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Industry 4.1

Intelligent Manufacturing with Zero Defects

Cheng, Fan-Tien (Editor)

IEEE Series on Systems Science and Engineering

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1. Edition January 2022
560 Pages, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-73989-0
John Wiley & Sons

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Industry 4.1 Intelligent Manufacturing with Zero Defects

Discover the future of manufacturing with this comprehensive introduction to Industry 4.0 technologies from a celebrated expert in the field

Industry 4.1: Intelligent Manufacturing with Zero Defects delivers an in-depth exploration of the functions of intelligent manufacturing and its applications and implementations through the Intelligent Factory Automation (iFA) System Platform. The book's distinguished editor offers readers a broad range of resources that educate and enlighten on topics as diverse as the Internet of Things, edge computing, cloud computing, and cyber-physical systems.

You'll learn about three different advanced prediction technologies: Automatic Virtual Metrology (AVM), Intelligent Yield Management (IYM), and Intelligent Predictive Maintenance (IPM). Different use cases in a variety of manufacturing industries are covered, including both high-tech and traditional areas.

In addition to providing a broad view of intelligent manufacturing and covering fundamental technologies like sensors, communication standards, and container technologies, the book offers access to experimental data through the IEEE DataPort. Finally, it shows readers how to build an intelligent manufacturing platform called an Advanced Manufacturing Cloud of Things (AMCoT).

Readers will also learn from:
* An introduction to the evolution of automation and development strategy of intelligent manufacturing
* A comprehensive discussion of foundational concepts in sensors, communication standards, and container technologies
* An exploration of the applications of the Internet of Things, edge computing, and cloud computing
* The Intelligent Factory Automation (iFA) System Platform and its applications and implementations
* A variety of use cases of intelligent manufacturing, from industries like flat-panel, semiconductor, solar cell, automotive, aerospace, chemical, and blow molding machine

Perfect for researchers, engineers, scientists, professionals, and students who are interested in the ongoing evolution of Industry 4.0 and beyond, Industry 4.1: Intelligent Manufacturing with Zero Defects will also win a place in the library of laypersons interested in intelligent manufacturing applications and concepts. Completely unique, this book shows readers how Industry 4.0 technologies can be applied to achieve the goal of Zero Defects for all product

