumers. The hypothesis on which game theory is mainly founded on can be seen as far from the realities of this world, hence why game theory may be considered as useless in the complex world of marketing. The criticism that keeps coming up in regards to the application of game theory in marketing is that game theory analyses rational players behaviours. In marketing, the relation between price and quality of goods are not the main reason for a consumers purchase. Irrational factors as well as intangible factors can sometimes come before physical and price factors. H&M could produce expensive products but because the products are sustainable or related to consumers desires, they won’t worry about how much the item costs, in the same way that if a product is very cheap but harms a lot of animals in the process or isn’t good for consumers then they wouldn’t purchase it as they don’t believe in the product. Game Theory Benefits Although there are many marketing practitioner’s who are against using game theory in marketing, there are some who are for this theory. Competition by many other models are often not handled well as other marketing models in the earlier days were mostly optimising and asymmetric because they took the view of ‘a single active decision maker’ [15]. Competitors are often thought of as non-reactive when in reality it is the complete opposite. Game theory, however, is the ideal model for interdependence and the effects of interactions that exists between competing firms as it doesn’t assume the competition will not react but addresses the competition directly and makes it an essential part of making a marketing decision [16]. Non-cooperative competition like this links well with Nash equilibrium, as it takes in consideration ways competitors may go against you which allows the business to prepare for the worse and use it to an

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advantage. This means that when deciding on marketing, you can see all potential outcomes [17]. This means that H&M are able to observe everything that may happen in regards to their competition and be able to find the best strategy suited to them considering all the competition. Bacharach [18] claims that there are many attributions to using game theory to determine a marketing strategy. He believes that game theory provides a well defined set of possibilities for all players, which allows all players to consider each possible result and then choose the best path. Big data allows game theory attribution to be feasible in this day and age because theoretically the system becomes more precise each time data is collected on a consumers buying journey. It may not be the ultimate solution as game theory’s main incompatibility with marketing is when it studies rational decision makers, but it has gone a lot further than before [19]. Instead of using typical models like the last click attribution, the game theory can share credit for sales across multiple points of a customers purchasing journey. This gives marketers a chance to paint a clearer picture of what the person should do more and where they can save money. The main reason why people don’t use game theory as a marketing tool is because consumers don’t make choices by considering the costs and benefits, but instead by thinking and choosing depending on how they emotionally feel about the product. This defies game theory as it defies logic. However, as long as the situation can be rationalised then game theory is actually very helpful. Game theory hasn’t been used much in marketing not because it is impossible but because it is a challenge [13]. Whether the outcomes are worth the effort it will be dependable on the individual but marketing is a notoriously competitive world and using game theory may just be the competitive edge the fashion industry needs. Lastly, Nash equilibrium focuses on non-cooperative competition which basically takes into account the ways in which competitors may stray away and go against you. Nash equilibrium is typically what companies want to consider when masking strategic decisions when the market is stable as no particular benefits can be gained from drastic changes. This is helpful to H&M incase their competitors aren’t in a cooperating environment as it takes in to account what other retailers may do to try and go against you. So, in H&Ms case, they have many competitors and it is hard to ensure that all competitors will cooperate to ensure everyone benefits as the fashion industry is a huge competitive market.

5 Conclusion Ultimately, game theory has been used in many fields and is a risk as well as a challenge when it is used in deciding marketing strategies as participants can be seen as irrational. However, by using game theory, H&M can see all possible outcomes and decide which strategy is best suited to them. Not only that, but they can also see potential actions their competitors may pursue, so in hindsight H&M is much better off using game theory to decide with route is best for them in terms of marketing strategy. Game theory may work the best for them as it can mimic a lot of real life situations well and it can be applied easily to any field.

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References 1. Montet, C., Serra, D.: Game Theory and Economics. Palgrave Macmillen, Houndsmill (2003) 2. Ott, U.: International business research and game theory: looking beyond the prisoner’s dilemma. Int. Bus. Rev. 22(2), 480–491 (2013) 3. Mohammadi, A., et al.: The combination of system dynamics and game theory in analyzing oligopoly markets. Manage. Sci. Lett. 19(1), 265–274 (2016) 4. H&M.: H&M Group, Vision & Strategy. About.hm.com (2019). https://about.hm.com/en/ sustainability/vision-and-strategy.html. Accessed 29 Apr 2019 5. Azar, O.: The influence of psychological game theory. J. Econ. Behav. Organ. 20(2), 234– 240 (2018) 6. Smith, C., Neumann, J., Morgenstern, O.: Theory of games and economic behaviour. Math. Gaz. 29(285), 131 (1945) 7. Peleckis, K.: The use of game theory for making rational decisions in business negations: a conceptual model. Entrepreneurial Bus. Econ. Rev. 3(4), 105–121 (2015) 8. Dufwenberg, M.: Game theory. Wiley Interdisc. Rev. Cogn. Sci. 2(2), 167–173 (2010) 9. Herbig, P.: Game theory in marketing: applications, uses and limits. J. Mark. Manage. 7(3), 285–298 (1991) 10. Mediavilla, M., Bernardos, C., Martínez, S.: Game theory and purchasing management: an empirical study of auctioning in the automotive sector. In: Umeda, S., (eds.) Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth, pp. 199–206. Springer, Cham (2015) 11. Wong, T.: To copy or not to copy, that is the question: the game theory approach to protecting fashion designs. Univ. PA Law Rev. 160(04), 1139–1193 (2012) 12. Rivett, P., Wagner, H.: Principles of operations research. Oper. Res. Q. (1970–1977) 21(4), 484 (1975) 13. Dominici, G.: Game theory as a marketing tool: uses and limitations. Elixir Mark. 36, 3524– 3528 (2011) 14. Moorthy, K.: Using game theory to model competition. J. Mark. Res. 22(3), 262 (1985) 15. Chatterjee, K., Lilien, G.: Game theory in marketing science uses and limitations. Int. J. Res. Mark. 3(2), 79–93 (1986) 16. Özer, O.: Determining the best sales time period for dried figs: a game theory application. J. Int. Food Agribusiness Mark. 27(2), 91–99 (2015) 17. Possajennikov, A.: Imitation dynamic and nash equilibrium in Cournot oligopoly with capacities. Int. Game Theory Rev. 05(03), 291–305 (2003) 18. Bacharach, M.: Economics and the Theory of Games. Westview Press, Boulder (1977) 19. Zheng, Z., et al.: Game theory for big data processing: multileader multifollower game-based ADMM. IEEE Trans. Signal Process. 66(15), 3933–3945 (2018)

Multidimensional Analysis Between High-Energy-Physics Theory Citation Network and Twitter Lapo Chirici1, Yi Wang2(&), and Kesheng Wang3 1 2

3

Department of Computer Science, University of Pisa, Pisa, Italy The School of Business, Plymouth University, Plymouth, UK [email protected] Department of Mechanical and Industrial Engineering, NTNU, Trondheim, Norway

Abstract. The knowledge of information propagation has always been the subject of multiple studies. Recent researches have shown that network with a certain degree of concentration of nodes act often as attractors to others, generating faster and more relevant connections. With this experiment a Highenergy-physics theory citation network was explored, in comparison with the influence of a Twitter network. Despite the impact of scientific publication is always not straightforward to capture and measure, a citation network can be represented as a fitting example of a generative process leading to innovation. The investigation has been carried out through network analysis tools, for the purpose to examine common patterns regarding valuable metrics arisen from both multidimensional graphs. Beyond a substantial difference in the usability of the two channels, the results emerged have highlighted important aspects of how the information propagation coefficients are based on similar principles for some metrics, but distant for others, such as the closeness of nodes. Keywords: Network discovery
Multidimensional analysis Network forecasting
Information flow


Shortest path


1 Introduction The project wants to examine in a comparative manner two networks of citations coming from different areas with the aim of highlighting common features and similar parameters. The research has been undertaken towards the choice of networks having comparable dimensions in terms of structure and behavior. On Twitter, the quotations of another’s tweets or user are called retweets and mention, respectively. In a scientific essay the mentions of parts of text produced by other writers are called citations. Therefore the choice fell on the following direct and unweighted datasets: 1. Twitter mentions/retweet network 2. Network of citations of a body of scientific texts: High-energy physics [1]

© Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 639–645, 2020. https://doi.org/10.1007/978-981-15-2341-0_80

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The first network consists of a subnet of Twitter mentions and retweets related to a given period (continuously updated) consisting of 3657 nodes and 188712 arches. Each node represents a user (@user_name). If the user @A mentions the user @B or retweets a tweet, a direct arc is created from the @A node to the @B node and not necessarily an arc from @B to @A. The second network concerns the e-prints of HEP-Th section, contained in arXiv archive, starting from January 1993 until April 2003. This latter contains 27770 nodes and 352807 arches. Each node represents a writer. If writer A cites writer B, there is a direct arc from node A to node B, and not necessarily an arc from B to A. The network of citations of theoretical physics has a giant connected component [2], composed of 27,400 nodes, equivalent to 98.7% of the total network. A few other components disconnected from the central one has been detected. In the Twitter network of mentions and retweets, on the other hand, the connected giant component is composed of 3,656 nodes, which equals 100% of the total network. Although the number of nodes of the two giant components is different, the percentage ratio confirms the possibility of comparing them. Network analysis produced the following results:

Table 1. Networks dimensions and valuable metrics Dimensions/Metrics Clustering coefficient Connected components Network diameter Network radius Shortest path Characteristic path length Avg. N. of neighbours Number of nodes Network density Isolated nodes Number of self-loops Multi-edge node pairs Analysis time

HEP-Th Twitter 0,157 0,174 2 1 37 12 1 1 224589720 (29%) 12737676 (95%) 8,460 3,764 25,372 84,673 27770 3657 0,00 0,00 1 0 39 2903 483 30985 11965,33 10213,95

Degree Distribution of Networks The degree of a node in a network corresponds to the number of its neighbors. If a network is directed, the arc that connects the node to its neighbor points in a precise direction and, therefore, the nodes have two different degrees: the in-degree, which corresponds to the number of the incoming arcs, and the out- degree which corresponds to the number of output arcs [3, 4]. Analyzing the degree distributions of the citations network and reporting the graphs in logarithmic scale (Fig. 1) it is observed that both distributions are very regular. A high number of nodes with a low degree of outgoing and incoming paths is reported in both the graph. A decrease in the number of nodes as the degree increases is also part

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will also be part of the study. In particular, in the in-degree, a greater accumulation of nodes with a degree between 100 and 500 is remarkable, probably due to the presence of texts that are more cited than others. The red line in the figure identifies to the power law [5], which is corresponding to both the trend’s evolutions, even if the in-degree distribution is closest.

