Wang / Strandhagen / Yu Advanced Manufacturing and Automation VII

1. Auflage 2018
ISBN: 978-981-10-5768-7
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 451, 627 Seiten

Reihe: Lecture Notes in Electrical Engineering

The proceedings brings together a selection of papers from the 7th International Workshop of Advanced Manufacturing and Automation (IWAMA 2017), held in Changshu Institute of Technology, Changshu, China on September 11-12, 2017. Most of the topics are focusing on novel techniques for manufacturing and automation in Industry 4.0. These contributions are vital for maintaining and improving economic development and quality of life. The proceeding will assist academic researchers and industrial engineers to implement the concepts and theories of Industry 4.0 in industrial practice, in order to effectively respond to the challenges posed by the 4th industrial revolution and smart factories.

Professor Kesheng Wang has been a Professor and director of the Knowledge Discovery Laboratory at the Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), Norway. He became an elected member of the Norwegian Academy of Technological Sciences in 2006. Prof. Wang has published 22 books, 10 book chapters and over 270 technical peer-reviewed papers in international journals and conferences. He also has extensive experience with coordinating cooperation projects with many industrial companies and national, international and EU projects. Professor Wang's current areas of interest are intelligent manufacturing systems, data mining and knowledge discovery, radio-frequency identification (RFID), predictive maintenance and Industry 4.0/Logistics 4.0.Dr. Yi Wang obtained his PhD from the Manufacturing Engineering Center, Cardiff University, UK in 2008. He is currently a Lecturer at the School of Business and Management, Plymouth University, UK. He holds various visiting professorships in several universities worldwide. Dr. Wang's research interests include supply chain management, logistics, operation management, culture management, information systems, game theory, data analysis, semantics and ontology analysis, and neuromarketing. He has published 35 technical peer-reviewed papers in international journals and conferences. He has co-authored two monographs: Operations Management for Business in 2008 and Data Mining for Zero-defect Manufacturing in 2012.Professor Jan Ola Strandhagen is a Professor at the Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), Norway. He was previously director of the research center SFI Norman in SINTEF. He holds a PhD in Production Engineering from NTNU (1994). His research has focused on production management and control, logistics, manufacturing economics and strategies. He has managed and executed R&D projects in close collaboration with a wide variety of Norwegian companies and participated as researcher and project manager in several European projects. Professor Tao Yu is the President of Shanghai Polytechnic University, Shanghai, China and Professor at Shanghai University. He is a committee member of the International Federation for Information Processing (IFIP)/TC5. Prof. Yu has published more than one hundred academic papers. His research interests include mechatronics, computer integrated manufacturing systems and grid manufacturing.

Autoren/Hrsg.

Wang, Kesheng

Wang, Yi

Strandhagen, Jan Ola

Yu, Tao

Inhaltsverzeichnis

1;Preface;6
2;Committees;8
3;Contents;11
4;About the Editors;17
5;1 How AI Affects the Future Predictive Maintenance: A Primer of Deep Learning;19
5.1;Abstract;19
5.2;1 Introduction;19
5.3;2 Deep Learning;21
5.3.1;2.1 Artificial Intelligence;21
5.3.2;2.2 Computational Intelligence;21
5.3.3;2.3 Artificial Neural Networks (ANN);22
5.3.4;2.4 Deep Learning (DL);23
5.4;3 Deep Learning for Predictive Maintenance;24
5.5;4 Discussions;26
5.6;5 Conclusions;27
5.7;References;27
6;2 Application of Long Short-Term Memory Neural Network to Sales Forecasting in Retail—A Case Study;28
6.1;Abstract;28
6.2;1 Introduction;28
6.3;2 Long Short-Term Memory Neural Network;29
6.4;3 Experiment and Results;31
6.5;4 Discussion and Conclusions;33
6.6;Acknowledgements;34
6.7;References;34
7;3 A Coarse-to-Fine Matching Method in the Line Laser Scanning System;35
7.1;Abstract;35
7.2;1 Introduction;36
7.3;2 The Framework of the Proposed Method;37
7.4;3 Flat Top Laser Model;38
7.5;4 A Coarse-to-Fine Matching Method;40
7.6;5 Experiment;43
7.6.1;5.1 Simulation;44
7.6.2;5.2 The Three-Dimensional Measurement Experiment;45
7.7;6 Conclusion;48
7.8;Acknowledgements;48
7.9;References;49
8;4 Pose and Position Measurement in Dynamic Optical Coordinate Measure System;50
8.1;Abstract;50
8.2;1 Introduction;50