Editor Biography xv

List of Contributors xvii

Preface xix

Acknowledgments xxi

Foreword xxiii

1 Evolution of Automation and Development Strategy of Intelligent Manufacturing with Zero Defects 1
Fan-Tien Cheng

1.1 Introduction 1

1.2 Evolution of Automation 1

1.2.1 e-Manufacturing 1

1.2.1.1 Manufacturing Execution System (MES) 3

1.2.1.2 Supply Chain (SC) 6

1.2.1.3 Equipment Engineering System (EES) 7

1.2.1.4 Engineering Chain (EC) 9

1.2.2 Industry 4.0 10

1.2.2.1 Definition and Core Technologies of Industry 4.0 10

1.2.2.2 Migration from e-Manufacturing to Industry 4.0 12

1.2.2.3 Mass Customization 12

1.2.3 Zero Defects - Vision of Industry 4.1 13

1.2.3.1 Two Stages of Achieving Zero Defects 14

1.3 Development Strategy of Intelligent Manufacturing with Zero Defects 14

1.3.1 Five-Stage Strategy of Yield Enhancement and Zero-Defects Assurance 15

1.4 Conclusion 18

Appendix 1.A - Abbreviation List 18

References 20

2 Data Acquisition and Preprocessing 25
Hao Tieng, Haw-Ching Yang, and Yu-Yong Li

2.1 Introduction 25

2.2 Data Acquisition 26

2.2.1 Process Data Acquisition 26

2.2.1.1 Sensing Signals Acquisition 26

2.2.1.2 Manufacturing Parameters Acquisition 35

2.2.2 Metrology Data Acquisition 36

2.3 Data Preprocessing 37

2.3.1 Segmentation 37

2.3.2 Cleaning 38

2.3.2.1 Trend Removal 39

2.3.2.2 Wavelet Thresholding 41

2.3.3 Feature Extraction 43

2.3.3.1 Time Domain 43

2.3.3.2 Frequency Domain 47

2.3.3.3 Time-Frequency Domain 49

2.3.3.4 Autoencoder 52

2.4 Case Studies 53

2.4.1 Detrending of the Thermal Effect in Strain Gauge Data 53

2.4.2 Automated Segmentation of Signal Data 55

2.4.3 Tool State Diagnosis 57

2.4.4 Tool Diagnosis using Loading Data 61

2.5 Conclusion 64

Appendix 2.A - Abbreviation List 64

Appendix 2.B - List of Symbols in Equations 65

References 67

3 Communication Standards 69
Fan-Tien Cheng, Hao Tieng, and Yu-Chen Chiu

3.1 Introduction 69

3.2 Communication Standards of the Semiconductor Equipment 69

3.2.1 Manufacturing Portion 69

3.2.1.1 SEMI Equipment Communication Standard I (SECS-I) (SEMI E4) 70

3.2.1.2 SEMI Equipment Communication Standard II (SECS-II) (SEMI E5) 75

3.2.1.3 Generic Model for Communications and Control of Manufacturing Equipment (GEM) (SEMI E30) 81

3.2.1.4 High-Speed SECS Message Services (HSMS) (SEMI E37) 84

3.2.2 Engineering Portion (Interface A) 91

3.2.2.1 Authentication & Authorization (A&A) (SEMI E132) 93

3.2.2.2 Common Equipment Model (CEM) (SEMI E120) 95

3.2.2.3 Equipment Self-Description (EqSD) (SEMI E125) 95

3.2.2.4 Equipment Data Acquisition (EDA) Common Metadata (ECM) (SEMI E164) 98

3.2.2.5 Data Collection Management (DCM) (SEMI E134) 102

3.3 Communication Standards of the Industrial Devices and Systems 107

3.3.1 Historical Roadmaps of Classic Open Platform Communications (OPC) and OPC Unified Architecture (OPC-UA) Protocols 108

3.3.1.1 Classic OPC 108

3.3.1.2 OPC-UA 109

3.3.2 Fundamentals of OPC-UA 110

3.3.2.1 Requirements 110

3.3.2.2 Foundations 111

3.3.2.3 Specifications 112

3.3.2.4 System Architecture 112

3.3.3 Example of Intelligent Manufacturing Hierarchy Applying OPC-UA Protocol 119

3.3.3.1 Equipment Application Program (EAP) Server 121

3.3.3.2 Use Cases of Data Manipulation 122

3.3.3.3 Sequence Diagrams of Data Manipulation 123

3.4 Conclusion 125

Appendix 3.A - Abbreviation List 125

References 128

4 Cloud Computing, Internet of Things (IoT), Edge Computing, and Big Data Infrastructure 129
Hung-Chang Hsiao, Min-Hsiung Hung, Chao-Chun Chen, and Yu-Chuan Lin