Fig. 1. Logarithmic scale of In-degree and Out-degree of HEP-Th network

Analyzing the degree distributions of the Twitter network (Fig. 2), a more evident uniformity regarding the output arcs is observable. That implies a regular trend with respect to the power law, a factor that is highlighted by a greatest number of nodes having only one arc in output. The effect is underlined by a decrease of the number of nodes in conjunction with the increase in the degree. In in-degree distribution, on the other hand, the trend is not so regular, since the greatest number of nodes has only one path in input, but there is a peak corresponding to a group of nodes with a number of incoming paths between 100 and 500, as in the citations network. Since the present network is composed of both mentions and retweets, the peak is probably due to two factors: a massive presence of users who are often cited or retweeted (like “influencers”) or tweets related to trending topics. In the second case the popularity of a trend (marked with hashtag #) can act as a “boost” to quickly increase the mentions of a specific user whose tweet was precisely marked as particularly relevant [6].

Fig. 2. Logarithmic scale of In-degree and Out-degree of Twitter network

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Both the graphs count a small number of nodes with a high degree: these are called hubs and denote the presence of a scale-free network [7].

2 Shortest Path Length In a Twitter network the distance between a randomly selected pair of mentions/retweets indicates the minimum number of interactions between them. For example, if the user @A mentions the user @B, and this in turn mentions the user @C, the distance between the mention @A and the mention @C is equal to 2 (in the case in which the user @A quoted the user @C, the distance between them would be reduced to 1). Similarly, for the network of citations in which the nodes A, B and C represent writers. For a shorter average distance (sum of minimum paths/total possible paths) a higher speed of transmissible information corresponds. Analyzing the two networks, the average shortest path is 8.46 in the citations network and 3.76 in the Twitter network (Table 1). If at first glance these two values may seem rather distant, it is fundamental to compare them to the size of the respective networks: the average distance, in fact, varies with the number of nodes present in the network. Although the average distance between two citations (value 8.46) exceeds the value of the “Six degrees of separation” [7], it is necessary to consider the huge number of nodes in the network (27.770). In this perspective, the 8 steps that separate a pair of randomly chosen nodes indicate a good index of the small world effect [8]. Furthermore, the shortest path distribution of this network (Fig. 3) underlines an initial peak indicative of the fact that more than half of the total number of possible paths (59%) has a geodesic distance value lower than 8 (the remaining 41% of paths therefore have a distance between 9 and 37) [9].

Fig. 3. High-energy physics theory citation network Shortest Path Length

Fig. 4. Twitter mentions e retweets network Shortest Path Length

The value of the average distance obtained in the network of mentions/retweets (3.76) is instead considerably lower than that of the aforementioned theory (Fig. 4). The network is therefore even less separated than a common real network. This feature is typical of social networks and is called “ultra-small world” [10]. Twitter, even compared to other social networks, is by nature an extremely dynamic dialogue

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platform and many of the mentions made over a certain period of time concern a limited number of popular themes (and users) at that time (trend-topic). Therefore, assuming that the user @C is a popular user, the probability that both the user @A and the user @B would mention it increase, contributing to a decrease in the average distance.

3 Clustering Coefficients Comparison The third metric analyzed is the clustering coefficient, which represents the estimation of how many nodes adjacent to another node are also related to each other. The detection of this index is useful in order to analyze the potential dissemination of information between the various nodes. The greater the coefficient, the lower the network’s efficiency in disseminating information, as it is a manifestation of greater closure of the network itself. In social networks, where arcs represent an interaction, the clustering coefficient provides an estimate of how close the group, or community, is respect to other nodes in the network. The expected result before proceeding with the scanning of the data let intuitively foresee a considerable gap between the two coefficients, meaning by far more (and therefore much more passive) that of textual quotations than that of Twitter. On the contrary, by scanning the two datasets, the result that emerges is a slight discrepancy of only a few tenths, with the social network coefficient even slightly higher: 0.157 found for citations against 0.174 of Twitter. However, before proceeding with definitive conclusion, a further observation based on results obtained by the interpretation of the different sizes of the networks has been turned necessary. In order to do so, the average of the number of neighboring nodes has been analyzed, since it represents the first index responsible for determining the clustering coefficient. The values obtained are 84.673 for Twitter against 25.372 for HEP-Th citations. Despite the average of the number of neighbors obtained in social network is more than 3 times that of citations, the clustering coefficient is almost similar. A result which is very low on Twitter, considering the different size in numerical terms of the two networks. Direct interpretation of this comparison is precisely the greater fluidity in finding, being part of it and sharing the flow of information on social networks, compared to other types of networks. In case of Twitter this flow can be triggered by retweeting or searching via hashtag. Not to be overlooked is also the fact that the interaction process on Twitter can be done very easily even among non-neighbors in the same “network route”. In fact, the information can be found not only through a tweet of a following, but also (and very often) by virtue of “threads of discussion” generated by the Trend-Topics of that particular moment. Even the conformation of the two distributions confirms what has been mentioned. The graph of Twitter (Fig. 5) appears in fact with a flow of nodes that does not follow a regular line of cohesion, despite the presence of areas characterized by hubs with greater density of relationships. Reading the graph of the physical citations network (Fig. 6) an apparent cleanliness in the distribution of relational flows is here reported, developed with curvilinear proportionality from left to right (the peak of the number of neighboring nodes we have between 0.1 and 0.01). This index shows how the

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coefficient decreases with the increase in the number of neighbors and therefore there is more openness in the dissemination of information.

Fig. 5. High-energy physics theory citation network Clustering Coefficient

Fig. 6. Twitter mentions e retweets network Clustering Coefficient

The relational dimension of Twitter, on the other hand, in addition to being very jagged, does not follow the theoretical logic of clustering coefficient (> close nodes ) < closure), since most of the aggregated data shows how the increase in the number of close nodes increases as well the coefficient. It follows, therefore, that the relational dynamics of retweets and mentions are not mainly based on the principle of “closeness” [11], but it gives privilege to other parameters such as the “richness” and “popularity” of the contents of the tweets.

4 Conclusion In conclusion it can be inferred that the analysis of the in/out degree, average shortest path length and clustering coefficient fully confirm Millgram’s studies on the “six degree of separation”, from which the small world effect is then extrapolated. According to this theory, each node is connected to a few other nodes, but it can reach any other node in the network thanks to the presence of hubs. Directly connected to these phenomena and widely developable in other environments (always with the same networks) could be the experiment of the information cascade, according to which the behavior of a node in managing the incoming communication flow is influenced by the degree of similarity of the others neighboring nodes in making choices of this type. Ultimately, it can be seen that the major difference between the behaviors of the two datasets lies in the regularity or otherwise in managing information flows. Where the textual citations correctly respond to the forecasts of the expected values, those of Twitter presumably suffer the influence of external factors that cannot be directly monitored.

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References 1. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 149–151 (2005) 2. Klimkova, E., Senkerik, R., Zelinka, I., Sysala, T.: Visualization of giant connected component in directed network - preliminary study, Mendel, pp. 412–416 (2011) 3. Zhang, J., Luo, Y.: Degree centrality, betweenness centrality, and closeness centrality in social network. Adv. Intell. Syst. Res. 132, 300–303 (2017) 4. Wang, Y., Ma, H.-S., Yang, J.-H., Wang, K.-S.: Industry 4.0: a way from mass customization to mass personalization production. Adv. Manuf. 5(4), 311–320 (2017) 5. Barabasi, A.-L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica 311, 590 (2002) 6. Newman, M.E.J.: Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E 64, 016131 (2001) 7. Barabási, A.: Linked: How Everything is Connected to Everything Else and What It Means for Business, Science, and Everyday Life. Plume, New York (2003) 8. Marvel, S.A., Martin, T., Doering, C.R., Lusseau, D., Newman, M.E.J.: The small-world effect is a modern phenomenon (2013) 9. Shamai, G., Kimmel, R.: Geodesic distance descriptors, pp. 3624–3632 (2017) 10. Sampaio, C., Moreira, A., Andrade, R., Herrmann, H.J.: Mandala networks: ultra-smallworld and highly sparse graphs. Sci. Rep. 13, 9082 (2015) 11. Okamoto, K., Chen, W., Li, X.-Y.: Ranking of closeness centrality for large-scale social networks. In: Preparata, F.P., Wu, X., Yin, J. (eds.) Frontiers in Algorithmics, pp. 186–195. Springer, Heidelberg (2008)

Application of Variable Step Size Beetle Antennae Search Optimization Algorithm in the Study of Spatial Cylindrical Errors Chen Wang1,2, Yi Wang3, and Kesheng Wang2(&) 1

College of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, China [email protected] 2 Department of Mechanical and Industrial Engineering, NTNU, Trondheim, Norway [email protected] 3 The School of Business, Plymouth University, Plymouth, UK [email protected]

Abstract. Based on the establishment of mathematical model of spatial cylindrical error evaluation, this article solves the objective function of the minimum cylindrical error area by beetle antennae search algorithm. At the same time, the problem of the beetle antennae search algorithm is not high, and it is easy to fall into the local optimal solution. The variable step beetle antennae algorithm is designed to improve the calculation accuracy. According to the actual data collected by three coordinate machine, the calculation results of this algorithm are compared with those of other methods, and the feasibility and superiority of this algorithm are verified. Keywords: Spatial cylindrical error algorithm


Variable step
Beetle antennae search

1 Introduction In the field of modern manufacturing, the requirement of manufacturing accuracy of parts is increasing, and precision measurement technology is developing constantly. Intelligent precision measurement of parts is also an indispensable part in the field of modern manufacturing. Spatial cylindrical error, as an important form and position error standard for axle and tube parts, its accuracy of evaluation results affects the evaluation accuracy of the whole part [1]. Minimum area method, traditional method and intelligent optimization algorithm are still the main methods for evaluating spatial cylindrical error. Intelligent optimization algorithm, such as GA, PSO, DE algorithm are also widely used in this field. However, there intelligent optimization algorithm not only have a great relationship with the selection of algorithm parameters, but also need to improve the complexity, accuracy and robustness of the algorithm. He Changyun, Wang Pei et al. [2] used least squares method to measured cylinder is roughly positioned, and the evaluation model of cylinder error is simplified by coordinate transformation based on least squares method. Finally, the minimum value © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 646–653, 2020. https://doi.org/10.1007/978-981-15-2341-0_81

