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E-raamat: Industry 4.1: Intelligent Manufacturing with Zero Defects

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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(24)
Fan-Tien Cheng
1.1 Introduction
1(1)
1.2 Evolution of Automation
1(13)
1.2.1 e-Manufacturing
1(2)
1.2.1.1 Manufacturing Execution System (MES)
3(3)
1.2.1.2 Supply Chain (SC)
6(1)
1.2.1.3 Equipment Engineering System (EES)
7(2)
1.2.1.4 Engineering Chain (EC)
9(1)
1.2.2 Industry 4.0
10(1)
1.2.2.1 Definition and Core Technologies of Industry 4.0
10(2)
1.2.2.2 Migration from e-Manufacturing to Industry 4.0
12(1)
1.2.2.3 Mass Customization
12(1)
1.2.3 Zero Defects - Vision of Industry 4.1
13(1)
1.2.3.1 Two Stages of Achieving Zero Defects
14(1)
1.3 Development Strategy of Intelligent Manufacturing with Zero Defects
14(4)
1.3.1 Five-Stage Strategy of Yield Enhancement and Zero-Defects Assurance
15(3)
1.4 Conclusion
18(7)
Appendix 1.A Abbreviation List
18(2)
References
20(5)
2 Data Acquisition and Preprocessing
25(44)
Hao Tieng
Haw-Ching Yang
Yu-Yong Li
2.1 Introduction
25(1)
2.2 Data Acquisition
26(11)
2.2.1 Process Data Acquisition
26(1)
2.2.1.1 Sensing Signals Acquisition
26(9)
2.2.1.2 Manufacturing Parameters Acquisition
35(1)
2.2.2 Metrology Data Acquisition
36(1)
2.3 Data Preprocessing
37(16)
2.3.1 Segmentation
37(1)
2.3.2 Cleaning
38(1)
2.3.2.1 Trend Removal
39(2)
2.3.2.2 Wavelet Thresholding
41(2)
2.3.3 Feature Extraction
43(1)
2.3.3.1 Time Domain
43(4)
2.3.3.2 Frequency Domain
47(2)
2.3.3.3 Time-Frequency Domain
49(3)
2.3.3.4 Autoencoder
52(1)
2.4 Case Studies
53(11)
2.4.1 Detrending of the Thermal Effect in Strain Gauge Data
53(2)
2.4.2 Automated Segmentation of Signal Data
55(2)
2.4.3 Tool State Diagnosis
57(4)
2.4.4 Tool Diagnosis using Loading Data
61(3)
2.5 Conclusion
64(5)
Appendix 2.A Abbreviation List
64(1)
Appendix 2.B List of Symbols in Equations
65(2)
References
67(2)
3 Communication Standards
69(60)
Fan-Tien Cheng
Hao Tieng
Yu-Chen Chiu
3.1 Introduction
69(1)
3.2 Communication Standards of the Semiconductor Equipment
69(38)
3.2.1 Manufacturing Portion
69(1)
3.2.1.1 SEMI Equipment Communication Standard I (SECS-I) (SEMI E4)
70(5)
3.2.1.2 SEMI Equipment Communication Standard II (SECS-II) (SEMI E5)
75(6)
3.2.1.3 Generic Model for Communications and Control of Manufacturing Equipment (GEM) (SEMI E30)
81(3)
3.2.1.4 High-Speed SECS Message Services (HSMS) (SEMI E37)
84(7)
3.2.2 Engineering Portion (Interface A)
91(2)
3.2.2.1 Authentication & Authorization (A&A) (SEMI E132)
93(2)
3.2.2.2 Common Equipment Model (CEM) (SEMI E120)
95(1)
3.2.2.3 Equipment Self-Description (EqSD) (SEMI E125)
95(3)
3.2.2.4 Equipment Data Acquisition (EDA) Common Metadata (ECM) (SEMI E164)
98(4)
3.2.2.5 Data Collection Management (DCM) (SEMI E134)
102(5)
3.3 Communication Standards of the Industrial Devices and Systems
107(18)
3.3.1 Historical Roadmaps of Classic Open Platform Communications (OPC) and OPC Unified Architecture (OPC-UA) Protocols