4.1 Introduction 129

4.2 Cloud Computing 131

4.2.1 Essentials of Cloud Computing 131

4.2.2 Cloud Service Models 132

4.2.3 Cloud Deployment Models 134

4.2.4 Cloud Computing Applications in Manufacturing 137

4.2.5 Summary 142

4.3 IoT and Edge Computing 142

4.3.1 Essentials of IoT 142

4.3.2 Essentials of Edge Computing 146

4.3.3 Applications of IoT and Edge Computing in Manufacturing 148

4.3.4 Summary 150

4.4 Big Data Infrastructure 150

4.4.1 Application Demands 150

4.4.2 Core Software Stack Components 152

4.4.3 Bridging the Gap between Core Software Stack Components and Applications 153

4.4.3.1 Hadoop Data Service (HDS) 153

4.4.3.2 Distributed R Language Computing Service (DRS) 156

4.4.4 Summary 159

4.5 Conclusion 159

Appendix 4.A - Abbreviation List 160

Appendix 4.B - List of Symbols in Equations 162

References 162

5 Docker and Kubernetes 169
Chao-Chun Chen, Min-Hsiung Hung, Kuan-Chou Lai, and Yu-Chuan Lin

5.1 Introduction 169

5.2 Fundamentals of Docker 173

5.2.1 Docker Architecture 173

5.2.1.1 Docker Engine 174

5.2.1.2 High-Level Docker Architecture 174

5.2.1.3 Architecture of Linux Docker Host 176

5.2.1.4 Architecture of Windows Docker Host 177

5.2.1.5 Architecture of Windows Server Containers 177

5.2.1.6 Architecture of Hyper-V Containers 178

5.2.2 Docker Operational Principles 178

5.2.2.1 Docker Image 178

5.2.2.2 Dockerfile 179

5.2.2.3 Docker Container 183

5.2.2.4 Container Network Model 184

5.2.2.5 Docker Networking 185

5.2.3 Illustrative Applications of Docker 187

5.2.3.1 Workflow of Building, Shipping, and Deploying a Containerized Application 188

5.2.3.2 Deployment of a Docker Container Running a Linux Application 189

5.2.3.3 Deployment of a Docker Container Running a Windows Application 191

5.2.4 Summary 194

5.3 Fundamentals of Kubernetes 195

5.3.1 Kubernetes Architecture 195

5.3.1.1 Kubernetes Control Plane Node 195

5.3.1.2 Kubernetes Worker Nodes 197

5.3.1.3 Kubernetes Objects 199

5.3.2 Kubernetes Operational Principles 200

5.3.2.1 Deployment 200

5.3.2.2 High Availability and Self-Healing 200

5.3.2.3 Ingress 202

5.3.2.4 Replication 204

5.3.2.5 Scheduler 204

5.3.2.6 Autoscaling 205

5.3.3 Illustrative Applications of Kubernetes 205

5.3.4 Summary 209

5.4 Conclusion 209

Appendix 5.A - Abbreviation List 210

References 211

6 Intelligent Factory Automation (iFA) System Platform 215
Fan-Tien Cheng

6.1 Introduction 215

6.2 Architecture Design of the Advanced Manufacturing Cloud of Things (AMCoT) Framework 215

6.3 Brief Description of the Automatic Virtual Metrology (AVM) Server 218

6.4 Brief Description of the Baseline Predictive Maintenance (BPM) Scheme in the Intelligent Prediction Maintenance (IPM) Server 218

6.5 Brief Description of the Key-variable Search Algorithm (KSA) Scheme in the Intelligent Yield Management (IYM) Server 219