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of the model is solved by sequential quadratic programming algorithm. This method reduces the computational complexity, the accuracy and speed of calculation are improved, and the problem was avoided at the same time. In many software, the least square method does not satisfy the minimum area principle. This algorithm only uses it for rough positioning, and the final quadratic programming algorithm conforms to the minimum area method. Xue Xiaoqiang and Feng Yong [3] proposed an iterative weighted least squares method by improving the constraints of measurement points. By modifying the weighted coefficients, the least squares method was continuously approximated to the minimum region, and the minimum cylindrical error was obtained. This method ultimately depends on the accuracy of the measured data, so it has a certain significance in practical application, but the results will not converge when the measurement points cannot meet the small error conditions. Based on the principle of minimum containment region, the unconstrained cylindrical error model is established by Chelinxian and Yi Jian [4]. The improved adaptive chaotic difference algorithm is used to solve the model. Compared with genetic algorithm and bee colony algorithm, the experimental results show that the algorithm is better. The cylindricity error evaluation method proposed by Lu Yuming and Wang Yanchao [5] is a biogeographic optimization algorithm with double mechanism. The algorithm combines the local search mechanism of the difference algorithm with the previous step of the biogeographic algorithm, and improves the mutation algorithm and the migration operator, thus improving the convergence and optimization ability of the biogeographic algorithm. Finally, the objective function of cylindrical error model is solved by the smallest area normal number, and the algorithm has a better solution effect. Zhao Yibing, Wen Xiulan, et al. [6] used quasi-particle swarm optimization to solve spatial cylindrical error, and achieved good evaluation accuracy. Considering the high accuracy and complexity of genetic algorithm and immune algorithm, setting more parameters and slower convergence speed, Ning Huifeng and Ma Guanglong [7] used the dichotomy iteration method to realize the online accurate evaluation of cylindrical error. Rossi and others [8] put forward a heuristic sampling strategy, which can calculate the center coordinates of the minimum area method and good evaluation accuracy, but the calculation process of the algorithm is complex and the calculation efficiency is low. Zhao Yibing et al. [9] established a mathematical model of the minimum area cylindrical error solution based on coordinate measuring machine detection, and proposed the minimum area cylindrical error method based on quasiparticle swarm optimization. Wen et al. [10] proposed a cylindrical error evaluation method based on Monte Carlo and GUM methods and a quasi-particle swarm optimization algorithm, and calculated the uncertainty of measurement results. The above methods have achieved good results in solving the problem of spatial cylindrical error evaluation, but there are still some problems such as low accuracy and general efficiency. At present, the BAS algorithm has the characteristics of fewer parameters, fast solving speed and high precision, and has been applied to many practical fields of engineering application. The above research shows that in the current evaluation methods of spatial cylindrical error, most of them adopt intelligent optimization algorithm to solve the mathematical model of spatial cylindrical error. However, there is still room for improvement when using intelligent optimization algorithm to solve the problems of

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more parameters and stronger sensitivity of optimization results to parameters. In this paper, a variable step beetle antennae search algorithm (VSBAS) is designed to further improve the accuracy and speed of solving the mathematical model of spatial cylindrical error.

2 Establishment of Mathematical Model of Spatial Cylindrical In fact, the evaluation of spatial cylindrical error by using minimum region hair is to measure the deviation of a cylinder relative to an ideal cylinder, that is, to determine the two best coaxial cylinders containing measuring points. The measured cylinder will have many spatial measurement points. These measuring points will be contained by different coaxial cylinders. According to the principle of minimum region, there will be an optimal two coaxial cylinders containing all measuring points [16]. The spatial cylindrical error is the radius difference between two coaxial cylindrical surfaces containing the measured points. Here it is shown as the difference between the maximum distance and the minimum distance between the measured point and the ideal axis. The spatial cylindrical error schematic diagram is shown in Fig. 1.

Fig. 1. Schematic diagram of spatial cylindrical error

As shown in the Fig. 1, the axis direction of the cylinder is assumed to be (a, b, c), and the coordinate origin is used as the plane in which (a, b, c) is found. The intersection point between the plane and the axis of the cylinder to be measured is set as (x0, y0, z0). Then the expression of the ideal axis of the cylinder to be measured is x  x0 y  y0 z  z 0 ¼ ¼ a b c

ð1Þ

When a group of measuring points of the cylinder to be measured are obtained by using a coordinate measuring machine, and the number of measuring points is Pi (xi, yi, zi) at any Pi (i = 1, 2… k0, k0), the ideal axis distance from the measuring point Pi to the cylindrical to be measured is ri by distance formula.

Application of Variable Step Size Beetle Antennae Search Optimization Algorithm

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A2 þ B2 þ C 2 ri ¼ a2 þ b2 þ c 2

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ð2Þ

Finally, the objective function of spatial cylindrical error can be expressed as follows: f ¼ minðmax ðri Þ  min ðri ÞÞ

ð3Þ

According to the minimum zone method, the solution of cylindrical error is the optimization problem of the objective function (3) with respect to the non-linear function of variables (x0, y0, z0).

3 Proposed Algorithm 3.1

Algorithm Flow

BAS algorithm is a new intelligent optimization algorithm. The original BAS algorithm imagines the beetle antennae as a point, two points must be on both sides of the point, the moving length of the point is proportional to the distance between the two fixed points, and the original BAS algorithm has a standard fixed step length. When the point moves at a fixed step distance, the two antenna directions of the antenna rotate randomly, which simulates the rotation of the head direction of the antenna and ensures that the antenna has a good search ability. Bas algorithm is similar to PSO algorithm. Compared with PSO algorithm, BAS algorithm needs less parameters. Even beetle antennae can search the solution space better without gradient information, which makes bas algorithm greatly reduce the calculation and achieve effective optimization. However, because the step length of standard bas algorithm is fixed, the search speed and efficiency of BAS algorithm in global search and local search are general. In this paper, the fixed step size of BAS algorithm is optimized, and the variable step size bas algorithm is designed. By changing the parameters of the step size, we can shorten the time of approaching the optimal solution by using the step length at the beginning of the iteration, and find the optimal solution accurately by using the small step length at the end of the iteration. The solution process of VSBAS is as follows: 1. First, the measured data are brought into the objective function (3), and then the algorithm is initialized. The initialization data of the variable step-size beetle antennae algorithm include: the distance DX between the two antennae, the step of beetle antennae, the variable step parameter Alpha, the dimension D of the problem, the initial solution x, and the total number of iterations n. 2. The initial solution x obtained in D − 1 is introduced into the algorithm and iteration begins. Firstly, the algorithm calculates the two-needle coordinates: The left beetle antennae

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XL ¼ x þ dx  rand/2

ð4Þ

XR ¼ x  dx  rand/2

ð5Þ

The right beetle antennae

Rand is a random value in D − 1. 3. Calculate the functional fitness (odor intensity) of the desired location. FL ¼ f ðXLÞ

ð6Þ

FL ¼ f ðXLÞ

ð7Þ

4. The next solution Calculated with variable step size algorithm:  x¼

x þ Alpha  step  randðXL  XRÞ; FL\FR x  Alpha  step  randðXL  XRÞ; FL [ FR

ð8Þ

5. If the number of iterations does not reach the maximum number of iterations, return to step 4 for iteration. When the number of iterations reaches the maximum number of iterations, all calculations stop. The result of the iteration is the error value of the space cylinder. The algorithm flow chart is shown in Fig. 2. 3.2

Algorithm Test

DTLZ suits is usually used as a test function. DTLZ 1-DTLZ 4 was selected as a test function to test the performance of VSBAS algorithm. At present, many experts put forward a variety of MOEA performance indicators. It can be divided into two categories: (1) one is used to evaluate the actual convergence of Pareto optimal solution; (2) the other is used to evaluate the distribution of solution. Classical quality indexes such as error ratio (ER), Supervolume (HV), generation distance (GD), inverse generation distance (IGD). In the further study of MOEA performance index, GD and IGD can be widely used to test the convergence and diversity of algorithms, and have achieved good results in mops test. GD reflects the convergence of the algorithm. IGD reflects that the convergence and diversity of solutions can be recorded at the same time. 1. Generational Distance (IGD) indicator Let P be the set of final non-dominated points obtained from the objective space, and P be a set of points uniformly spread over the true PF. The GD can indicate only the convergence of an algorithm and a smaller value indicates better quality. The GD is computed as:

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Establishment of objective function and acquisition of measurement point data

Initialization of algorithmic parameters of BAS

Calculating the coordinates of the left and right by beetle antenae

Calculate the required odor intensity (function fitness value)

No

Calculate the next step of longicorn (variable step method)

Whether the iteration parameters are reached or not

Yes The calculation is completed, and the error value of spatial cylindrical and the solution of objective function are obtained.

Fig. 2. Algorithm flow chart

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P  u2P dðu; P Þ GDðP; P Þ ¼ jPj 

ð13Þ

2. Inverted Generational Distance (IGD) indicator Let P be the set of final non-dominated points obtained from the objective space and P be a set of points uniformly spread over the true PF. The IGD can indicate both the convergence and diversity, and a smaller value indicates better quality. The IGD is computed as: P  dðv; PÞ IGDðP; PÞ ¼ v2P  ð14Þ jP j

4 Experimental Results and Discussion The measurement data of CMM in this paper come from reference. See Table 1 for the original data. Then the variable step size bas algorithm is verified by experiments. The parameters of VSBAS are as follows: population size NP = 100, variable step

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parameter a = 0.95. The algorithm is programmed in MATLAB 2018a. The computer is configured with 16 g memory, 3.20 GHz dual core CPU and windows 10 professional operating system.

Table 1. Cylindrical measurement point coordinates X 1 11.0943 2 5.0940 3 −6.9063 4 −12.9065 5 −6.9063 6 5.0940 7 10.9546 8 4.9544 9 −7.0459 10 −13.0461

Y 0.4522 10.8450 10.8439 0.4498 −9.9429 −9.9418 0.5220 10.9148 10.9137 0.5196

Z 65.2328 65.0765 65.0089 65.0879 65.0540 65.2216 75.2316 75.0752 75.0770 74.8964

11 12 13 14 15 16 17 18 19 20

X −7.0459 4.95447 10.8150 4.8148 −7.1855 −13.1858 −7.1855 4.8149 10.6754 4.6752

Y −9.8731 −9.8720 0.5918 10.9846 −9.8033 0.5849 −9.8033 −9.8022 0.6616 11.0544

Z 75.0528 75.2204 85.2304 85.0740 85.0516 84.8952 85.0516 85.2171 95.2291 95.0940

Table 2 shows the final results obtained by using secondary annealing teaching and learning algorithm (2ATLBO), particle swarm optimization (PSO), and variable step size teaching and learning algorithm VSBAS designed in this paper based on the data measured in Table 1. According to Table 2, compared with other algorithms, the VSBAS algorithm has the highest accuracy and better convergence speed. It shows that the VSBAS algorithm has better results in solving the problem of spatial cylindrical error evaluation. Figure 3 shows the iteration curves of the three algorithms mentioned above. As shown in Fig. 3, VSBAS algorithm has better accuracy and convergence speed than 2ATLBO and PSO algorithm.

Fig. 3. Iterative curve of the algorithms

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Table 2. Calculation results Method PSO 2ATLBO VSBAS

Iteration 300 300 300

Cylindrical error Improvement rate 0.00213 / 0.00192 9.8% 0.00175 17.8%

5 Conclusions In this paper, the BAS is improved and a variable step BAS algorithm (VSBAS) is designed. The VSBAS algorithm is applied to solve the mathematical model of spatial cylindrical error. Variable step size method can help BAS algorithm avoid falling into local optimal. The VSBAS algorithm is tested by the test functions GD, and IGD. The test results show that the algorithm has good convergence and convergence speed. It is found that the VSBAS algorithm is superior to other algorithms (2ATLBO, PSO) in solving accuracy and convergence speed. At the same time, compared with other algorithms, VSBAS algorithm has fewer parameters and more convenient programming solution. The data measured by the three coordinate machine are brought into the algorithm and applied to the evaluation of spatial cylindrical error, better results can be obtained. Acknowledgements. The work is supported by MonitorX project, which is granted the Research Council of Norway (grant no. 245317).