108(1)
3.3.1.1 Classic OPC
108(1)
3.3.1.2 OPC-UA
109(1)
3.3.2 Fundamentals of OPC-UA
110(1)
3.3.2.1 Requirements
110(1)
3.3.2.2 Foundations
111(1)
3.3.2.3 Specifications
112(1)
3.3.2.4 System Architecture
112(7)
3.3.3 Example of Intelligent Manufacturing Hierarchy Applying OPC-UA Protocol
119(2)
3.3.3.1 Equipment Application Program (EAP) Server
121(1)
3.3.3.2 Use Cases of Data Manipulation
122(1)
3.3.3.3 Sequence Diagrams of Data Manipulation
123(2)
3.4 Conclusion
125(4)
Appendix 3.A Abbreviation List
125(3)
References
128(1)
4 Cloud Computing, Internet of Things (loT), Edge Computing, and Big Data Infrastructure
129(40)
Hung-Chang Hsiao
Min-Hsiung Hung
Chao-Chun Chen
Yu-Chuan Lin
4.1 Introduction
129(2)
4.2 Cloud Computing
131(11)
4.2.1 Essentials of Cloud Computing
131(1)
4.2.2 Cloud Service Models
132(2)
4.2.3 Cloud Deployment Models
134(3)
4.2.4 Cloud Computing Applications in Manufacturing
137(5)
4.2.5 Summary
142(1)
4.3 IoT and Edge Computing
142(8)
4.3.1 Essentials of IoT
142(4)
4.3.2 Essentials of Edge Computing
146(2)
4.3.3 Applications of IoT and Edge Computing in Manufacturing
148(2)
4.3 A Summary
150(1)
4.4 Big Data Infrastructure
150(9)
4.4.1 Application Demands
150(2)
4.4.2 Core Software Stack Components
152(1)
4.4.3 Bridging the Gap between Core Software Stack Components and Applications
153(1)
4.4.3.1 Hadoop Data Service (HDS)
153(3)
4.4.3.2 Distributed R Language Computing Service (DRS)
156(3)
4.4.4 Summary
159(1)
4.5 Conclusion
159(10)
Appendix 4.A Abbreviation List
160(2)
Appendix 4.B List of Symbols in Equations
162(1)
References
162(7)
5 Docker and Kubernetes
169(46)
Chao-Chun Chen
Min-Hsiung Hung
Kuan-Chou Lai
Yu-Chuan Lin
5.1 Introduction
169(4)
5.2 Fundamentals of Docker
173(22)
5.2.1 Docker Architecture
173(1)
5.2.1.1 Docker Engine
174(1)
5.2.1.2 High-Level Docker Architecture
174(2)
5.2.1.3 Architecture of Linux Docker Host
176(1)
5.2.1.4 Architecture of Windows Docker Host
177(1)
5.2.1.5 Architecture of Windows Server Containers
177(1)
5.2.1.6 Architecture of Hyper-V Containers
178(1)
5.2.2 Docker Operational Principles
178(1)
5.2.2.1 Docker Image
178(1)
5.2.2.2 Dockerfile
179(4)
5.2.2.3 Docker Container
183(1)
5.2.2.4 Container Network Model
184(1)
5.2.2.5 Docker Networking
185(2)
5.2.3 Illustrative Applications of Docker
187(1)
5.2.3.1 Workflow of Building, Shipping, and Deploying a Containerized Application
188(1)
5.2.3.2 Deployment of a Docker Container Running a Linux Application
189(2)
5.2.3.3 Deployment of a Docker Container Running a Windows Application
191(3)
5.2.4 Summary
194(1)
5.3 Fundamentals of Kubernetes
195(14)
5.3.1 Kubernetes Architecture
195(1)
5.3.1.1 Kubernetes Control Plane Node
195(2)
5.3.1.2 Kubernetes Worker Nodes
197(2)
5.3.1.3 Kubernetes Objects
199(1)
5.3.2 Kubernetes Operational Principles
200(1)
5.3.2.1 Deployment
200(1)
5.3.2.2 High Availability and Self-Healing
200(2)
5.3.2.3 Ingress
202(2)
5.3.2.4 Replication
204(1)
5.3.2.5 Scheduler