8.3;2 Principle of Dynamic Optical Coordinate Measure System;52
8.4;3 Pose Measurement in Dynamic Optical Coordinate Measure System;53
8.4.1;3.1 Design of V-8 Target;53
8.4.2;3.2 Simulation Experiment;54
8.4.3;3.3 Practical Experiment;56
8.4.4;3.4 Recognition Algorithm;56
8.4.5;3.5 Complement to Recognition Algorithm;57
8.5;4 Position Measurement in Dynamic Optical Coordinate Measure System;58
8.5.1;4.1 Design of Coded Targets;58
8.5.2;4.2 Recognition Algorithm of Coded Targets;58
8.6;5 Application;61
8.7;6 Conclusion;64
8.8;Acknowledgements;65
8.9;References;65
9;5 Design of Pin-Point Gate Injection Mould for Shells of Earplugs;67
9.1;Abstract;67
9.2;1 Process Analysis of Plastic Part;67
9.3;2 Design of Injection Mould Structure;68
9.3.1;2.1 Design of Parting Surface;68
9.3.2;2.2 Cavity Number and Layout;69
9.3.3;2.3 Design of Gating System;70
9.3.3.1;2.3.1 Design of Primary Runner;70
9.3.3.2;2.3.2 Design of Sub-runner;70
9.3.4;2.4 Design of Shaped Parts;71
9.3.4.1;2.4.1 Design of Cavity;71
9.3.4.2;2.4.2 Design of Core;71
9.3.5;2.5 Design of Cooling System;72
9.4;3 Structure of Injection Molds;73
9.5;4 Summary;74
9.6;References;75
10;6 Research on the Principle of Automatic Correcting Machine;76
10.1;Abstract;76
10.2;1 Introduction;76
10.3;2 Method for Correcting the Spool;77
10.4;3 Bending Correcting and T-S Fuzzy Neural Network for Predicting Correcting Stroke;78
10.5;4 The Design of Automatic Correcting Machine;80
10.6;5 Conclusion;82
10.7;Acknowledgements;82
10.8;References;82
11;7 Development on Management System of Automated High-Rise Warehouse for Mid-Small Enterprises Based on Django;83
11.1;Abstract;83
11.2;1 Introduction;83
11.3;2 Introduction and Design of Management System of Automated High-Rise Warehouse;84
11.3.1;2.1 Requirement Analysis;84
11.3.2;2.2 System Analysis [2];85
11.3.3;2.3 Functional Selection;85
11.4;3 Modularization Development of Management System of Automated High-Rise Warehouse;86
11.4.1;3.1 Introduction of Development Framework;86
11.4.2;3.2 Part of the Program Design of Management System of Automated High-Rise Warehouse;87
11.4.3;3.3 Communication Protocol Between PLC and the Monitor [8];88
11.4.4;3.4 Introduction of in-Stock Module in Management System of Automated High-Rise Warehouse;89
11.5;4 Conclusions;90
11.6;Acknowledgements;90
11.7;References;91
12;8 Underwater Image Enhancement by Fusion;92
12.1;Abstract;92
12.2;1 Introduction;92
12.3;2 Underwater Image Model;94
12.3.1;2.1 Transmission Estimation;95
12.3.2;2.2 Back-Scattering Estimation;95
12.3.3;2.3 Image Restoration Method;96
12.4;3 Image Enhancement by Multi-scale Fusion;96
12.4.1;3.1 Inputs;96
12.4.2;3.2 Weight Maps;98
12.4.3;3.3 Multi-scale Fusion;99
12.5;4 Experimental Result and Discussion;100
12.6;5 Conclusion;103
12.7;Acknowledgements;103
12.8;References;103
13;9 Restoration and Enhancement of Underwater Light Field Image;104
13.1;Abstract;104
13.2;1 Introduction;104
13.3;2 Restoration and Enhancement of Light Field Image;105
13.3.1;2.1 Underwater Light Field Image Model;106
13.3.2;2.2 Refocus and All-Focus;108
13.3.3;2.3 The Algorithm for Restoration and Enhancement;108
13.4;3 Experiment;109
13.4.1;3.1 Result of the Contrast Algorithm;109
13.4.2;3.2 Result of the Proposed Algorithm;111
13.4.3;3.3 Clarity Evaluation;112
13.5;4 Conclusion;115
13.6;Acknowledgements;115
13.7;References;116
14;10 Optimizing Leather Cutting Process in Make-to-Order Production to Increase Hide Utilization;117
14.1;Abstract;117
14.2;1 Introduction;117
14.3;2 Literature Review;118
14.3.1;2.1 Leather Nesting Problem;118
14.3.2;2.2 Leather Cutting Process;119
14.4;3 Process Reorganisation for Improved Leather Utilization;120
14.5;4 Discussion, Conclusion and Recommendation;121
14.6;Acknowledgements;123
14.7;References;123
15;11 A Study on Material Properties of Sealed Rubber Cylinder for Compressible Packer;125
15.1;Abstract;125
15.2;1 Introduction;125
15.3;2 Mathematical Model for Rubber Material;126
15.4;3 Experiment and Result;127
15.5;4 Conclusion;131
15.6;Acknowledgements;131
15.7;References;132
16;12 Finite Element Analysis of Sealing Performance for Multi Cylinder Packer;133
16.1;Abstract;133
16.2;1 Introduction;133
16.3;2 Establishment of Finite Element Model;134
16.4;3 Analytical Methods and Procedures;135
16.5;4 Conclusion;138
16.6;Acknowledgements;140
16.7;References;140
17;13 Experimental Study on the Effect of Surface Texture on the Dynamic Performance of Journal Bearing;141
17.1;Abstract;141
17.2;1 Introduction;142
17.3;2 Experimental Method;143
17.3.1;2.1 Experiment Apparatus;143
17.3.2;2.2 Test Bearing;144
17.3.3;2.3 Measuring Method;144
17.3.4;2.4 Dynamic Characteristics of Journal Bearing;145
17.4;3 Experimental Results and Discussion;147