6.6 The iFA System Platform 220

6.6.1 Cloud-based iFA System Platform 220

6.6.2 Server-based iFA System Platform 221

6.7 Conclusion 222

Appendix 6.A - Abbreviation List 222

Appendix 6.B - List of Symbols 224

References 224

7 Advanced Manufacturing Cloud of Things (AMCoT) Framework 225
Min-Hsiung Hung, Chao-Chun Chen, and Yu-Chuan Lin

7.1 Introduction 225

7.2 Key Components of AMCoT Framework 227

7.2.1 Key Components of Cloud Part 227

7.2.2 Key Components of Factory Part 229

7.2.3 An Example Intelligent Manufacturing Platform Based on AMCoT Framework 229

7.2.4 Summary 231

7.3 Framework Design of Cyber-Physical Agent (CPA) 231

7.3.1 Framework of CPA 231

7.3.2 Framework of Containerized CPA (CPAC) 232

7.3.3 Summary 233

7.4 Rapid Construction Scheme of CPAs (RCSCPA) Based on Docker and Kubernetes 234

7.4.1 Background and Motivation 234

7.4.2 System Architecture of RCSCPA 235

7.4.3 Core Functional Mechanisms of RCSCPA 236

7.4.3.1 Horizontal Auto-Scaling Mechanism 237

7.4.3.2 Load Balance Mechanism 238

7.4.3.3 Failover Mechanism 238

7.4.4 Industrial Case Study of RCSCPA 239

7.4.4.1 Experimental Setup 239

7.4.4.2 Testing Results 239

7.4.5 Summary 242

7.5 Big Data Analytics Application Platform 242

7.5.1 Architecture of Big Data Analytics Application Platform 242

7.5.2 Performance Evaluation of Processing Big Data 243

7.5.3 Big Data Analytics Application in Manufacturing - Electrical Discharge Machining 245

7.5.4 Summary 247

7.6 Manufacturing Services Automated Construction Scheme (MSACS) 248

7.6.1 Background and Motivation 248

7.6.2 Design of Three-Phase Workflow of MSACS 249

7.6.3 Architecture Design of MSACS 251

7.6.4 Designs of Core Components 252

7.6.4.1 Design of Key Information (KI) Extractor 252

7.6.4.2 Design of Library Information (Lib. Info.) Template 255

7.6.4.3 Design of Service Interface Information (SI Info.) Template 256

7.6.4.4 Design of Web Service Package (WSP) Generator 256

7.6.4.5 Design of Service Constructor 261

7.6.5 Industrial Case Studies 262

7.6.5.1 Web Graphical User Interface (GUI) of MSACS 262

7.6.5.2 Case Study 1: Automated Construction of the AVM Cloud-based Manufacturing (CMfg) Service for Validating the Efficacy of MSACS 262