References 1. Kjølle, A.: Mechanical Equipment. Hydropower in Norway, Trondheim, December 2001 2. Mobley, R.K.: An Introduction to Predictive Maintenance, 2nd edn. Butterworth Heinemann, Boston (2003) 3. Wang, K., Wang, Y.: How AI affects the future predictive maintenance: a primer of deep learning. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds.) Advanced Manufacturing and Automation VII, IWAMA 2017. Lecture Notes in Electrical Engineering, vol. 451, pp. 1–9. Springer, Singapore (2017) 4. Wang, Y., Ma, H.-S., Yang, J.-H., Wang, K.-S.: Industry 4.0: a way from mass customization to mass personalization production. Adv. Manuf. 5(4), 311–320 (2017) 5. Bram, J., Ruud, T., Tiedo, T.: The influence of practical factors on the benefits of conditionbased maintenance over time-based maintenance. Reliab. Eng. Syst. Saf. 158, 21–30 (2017) 6. Gao, Z., Sheng, S.: Real-time monitoring, prognosis, and resilient control for wind turbine systems. Renew. Energy 116(B), 1–4 (2018) 7. Matheus, P.P., Licínio, C.P., Ricardo, K., Ernani, W.S., Fernanda, G.C., Luiz, M.: A case study on thrust bearing failures at the SÃO SIMÃO hydroelectric power plant. Case Stud. Therm. Eng. 1(1), 1–6 (2013) 8. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997) 9. Martin, L., Lars, K., Amy, L.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn. Lett. 42, 11–24 (2014) 10. Subutai, A., Alexander, L., Scott, P., Zuha, A.: Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262, 134–147 (2017)

A Categorization Matrix and Corresponding Success Factors for Involving External Designers in Contract Product Development Aleksander Wermers Nilsen1(&) and Erlend Alfnes2 1

Inventas AS, Innherredsveien 7, 7014 Trondheim, Norway [email protected] 2 NTNU, Trondheim, Norway [email protected]

Abstract. This article addresses the involvement of external designers in contract development projects. A matrix is proposed for how buyers can structure the role and involvement of design suppliers in a joint development team. The degree of involvement depends on the buyer’s need for capacity and competence from external designers, and the development risk in the project. Four main roles are proposed for the design supplier: Purchased design capacity; Module design specialist; Design team member; System architect. A set of success factors for team involvement and information sharing is proposed for each role. Keywords: Supplier involvement development
Success factors


Contract development
Product

1 Introduction Contract product development is common in engineer-to-order projects. The main difference from conventional market-driven product development is that the development process is based on a contract with a buyer [1]. The sale takes place first and then the main part of the development is done after the buyer has committed to the purchase. The engineer-to-order business is characterized by a high degree of volatility and uncertainty. The type of products and the amount of design workload can change dramatically from one year to another. The product development times is often short, and end-users require a solution in a hurry. To only rely on internal design capacity and competences can be risky. Many companies therefor buy external design services to ensure end-user satisfaction. The services can range from the design of a minor component to being responsible for the entire systems design. Not only do the need for design capacity vary, the buyer’s needs may also vary regarding level of competence, as some projects may require specialists to be involved. For these reason design suppliers are prevalent in contract product development. This paper addresses the buyer’s involvement of design suppliers. The objective is to investigate the role of the design supplier in contract development and corresponding success factors for involvement of design suppliers. © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 654–661, 2020. https://doi.org/10.1007/978-981-15-2341-0_82

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The structure of the paper is as follows: a brief theoretical background of supplier involvement and established success factors in the field. The results of a survey are presented. The survey was designed to study Norwegian companies and learn how they involve design supplier in their product development. The paper is concluded with a discussion which provides a matrix to categorize the role of a design supplier. The discussion also includes success factors for involvement of design supplier in contract product development corresponding to each of the roles in the matrix.

2 Supplier Involvement Historically, developing new products was mainly considered an in-house expertise. Involving suppliers and the supporting academic research into this field has been conducted since the 1980’s, see [2]. In the 1980’s the leading research, led by the automotive industry, focused on the performance gap between US and Japanese manufacturers. This was of interest at the time as US car companies usually did not involve suppliers in development while the Japanese car companies increasingly started to include suppliers. The findings, according to [3], showed that the Japanese companies which involved suppliers in product development, had a reduced time to market, resulting in a more economic production, technologically superior products and a sustained competitive advantage. 2.1

Relevant Supplier Categories for Contract Product Development

According to [4], when a supplier is involved early in the development process it is critical that the supplier not only have the technical abilities needed but also have the correct culture. Not all supplier involvement is equal, four scenarios with different communication setups or categories are suggested in [5]. It identifies four scenarios with different communication setups or categories. The model identified in [5] considers two axes, the y axis is the degree of supplier responsibility, from low to high, and on the x axis the development risk, from low to high. This article will consider the categories that have a high degree of responsibility. The categories with low degree of supplier responsibility are characterized by little to no supplier involvement; thus, the focus is on the buyers needs and less on the buyer-supplier relationship. The degree of development risk is divided into two categories: arm’s length development, and strategic development. Previous research by [5] finds that projects in the arms-length category require less information sharing than the strategic development category. In an arm’s- length development project, the supplier requires little direct communication, usually the only communication is centered around status of the project and time to completion. This communication is usually initiated by the supplier. The strategic development group requires a close collaboration between supplier and buyer, resulting in technical information sharing, in face-2-face meetings and working groups by many individuals across several fields in the two different companies. Research such as, [6] argues that it is difficult to gain a positive effect from involving suppliers, as increase communication and coordination in projects lead to longer development time and increased costs. Furthermore [6] postulates that two

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elements must be in place for sufficient supplier involvement: contingency factors on the organizational level, and management of supplier involvement on the project level. A categorization is proposed in [6] in order to reduce excess coordination by defining two types of projects; know-how projects and capacity projects. The first, know-how projects, are when then supplier has the in-depth knowledge and technical understanding needed to carry out the project. Capacity projects are when the buyer’s needs more resources to complete the project. The capacity project buyer has a goal of overcoming the shortages of their own organization, often the supplier takes on less important responsibilities in order for the buyer to focus on the critical elements of the project. The know-how project buyer realizes that they do not possess the knowledge required to perform a task and therefore the supplier is given responsibility for that component or part. The component or part may or may not be critical as far as the overall project is concerned. 2.2

Success Factors

In order to successfully involve suppliers in the development process, several reports have assessed what factors need to be in place for such an endeavor to be deemed successful. There are three main factors according to [2], that influence the success of supplier involvement; supplier selection, supplier relationship development and adaptation, and internal customer capabilities. Selection of supplier concerns which suppliers to use in the development process and which to involve early. The second factor is of interest for the purpose of this article, namely the supplier relationship development and adaptation, which [2] documents to be frequently overlooked by managers. Supplier relationship development and adaptation is achieved by looking at mutual trust, commitment, and mutual understanding of performance targets. One way to build supplier relationships is by including suppliers’ employees on the development team as discussed by [4]. The third factor found in [2], is about the buyer’s internal capabilities, such as the ability to manage internal cross-discipline teams. The research by [7] finds that the three factors found in [2] are among the factors that promote successful supplier involvement. The article goes into greater depth, finding that supplier membership on the buyer’s development team is the greatest success factor. They found that open and direct intra-company communication most often resulted in a rapid fix of most problems. Co-location was found to be more relevant with technologically complex projects, as well as in long term development projects. Furthermore, factors such as formal trust, customer requirements sharing, technology information sharing, and shared physical assets also contribute to successful supplier integration. The article by [7], groups the factors that lead to successful supplier integration into two groups: relationship structuring factors, and asset allocation factors. They find that the asset allocation factors directly influence the new product development, while the relationship structuring factors are what they describe as facilitating factors, by which that they mean the relationship structuring factors facilitate the sharing of assets.

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3 Methodology A list of success factors for supplier team involvement and information sharing was derived from literature. The success factor that were found in research pertain to offline product development [2, 4] and [7]. The impact of these factors for design supplier involvement in contract development project success was investigated through a survey. Data was collected via a survey from three groups. Group A are companies which recently had one or more projects that included design supplier integration. Group B are companies that do not integrate design suppliers. Group C consist of consultants, project managers and professionals that are not employed by the design supplier or the buyer. These professionals work with coordinating development projects that involve design suppliers. The companies selected are involved with the authors company, therefore some prior knowledge about their business is known. This article focusses on technology companies based in Norway that do product development. The companies in focus have products that have low volume single batch production, i.e. they only make one or few batches of a products before they change the design. The goal for collecting this data is to investigate if it is possible to create a model for categorizing the role of the design supplier in contract product development along with corresponding success factors.

4 A Categorization Approach/Matrix In the literature section, we described two important design supplier categorizations. As mentioned earlier, while [6] focusses on the types of projects, [5] looks at two overarching supplier dimensions – capacity and knowledge. The surveyed companies indicated that communication is an important factor when choosing to involve a design supplier in development. Companies that have not involved design suppliers, group B, indicated that communication would be critical if they did decide to involve design suppliers in the future. Companies not currently involving design suppliers (group B) indicated that limitations on their own internal capacity would also be critical if they were to involve design suppliers. Companies currently involving design suppliers, group A, consider the experience of the design supplier as well as cost to be important when choosing which design supplier to use. Furthermore, the companies currently not involving design suppliers, group B, indicated that they would, on average, consider capacity to be a more important motive for design supplier integration. Conversely, the companies involving design suppliers, group A, considered performance quality, reduced cost and the experience of the design supplier to be their motivation. Thus, a parallel to [6] can be drawn, two project types for design supplier involvement: know-how projects, and capacity projects, which relates directly to the buyer’s needs. The buyer’s needs can range needing a highly specialized and skilled competence to no real qualification. The latter implying that the buyer just needs more manpower. This article will use this distinction between project types going forward. The distinction between degrees of development risk

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involved, as found in [7] are: arm’s length, and strategic involvement. The development risk concerns the complexity of the project, and thereby the degree of involvement. High risk implies long development time and high degree of supplier involvement, leading to the buyer’s company needing to strategically choose their collaboration partners wisely. Low risk involvement puts less critical decisions on the supplier, utilizing them more as support. In this study, we combine the two categorization approaches found in [5] and [6] for project type and supplier involvement respectively. Using the survey data this article proposes combining the type of project and the degree of risk into a matrix, as shown in Fig. 1. The figure shows a categorization of the roles of the design suppliers in contract development, split into four groups. Firstly, a split of the buyer’s needs between capacity and know-how projects. Secondly, a split between the degree of development risk (low and high) which correspond to arm’s-length involvement and strategic involvement of the supplier. Supplier’s involvement Arm’s length Strategic Buyer’s needs

Capacity Know-How

Purchased design capacity

Design team member

Module design specialist

Systems architect

Fig. 1. Design supplier roles based on buyers needs and supplier involvement.