204(1)
5.3.2.6 Autoscaling
205(1)
5.3.3 Illustrative Applications of Kubernetes
205(4)
5.3.4 Summary
209(1)
5.4 Conclusion
209(6)
Appendix 5.A Abbreviation List
210(1)
References
211(4)
6 Intelligent Factory Automation (iFA) System Platform
215(10)
Fan-Tien Cheng
6.1 Introduction
215(1)
6.2 Architecture Design of the Advanced Manufacturing Cloud of Things (AMCoT) Framework
215(3)
6.3 Brief Description of the Automatic Virtual Metrology (AVM) Server
218(1)
6.4 Brief Description of the Baseline Predictive Maintenance (BPM) Scheme in the Intelligent Prediction Maintenance (IPM) Server
218(1)
6.5 Brief Description of the Key-variable Search Algorithm (KSA) Scheme in the Intelligent Yield Management (IYM) Server
219(1)
6.6 The iFA System Platform
220(2)
6.6.1 Cloud-based iFA System Platform
220(1)
6.6.2 Server-based iFA System Platform
221(1)
6.7 Conclusion
222(3)
Appendix 6.A Abbreviation List
222(2)
Appendix 6.B List of Symbols
224(1)
References
224(1)
7 Advanced Manufacturing Cloud of Things (AMCoT) Framework
225(50)
Min-Hsiung Hung
Chao-Chun Chen
Yu-Chuan Lin
7.1 Introduction
225(2)
7.2 Key Components of AMCoT Framework
227(4)
7.2.1 Key Components of Cloud Part
227(2)
7.2.2 Key Components of Factory Part
229(1)
7.2.3 An Example Intelligent Manufacturing Platform Based on AMCoT Framework
229(2)
7.2.4 Summary
231(1)
7.3 Framework Design of Cyber-Physical Agent (CPA)
231(3)
7.3.1 Framework of CPA
231(1)
7.3.2 Framework of Containerized CPA (CPAc)
232(1)
7.3.3 Summary
233(1)
7.4 Rapid Construction Scheme of CPAs (RCSCpa) Based on Docker and Kubernetes
234(8)
7.4.1 Background and Motivation
234(1)
7.4.2 System Architecture of RCScpa
235(1)
7.4.3 Core Functional Mechanisms of RCScpa
236(1)
7.4.3.1 Horizontal Auto-Scaling Mechanism
237(1)
7.4.3.2 Load Balance Mechanism
238(1)
7.4.3.3 Failover Mechanism
238(1)
7.4.4 Industrial Case Study of RCSCPA
239(1)
7.4.4.1 Experimental Setup
239(1)
7.4.4.2 Testing Results
239(3)
7.4.5 Summary
242(1)
7.5 Big Data Analytics Application Platform
242(6)
7.5.1 Architecture of Big Data Analytics Application Platform
242(1)
7.5.2 Performance Evaluation of Processing Big Data
243(2)
7.5.3 Big Data Analytics Application in Manufacturing - Electrical Discharge Machining
245(2)
7.5.4 Summary
247(1)
7.6 Manufacturing Services Automated Construction Scheme (MSACS)
248(18)
7.6.1 Background and Motivation
248(1)
7.6.2 Design of Three-Phase Workflow of MSACS
249(2)
7.6.3 Architecture Design of MSACS
251(1)
7.6.4 Designs of Core Components
252(1)
7.6.4.1 Design of Key Information (KI) Extractor
252(3)
7.6.4.2 Design of Library Information (Lib. Info.) Template
255(1)
7.6.4.3 Design of Service Interface Information (SI Info.) Template
256(1)
7.6.4.4 Design of Web Service Package (WSP) Generator
256(5)
7.6.4.5 Design of Service Constructor
261(1)
7.6.5 Industrial Case Studies
262(1)
7.6.5.1 Web Graphical User Interface (GUI) of MSACS
262(1)
7.6.5.2 Case Study 1: Automated Construction of the AVM Cloud-based Manufacturing (CMfg) Service for Validating the Efficacy of MSACS