17.4.1;3.1 Dynamic Characteristics of Journal Bearing and Textured Bearing Under Different Loads;147
17.4.2;3.2 Vibration Characteristics of Journal Bearing and Textured Bearing Under Different Loads;149
17.5;4 Conclusions;150
17.6;Acknowledgements;150
17.7;Appendix;151
17.8;References;151
18;14 The Effect of Temperature on Mechanical Properties of Polypropylene;153
18.1;Abstract;153
18.2;1 Introduction;153
18.3;2 Experimental Investigation;154
18.3.1;2.1 Material;154
18.3.2;2.2 Tensile Testing;154
18.4;3 Results and Discussion;156
18.5;4 Conclusion;157
18.6;Acknowledgements;158
18.7;References;158
19;15 The Effect of Extensometers on the Mechanical Properties of the Polypropylene Under Uniaxial Tensile Loading;160
19.1;Abstract;160
19.2;1 Introduction;160
19.3;2 Test Method for Properties of Plastic Materials at High and Low Temperature;161
19.4;3 Results and Discussion;162
19.5;4 Verification Test;162
19.6;5 Conclusion;162
19.7;Acknowledgements;165
19.8;References;165
20;16 Seal Property of Rubber Cylinder Shoulder in Packer;166
20.1;Abstract;166
20.2;1 Introduction;166
20.3;2 Shoulder Protector Device for Sealing Cylinder of Packer;167
20.4;3 Sealing Test Process and Result Analysis;168
20.5;4 Conclusion;171
20.6;Acknowledgements;171
20.7;References;171
21;17 Data Acquisition and Storage Network Based on CAN-Bus;172
21.1;Abstract;172
21.2;1 Introduction;172
21.3;2 Overall Scheme Design;173
21.4;3 Data Acquisition;173
21.5;4 Data Storage and Communication;175
21.6;5 Data Analysis and Verification;175
21.7;6 Conclusion;177
21.8;Acknowledgements;177
21.9;References;177
22;18 Effect of Pressure on Packer’s Sealing Performance;178
22.1;Abstract;178
22.2;1 Introduction;178
22.3;2 Finite Element Model;179
22.4;3 Analysis on Sealing Performance;180
22.5;4 Conclusion;182
22.6;Acknowledgements;182
22.7;References;183
23;19 Acquisition and Control Systems of Distributed Data Based on STM32;184
23.1;Abstract;184
23.2;1 Introduction;184
23.3;2 Hardware Design;185
23.4;3 Software Design;187
23.5;4 Results;188
23.6;5 Conclusions;189
23.7;Acknowledgements;189
23.8;References;189
24;20 Introduction of Cyber-Physical System in Robotized Press-Brake Line for Metal Industry;190
24.1;Abstract;190
24.2;1 Introduction;190
24.3;2 Software Based Control;191
24.4;3 Application;193
24.5;4 Conclusion;195
24.6;Acknowledgements;195
24.7;References;195
25;21 Dynamic Balance System Design and Control for High-Branch Pruning Machine;196
25.1;Abstract;196
25.2;1 Introduction;196
25.3;2 Principle of Dynamic Balance System;198
25.4;3 Manipulator Kinematic Analysis and Tilt Torque;199
25.4.1;3.1 Kinematic Analysis;199
25.4.2;3.2 Tilt Torque Calculation;200
25.5;4 Dynamic Balance System Structure Design;201
25.5.1;4.1 Basic Structural Composition;201
25.5.2;4.2 Control Method of Dynamic Balance System;202
25.6;5 Tests and Verification of Dynamic Balance System;202
25.7;6 Conclusions;204
25.8;References;204
26;22 Braking Distance Monitoring System for Escalator;206
26.1;Abstract;206
26.2;1 Introduction;206
26.3;2 Design Principle and Requirement;207
26.4;3 Braking Distance Testing Techniques;209
26.5;4 Conclusion;213
26.6;Acknowledgments;214
26.7;References;214
27;23 Design of Large Tonnage Elevator Sheave Block;215
27.1;Abstract;215
27.2;1 Introduction;215
27.3;2 Structure Design of Sheave Block;216
27.3.1;2.1 Sheave Traction Ratio and Rope Winding Selection;216
27.3.2;2.2 Design of Basic Structure of Car Top Sheave Block;217
27.3.3;2.3 Design of Basic Structure of Counterweight Top Sheave Block;219
27.3.4;2.4 Life and Static Strength Check;220
27.4;3 Finite Element Analysis;222
27.5;4 Conclusion;224
27.6;Acknowledgments;224
27.7;References;224
28;24 A Case Study of Tacit Knowledge Diffusion Object Preference in R&D Teams;226
28.1;Abstract;226
28.2;1 Introduction;226
28.3;2 Research Ideas and Questionnaire Design;227
28.3.1;2.1 Independent Variable Design;227
28.3.2;2.2 Dependent Variable Design;228
28.4;3 Example Analysis;229
28.4.1;3.1 Research Data Sources and Analysis;229
28.4.2;3.2 The Selection of Tacit Knowledge Diffusion Among Members Based on Knowledge Community;229
28.4.3;3.3 The General Situation of Implicit Knowledge Diffusion Preference Among Members;229
28.5;4 Hypothesis Testing of Related Data;230
28.5.1;4.1 QAP Correlation Analysis;230
28.5.2;4.2 QAP Regression Analysis;233
28.6;5 Summary;234
28.7;References;234
29;25 Strategic Framework for Manual Assembly System Design;235
29.1;Abstract;235
29.2;1 Introduction;236
29.3;2 Assembly Systems;236
29.4;3 Assembly System Impact Factors and Decisions;237
29.5;4 Manual Assembly System Framework;239
29.6;5 Case Study—Mid-Norwegian Industrial Company;240
29.6.1;5.1 AS-IS Situation for the Assembly System;240
29.6.2;5.2 Redesign of the Assembly System and TO-BE Situation;240