The first role, “purchased design capacity”, pertains to projects where the design suppliers have responsibility for less critical parts or components. The project is firmly inside the design supplier’s main core working area and they are considered competent in their field. The buyer typically asks for a solution to their problem, the design supplier delivers the part or component with minimal interaction after the first inquiry. While the part or component in question is designed by the supplier it may be a standard solution but looking from the buyer side this is a component developed by the supplier. The second role, “module design specialist”, is somewhat similar in that the supplier-buyer interaction is limited. However, “module design specialist requires more information sharing as the design supplier provides a custom product that meets the specifications of the buyer. This may be a customized made-to-order development and the design supplier will customize one of their standard products to meet the buyer’s needs. The design supplier is considered an expert in the field and has been selected by the buyer for this reason. The “design team member” role requires a significant amount of information sharing as the design supplier is involved in development of a complex system. The design supplier responsibility is not on the critical components, but the complex nature of the development requires coordination of information such as product specifications, interfaces and other non-trivial information. The design supplier is considered part of the development team but does not take the lead role in specification of the entire

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system. The buyer requires the capacity of the design supplier in order to complete the project. The last role is “systems architect”, here the design supplier has special knowledge of the critical sub systems of the project. The design supplier is included in the development team. Information sharing is critical to the success of the product. Product specification, interfaces, production methods and most of the key decisions concerning the development is done in coordination with the buyer. Often the design supplier and buyer will co-locate in order to maximize the coordination and allow for informal information sharing. The design supplier is considered an expert in the field. The design supplier will design critical parts or components and make technical decisions concerning the development. From the survey, the results for companies not currently involving design suppliers (group B), show that they would consider involving design suppliers using the “purchased design capacity” or “design team partner” roles. The respondents in general want to supplement their current activities by outsourcing some of the development of less complex tasks, this is done to free up internal capacity or general lack of project engineers. Costs and quality are important. While they could have involved design suppliers, they have chosen to do the development in-house. The survey results for companies involving design suppliers (group A) correlate to the know-how projects. They want to reduce cost and increase quality by having a specialist perform the development. The reasoning may be that a specialist can perform the task in less time than developing the expertise in-house. It is not evident how the respondents are distributed between the “module design specialist” and “systems architect” roles. The professionals survey data (group C) show no clear signs of belonging to either the capacity or know-how projects, which is natural as they have worked in a wide variety of projects, leading to no clearly defined position. However, this article assumes that they were involved in the high degree of development risk roles, “design team partner” or “systems architect”. This is because the professionals are hired to lead projects that have a high degree of technical complexity.

5 Success Factors In order to determine the success factors that correlate to each role, this article leans on the research done by [2, 4] and [7]. In [7] success factors are grouped in two: relationship structuring factors and asset allocation factors. The asset allocation factors are directly linked to the successful involvement of suppliers on the development team. The relationship structuring factors improve the effect of the asset allocation factors. Using the success factors found and the survey data this article suggests a structuring of the factors so that they are associated with the roles presented in Fig. 1. The success factors are shown in Fig. 2 for each categorization of design supplier roles. Factors are organized so arm’s-length development (“purchased design capacity” and “module design specialist”) have less long-term focus. The success factors are organized in a way that the relationship structuring factors are more prevalent in the strategic involvement roles (“design team member” and “systems architect”). Each

Purchased design capacity

Module design specialist

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Systems architect

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Co-location Joint agreement on system functions and performance Buyer and supplier management commitment Shared end user requirements Common and linked information systems Supplier is trusted partner on development team Joint agreement on module function and performance Technology sharing Buyer confidence in suppliers’ capabilities Joint agreement on module performance Specify functions and performance Coordinating development activities Formulated communication and sharing guidelines

Fig. 2. Success factors for the design supplier roles in contracted development.

level in Fig. 2 should include the factors of the levels below, so the systems architect level includes factors from all four groups. For all four roles the success factors; “specify functions and performance”, “coordinating development activities with suppliers” and “formulate communication and information sharing guidelines” are all included. The last factor is of special interest as establishing and formulating information sharing guidelines are directly connected to the quality of the relationship between buyer and supplier. These guidelines should be introduced at the start of a project or collaboration. This will ensure that both the buyer and supplier have agreed on the reporting methods. The buyer and supplier also agree how information is shared within the development team. Note that in Fig. 2, the factors “joint agreement on module/system functions and/or performance” replace each other in the different supplier roles. The goal of the model (Figs. 1 and 2) is to increase trust, commitment, information sharing and cooperation so that the design supplier can maximize the development performance. The insight into the role of the design supplier, based on the buyer’s needs (project type) and the risk the design supplier takes on, provides decision makers with an ability to assess their allocation of resources. For example, if the project is a capacity project, the design supplier need not allocate their own internal expert. The categorization can also limit ambiguity regarding whom should make the critical decisions. The two axes represent the two sides of the relationship, the project type describes the buyer’s needs, the supplier’s development risk is the willingness to take an active role in the development. Pairing the role of the supplier and the corresponding success factors also allows for the buyer and supplier to effectively set the parameters of the project, for example co-locating only if the role of the supplier indicates that it is prudent.

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6 Conclusion This article proposes a new model for considering the role of a design supplier in a development team. The degree of development risk and buyer’s needs will identify the role of the design supplier. This article applies the theory for supplier involvement in product development to the field of contract product development. By using the identified roles, a buyer can facilitate the type of relationship needed to have an effective collaboration with a design supplier. A reflected approach to why a certain design supplier is selected and what kind of role the design supplier has in the project can help define what success factors that need be in place in order to successfully involve the design supplier in contract product development. The model presented helps decision-makers to facilitate an effective cooperation between a buyer and a supplier, thereby increasing the likelihood for a successfully completed project. Acknowledgements. We would like to acknowledge the Norwegian Research Council and Inventas AS for their support. Informed consent was obtained from all individual participants included in the study.

References 1. Alderman, N., Thwaites, A., Maffin, D.: Project-level influences on the management and organisation of product development in engineering. Int. J. Innov. Manage. 05(04), 517–542 (2001) 2. Johnsen, T.: Supplier involvement in new product development and innovation: taking stock and looking to the future. J. Purchasing Supply Manage. 15(3), 187–197 (2009) 3. Clark, K.B.: Project scope and project performance: the effect of parts strategy and supplier involvement on product development. Manage. Sci. 35(10), 1247–1263 (1989) 4. Ragatz, G.L., Handfield, R.B., Petersen, K.J.: Benefits associated with supplier integration into new product development under conditions of technology uncertainty. J. Bus. Res. 55(5), 389–400 (2002) 5. Wynstra, F., Ten Pierick, E.: Managing supplier involvement in new product development: a portfolio approach. Eur. J. Purchasing Supply Manage. 6(1), 49–57 (2000) 6. Wagner, S.M., Hoegl, M.: Involving suppliers in product development: Insights from R&D directors and project managers. Ind. Mark. Manage. 35(8), 936–943 (2006) 7. Ragatz, G.L., Handfield, R.B., Scannell, T.V.: Success factors for integrating suppliers into new product development. J. Product Innov. Manage. 14(3), 190–202 (1997)

Engineering Changes in the Engineer-to-Order Industry: Challenges of Implementation Luis F. Hinojos A.(&), Natalia Iakymenko, and Erlend Alfnes Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway [email protected]

Abstract. Market success in the Engineer-to-Order (ETO) industry, implies, among other things, competitive prices, no delays in delivery and high customization. Engineering Changes (EC) have a large impact on delivery time, cost, and resources allocated, but they are also desirable because they enhance the product. ETO companies present characteristics that differentiate their products and processes from those in mass production. Also, they experience frequent changes to product specifications. Hence, ETO requires a different approach for Engineering Change Management (ECM) thus becoming more challenging. The purpose of ECM is to implement the modifications to products in a controlled manner. Efficient EC implementation is necessary to guarantee customer satisfaction and to remain successful. The objective of this research is to analyze the factors that influence the implementation of EC in the ETO industry. Keywords: Engineer-to-order management


Engineering changes
Engineering change

1 Introduction The ETO production environment has a range of characteristics that differentiate it from other types of environments. ETO products are typically complex with deep product structures, are highly customized, and often one-of-a-kind. Production is done in low volumes with no stock of finalized goods, and the product design does not commence until there is an order placed. Each customer order requires engineering to create or adapt a product [1]. The high level of customization makes the repetitiveness of the production processes low [2]. The ETO production environment is characterized by having a complicated information flow. Besides, activities in ETO (e.g. engineering, production, and procurement) in some cases overlap during the project rather than occurring consecutively like they do in mass production [3]. ETO manufacturing companies experience frequent changes to product specifications [4]. The implementation of EC generates time delays, increases project costs and uses up a considerable amount of engineering capacity and other resources. Poorly managed EC lead to affected product customization and market opportunities, obsolete inventories, materials shortages, and decreased quality [5–8]. Despite the negative effects, EC are desirable because they improve the product [9]. They enable customer © Springer Nature Singapore Pte Ltd. 2020 Y. Wang et al. (Eds.): IWAMA 2019, LNEE 634, pp. 662–670, 2020. https://doi.org/10.1007/978-981-15-2341-0_83

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satisfaction, especially in ETO. Also, efficient EC implementation can achieve higher quality on the end-product [10]. The purpose of ECM is to implement the modifications to products in a controlled manner [6]. Negative effects can be reduced by efficient ECM [10]. There is a need to study the practices for ECM under the ETO context. Manufacturers in the ETO environment cannot implement EC in the same way as those in mass production. The customers have direct contact with manufacturers and demand products that satisfy unique needs. EC are an approach to implement the input coming from the customer along the product life cycle [10]. To remain competitive in the market, these companies need to be flexible and to be able to incorporate customer requirements to their products, with an attractive price and delivery time [11]. The implementation of EC cannot be postponed and the modifications have to be introduced when requested since the one-off product made is linked to a customer order [12]. In addition, most of the research available in ECM does not differentiate between production environments. Even though recent research papers study the overlap in ECM and ETO, Iakymenko, Romsdal [12], reveal the needs of further research of ECM from the ETO perspective. The objective of this research is to explore what influences the implementation of EC in the ETO production environment. The structure of the paper is as follows: a brief theoretical background for ECM, where after the factors influencing EC implementation are listed. A Norwegian company is presented as a case study which operates in the maritime industry and provides customized propulsion systems for advanced vessels. The factors are then analyzed in the case company studying six EC. The paper continues with the results of the focus group. Finally, a discussion provides an understanding of the areas of opportunity to improve EC implementation.