262(2)
7.6.5.3 Case Study 2: Performance Evaluation of MSACS
264(1)
7.6.6 Summary
265(1)
7.7 Containerized MSACS (MSACSC)
266(2)
7.8 Conclusion
268(7)
Appendix 7.A Abbreviation List
269(1)
Appendix 7.B Patents (AMCoT + CPA)
270(1)
References
271(4)
8 Automatic Virtual Metrology (AVM)
275(56)
Fan-Tien Cheng
8.1 Introduction
275(7)
8.1.1 Survey of Virtual Metrology (VM)-Related Literature
276(1)
8.1.2 Necessity of Applying VM
277(1)
8.1.3 Benefits of VM
278(4)
8.2 Evolution of VM and Invention of AVM
282(5)
8.2.1 Invention of AVM
283(4)
8.3 Integrating AVM Functions into the Manufacturing Execution System (MES)
287(5)
8.3.1 Operating Scenarios among AVM, MES Components, and Run-to-Run (R2R) Controllers
289(3)
8.4 Applying AVM for Workpiece-to-Workpiece (W2W) Control
292(21)
8.4.1 Background Materials
293(2)
8.4.2 Fundamentals of Applying AVM for W2W Control
295(4)
8.4.3 R2R Control Utilizing VM with Reliance Index (RI) and Global Similarity Index (GSI)
299(1)
8.4.4 Illustrative Examples
300(13)
8.4.5 Summary
313(1)
8.5 AVM System Deployment
313(5)
8.5.1 Automation Levels of VM Systems
313(2)
8.5.2 Deployment of the AVM System
315(3)
8.6 Conclusion
318(13)
Appendix 8.A Abbreviation List
319(2)
Appendix 8.B List of Symbols in Equations
321(2)
Appendix 8.C Patents (AVM)
323(3)
References
326(5)
9 Intelligent Predictive Maintenance (IPM)
331(46)
Yu-Chen Chiu
Yu-Ming Hsieh
Chin-Yi Lin
Fan-Tien Cheng
9.1 Introduction
331(3)
9.1.1 Necessity of Baseline Predictive Maintenance (BPM)
332(1)
9.1.2 Prediction Algorithms of Remaining Useful Life (RUL)
333(1)
9.1.3 Introducing the Factory-wide IPM System
334(1)
9.2 BPM
334(10)
9.2.1 Important Samples Needed for Creating Target-Device Baseline Model
337(1)
9.2.2 Samples Needed for Creating Baseline Individual Similarity Index (ISIB) Model
338(1)
9.2.3 Device-Health-Index (DHI) Module
338(1)
9.2.4 Baseline-Error-Index (BEI) Module
339(1)
9.2.5 Illustration of Fault-Detection-and-Classificauon (FDC) Logic
340(1)
9.2.6 Flow Chart of Baseline FDC Execution Procedure
340(1)
9.2.7 Exponential-Curve-Fitting (ECF) RUL Prediction Module
340(4)
9.3 Time-Series-Prediction (TSP) Algorithm for Calculating RUL
344(10)
9.3.1 ABPM Scheme
345(1)
9.3.2 Problems Encountered with the ECF Model
346(1)
9.3.3 Details of the TSP Algorithm
346(2)
9.3.3.1 AR Model
348(1)
9.3.3.2 MA Model
349(1)
9.3.3.3 ARMA and ARIMA Models
349(1)
9.3.3.4 TSP Algorithm
349(3)
9.3.3.5 Pre-Alarm Module
352(1)
9.3.3.6 Death Correlation Index
353(1)
9.4 Factory-Wide IPM Management Framework
354(5)
9.4.1 Management View and Equipment View of a Factory
354(1)
9.4.2 Health Index Hierarchy (HIH)
355(1)
9.4.3 Factory-wide IPM System Architecture
356(3)
9.5 IPM System Implementation Architecture
359(5)
9.5.1 Implementation Architecture of IPMC based on Docker and Kubernetes
359(2)
9.5.2 Construction and Implementation of the IPMC
361(3)
9.6 IPM System Deployment
364(3)
9.7 Conclusion