29.7;6 Discussion;242
29.8;7 Conclusion;242
29.9;References;242
30;26 The Research and Development of Preventing the Accidental Movement of the Elevator Car Safety Protection Device;244
30.1;Abstract;244
30.2;1 Introduction;244
30.3;2 Profile of Car Accidental Movement Protective Device;245
30.4;3 Design on Safety Protection Device to Prevent the Accidental Movement of the Elevator Car;245
30.4.1;3.1 Working Principle;245
30.4.2;3.2 Safety Gear;247
30.5;4 Conclusion;249
30.6;References;249
31;27 Ultrasonic Sensing System Design and Accurate Target Identification for Targeted Spraying;250
31.1;Abstract;250
31.2;1 Introduction;250
31.3;2 Target Spray System of Sprayer;251
31.3.1;2.1 Canopy Scanning Identification of Target Spray System;251
31.3.2;2.2 Hardware Design of Target Spray System;252
31.3.3;2.3 Target Identification Method;252
31.3.4;2.4 Software Design of Target Spray System;254
31.4;3 Determine Advance Spray Distance and Spray Time;254
31.5;4 Test and Analysis for Target Spray System;256
31.6;5 Conclusion;257
31.7;References;257
32;28 Fault Location in Power System Based on Different Modes of Traveling Wave and Artificial Neural Network;259
32.1;Abstract;259
32.2;1 Introduction;259
32.3;2 The Process of Fault Area Identification;261
32.4;3 Fault Location by ANN;262
32.4.1;3.1 ANN;262
32.4.2;3.2 Fault Location Algorithm;263
32.5;4 Performance Evaluation;265
32.6;5 Conclusion;267
32.7;References;267
33;29 Review of Technology for Strengthening Effect of Fe Element in Al Alloys;269
33.1;Abstract;269
33.2;1 Introduction;269
33.3;2 The Role of Fe Element in Al Alloys;270
33.4;3 Technical Research Work of Eliminating Harmful Effects of Fe in Al Alloys;271
33.4.1;3.1 Technology of Reducing the Fe Content in Al Alloys by Means of Feedstock Pre-processing or Melt-Treatment;272
33.4.2;3.2 Technology of Morphology Controlling of Fe-Rich Precipitated Phases;273
33.5;4 Development of Al Alloys with High Fe Content;275
33.6;5 Conclusion;276
33.7;References;277
34;30 Characterization the Fatigue Life of the Flame Thermal Spray Parts Applying the Power Function Energy Method;278
34.1;Abstract;278
34.2;1 Introduction;278
34.3;2 Fatigue Life Prediction Model with Applying 3USE Method;279
34.4;3 Combined Bending and Torsion Fatigue Life Test;280
34.4.1;3.1 Test Material;280
34.4.2;3.2 Test Method;280
34.5;4 Results and Discussions;281
34.5.1;4.1 Fatigue Life of Specimen;281
34.5.2;4.2 Data from Fatigue Life Test;281
34.5.3;4.3 Least Square Method to Fit Parameters in Remberg-Osgood Equation;281
34.5.4;4.4 Least Square Method to Fit Parameters in 3USE-Fatigue Life Prediction Model;283
34.6;5 Results;285
34.7;References;285
35;31 Design of Poultry Egg Quality Detection System Based on LABVIEW and PLC;287
35.1;Abstract;287
35.2;1 Introduction;287
35.3;2 The Structure and Control Requirements of the Egg Detection System;288
35.3.1;2.1 The Structure of the Egg Detection System;288
35.3.2;2.2 The Design of Control System;289
35.3.3;2.3 A Communication Connection with the PLC and LABVIEW;290
35.4;3 The Image Processing of Dirty Spots and Cracks Egg;291
35.4.1;3.1 The Image of Egg Collection Process;291
35.4.2;3.2 Image Grayscale;292
35.4.3;3.3 Dirty Spots Feature Extraction;292
35.4.4;3.4 Cracks Feature Extraction;292
35.4.5;3.5 Identification Dirty Spots and Cracks;294
35.5;4 Analysis of Test Results;295
35.6;5 Conclusion;296
35.7;References;296
36;32 Thermal Deformation of a Vertical Plate According to Various Plastic Deformation Regions;297
36.1;Abstract;297
36.2;1 Introduction;297
36.3;2 Materials and Methods;298
36.3.1;2.1 Induction Heating;298
36.3.2;2.2 Arc Heating and Laser Beam Heating;298
36.4;3 Numerical Analysis;299
36.4.1;3.1 Induction Heating Modeling;300
36.4.2;3.2 Arc Heating Modeling;300
36.4.3;3.3 Laser Beam Heating Modeling;301
36.5;4 Results and Discussion;302
36.5.1;4.1 Thermal Deformation by Induction Heating;303
36.5.2;4.2 Thermal Deformation by Arc Heating;303
36.5.3;4.3 Thermal Deformation by Laser Beam Heating;303
36.6;5 Conclusions;304
36.7;References;305
37;33 The Comparison of Different Models of Government Purchasing Home-Based Aged-Care Services;306
37.1;Abstract;306
37.2;1 Introduction;306
37.3;2 The Level of Competition of the Government Purchasing Home-Based Aged-Care Services;307
37.4;3 Inter-subjective Relationship;308
37.5;4 Levels of Institutionalization of the Government Purchasing Homed-Based Aged-Care Services;309
37.6;5 Quantities and Qualities of the Services;311
37.7;Reference;312
38;34 Study on Value Increment of Channel Value Chain Between Manufacturer and Distributor-Based on Game Theory;313
38.1;Abstract;313
38.2;1 Introduction;313
38.3;2 Literature Review;314
38.4;3 Case Analysis and Propositions;315