2 Methodology This research has been performed as a single in-depth case study in an ETO manufacturing company. Under the ETO context, the practices for EC implementation were investigated. Furthermore, the case studied six EC as multiple embedded units of research. Efficient EC implementation is affected by factors that contribute to negative effects. These factors are any circumstance, fact or influence that contributes to ECM performance. A range of factors that potentially influence EC implementation were identified through a literature study. The data collection methods included semistructured interviews with four project managers, field observations and documentation review. The final step in the research process was to perform a focus group that was performed to validate the list of factors. The participants all have leadership positions from both engineering and project management. The participants’ work experience ranged from six to over 20 years of relevant experience. The participants were asked to rate the influence each factor has on the negative effects of EC implementation, on a scale from one to five (strongly disagree to strongly agree).

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3 Engineering Change Management EC refers to modifications to the released structure (fits, forms and dimensions, surfaces, materials etc.), behavior (stability, strength, corrosion etc.), function (speed, performance, efficiency, etc.), or the relations between functions and behavior (design principles), or behavior and structure (physical laws) of a technical artefact [9]. EC are categorized as either emergent or initiated [13]. From a simplified point of view, emergent EC are requested to remove errors from a product and initiated to enhance it in some way [6]. Furthermore, EC can be distinguished as early, mid-production and late EC in the product development process [14]. This distinction is relevant because the degree of negative impact will vary according to the point of time of the project when the EC is requested. As established, EC lead to negative effects such as increased delivery time, reduced profit margins, production schedules disturbances and increased resource allocation [1, 6]. ECM refers to the organization and control of the process of making alterations to products [6]. ECM involves planning, controlling, monitoring and recording the EC within many departments of a manufacturing company and systematic means of communication are required. Several researchers have studied the process for ECM [5, 6, 14, 15]. The following six steps are referred to as the ECM generic process for this paper: – – – – – –

Identify and request engineering change Identify possible solutions to change request Perform an impact evaluation of possible solutions Select and approve a solution Release and implement engineering change Perform post-implementation review The following factors are identified to influence EC implementation:

1. Product complexity. Refers to the number of components, the number of levels in the product structure and the number of design interdependencies as defined in the Bill of Materials [6, 7, 15, 16]. 2. Product customization. This refers to the level to which a product accommodates the customer-specific and individual requirements [17] 3. Product innovation. Refers to the introduction of new technology in the product [14, 16]. 4. EC timing. Refers to the project stage at which the EC is requested [14, 16, 18]. 5. EC propagation. Refers to the phenomenon by which one single modification to a component initiates a series of other changes to parts and systems the component interacts with [13, 16]. 6. Intra-organizational integration. This refers to adequate information sharing and interaction between internal disciplines involved in EC implementation [8, 15, 18]. 7. Cross-organizational integration. This refers to adequate information sharing and interaction across organizations involved in EC implementation [1, 10]. 8. Established ECM process. Refers to whether the company has adopted and follows the activities involved in the ECM generic process [5, 6, 14, 15].

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4 Results The case company provides customized propulsion and position system for advanced vessels. First, it was found that the company does not have dedicated tools for ECM. Although the ERP system and the IBM planning system aid the ECM process, they are only used in their respective departments. The business systems do not have ECM dedicated capabilities and do not provide a streamlined workflow nor decision-making support for EC implementation. Second, project managers are responsible for the ECM process. Even though they utilize aids, such as a sales configurator and the ERP system, the EC impact evaluation was found to be very dependent on the experience of the project manager. Third, EC approval from the customer is done through EC forms exchanged by email. Lastly, it is difficult to assess the impact evaluation accuracy and implementation performance since the company does not perform a post-implementation review of EC. The study selected six EC as multiple units of research. The primary criterion of selection was disruptive EC with significant negative effects. In addition, the six EC differed in timing (i.e. early, mid-production, and late) and origin (i.e. internally or externally). They were selected from different projects and customers to have a representative sample. The projects varied in terms of duration and number of engineering hours. In every case, the amount of equipment provided was diverse and so the degree of product customization. All six EC incurred in either delay in delivery times, increased costs or other unwanted consequences. Table 1 provides a summary of the characteristics of the EC mapped during the semi-structured interviews. Table 1. Summary of engineering changes EC #

Engineering change description

Reason for change

Project stage

Initiated by

Project duration

EC 1

Modification of gravitybased tanks to pressurized tanks for the tunnel thrusters Introduction of a big two-piece shim ring to replace multiple shim rings in the bolt connections Change of position for stiffeners, change of shape tunnel and paint specification Change the shape of the tunnel for the thruster Change to a multioperational control panel Change to an electrical motor with a higher IP grade

Error correction

Terminated

Customer. shipowner

4 years 500 (terminated)

Improve quality and reliability

Engineering Customer. shipyard

3 years 500 (terminated)

Error correction

Production

Customer. shipyard

2 years (estimated)

682

Error correction Technological evolution

Engineering Customer. shipyard Tendering Internal. sales dep.

8 months (estimated) 15 months (estimated)

387

Error correction

Delivery

6 months (estimated)

84

EC 2

EC 3

EC 4 EC 5

EC 6

Customer. shipyard

Engineering hours

265

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Next, it is described the EC where each factor was identified to influence the EC implementation. 1. Product Complexity. – EC2: The manufacturing complexity of the ring was underestimated and increased the effort and time required in its manufacturing processes. 2. Product innovation. – EC2: The adoption of the new solution led to unforeseen complications in the project and subsequent projects. – EC5: Since it was the first time this solution was offered, it was uncertain the cost and the hours needed for development. 3. Product Customization. – EC1, EC2, EC3: The high number in engineering hours is an indication of a high level of product customization. – EC5: The solution required the development of customized software in addition to the customized propulsion equipment. There was no access to the number of engineering hours used in customizing the control panel. Early in the project, the specifications were unknown for the engines and the power management system. 4. EC timing. – EC1: The timing of the change had the greatest influence on the negative effects since the ship was already built, and the thrusters and the tanks were already installed. Hence, the development of pressurized tanks consisted of a more complex solution and had to be implemented by the ship-owner on site. – EC4: In this case a positive circumstance, the change was identified early before the project was issued for production and avoided large increments in cost. – EC6: The mistake was identified once the equipment was produced and ready for delivery. This increased the negative effects considerably. 5. EC propagation. – EC1: The EC led to other changes in the ship, the tanks required a pressurized air source which had to be made available where the tanks were placed. – EC2, EC6: The implementation of the ring caused additional changes in the motor frame. Also, technical documentation became outdated. 6. Intra and cross-organizational integration. – EC3: The lack of efficient information flow internally and externally led to misunderstandings. – EC4: Poor communication with the customer lead to mistakes in the specifications.

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7. Established ECM process. – EC3 and EC4: The lack of formal processes for ECM did not allow to perform a post-implementation control to verify if the costs were covered by the charge made to the customer. 8. Different objectives between Sales and Project Management. – EC 6 led to identifying a factor that had not been discussed earlier. Production and sales have different objectives in terms of good performance. Each department had its objectives to fulfill. Sales wanted to retain customer satisfaction by offering the lowest price possible, ignoring internal costs. On the other hand, project management wanted to retain profit margin and stick to ECM procedures. The most recurrent and relevant factors influencing EC implementation in the ETO context were identified by the previous factor analysis and by the focus group. Table 2 presents the average scores of the factors and recapitulates the EC where they were present. In general, the highest-rated factors were related to the increase in costs and delay in delivery time. EC propagation achieved a top rating score and EC timing had the second-highest rating. Both EC-related factors were considered extremely influential according to the score and they were the most encountered in the case study. The respondents agreed that late EC (post equipment delivery) are the hardest to implement because normally this must be on-site (shipyard). In addition, it was identified that late EC leads to resources being allocated from other projects. The factor of Different objectives between sales and project management was acknowledged both as highly influential and challenging to address. The challenge originates from cultural aspects in the industry, where the customer has a high bargaining power when it comes to EC implementation. Cross-organizational integration and Intra-organizational integration were factors affirmed by most respondents to be notably influential in EC implementation. The opportunity for improvement regarding internal collaboration was highlighted during the discussion. Manual methods of communication were related to increasing the time spent in ECM. Product complexity was linked to change propagation. Product Innovation had contradictory reactions. Some respondents found it as a cause for further changes and others noted that it did not have any influence on negative effects but only had a positive effect (i.e. positive for later projects).

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Most relevant factors Present in EC Score EC propagation EC1, EC2, EC6 4.8 Different objectives between sales and project management EC6 4.8 EC timing EC1, EC4, EC6 4.4 Product complexity EC2 4.4 Intra-organizational integration EC3, EC4 4.2 Cross-organizational integration EC3, EC4 4.2 Product customization EC1, EC2, EC3, EC5 4 Established ECM process EC3, EC4 3.8 Production innovation EC2 3

5 Discussion The case study allowed the identification of areas of opportunity to improve EC implementation. The factors of EC propagation and Product Complexity show the importance of predicting the change propagation to improve the evaluation of the scale and cost of the EC. The uncertainty coming from change propagation is difficult to be systematically assessed. Change propagation identification helps to identify the consequences of EC as early as possible by identifying dependencies in a system. The factors of EC timing and Established ECM Process show the importance of having streamlined procedures for ECM in place. Late EC changes are the most damaging to project planning and required quick implementation. Besides, the impact evaluation was dependent on the experience of the project managers in the case company. The lack of dedicated impact assessment tools exposes project managers to calculate inaccurate time and cost for implementation. The factors of Cross and Intra-organizational integration show the importance of encouraging collaboration and enhance the information flow among actors that are dispersed at different physical locations, including customers. Collaboration can be challenging in ETO manufacturing companies due to the number and location of actors involved. In the case company, the collaboration between departments in all cases is done manually through meetings, email exchange, and phone calls. The process is not aided by automatic notifications or prompts to facilitate the approval workflow. The factor of product innovation and product customization shows the importance of early identification of customer requirements. Product innovations are likely to increase the risk associated with its adoption resulting in EC to propagate along in the product structure. But adequate requirement identification can potentially avoid EC. It can prevent mistakes in product development by correctly translating customer requirements to product specifications. Also, it can reduce the risk of further changes by ensuring the product being built is the product needed by the market. Tools and practices to support ECM were studied as part of this research. A classification is proposed according to the area of opportunity they propose to improve in EC implementation. However, it is not included in the scope of this paper.

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6 Conclusion This research provides an understanding of the production environment where the EC occurs and identifies areas of improvement of EC implementation in industry. The basic assumption of the factor analysis is that there are underlying causes that can be used to explain the negative effects caused by EC. The identified factors provide managers with a set of potentially important considerations that should help them mitigate the negative effects generated by disruptive EC. There are limitations in this study, besides the factors considered in this study, other environmental, organizational or technological factors might have been missed by this research that may also affect the implementation of EC. This research was conducted in the context of only one company case. Further case studies should be done to deepen the knowledge and generalizability of the study. It would be interesting to perform the factor analysis performed in this research in a manufacturing company at the end of the value chain. Also, companies who experience a larger quantity of EC requests per project should be considered. Informed consent was obtained from all individual participants included in the study. Acknowledgements. This work was supported by The Research Council of Norway.