367(10)
Appendix 9.A Abbreviation List
367(3)
Appendix 9.B List of Symbols in Equations
370(1)
Appendix 9.C Patents (IPM)
371(1)
References
372(5)
10 Intelligent Yield Management (IYM)
377(32)
Yu-Ming Hsieh
Chin-Yi Lin
Fan-Tien Cheng
10.1 Introduction
377(4)
10.1.1 Traditional Root-Cause Search Procedure of a Yield Loss
379(1)
10.1.2 IYM System
380(1)
10.1.3 Procedure for Finding the Root Causes of a Yield Loss by Applying the Key-variable Search Algorithm (KSA) Scheme
380(1)
10.2 KSA Scheme
381(20)
10.2.1 Data Preprocessing Module
382(1)
10.2.2 KSA Module
382(1)
10.2.2.1 Triple Phase Orthogonal Greedy Algorithm (TPOGA)
382(2)
10.2.2.2 Automated Least Absolute Shrinkage and Selection Operator (ALASSO)
384(1)
10.2.2.3 Reliance Index of KSA (RIK) Module
385(1)
10.2.3 Blind-stage Search Algorithm (BSA) Module
386(1)
10.2.3.1 Blind Cases
387(3)
10.2.3.2 Blind-stage Search Algorithm
390(3)
10.2.4 Interaction-Effect Search Algorithm (IESA) Module
393(1)
10.2.4.1 Interaction-Effect
393(3)
10.2.4.2 Interaction-Effect Search Algorithm
396(5)
10.3 IYM System Deployment
401(1)
10.4 Conclusion
402(7)
Appendix 10.A Abbreviation List
402(1)
Appendix 10.B List of Symbols in Equations
403(2)
Appendix 10.C Patents (IYM)
405(1)
References
406(3)
11 Application Cases of Intelligent Manufacturing
409(107)
Fan-Tien Cheng
Yu-Chen Chiu
Yu-Ming Hsieh
Hao Tieng
Chin-Yi Lin
Hsien-Cheng Huang
11.1 Introduction
409(1)
11.2 Application Case I: Thin Film Transistor Liquid Crystal Display (TFT-LCD) Industry
409(23)
11.2.1 Automatic Virtual Metrology (AVM) Deployment Examples in the TFT-LCD Industry
409(1)
11.2.1.1 Introducing the TFT-LCD Production Tools and Manufacturing Processes for AVM Deployment
410(3)
11.2.1.2 AVM Deployment Types for TFT-LCD Manufacturing
413(5)
11.2.1.3 Illustrative Examples
418(7)
11.2.1.4 Summary
425(1)
11.2.2 Intelligent Yield Management (IYM) Deployment Examples in the TFT-LCD Industry
425(1)
11.2.2.1 Introducing the TFT-LCD Production Tools and Manufacturing Processes for IYM Deployment
425(1)
11.2.2.2 KSA Deployment Example
426(6)
11.2.2.3 Summary
432(1)
11.3 Application Case II: Solar Cell Industry
432(21)
11.3.1 Introducing the Solar Cell Manufacturing Process and Requirement Analysis of Intelligent Manufacturing
433(1)
11.3.2 T2T Control with AVM Deployment Examples
434(1)
11.3.2.1 T2T+VM Control Scheme with RI&GSI
435(2)
11.3.2.2 Illustrative Examples of T2T Control with AVM
437(7)
11.3.3 Factory-Wide Intelligent Predictive Maintenance (IPM) Deployment Examples
444(1)
11.3.3.1 Illustrative Examples of BPM and RUL Prediction
444(7)
11.3.3.2 Illustrative Example of Factory-Wide IPM System
451(2)
11.3.4 Summary
453(1)
11.4 Application Case III: Semiconductor Industry
453(1)
11.4.1 AVM Deployment Example in the Semiconductor Industry
453(1)
11.4.1.1 AVM Deployment Example of the Etching Process
454(2)
11.4.1.2 Summary
456(1)
11.4.2 IPM Deployment Examples in the Semiconductor Industry
456(1)