38.4.1;3.1 Model Assumptions and Definitions;316
38.4.2;3.2 Model Analysis and Recommendations;317
38.5;4 Conclusions;323
38.6;Acknowledgements;323
38.7;References;324
39;35 The Study on Evaluation Index System of Restructuring Construction Industry Under the Green Development Model;325
39.1;Abstract;325
39.2;1 Current Situations;325
39.3;2 Theories Relevant to the Green Development Model and Economic Transition;326
39.3.1;2.1 The Sustainable Development Theory;326
39.3.2;2.2 Theory of Corporate Transition;327
39.3.3;2.3 Introduction to the Evaluation Systems of Construction Industry Under the Green Development Mode in Foreign Countries;327
39.4;3 Analysis of Obstacles in Economic Restructure in Construction Industry;328
39.4.1;3.1 Internal Environment;328
39.4.2;3.2 External Environment;329
39.5;4 The Objectives and Principles of Evaluation Index System of Economic Restructure in the Construction Industry;329
39.5.1;4.1 Transition Objectives;329
39.5.2;4.2 Transition Principles;330
39.6;5 Recommended Indices for the Evaluation Index System of Economic Restructure in the Construction Industry;330
39.6.1;5.1 Internal Index;332
39.6.2;5.2 External Index;332
39.7;6 Summary and Prospect;333
39.8;References;334
40;36 Correcting Strategy for Distortion of Ring Part Based on Genetic Algorithm;335
40.1;Abstract;335
40.2;1 Introduction;336
40.3;2 Correcting Method for Distorted Ring Shaped Parts;336
40.3.1;2.1 Deformation Analysis of Ring Parts;336
40.3.2;2.2 Optimizing Model for Correcting Parameters;337
40.4;3 GA Algorithm Based Correcting Strategy;339
40.4.1;3.1 FEM Based Correcting Model;339
40.4.2;3.2 DGA Based Optimizing Method;341
40.5;4 Case Study;342
40.5.1;4.1 Modeling for Distorted Ring Shaped Part;342
40.5.2;4.2 Results and Discussion;344
40.6;5 Conclusions;346
40.7;References;346
41;37 Human Centered Automation System for ELV Disassembly Line;347
41.1;Abstract;347
41.2;1 Introduction;347
41.3;2 Disassembly Operation Mode of End-of-Life Vehicles;348
41.4;3 Hardware Structure of ELV Disassembly Line Scheduling System;349
41.5;4 Software Structure of ELV Disassembly Line Scheduling System;350
41.5.1;4.1 Data Model for Disassembly Process;351
41.5.2;4.2 Disassembly Job Planning Based on Rule Reasoning;352
41.5.3;4.3 Information Management and Scheduling of Disassembly Station;353
41.6;5 Conclusion;355
41.7;Acknowledgements;355
41.8;References;355
42;38 Rolling Bearing Fault Diagnosis Using Deep Learning Network;356
42.1;Abstract;356
42.2;1 Introduction;356
42.3;2 The Basic Theories of DBN;357
42.3.1;2.1 Restricted Boltzmann Machine (RBM) Architecture;357
42.3.2;2.2 DBN Theory;359
42.4;3 Theory of the Proposed Model;360
42.4.1;3.1 Nesterov Momentum;360
42.4.2;3.2 NM-DBM Fault Diagnosis;360
42.5;4 Experimental Validation;361
42.5.1;4.1 Experimental Data-Sets;361
42.5.2;4.2 Results and Discussion;362
42.6;5 Conclusion;363
42.7;Acknowledgements;364
42.8;References;364
43;39 An Automatic Feature Learning and Fault Diagnosis Method Based on Stacked Sparse Autoencoder;365
43.1;Abstract;365
43.2;1 Introduction;365
43.3;2 A Brief Introduction to the Stacked SAE Network;366
43.3.1;2.1 SAE;366
43.3.2;2.2 Stacked SAE Network;368
43.4;3 Proposed Stacked SAE-Based Fault Diagnosis Method;369
43.5;4 Experiments for Validation;369
43.5.1;4.1 Data Introduction;369
43.5.2;4.2 Validation Results;370
43.6;5 Conclusions;372
43.7;Acknowledgements;372
43.8;References;372
44;40 Application of Intelligent Hanging Production System in Garment Industry;374
44.1;Abstract;374
44.2;1 Introduction;374
44.3;2 Development Process and Working Principle of Intelligent Clothing Hanging Production System;375
44.3.1;2.1 Development Process of Intelligent Clothing Hanging Production System;375
44.3.2;2.2 Working Principle of Intelligent Clothing Hanging Production System;375
44.4;3 Superiority Analysis of Intelligent Clothing Hanging Production System;376
44.4.1;3.1 Parallel Production into Multiple Styles;376
44.4.2;3.2 Computer Real-Time Management;377
44.4.3;3.3 Material Automatic Sorting;377
44.4.4;3.4 Automatic Balancing Distribution of Materials;378
44.5;4 Production Efficiency Analysis of Garment Intelligent Suspension Production System;378
44.6;5 Conclusion;379
44.7;References;379
45;41 Objective Evaluation of Clothing Modeling Based on Eye Movement Experiment;380
45.1;Abstract;380
45.2;1 Introduction;380
45.3;2 Experiments;381
45.3.1;2.1 Material Preparation;381
45.3.2;2.2 Instrument Preparation;381
45.3.3;2.3 Experiment Scheme;382
45.3.4;2.4 Test Methods and Requirements;382
45.4;3 Results and Discussions;382
45.4.1;3.1 Results and Analysis of Eye Movement Test;382
45.4.1.1;3.1.1 Analysis of the Degree of Attention of Each Garment Styling Element;382
