Highlights of the current and highly relevant topics in manufact
Table of Contents
Preface xvii
Part I: Smart Technologies in Manufacturing 1
1 Smart Manufacturing Systems for Industry 4.0 3
Gaijinliu Gangmei and Polash Pratim Dutta
Abbreviations 3
1.1 Introduction 4
1.2 Research Methodology 5
1.3 Pillars of Smart Manufacturing 6
1.3.1 Manufacturing Technology and Processes 6
1.3.2 Materials 7
1.3.3 Data 8
1.3.4 Sustainability 8
1.3.5 Resource Sharing and Networking 9
1.3.6 Predictive Engineering 9
1.3.7 Stakeholders 10
1.3.8 Standardization 10
1.4 Enablers and Their Applications 11
1.4.1 Smart Design 12
1.4.2 Smart Machining 12
1.4.3 Smart Monitoring 13
1.4.4 Smart Control 13
1.4.5 Smart Scheduling 14
1.5 Assessment of Smart Manufacturing Systems 14
1.6 Challenges in Implementation of Smart Manufacturing Systems 15
1.6.1 Technological Issue 16
1.6.2 Methodological Issue 16
1.7 Implications of the Study for Academicians and Practitioners 17
1.8 Conclusion 17
References 18
2 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities 23
S. Deepak Kumar, G. Arun Manohar, R. Surya Teja, P. S. V. Ramana Rao, A. Mandal, Ajit Behera and P. Srinivasa Rao
Abbreviations 24
2.1 Introduction to Smart Manufacturing 24
2.1.1 Background of SM 24
2.1.2 Traditional Manufacturing versus Smart Manufacturing 25
2.1.3 Concept and Evolution of Industry 4.0 25
2.1.4 Motivations for Research in Smart Manufacturing 28
2.1.5 Objectives and Need of Industry 4.0 29
2.1.6 Research Methodology 30
2.1.7 Principles of I4. 0 30
2.1.8 Benefits/Advantages of Industry 4.0 31
2.2 Technology Pillars of Industry 4.0 31
2.2.1 Automation in Industry 4.0 33
2.2.1.1 Need of Automation 33
2.2.1.2 Components of Automation 33
2.2.1.3 Applications of Automation 34
2.2.2 Robots in Industry 4.0 34
2.2.2.1 Need of Robots 35
2.2.2.2 Advantages of Robots 35
2.2.2.3 Applications of Robots 37
2.2.2.4 Advances Robotics 37
2.2.3 Additive Manufacturing (AM) 38
2.2.3.1 Additive Manufacturing’s Potential Applications 39
2.2.4 Big Data Analytics 40
2.2.5 Cloud Computing 41
2.2.6 Cyber Security 43
2.2.6.1 Cyber-Security Challenges in Industry 4.0 43
2.2.7 Augmented Reality and Virtual Reality 44
2.2.8 Simulation 46
2.2.8.1 Need of Simulation in Smart Manufacturing 46
2.2.8.2 Advantages of Simulation 47
2.2.8.3 Simulation and Digital Twin 47
2.2.9 Digital Twins 47
2.2.9.1 Integration of Horizontal and Vertical Systems 48
2.2.10 IoT and IIoT in Industry 4.0 48
2.2.11 Artificial Intelligence in Industry 4.0 49
2.2.12 Implications of the Study for Academicians and Practitioners 51
2.3 Summary and Conclusions 51
2.3.1 Benefits of Industry 4.0 51
2.3.2 Challenges in Industry 4.0 52
2.3.3 Future Directions 52
Acknowledgement 53
References 53
3 IoT-Based Intelligent Manufacturing System: A Review 59
Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Pritam Chakraborty
3.1 Introduction 60
3.2 Literature Review 60
3.3 Research Procedure 64
3.3.1 The Beginning and Advancement of SM/IM 64
3.3.2 Beginning of SM/IM 64
3.3.3 Defining SM/IM 65
3.3.4 Potential of SM/IM 66
3.3.5 Statistical Analysis of SM/IM 68
3.3.6 Future Endeavour of SM/IM 68
3.3.7 Necessary Components of IoT Framework 69
3.3.8 Proposed System Based on IoT 71
3.3.9 Development of IoT in Industry 4.0 72
3.4 Smart Manufacturing 73
3.4.1 Re-Configurability Manufacturing System 73
3.4.2 RMS Framework Based Upon IoT 75
3.4.3 Machine Control 76