References 1. Mello, M.H.: Coordinating an engineer-to-order supply chain: a study of shipbuilding projects. Norwegian University of Science and Technology, Faculty of Science and Technology, Department of Production and Quality Engineering, Trondheim (2015) 2. Adrodegari, F., et al.: Engineer-to-order (ETO) production planning and control: an empirical framework for machinery-building companies. J. Prod. Plann. Control 26, 910– 932 (2015) 3. Semini, M., et al.: Strategies for customized shipbuilding with different customer order decoupling points. J. Eng. Marit. Environ. 228(4), 362–372 (2014) 4. Stavrulaki, E., Davis, M.: Aligning products with supply chain processes and strategy. Int. J. Logistics Manage. 21(1), 127–151 (2010) 5. Terwiesch, C., Loch, C.H.: Managing the process of engineering change orders: the case of the climate control system in automobile development. J. Prod. Innov. Manage 16(2), 160– 172 (1999) 6. Jarratt, T., Clarkson, J., Eckert, C.: Engineering change. In: Clarkson, J., Eckert, C. (eds.) Design process Improvement, pp. 262–285. Springer, London (2005) 7. Wänström, C., Jonsson, P.: The impact of engineering changes on materials planning. J. Manuf. Technol. Manage. 17(5), 561–584 (2006) 8. Lin, Y., Zhou, L.: The impacts of product design changes on supply chain risk: a case study. Int. J. Phys. Distrib. Logistics Manage. 41(2), 162–186 (2011) 9. Hamraz, B., Caldwell, N.H.M., Clarkson, P.J.: A holistic categorization framework for literature on engineering change management. J. Syst. Eng. 16(4), 473–505 (2013)

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10. Wasmer, A., Staub, G., Vroom, R.W.: An industry approach to shared, cross-organisational engineering change handling-the road towards standards for product data processing. J. Comput. Aided Des. 43(5), 533–545 (2011) 11. Zennaro, I., et al.: Big size highly customised product manufacturing systems: a literature review and future research agenda. Int. J. Prod. Res. 57, 1–24 (2019) 12. Iakymenko, N., et al.: Managing engineering changes in the engineer-to-order environment: challenges and research needs. IFAC-PapersOnLine 51(11), 144–151 (2018) 13. Eckert, C., Clarkson, P., Zanker, W.: Change and customisation in complex engineering domains. J. Res. Eng. Des. 15(1), 1–21 (2004) 14. Reidelbach, M.A.: Engineering change management for long-lead-time production. Prod. Inventory Manage. J. 32(2), 84 (1991) 15. Tavčar, J., Duhovnik, J.: Engineering change management in individual and mass production. J. Robot. Comput. Integr. Manuf. 21(3), 205–215 (2005) 16. Jarratt, T., et al.: Engineering change: an overview and perspective on the literature. J. Res. Eng. Des. 22(2), 103–124 (2011) 17. Hicks, C., McGovern, T., Earl, C.F.: Supply chain management: a strategic issue in engineer to order manufacturing. Int. J. Logistics 65(2), 179–190 (2000) 18. Fricke, E., et al.: Coping with changes: causes, findings, and strategies. J. Syst. Eng. 3(4), 169–179 (2000)

Impact of Carbon Price on Renewable Energy Using Power Market System Xiangping Hu1(&), Xiaomei Cheng2(&), and Xinlu Qiu3 1

Industrial Ecology Programme, Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway [email protected] 2 Department of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, Norway [email protected] 3 Department of Industrial Economics and Technology, Norwegian University of Science and Technology, Trondheim, Norway [email protected]

Abstract. Reducing anthropogenic greenhouse gas emissions is a critical element to keep global warming below 2 °C. In terms of the IEA report, the largest sources of emission in 2016, which approaches to 42% of global total, is generated by power sector and heat sector. This indicates that reducing the emission in the power sector can play a crucial role to limit global warming. Large shares of low-carbon generators such as renewables, power plants with carbon capture and storage and implementing a sustainable environmental tax or carbon price are the possible approaches to reduce the emissions from the power sector. The paper investigates how carbon prices affect the Northern European power system. The power system model is net transfer capacity-based model which aims to minimize economic performance, such as operational cost and environmental cost, with the common power system constraints and large expansions of sustainable energy development, i.e., solar and wind energy. The carbon prices are based on scenarios of the Shared Socioeconomic Pathways (SSPs) which aims to limit global warming to below 2 °C with a probability greater than 66%. Four scenarios are conducted based on SSPs carbon prices. Results show that the carbon prices have a great impact on the economic performance of the power system, i.e., the higher carbon price, the higher power prices. Increasing carbon prices result in decreasing of coal production including hard coal and lignite coal production but increasing the gas production. This is due to different fuel carbon prices. Furthermore, renewable energy such as wind production continues to increase. This implies a positive relationship between renewable energy and carbon prices, such as the higher the carbon prices, the higher renewable energy production. Keywords: Carbon price model


Sustainable energy development
Power market

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1 Introduction The ambition of the Paris Agreement on climate change is to keep a global temperature rise this century below 2 °C compared to pre-industrial level, which changes the path of the energy sector development. In 2016, 42% of global CO2 was from the electricity and heat generation [1]. Therefore, this implies that the power sectors have great potential for limiting global warming to no more than 2 °C. Many approaches, such as increasing the share of renewable energy and introduce the carbon prices into power sectors, have been developed to reduce the emission from power sectors. Introducing the carbon price into the energy sector is one of the important approaches to alleviate the carbon emission, and hence achieving decarbonization goal [2, 3]. To achieve low emissions scenarios, an implicit carbon shadow price is usually assumed, and this price can also be used as policy instruments [3]. The carbon prices vary with different scenarios for climate change mitigation and adaptation, especially in the long run [3–6]. The Shared Socioeconomic Pathways (SSPs) are established by the scientific community and are part of a new scenario framework [7]. These pathways describe five different development trends in future by considering different scenarios for climate change projections, challenges for mitigation and adaptation to climate change, socioeconomic conditions and policies [7–10], and they are used to facilitate a harmonized framework for integrated analysis for interdisciplinary research of climate impact, and the aim of these pathways is to investigate future changes in different sectors or countries [7, 11]. In this paper, we investigate the impact of carbon prices on the environment and sustainable energy development based on the Northern European power system. The power system model used in this paper is the net transfer capacity-based model (NTC). These carbon prices are based on different pathways in the SSPs framework. Scenarios with possibility to achieve the 2 °C target are considered, and therefore, only four scenarios, i.e., SSP1, SSP2, SSP2, and SSP5, with different carbon prices are used. The rest of the paper is organized as follows. Section 2 introduces the data and methodology, followed by results and discussions in Sect. 3. Conclusions in Sect. 4 ends the paper.

2 Data and Methodology 2.1

Power System Modeling

The power system model is used to simulate the Northern European power grid for six countries. The countries include Norway, Sweden, Finland, Denmark, Germany, and Netherland. The objective of the model is to minimize the operating cost and environmental cost to confirm the hourly energy balance, transfer capacity limits and operational security standards [12]. Fundamental input parameters for power plants, i.e., fixed cost, marginal cost, start-up cost, transmission capacity limitations and so on, are obtained from [13]. The production capacity, demand and transmission constraints in the state of 2010 are used as the initial conditions for simulation in our model. The last point of the

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previous year’s hydro reservoir level is used as the initial condition of the next year’s hydro reservoir level. Environmental costs for thermal units are equal to the environmental tax multiplying by the total amount of emissions within the planning periods. The environmental variables, i.e., emission factor, energy efficiencies, and energy conversion factors, originates from the International Energy Agency (IEA) [14]. The main outputs for the model are the spot power prices, mixed production and the amount of carbon emissions. The detailed desperation of the numerical model can be found in [2] and the optimization is conducted using GAMS [15]. 2.2

Carbon Price and Scenarios Design

For the sake of generality, the environmental tax mentioned in the previous paragraph is the amount that must be paid for the right to emit one ton of carbon dioxide into the atmosphere. The main target for these carbon prices is to reduce the amount of carbon emission, and further reflects the carbon price’s influence on the energy system. The carbon prices used in this work are abstracted within the Shared Socioeconomic Pathways (SSPs) framework [3, 7, 16]. With the same target as the carbon price in the energy system, i.e., reducing the carbon emission amount, the other meaning of the carbon prices under the SSPs framework is to explain the carbon prices’ socioeconomic impact, not only the impact on the energy system. There are five different carbon prices within the SSPs framework [7, 16–21]. However, only four types of carbon prices are examined, i.e., SSP1, SSP2, SSP4, and SSP5, since it is not possible to achieve the 2 °C target under SSP3 [3]. The carbon prices of four scenarios until the year 2050 are shown in Fig. 1.

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3 Results and Discussions

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In this section, we investigate these four scenarios and analyze the results in terms of economic and environmental perspectives. Figure 2 shows the annual power prices which is equal to average all countries’ prices by year, together with the carbon prices for each scenario. It can be observed that power prices are increased with the rise in carbon prices. This reflects that one of the carbon prices’ key role in economic performance in the power system is to regulate power price.

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Fig. 2. Annual power prices and carbon prices for each scenario: The annual power prices are given in solid lines with values shown in the left y-axis, and the carbon prices are shown in dashed lines with values shown in the right y-axis.

Figure 3 illustrates the energy mix for each scenario. We can observe that the variations are primarily with gas and coal. The gas production is increased with the rise in the carbon prices, while the coal production including hard coal and lignite coal is decreased. The reason is that with the increasing carbon prices over time, the gas power with low carbon prices is increased to replace power production with high carbon prices, such as hard coal and lignite coal. The wind power continues to increase due to the cheaper power prices. The hydropower production is stable due to the same reservoir level for each year. The reservoir level assumptions for future years could be an interesting topic to be investigated for future study.

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The environmental impact, i.e., the total amount of carbon emission, is shown in Fig. 4. From this figure, we can notice that before the year 2030, the highest carbon prices scenario, i.e., SSP5, has the lowest total amount of carbon emission, which is opposite to the lower carbon prices scenario, i.e., SSP1. This implies that the carbon prices before the year 2030 have a positive impact on the total amount of carbon emission. However, the total amount of carbon emission converges to the same amount around 220 Mton. This indicates that carbon prices have limited impacts on the total

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amount of carbon emission in the long-term. This result illustrates that further increasing carbon prices might not have any impacts or may only have a few influences on carbon emission from a long-term perspective.