45.4.1.2;3.1.2 Analysis of Overall Attention Degree of Clothing Modeling Elements;383
45.4.1.3;3.1.3 Analysis of Overall Attention Degree of Clothing Modeling Elements;384
45.4.2;3.2 Results and Analysis of Language Description of Testers;384
45.4.2.1;3.2.1 Subjective Description of the Degree of Concern in Modeling Elements;384
45.4.2.2;3.2.2 Subjective Description of the Overall Attention Level of the Design Renderings;384
45.5;4 Conclusions;385
45.6;5 Compliance with Ethics Standards;386
45.7;References;386
46;42 Simulation Analysis of Rotor Indirect Field Oriented Vector Control System for AC Induction Motor in Low Speed Electric Vehicles;387
46.1;Abstract;387
46.2;1 Introduction;387
46.3;2 Mathematical Model of the AC Induction Motor;388
46.4;3 Coordinate Transformation of the AC Induction Motor;390
46.5;4 Modeling of Vector Control System Based on Rotor Indirect Field Orientation;392
46.6;5 Simulation of AC Induction Motor Control System Based on MATLAB/Simulink;394
46.6.1;5.1 Main Circuit Model;394
46.6.2;5.2 Control Circuit Model;394
46.6.3;5.3 Simulation Model of Vector Control Speed Regulation System Based on Rotor Indirect Field Oriented;395
46.6.4;5.4 Parameter Setting of the Induction AC Motor;396
46.6.5;5.5 Simulation Results and Analysis;397
46.7;References;399
47;43 Vision Based Quality Control and Maintenance in High Volume Production by Use of Zero Defect Strategies;401
47.1;Abstract;401
47.2;1 Zero Defect Strategies in Multi Stage Production;402
47.3;2 Quality Approach in Modern Manufacturing;402
47.3.1;2.1 Different Quality Methods;402
47.3.2;2.2 Zero Defect Manufacturing;403
47.4;3 Cyber Physical System and Maintenance;404
47.4.1;3.1 The Maintenance View;404
47.4.2;3.2 Cyber Physical Production Systems and Life Cycle Processes;404
47.5;4 Machine Vision Inspection and Maintenance in a Real Case;405
47.6;5 Conclusion;407
47.7;References;408
48;44 A RFID Based Solution for Managing the Order-Picking Operation in Warehouse;409
48.1;Abstract;409
48.2;1 Introduction;409
48.3;2 Methodology;410
48.3.1;2.1 Localization Scheme;411
48.3.2;2.2 Route Optimization;412
48.4;3 Simulation;413
48.5;4 Conclusions;415
48.6;References;415
49;45 Improving the Decision-Making of Reverse Logistics Network Design Part II: An Improved Scenario-Based Solution Method and Numerical Experimentation;416
49.1;Abstract;416
49.2;1 Introduction;416
49.3;2 An Improved Scenario-Based Solution Method to the Stochastic Problem;417
49.4;3 Numerical Experimentations;419
49.5;4 Conclusion;423
49.6;References;423
50;46 Improving the Decision-Making of Reverse Logistics Network Design Part I: A MILP Model Under Stochastic Environment;425
50.1;Abstract;425
50.2;1 Introduction;425
50.3;2 Problem and Modeling;427
50.3.1;2.1 Constraints;428
50.3.2;2.2 Objective Function;430
50.4;3 Summary;430
50.5;References;431
51;47 Predictive Maintenance for Synchronizing Maintenance Planning with Production;433
51.1;Abstract;433
51.2;1 Introduction;434
51.3;2 Literature;434
51.3.1;2.1 Digitalizing Maintenance;434
51.3.2;2.2 Remaining Useful Life Algorithm in Predictive Maintenance;435
51.3.3;2.3 Predictive Maintenance and Maintenance Planning;436
51.4;3 IPL Approach;436
51.4.1;3.1 Step 1: Initial Maintenance Plan;436
51.4.2;3.2 Step 2: Modelling of RUL;437
51.4.3;3.3 Step 3: Synchronizing Maintenance Plan;438
51.5;4 Concluding Remarks;439
51.6;References;440
52;48 Recognition of Garlic Cloves Orientation Based on Machine Vision;441
52.1;Abstract;441
52.2;1 Introduction;441
52.3;2 Image Acquisition;442
52.4;3 Image Preprocessing;443
52.4.1;3.1 Image Segmentation;443
52.4.2;3.2 Image Expansion;444
52.5;4 Garlic Flap Recognition;445
52.5.1;4.1 Garlic Characteristic Parameters;445
52.5.2;4.2 Recognition of Flap Direction Based on Area Feature;445
52.6;5 The Results and Analysis of Experiment;447
52.7;6 Conclusion;448
52.8;References;448
53;49 Metal Penetration in Additively Manufactured Venting Slots for Low-Pressure Die Casting;450
53.1;Abstract;450
53.2;1 Introduction;450
53.2.1;1.1 Additive Manufacturing;451
53.2.2;1.2 Low-Pressure Die Casting;451
53.2.3;1.3 Test Parameters;452
53.2.4;1.4 Surface Tension and Oxide Films;452
53.3;2 Experiment;453
53.3.1;2.1 Degree of Metal Penetration;453
53.3.2;2.2 Actual Slot Size;455
53.4;3 Results and Discussion;455
53.4.1;3.1 Metal Penetration;455
53.4.2;3.2 Metal Retraction;458
53.4.3;3.3 Surface Tension and Metal Penetration;459
53.5;4 Summary and Conclusions;460
53.6;Acknowledgements;460
53.7;References;460
54;50 Analysis and Impact Assessment in Sustainable Industrial and Infrastructural Development Projects;462
54.1;Abstract;462
54.2;1 Introduction;462
54.3;2 Industry in Biodiversity and Ecosystems;464
54.4;3 Consequence and Risk Analyses;466
54.5;4 Circular Economy;466