3.4.4 Machine Intelligence 77
3.4.5 Innovation and the IIoT 78
3.4.6 Wireless Technology 78
3.4.7 IP Mobility 78
3.4.8 Network Functionality Virtualization (NFV) 79
3.5 Academia Industry Collaboration 79
3.6 Conclusions 80
References 81
4 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process 85
Kali Charan Rath, Prasenjit Chatterjee, Pankajkumar Munibara Patro, Polaiah Bojja, Amaresh Kumar and Rashmi Prava Das
Abbreviations 86
4.1 Introduction and Literature Reviews 86
4.1.1 Motivation Behind the Study 88
4.1.2 Objective of the Chapter 89
4.2 Network in Smart Manufacturing System 89
4.2.1 Challenges for Smart Manufacturing Industries 90
4.2.2 Smart Manufacturing Current Market Scenario 93
4.3 Data Drives in Smart Manufacturing 93
4.3.1 Benefits of Data-Driven Manufacturing 94
4.4 Manufacturing of Product Through 3D Printing Process 97
4.4.1 3D Printing Technology 99
4.4.2 3D Printing Technologies Classification 100
4.4.3 3D Printer Parameters 101
4.4.4 Significance of Honeycomb Structure 102
4.4.5 Acrylonitrile Butadiene Styrene (ABS) Thermoplastic Polymer Used for Honeycomb Structures Model 103
4.4.6 3D Printing Parameters and Their Descriptions 107
4.5 Conclusion 107
References 109
5 Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management 113
Hiranmoy Samanta and Kamal Golui
5.1 Introduction 114
5.2 Objectives 114
5.3 Research Methodology 114
5.4 Literature Review 115
5.5 Components of SIM 116
5.5.1 Supply Chain Management (SCM) 116
5.5.2 Inventory Management System (IMS) 117
5.5.3 Internet of Things (IoT) 120
5.5.4 RFID System 121
5.5.5 Maintenance, Repair, and Operations 123
5.5.6 Deep Reinforcement Learning 125
5.6 Framework 127
5.7 Optimization 130
5.7.1 Inventory Optimization 130
5.8 Results and Discussion 131
5.9 A Mirror to Researchers and Managers 132
5.10 Conclusions 133
5.11 Future Scope 133
References 134
6 Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0 141
Bikash Chandra Behera, Bikash Ranjan Moharana, Matruprasad Rout and Kishore Debnath
6.1 Introduction 142
6.2 Machine Learning 143
6.3 Smart Factory 146
6.4 Intelligent Machining 148
6.5 Machine Learning Processes Used in Machining Process 150
6.6 Performance Improvement of Machine Structure Using Machine Learning 152
6.7 Conclusions 153
References 153
7 Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies 157
Jagadeesha T., Thutupalli Srinivasa Advaith, Choppala Sarath Wesley, Grandhi Sri Sai Charith and Doppalapudi Manohar
Abbreviations 158
7.1 Introduction 158
7.2 Literature Review 159
7.3 Methodology 161
7.3.1 Dataset Preparation 161
7.3.2 CWRU Dataset 161
7.3.3 Methodology Flow Chart 161
7.3.4 Data Pre-Processing 162
7.3.5 Models Deployed 163
7.3.6 Training and Testing 163
7.4 Analysis 164
7.4.1 Datasets 164
7.4.2 Feature Extraction 168
7.4.3 Splitting of Data into Samples 168
7.4.4 Algorithms Used 169
7.4.4.1 Multinomial Logistic Regression 169
7.4.4.2 K-Nearest Neighbors 170
7.4.4.3 Decision Tree 172
7.4.4.4 Support Vector Machine (SVM) 173
7.4.4.5 Random Forest 175
7.5 Results and Discussion 177
7.5.1 Importance of Classification Reports 177
7.5.2 Importance of Confusion Matrices 177
7.5.3 Decision Tree 178
7.5.4 Random Forest 180
7.5.5 K-Nearest Neighbors 182
7.5.6 Logistic Regression 185
7.5.7 Support Vector Machine 185
7.5.8 Comparison of the Algorithms 188
7.5.8.1 Accuracies 188
7.5.8.2 Precision and Recall 188
7.6 Conclusions 191
7.7 Scope of Future Work 191