7.6.5.3 Case Study 2: Performance Evaluation of MSACS 264

7.6.6 Summary 265

7.7 Containerized MSACS (MSACSC) 266

7.8 Conclusion 268

Appendix 7.A - Abbreviation List 269

Appendix 7.B - Patents (AMCoT + CPA) 270

References 271

8 Automatic Virtual Metrology (AVM) 275
Fan-Tien Cheng

8.1 Introduction 275

8.1.1 Survey of Virtual Metrology (VM)-Related Literature 276

8.1.2 Necessity of Applying VM 277

8.1.3 Benefits of VM 278

8.2 Evolution of VM and Invention of AVM 282

8.2.1 Invention of AVM 283

8.3 Integrating AVM Functions into the Manufacturing Execution System (MES) 287

8.3.1 Operating Scenarios among AVM, MES Components, and Run-to-Run (R2R) Controllers 289

8.4 Applying AVM for Workpiece-to-Workpiece (W2W) Control 292

8.4.1 Background Materials 293

8.4.2 Fundamentals of Applying AVM for W2W Control 295

8.4.3 R2R Control Utilizing VM with Reliance Index (RI) and Global Similarity Index (GSI) 299

8.4.4 Illustrative Examples 300

8.4.5 Summary 313

8.5 AVM System Deployment 313

8.5.1 Automation Levels of VM Systems 313

8.5.2 Deployment of the AVM System 315

8.6 Conclusion 318

Appendix 8.A - Abbreviation List 319

Appendix 8.B - List of Symbols in Equations 321

Appendix 8.C - Patents (AVM) 323

References 326

9 Intelligent Predictive Maintenance (IPM) 331
Yu-Chen Chiu, Yu-Ming Hsieh, Chin-Yi Lin, and Fan-Tien Cheng

9.1 Introduction 331

9.1.1 Necessity of Baseline Predictive Maintenance (BPM) 332

9.1.2 Prediction Algorithms of Remaining Useful Life (RUL) 333

9.1.3 Introducing the Factory-wide IPM System 334

9.2 BPM 334

9.2.1 Important Samples Needed for Creating Target-Device Baseline Model 337

9.2.2 Samples Needed for Creating Baseline Individual Similarity Index (ISIB) Model 338

9.2.3 Device-Health-Index (DHI) Module 338

9.2.4 Baseline-Error-Index (BEI) Module 339

9.2.5 Illustration of Fault-Detection-and-Classification (FDC) Logic 340

9.2.6 Flow Chart of Baseline FDC Execution Procedure 340

9.2.7 Exponential-Curve-Fitting (ECF) RUL Prediction Module 340

9.3 Time-Series-Prediction (TSP) Algorithm for Calculating RUL 344

9.3.1 ABPM Scheme 345

9.3.2 Problems Encountered with the ECF Model 346

9.3.3 Details of the TSP Algorithm 346

9.3.3.1 AR Model 348

9.3.3.2 MA Model 349

9.3.3.3 ARMA and ARIMA Models 349

9.3.3.4 TSP Algorithm 349

9.3.3.5 Pre-Alarm Module 352

9.3.3.6 Death Correlation Index 353

9.4 Factory-Wide IPM Management Framework 354

9.4.1 Management View and Equipment View of a Factory 354

9.4.2 Health Index Hierarchy (HIH) 355

9.4.3 Factory-wide IPM System Architecture 356

9.5 IPM System Implementation Architecture 359

9.5.1 Implementation Architecture of IPMC based on Docker and Kubernetes 359

9.5.2 Construction and Implementation of the IPMC 361

9.6 IPM System Deployment 364

9.7 Conclusion 367

Appendix 9.A - Abbreviation List 367

Appendix 9.B - List of Symbols in Equations 370

Appendix 9.C - Patents (IPM) 371

References 372

10 Intelligent Yield Management (IYM) 377
Yu-Ming Hsieh, Chin-Yi Lin, and Fan-Tien Cheng

10.1 Introduction 377

10.1.1 Traditional Root-Cause Search Procedure of a Yield Loss 379

10.1.2 IYM System 380

10.1.3 Procedure for Finding the Root Causes of a Yield Loss by Applying the Key-variable Search Algorithm (KSA) Scheme 380

10.2 KSA Scheme 381

10.2.1 Data Preprocessing Module 382

10.2.2 KSA Module 382

10.2.2.1 Triple Phase Orthogonal Greedy Algorithm (TPOGA) 382

10.2.2.2 Automated Least Absolute Shrinkage and Selection Operator (ALASSO) 384

10.2.2.3 Reliance Index of KSA (RIK) Module 385

10.2.3 Blind-stage Search Algorithm (BSA) Module 386

10.2.3.1 Blind Cases 387

10.2.3.2 Blind-stage Search Algorithm 390

10.2.4 Interaction-Effect Search Algorithm (IESA) Module 393

10.2.4.1 Interaction-Effect 393

10.2.4.2 Interaction-Effect Search Algorithm 396

10.3 IYM System Deployment 401

10.4 Conclusion 402

Appendix 10.A - Abbreviation List 402

Appendix 10.B - List of Symbols in Equations 403

Appendix 10.C - Patents (IYM) 405

References 406

11 Application Cases of Intelligent Manufacturing 409
Fan-Tien Cheng, Yu-Chen Chiu, Yu-Ming Hsieh, Hao Tieng, Chin-Yi Lin, and Hsien-Cheng Huang