54.6;5 An Industrial Example; Finnfjord Smelting Plant (“Finnfjord Smelteverk”);468
54.7;6 Conclusions;468
54.8;References;469
55;51 Lightweight Design of LED Street Lamp Based on Response Surface Method;471
55.1;Abstract;471
55.2;1 Introduction;471
55.3;2 LED Street Lamp Model;472
55.4;3 Model and Sensitivity Analysis of LED Street Lamp Junction Temperature;472
55.4.1;3.1 Summary of Response Surface Method;472
55.4.2;3.2 Model of LED Street Lamp Junction Temperature;474
55.4.3;3.3 Modeling of LED Street Lamp Junction Temperature Based on Response Surface Method;474
55.4.4;3.4 Sensitivity Analysis;477
55.5;4 Lightweight Design of LED Street L478
55.5.1;4.1 Lightweight Design Mathematical Model of LED Street L478
55.5.2;4.2 An Improved Particle Swarm Optimization Algorithm;478
55.5.3;4.3 Comparative Analysis of Optimization Results;480
55.6;5 Conclusions;480
55.7;Acknowledgements;480
55.8;References;480
56;52 Industry 4.0 and Cyber Physical Systems in a Norwegian Industrial Context;482
56.1;Abstract;482
56.2;1 Introduction;483
56.3;2 Relevant Literature and Industry 4.0 Survey’s;483
56.3.1;2.1 The “Norwegian Model” and Management Role;483
56.3.2;2.2 Lean Manufacturing Versus Industry 4.0, CPS and Zero Defect Manufacturing;484
56.3.3;2.3 Lean Manufacturing Versus Industry 4.0, CPS and Zero Defect Manufacturing;485
56.4;3 Introduction to a Manufacturing Survey on Industry 4.0;487
56.5;4 Can “the Norwegian Model” Be an Effective Implementation of Digital Solutions in a Global Market?;488
56.6;5 Conclusions;488
56.7;References;489
57;53 Mechanical Analysis of a Customized Hand Orthosis Based on 3D Printing;491
57.1;Abstract;491
57.2;1 Introduction;491
57.3;2 Customized Hand Orthosis;492
57.3.1;2.1 3D Model Acquisition of Hand Orthosis;492
57.3.2;2.2 3D Printing Process;493
57.4;3 Rehabilitation Therapy of the Clinical Hand Spasm;493
57.4.1;3.1 The Physiological Mechanism of Hand Spasm;493
57.4.2;3.2 Rehabilitation Therapy of the Hand Orthosis;494
57.5;4 Mechanical Analysis of the Hand Orthosis;495
57.5.1;4.1 Mechanical Property Test;495
57.5.2;4.2 Construction of Finite Element Model;496
57.5.3;4.3 Result of Finite Element Analysis;496
57.6;5 Conclusion;497
57.7;References;498
58;54 Fault Classification and Degradation Assessment Based on Wavelet Packet Decomposition for Rotary Machinery;499
58.1;Abstract;499
58.2;1 Introduction;499
58.3;2 Vibration Condition Monitoring;500
58.4;3 Wavelet Packet Decomposition;501
58.5;4 Set up and Data Collection;503
58.6;5 Numerical Result;504
58.7;6 Conclusion;505
58.8;References;505
59;55 Research on Assembly and Disassembly of Reducer with the Combination of Virtual and the Actual Reality;507
59.1;Abstract;507
59.2;1 Introduction;507
59.3;2 General Research Idea;508
59.4;3 Development of Courseware System Based on Unity3D;509
59.4.1;3.1 Preliminary Material Preparation;509
59.4.2;3.2 Development Process of Virtual Speed Reducer System;510
59.5;4 Development of Training System Based on Zspace;513
59.5.1;4.1 Zspace Profile;513
59.5.1.1;4.1.1 System Development Characteristics;514
59.5.1.2;4.1.2 System Interaction and Immersive Development;514
59.5.1.3;4.1.3 The Display of Combination of Virtual and Reality;515
59.5.2;4.2 Augmented Reality Applications;516
59.6;5 Summary;518
59.7;References;518
60;56 Equipment Condition Monitoring System Based on LabVIEW;520
60.1;Abstract;520
60.2;1 Introduction;520
60.3;2 Overall Scheme Design;521
60.4;3 Key Technologies for System Design;522
60.4.1;3.1 Structure Design of Acquisition Instrument;522
60.4.2;3.2 Communication Design and Data Processing;523
60.4.2.1;3.2.1 UDP and Multi-channel Synchronization Acquisition Technology;523
60.4.2.2;3.2.2 Data Analysis and Storage;524
60.4.3;3.3 Signal Analysis;525
60.4.3.1;3.3.1 Time Domain Analysis;525
60.4.3.2;3.3.2 Frequency Domain Analysis;526
60.4.3.3;3.3.3 Example Validation;527
60.5;4 Conclusion;527
60.6;Acknowledgements;528
60.7;References;528
61;57 The Algorithm Knowledge Base for Steel Production Process Optimization;529
61.1;Abstract;529
61.2;1 Introduction;529
61.3;2 Demand Analysis;530
61.3.1;2.1 Analysis of Optimization Algorithms for Iron and Steel Production Process;530
61.3.2;2.2 Functional Requirements Analysis;531
61.4;3 Design of the Algorithm Base System;534
61.4.1;3.1 Overall Design of Algorithm Knowledge Base Structure;534
61.4.2;3.2 Database Design;536
61.5;4 Application of Knowledge Base;537
61.5.1;4.1 Development Environment of Knowledge Base;537
61.5.2;4.2 Knowledge Base System Interface Display;538
61.6;5 Conclusions;540
61.7;Acknowledgements;540
61.8;References;540
62;58 Study on the Friction Characteristics of Two Polytetrafluoroethylene (PTFE) Coatings;541
62.1;Abstract;541
62.2;1 Introduction;541
62.3;2 Materials and Preparations;542