References 192
8 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment 195
K. Sujatha, R. Krishnakumar, N.P.G. Bhavani, U. Jayalatsumi, V. Srividhya, C. Kamatchi and R. Vani
8.1 Introduction 196
8.1.1 Color Image Processing 197
8.1.2 Motivation 199
8.1.3 Objectives 199
8.2 Literature Review 200
8.2.1 Gas Turbine Power Plants 200
8.2.2 Artificial Intelligent Methods 201
8.3 Materials and Methods 202
8.3.1 Feature Extraction 202
8.3.2 Classification 203
8.4 Results and Discussion 204
8.4.1 Fisher’s Linear Discriminant Function (FLDA) and Curvelet 204
8.5 Conclusion 219
8.5.1 Future Scope of Work 220
References 221
9 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate 223
Sidharth Patro, Trupti Ranjan Mahapatra, Romeo S. Fono Tamo, Allu Vikram Kishore Murty, Soumya Ranjan Parimanik and Debadutta Mishra
Abbreviations 224
9.1 Introduction 224
9.2 Numerical Experimentation Program 227
9.3 Discussion of the Results 239
9.4 Conclusion 244
Acknowledgements 245
References 245
Part II: Integration of Digital Technologies to Operations 249
10 Edge Computing-Based Conditional Monitoring 251
Granville Embia, Aezeden Mohamed, Bikash Ranjan Moharana and Kamalakanta Muduli
10.1 Introduction 252
10.1.1 Problem Statement 252
10.2 Literature Review 253
10.3 Edge Computing 257
10.4 Methodology 259
10.5 Discussion 263
10.5.1 Predictive Maintenance 263
10.5.2 Energy Efficiency Management 264
10.5.3 Smart Manufacturing 265
10.5.4 Conditional Monitoring via Edge Computing Locally 266
10.5.5 Lesson Learned 266
10.6 Conclusion 267
References 267
11 Optimization Methodologies in Intelligent Manufacturing Systems: Application and Challenges 271
Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Atiur Alam
11.1 Introduction 272
11.2 Literature Review 273
11.3 Intelligent Manufacturing System Framework 275
11.3.1 Principles of Developing Industry 4.0 Solutions 277
11.3.2 Quantitative Analysis 279
11.3.2.1 Optimization Characteristics and Requirements of Industry 4.0 279
11.3.3 Optimization Methodologies and Algorithms 281
11.4 Bayesian Networks (BNs) 287
11.4.1 Instance-Based Learning (IBL) 288
11.4.2 The IB1 Algorithm 288
11.4.3 Artificial Neural Networks 289
11.4.4 A Comparison Between Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) 291
11.5 Problems of Implementing Machine Learning in Manufacturing 293
11.6 Conclusions 293
References 294
12 Challenges of Warehouse Management Towards Smart Manufacturing: A Case of an Indian Consumer Electrical Company 297
Natarajan Ramanathan, Neeraj Vairagi, Sakti Parida, Sushanta Tripathy, Ashok Kumar Sar, Kumar Mohanty and Alisha Lakra
12.1 Introduction 298
12.2 Literature Review 300
12.2.1 Shortage of Space 301
12.2.2 Non-Moving Materials 301
12.2.3 Lack of Action on Liquidation 302
12.2.4 Defective Material from Both Ends 302
12.2.5 Gap Between the Demand and the Supply 302
12.2.6 Multiple Price Revision 303
12.2.7 More Manual Timing for Loading and Unloading 303
12.2.8 Operational Challenges for Seasonal Products 303
12.2.9 Lack of Automation 303
12.2.10 Manpower Balancing Between Peak and Off 304
12.3 The Proposed ISM Methodology 304
12.3.1 Establishment of the Structural Self-Interaction Matrix (SSIM) 306
12.3.2 Creation of the Reachability Matrix 307
12.3.3 Implementation of the Level Partitions 308
12.3.4 Classification of the Selected Challenges 309
12.3.5 Development of the Final ISM Model 310