11.4.2.1 Introducing the Bumping Production Tools for IPM Deployment
456(1)
11.4.2.2 Illustrative Example
456(4)
11.4.2.3 Summary
460(1)
11.4.3 IYM Deployment Examples in the Semiconductor Industry
460(1)
11.4.3.1 Introducing the Bumping Process of Semiconductor Manufacturing for IYM Deployment
460(1)
11.4.3.2 Illustrative Example
460(4)
11.4.3.3 Summary
464(1)
11.5 Application Case IV: Automotive Industry
464(14)
11.5.1 AMCoT and AVM Deployment Examples in Wheel Machining Automation (WMA)
464(1)
11.5.1.1 Integrating GED-plus-AVM (GAVM) into WMA for Total Inspection
464(2)
11.5.1.2 Applying AMCoT to WMA
466(3)
11.5.1.3 Applying AVM in AMCoT to WMA
469(3)
11.5.1.4 Summary
472(1)
11.5.2 Mass Customization (MC) Example for WMA
472(1)
11.5.2.1 Requirements of MC Production for WMA
472(1)
11.5.2.2 Considerations for Applying AVM in MC-Production of WMA
473(1)
11.5.2.3 The AVM-plus-Target-Value-Adjustment (TVA) Scheme for MC
473(4)
11.5.2.4 AVM-plus-TVA Deployment Example for WMA
477(1)
11.5.2.5 Summary
478(1)
11.6 Application Case V: Aerospace Industry
478(14)
11.6.1 Introducing the Engine-Case (EC) Manufacturing Process
479(1)
11.6.1.1 Manufacturing Processes of an EC
479(1)
11.6.1.2 Inspection Processes of the Flange Holes
479(1)
11.6.1.3 Literature Reviews
480(1)
11.6.2 Integrating GAVM into EC Manufacturing for Total Inspection
481(1)
11.6.2.1 Considerations of Applying AVM in EC Manufacturing
481(1)
11.6.3 The DF Scheme for Estimating the Flange Deformation of an EC
482(1)
11.6.3.1 Probing Scenario
482(1)
11.6.3.2 Ellipse-like Deformation of an EC
483(3)
11.6.3.3 Position Error
486(2)
11.6.3.4 Integrating the On-Line Probing, the DF Scheme, and the AVM Prediction
488(1)
11.6.4 Illustrative Examples
488(2)
11.6.4.1 Diameter Prediction
490(1)
11.6.4.2 Position Prediction
490(2)
11.6.5 Summary
492(1)
11.7 Application Case VI: Chemical Industry
492(10)
11.7.1 Introducing the Carbon-Fiber Manufacturing Process
492(1)
11.7.2 Three Preconditions of Applying AVM
493(1)
11.7.3 Challenges of Applying AVM to Carbon-Fiber Manufacturing
494(1)
11.7.3.1 CPA+AVM (CPAVM) Scheme for Carbon-Fiber Manufacturing
494(4)
11.7.3.2 AMCoT for Carbon-Fiber Manufacturing
498(1)
11.7.4 Illustrative Example
498(1)
11.7.4.1 Production Data Traceback (PDT) Mechanism for Work-in-Process (WIP) Tracking
499(1)
11.7.4.2 AVM for Carbon-Fiber Manufacturing
500(1)
11.7.5 Summary
501(1)
11.8 Application Case VII: Bottle Industry
502(6)
11.8.1 Bottle Industry and Its Intelligent Manufacturing Requirements
502(1)
11.8.1.1 Introducing the Blow-Molding Manufacturing Process
502(1)
11.8.2 Applying AVM to Blow Molding Manufacturing Process
502(1)
11.8.3 AVM-Based Run-to-Run (R2R) Control for Blow Molding Manufacturing Process
503(1)
11.8.4 Illustrative Example
504(3)
11.8.5 Summary
507(1)
Appendix 11.A Abbreviation List
508(4)
Appendix 11.B List of Symbols in Equations
512(4)
References 516(5)
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.
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