62.3.1;2.1 The Sintering Process;542
62.3.2;2.2 The Bonding Process;543
62.3.3;2.3 Preparation for Samples;544
62.4;3 Experiment;544
62.5;4 Results and Discussion;545
62.5.1;4.1 Coefficient of Friction;545
62.5.2;4.2 Analysis;547
62.6;5 Conclusion;548
62.7;Acknowledgements;549
62.8;References;549
63;59 A Hybrid Nested Partitions Method for Bi-objective Job Shop Scheduling Problem Considering Energy Consumption and Makespan;550
63.1;Abstract;550
63.2;1 Introduction;550
63.3;2 Mathematical Model;551
63.3.1;2.1 Optimization Objective of Makespan;551
63.3.2;2.2 Energy Criterion for the Problem;552
63.3.3;2.3 Mathematical Model;553
63.4;3 Scheduling Algorithm;554
63.4.1;3.1 Partitioning Scheme;555
63.4.2;3.2 Sampling Scheme;555
63.4.3;3.3 Select Promising Index Scheme;556
63.4.4;3.4 Scheduling Backtracking Scheme;556
63.5;4 Case Study;556
63.6;5 Conclusion;557
63.7;References;558
64;60 Research on the Framework of Quality Prediction in Intelligent Manufacturing;559
64.1;Abstract;559
64.2;1 Introduction;559
64.3;2 The Framework of Combinational Prediction Model in Intelligent Manufacturing;560
64.3.1;2.1 The Characteristics of Intelligent Manufacturing;561
64.3.2;2.2 The Selection of Single Prediction Model;561
64.3.3;2.3 The Framework of Support Vector Regression Combinational Prediction Model;562
64.4;3 Conclusion;564
64.5;Acknowledgements;564
64.6;References;564
65;61 Deep Learning Approach to Multiple Features Sequence Analysis in Predictive Maintenance;566
65.1;Abstract;566
65.2;1 Introduction;567
65.3;2 Methodology of Multiple Features Sequence Analysis in Predictive Maintenance;568
65.3.1;2.1 Architecture of Multiple Features Sequence Analysis and Segmentation;569
65.3.2;2.2 Two Autoencoder Network Structures;570
65.3.3;2.3 Visualization and Segmentation;570
65.4;3 Experiments and Discussion;571
65.4.1;3.1 Bearing Life Cycle Dataset;571
65.4.2;3.2 Feature Extraction and Representation Learning with Autoencoder;571
65.4.3;3.3 Visualization and Segmentation;572
65.5;4 Conclusions;574
65.6;Acknowledgements;575
65.7;References;575
66;62 Sorting System of Robot Based on Vision Detection;576
66.1;Abstract;576
66.2;1 Introduction;576
66.3;2 System Hardware Design;577
66.4;3 Software Design;577
66.4.1;3.1 Host Computer Program Design;578
66.4.1.1;3.1.1 Program Flow Design;578
66.4.1.2;3.1.2 Image Acquisition, Processing and Coordinate Transformation;578
66.4.2;3.2 Robot Actuation Program Design;580
66.5;4 System Operation Effect;581
66.6;5 Conclusion;582
66.7;Acknowledgements;582
66.8;References;582
67;63 The Challenges and Promises of Big Data—An Engineering Perspective;583
67.1;Abstract;583
67.2;1 Introduction;583
67.3;2 Criticism of Big Data Paradigm;584
67.4;3 New Big Data Paradigm;585
67.5;References;586
68;64 A Modified Teaching and Learning Based Optimization Algorithm and Application in Deep Neural Networks Optimization for Electro-Discharge Machining;589
68.1;Abstract;589
68.2;1 Introduction;589
68.3;2 Teaching and Learning Based Optimization and Deep Neural Networks;591
68.3.1;2.1 Teaching and Learning Based Optimization;591
68.3.2;2.2 Deep Neural Networks;593
68.3.3;2.3 A Modified Teaching and Learning Based Optimization Algorithm;594
68.3.4;2.4 DNN Optimization Based on Mtlbo;596
68.4;3 Experimental Results;596
68.5;4 Conclusions;598
68.6;Acknowledgements;598
68.7;References;598
69;65 A Study on a Novel Application of Eye Tracking Technology in Product Customization;600
69.1;Abstract;600
69.2;1 Introduction;601
69.3;2 Literature Review;602
69.3.1;2.1 Personalized Customization;602
69.3.2;2.2 Eye Tracking Technology;603
69.4;3 Materials and Methods;604
69.4.1;3.1 Stimuli;604
69.4.2;3.2 Subjects;604
69.4.3;3.3 Methods;604
69.5;4 Results and Discussion;606
69.5.1;4.1 Eye Tracking Data Analysis;606
69.5.2;4.2 Recommendation Scheme;608
69.5.3;4.3 Evaluation and Analysis;609
69.6;5 Conclusion;609
69.7;References;610
70;66 Big Data Analysis in Click Prediction;612
70.1;Abstract;612
70.2;1 Introduction;612
70.3;2 Data Processing;613
70.3.1;2.1 Datasets Provided;613
70.3.2;2.2 Data Understanding;614
70.4;3 Data Cleaning;614
70.4.1;3.1 Data Training;614
70.4.2;3.2 Preliminary Operation;615
70.5;4 Attribute Manipulation;615
70.5.1;4.1 Attribute Manipulation: Geo_Location;616
70.6;5 Data Exploration;616
70.7;6 Data Distribution;617
70.7.1;6.1 Click Tendency Time-Based;618
70.7.2;6.2 Data Distribution Platform-Based;620
70.8;7 Data Geolocation;621
70.8.1;7.1 GRP’s Calculation on US Population;622
70.9;8 CTR Comparison with GRPs;622
70.10;9 Model Construction;623
70.10.1;9.1 Logistic Regression;623
70.11;10 Model Validation;624
70.11.1;10.1 Logit Model Construction;624
70.12;11 Result Exploitation;626
70.13;12 Conclusions;626
70.14;References;626