12.4 Results and Discussion 311
12.5 Practical Implications 312
12.6 Conclusions 313
References 314
13 The Impact of Organizational Ergonomics on Teaching Rapid Prototyping 319
Yaone Rapitsenyane, Patience Erick, Oanthata Jester Sealetsa and Richie Moalosi
Abbreviations 320
13.1 Introduction 320
13.2 Organizational Ergonomics 322
13.2.1 Aim of Organizational Ergonomics 323
13.3 Rapid Prototyping and Teaching Rapid Prototyping 323
13.4 Industry 4.0 Factors Associated with Organizational Ergonomics in a Rapid Prototyping/Manufacturing Facility 325
13.4.1 Technology 326
13.4.2 Communication 327
13.4.3 Teamwork 328
13.4.4 Human Resource 328
13.4.5 Quality Management 329
13.5 Implications of Industry 4.0 on Prototyping and Prototyping Facilities in Design Schools 329
13.6 The Influence of Cooperative Working Ergonomics of Distributed Manufacturing in Teaching and Learning Rapid Prototyping 332
13.7 Health and Safety in Rapid Prototyping Laboratories 333
13.7.1 Common Health Hazards in 3D Printing 333
13.7.2 Chemical Hazards 335
13.7.3 Flammable/Explosion Hazards 336
13.7.4 UV and Laser Radiation Hazard 336
13.7.5 Other Hazards 336
13.7.6 Hazard Controls 337
13.7.7 Engineering Controls 337
13.7.8 Administrative Controls 338
13.7.9 Personal Protective Equipment 338
13.8 Impact of Digital/Rapid Prototyping on Organizational Ergonomics 339
13.9 Implications of the Study for Academicians and Practitioners 340
13.10 Conclusions and Future Work 341
References 343
14 Sustainable Manufacturing Practices through Additive Manufacturing: A Case Study on a Can-Making Manufacturer 349
Kiren Piso, Aezeden Mohamed, Bikash Ranjan Moharana, Kamalakanta Muduli and Noorhafiza Muhammad
14.1 Introduction 350
14.2 Literature Review 352
14.3 Research Set Up 354
14.4 Additive Manufacturing Techniques 356
14.4.1 Types of Additive Manufacturing 356
14.4.1.1 Fused Deposition Modelling (FDM) 356
14.4.1.2 Stereolithography (SLA) 356
14.4.1.3 Selective Laser Sintering (SLS) 357
14.4.1.4 Direct Energy Deposition (DED) 357
14.4.1.5 Digital Light Processing (DLP) 358
14.5 Strategies Used by Production Company 358
14.5.1 Maintenance Strategies 358
14.5.1.1 Breakdown Maintenance (BM) 358
14.5.1.2 Preventive Maintenance (PM) 358
14.5.1.3 Periodic Maintenance (Time Based Maintenance – TBM) 359
14.5.1.4 Predictive Maintenance (PM) 359
14.5.1.5 Corrective Maintenance (CM) 359
14.5.1.6 Maintenance Prevention (PM) 359
14.5.2 Inventory Control in Manufacturing 359
14.5.2.1 Inventory Control and Maintenance in Manufacturing 360
14.5.2.2 Warehouse Storages 360
14.5.3 Time Factor in Manufacturing 361
14.5.3.1 Breakdown Time 361
14.5.3.2 Set-Up Time 361
14.5.3.3 Manned Time (Available Time) 361
14.5.3.4 Operating Working Time 361
14.5.3.5 Operating Time 362
14.5.3.6 Production Time 362
14.6 Sustainable Manufacturing 362
14.6.1 Social Aspect of Sustainable Manufacturing 363
14.6.2 Environmental Aspects of Sustainable Manufacturing 364
14.6.3 Economical Aspect of Sustainable Manufacturing 364
14.7 Sustainable Additive Manufacturing 365
14.7.1 Energy 365
14.7.2 Cost 366
14.7.2.1 Downtime Cost 366
14.7.3 Supply Chain 368
14.7.4 Maintenance with Additive Manufacturing 368
14.8 Additive Manufacturing with IFC CMD: A Case Study 369
14.9 Contribution of Additive Manufacturing Towards Sustainability 370
14.10 Limitations of Additive Manufacturing 372
14.11 Conclusions and Recommendations 373
References 373
Index 377