11.1 Introduction 409

11.2 Application Case I: Thin Film Transistor Liquid Crystal Display (TFT-LCD) Industry 409

11.2.1 Automatic Virtual Metrology (AVM) Deployment Examples in the TFT-LCD Industry 409

11.2.1.1 Introducing the TFT-LCD Production Tools and Manufacturing Processes for AVM Deployment 410

11.2.1.2 AVM Deployment Types for TFT-LCD Manufacturing 413

11.2.1.3 Illustrative Examples 418

11.2.1.4 Summary 425

11.2.2 Intelligent Yield Management (IYM) Deployment Examples in the TFT-LCD Industry 425

11.2.2.1 Introducing the TFT-LCD Production Tools and Manufacturing Processes for IYM Deployment 425

11.2.2.2 KSA Deployment Example 426

11.2.2.3 Summary 432

11.3 Application Case II: Solar Cell Industry 432

11.3.1 Introducing the Solar Cell Manufacturing Process and Requirement Analysis of Intelligent Manufacturing 433

11.3.2 T2T Control with AVM Deployment Examples 434

11.3.2.1 T2T+VM Control Scheme with RI&GSI 435

11.3.2.2 Illustrative Examples of T2T Control with AVM 437

11.3.3 Factory-Wide Intelligent Predictive Maintenance (IPM) Deployment Examples 444

11.3.3.1 Illustrative Examples of BPM and RUL Prediction 444

11.3.3.2 Illustrative Example of Factory-Wide IPM System 451

11.3.4 Summary 453

11.4 Application Case III: Semiconductor Industry 453

11.4.1 AVM Deployment Example in the Semiconductor Industry 453

11.4.1.1 AVM Deployment Example of the Etching Process 454

11.4.1.2 Summary 456

11.4.2 IPM Deployment Examples in the Semiconductor Industry 456

11.4.2.1 Introducing the Bumping Production Tools for IPM Deployment 456

11.4.2.2 Illustrative Example 456

11.4.2.3 Summary 460

11.4.3 IYM Deployment Examples in the Semiconductor Industry 460

11.4.3.1 Introducing the Bumping Process of Semiconductor Manufacturing for IYM Deployment 460

11.4.3.2 Illustrative Example 460

11.4.3.3 Summary 464

11.5 Application Case IV: Automotive Industry 464

11.5.1 AMCoT and AVM Deployment Examples in Wheel Machining Automation (WMA) 464

11.5.1.1 Integrating GED-plus-AVM (GAVM) into WMA for Total Inspection 464

11.5.1.2 Applying AMCoT to WMA 466

11.5.1.3 Applying AVM in AMCoT to WMA 469

11.5.1.4 Summary 472

11.5.2 Mass Customization (MC) Example for WMA 472

11.5.2.1 Requirements of MC Production for WMA 472

11.5.2.2 Considerations for Applying AVM in MC-Production of WMA 473

11.5.2.3 The AVM-plus-Target-Value-Adjustment (TVA) Scheme for MC 473

11.5.2.4 AVM-plus-TVA Deployment Example for WMA 477

11.5.2.5 Summary 478

11.6 Application Case V: Aerospace Industry 478

11.6.1 Introducing the Engine-Case (EC) Manufacturing Process 479

11.6.1.1 Manufacturing Processes of an EC 479

11.6.1.2 Inspection Processes of the Flange Holes 479

11.6.1.3 Literature Reviews 480

11.6.2 Integrating GAVM into EC Manufacturing for Total Inspection 481

11.6.2.1 Considerations of Applying AVM in EC Manufacturing 481

11.6.3 The DF Scheme for Estimating the Flange Deformation of an EC 482

11.6.3.1 Probing Scenario 482

11.6.3.2 Ellipse-like Deformation of an EC 483

11.6.3.3 Position Error 486

11.6.3.4 Integrating the On-Line Probing, the DF Scheme, and the AVM Prediction 488

11.6.4 Illustrative Examples 488

11.6.4.1 Diameter Prediction 490

11.6.4.2 Position Prediction 490

11.6.5 Summary 492

11.7 Application Case VI: Chemical Industry 492

11.7.1 Introducing the Carbon-Fiber Manufacturing Process 492

11.7.2 Three Preconditions of Applying AVM 493

11.7.3 Challenges of Applying AVM to Carbon-Fiber Manufacturing 494

11.7.3.1 CPA+AVM (CPAVM) Scheme for Carbon-Fiber Manufacturing 494

11.7.3.2 AMCoT for Carbon-Fiber Manufacturing 498

11.7.4 Illustrative Example 498

11.7.4.1 Production Data Traceback (PDT) Mechanism for Work-in-Process (WIP) Tracking 499

11.7.4.2 AVM for Carbon-Fiber Manufacturing 500

11.7.5 Summary 501

11.8 Application Case VII: Bottle Industry 502

11.8.1 Bottle Industry and Its Intelligent Manufacturing Requirements 502

11.8.1.1 Introducing the Blow-Molding Manufacturing Process 502

11.8.2 Applying AVM to Blow Molding Manufacturing Process 502

11.8.3 AVM-Based Run-to-Run (R2R) Control for Blow Molding Manufacturing Process 503

11.8.4 Illustrative Example 504

11.8.5 Summary 507

Appendix 11.A - Abbreviation List 508

Appendix 11.B - List of Symbols in Equations 512

References 516

Index 521
Fan-Tien Cheng, PhD, is Director of the Intelligent Manufacturing Research Center at the National Cheng Kung University in Taiwan, ROC. He received his doctorate in Electrical Engineering from the Ohio State University in 1989. His research foci are on topics related to intelligent manufacturing and Industry 4.0.

F.-T. Cheng, National Cheng Kung University in Taiwan, ROC