4 Conclusions and Discussion In this paper, the environmental impact of carbon prices based on SSPs Scenarios on the Northern European power system is investigated. Four scenarios are conducted. The fact that the carbon prices play an important role in the power prices is illustrated, such as the higher carbon prices, the higher power prices. Within the increasing carbon prices framework, coal production including hard coal and lignite coal is decreased, which is opposite to the gas production. This could be explained by the low gas carbon prices and high coal carbon prices. In addition, renewable production, for instance, wind power production continues to increase as carbon price rises. Furthermore, our simulation results also illustrate that further increasing carbon prices might have few influences on carbon emission in long-term. There is a potential limitation in our simulation that the reservoir level is similar for each year, which leads to stable hydropower production. This could be an interesting topic for further study.

References 1. IEA, Birol, F. (ed.): CO2 Emissions from Fuel Combustion Highlights. International Energy Agency, France (2016) 2. Cheng, X., Korpås, M., Farahmand, H.: The impact of electrification on power system in Northern Europe. In: 2017 14th International Conference on the European Energy Market (EEM). IEEE (2017) 3. Guivarch, C., Rogelj, J.: Carbon price variations in 2 °C scenarios explored (2017) 4. Creti, A., Jouvet, P.-A., Mignon, V.: Carbon price drivers: Phase I versus Phase II equilibrium? Energy Econ. 34(1), 327–334 (2012) 5. Feng, Z.-H., Zou, L.-L., Wei, Y.-M.: Carbon price volatility: evidence from EU ETS. Appl. Energy 88(3), 590–598 (2011) 6. Chevallier, J.: A model of carbon price interactions with macroeconomic and energy dynamics. Energy Econ. 33(6), 1295–1312 (2011) 7. Riahi, K., et al.: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change Hum. Policy Dimensions 42, 153–168 (2017) 8. O’Neill, B.C., et al.: A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122(3), 387–400 (2014) 9. Ebi, K.L., et al.: A new scenario framework for climate change research: background, process, and future directions. Clim. Change 122(3), 363–372 (2014) 10. Van Vuuren, D.P., et al.: A new scenario framework for climate change research: scenario matrix architecture. Clim. Change 122(3), 373–386 (2014) 11. Hu, X.P., Iordan, C.M., Cherubini, F.: Estimating future wood outtakes in the Norwegian forestry sector under the shared socioeconomic pathways. Glob. Environ. Change Hum. Policy Dimensions 50, 15–24 (2018)

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12. Farahmand, H.: Integrated power system balancing in Northern Europe-models and case studies, p. 150 (2012) 13. Farahmand, H., et al.: Possibilities of Nordic hydro power generation flexibility and transmission capacity expansion to support the integration of Northern European wind power production: 2020 and 2030 case studies. SINTEF Energy Research (2013) 14. IEA: World Energy Outlook 2011 (2011). www.worldenergyoutlook.org/weo2011. Accessed 12 Feb 2019 15. GAMS: General Algebraic Modeling System (GAMS), Washington, DC, USA (2017) 16. Bauer, N., et al.: Shared socio-economic pathways of the energy sector - quantifying the narratives. Glob. Environ. Change Hum. Policy Dimensions 42, 316–330 (2017) 17. O’Neill, B.C., et al.: The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change Hum. Policy Dimensions 42, 169–180 (2017) 18. Kriegler, E., et al.: Fossil-fueled development (SSP5): an energy and resource intensive scenario for the 21st century. Glob. Environ. Change Hum. Policy Dimensions 42, 297–315 (2017) 19. Fujimori, S., et al.: SSP3: AIM implementation of Shared Socioeconomic Pathways. Glob. Environ. Change Hum. Policy Dimensions 42, 268–283 (2017) 20. Fricko, O., et al.: The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road scenario for the 21st century. Glob. Environ. Change Hum. Policy Dimensions 42, 251–267 (2017) 21. Calvin, K., et al.: The SSP4: a world of deepening inequality. Glob. Environ. Change Hum. Policy Dimensions 42, 284–296 (2017)

Author Index

A Aleksandrova, Olga, 600 Alfnes, Erlend, 654, 662 Alonso-Ramos, Victor, 402 Antequera-Garcia, Gema, 402 Aschehoug, Silje, 358 Aukrust, Trond, 98 Azarian, Mohammad, 258 B Ban, Shuhao, 517 Barnard, Taylor, 396 Batu, Temesgen, 106 Berg, Olav Åsebø, 98 Bernhardsen, Thor Inge, 317 C Cao, Jiejie, 126 Chelishchev, Petr, 434 Chen, Bo, 283, 379, 410 Chen, Shifeng, 283, 379 Cheng, Bin, 480 Cheng, Xiaomei, 671 Chirici, Lapo, 639 D Dacal-Nieto, Angel, 402 Deng, Jiaming, 517 Deng, Xuechao, 234 Dong, Jinzhong, 151 Dong, Mengyao, 267 Dou, Yan, 44, 309, 523 Drobintsev, Pavel, 600 Du, Zhipeng, 219

E Eleftheriadis, Ragnhild J., 373, 608 F Feng, Bowen, 342 Feng, Xiangcai, 480 Feng, Xiong, 451 Fernandez-Gonzalez, Carmen, 402 Fordal, Jon Martin, 317 G Gamme, Inger, 358 Gao, Lingyan, 242, 275 Gao, Xiue, 283, 379 Gao, Zenggui, 267 Ge, Yang, 37, 59, 176, 309 Ghosh, Tamal, 283, 379 Guan, Xin, 292 Gui-qin, Li, 203 Guo, Lanzhong, 44, 52, 142, 169, 176, 309 H Hinojos A., Luis F., 662 Hong, Zhenyu, 185 Hovig, Even Wilberg, 98, 466 Hu, Chaobin, 151, 160 Hu, Xiangping, 3, 11, 134, 418, 427, 671 Huang, Junjie, 67 Huang, Qi, 242, 342, 457 I Iakymenko, Natalia, 662

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680 J Jian, Wu, 52 Jiang, Wei, 625 Jiang, Xiaomei, 59, 142, 151, 160 Jiao, Peijun, 52, 169 Jie, Cao, 52 Jin, Yujie, 211 Johannessen, Espen, 250 K Kang, Yuchi, 134 Karlsen, Øyvind, 116 Kotlyarova, Lina, 600 L Lan, Jian, 219, 234 Lemu, Hirpa G., 106, 116 Leng, Xuemei, 480 Li, Guiqin, 20, 29, 211, 219, 227, 234, 442, 451, 552, 560 Li, Jinguang, 495 Li, Jingyue, 195, 608 Li, Ming, 625 Li, Wenmeng, 44, 309 Li, Xiaolong, 242 Li, Xinyong, 37, 52, 169 Li, Yang, 20, 593 Li, Zhe, 195, 608 Li, Zhengqian, 451 Li, Zhiqiang, 366 Liao, Taohong, 3, 427 Lin, Shengyi, 227 Liu, Changzheng, 388 Liu, Fuyu, 418 Liu, Gong, 72 Liu, Hongbin, 511 Liu, Hongmei, 517 Liu, Junjun, 176 Liu, Lilan, 242, 267, 275, 333, 342, 457, 473, 585 Liu, Lin, 517 Liu, Meihong, 134, 427 Liu, Shouzheng, 473 Liu, Xiaoyu, 366, 511 Liu, Xinghua, 89 Liu, Xuedong, 517 Liu, Xuemei, 81, 89, 349, 495, 593 Liu, Yao, 625 Liu, Yunfei, 488 Li-xin, Lu, 203 Lodgaard, Eirin, 358 Lu, Jianfeng, 169 Lu, Lixin, 29, 211, 442, 552, 560 Lu, Yang, 219

Author Index Luo, Chloe, 633 Luo, Yinhua, 480 Lv, Leibing, 560 Lv, Yana, 410 M Ma, Haishu, 504 Ma, Haoyu, 81 Ma, Hongliang, 388 Ma, Jiaxin, 44, 176, 523 Ma, Junwei, 535 Ma, Zhanrong, 176 Ma, Zongzheng, 504 Martinsen, Kristian, 283, 379 Miao, Qiang, 585 Mitrouchev, Peter, 20, 29, 203, 211, 219, 227, 234, 442, 552, 560 Myklebust, Odd, 373 N Namokel, Michael, 151, 160 Nie, Jianjun, 504 Nilsen, Aleksander Wermers, 654 Niu, Shuguang, 67 Niu, Ziru, 72 Noureddine, Rami, 250 P Pan, Chengsheng, 410 Pedersen, Tom I., 299 Q Qiu, Xinlu, 671 R Ren, Xiaolei, 185 Ríos, Cristian, 402 Rødseth, Harald, 317, 608 S Sang, Haitao, 616 Schjølberg, Per, 299, 317 Selin, Ivan, 600 Shi, Jinfeng, 20 Shu, Beibei, 258 Solhaug, Harald, 98 Solvang, Wei Deng, 250, 258, 567, 577 Song, Laiqi, 89 Song, Pengyun, 11 Song, Xiaolei, 134 Sørby, Knut, 434, 466 Sun, Junfeng, 3, 427 Sun, Xu, 567, 577

Author Index Sun, Xuejian, 11 Sziebig, Gabor, 545 T Tan, Chao, 480 Tang, Hehui, 442 Tang, Xiuying, 480 Tian, Ran, 151, 160 Tian, Yonglin, 410 V Voinov, Nikita, 600 W Wan, Xiang, 242, 275, 333, 342, 457, 473, 585 Wang, Chao, 37 Wang, Chen, 646 Wang, Hanlin, 227 Wang, Jianhua, 535 Wang, Kesheng, 283, 379, 457, 473, 639, 646 Wang, Sen, 333, 511 Wang, Weicong, 29 Wang, Yi, 283, 333, 379, 396, 633, 639, 646 Wu, Fang, 275, 473 Wu, Jian, 59, 126, 169, 176 Wu, Pengfei, 585 X Xi, Zhang, 325 Xia, Hong, 234 Xie, Wenxue, 379 Xin, Wang, 325 Xin, Zhenbo, 72 Xing, Zhiwei, 185 Xu, Jingjing, 488 Xu, Weigang, 517

681 Xu, Yuanzhi, 325 Xu, Zhen, 3, 427 Y Yan, Xiupeng, 504 Yang, Ge, 52 Ye, Gu, 203 Ye, Zhiwen, 67 Yin, Ranguang, 349 Yu, Hao, 250, 258, 567, 577 Yu, Haoshui, 418 Yu, Tao, 227 Yuan, Jin, 72, 81, 349, 379, 495, 593 Z Zhang, Baodi, 134 Zhang, Baosheng, 418 Zhang, Guichang, 185 Zhang, Guowei, 366 Zhang, Haishu, 89 Zhang, Haohan, 366, 511 Zhang, Li, 511 Zhang, Ping, 593 Zhang, Ronghua, 388 Zhang, Shijin, 20 Zhang, Tianshu, 283 Zhang, Xiangyu, 457 Zhang, Xuedong, 3 Zhao, Weixing, 552 Zhao, Zhiping, 37 Zhou, Faqi, 517 Zhou, Maoheng, 219, 234 Zhu, Qiuyu, 219, 234 Zou, Liangliang, 81, 495 Zou, Wei, 585