Industry 4.1 E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Series on Systems Science and Engineering
• Sprache: Englisch

Seitenzahl: 974

Veröffentlichungsjahr: 2021

Beschreibung

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

Leseprobe

Table of Contents

Cover

Series Page

Title Page

Copyright Page

Editor Biography

List of Contributors

Preface

Overview and Goals

Organization and Features

Acknowledgments

Foreword

1 Evolution of Automation and Development Strategy of Intelligent Manufacturing with Zero Defects

1.1 Introduction

1.2 Evolution of Automation

1.3 Development Strategy of Intelligent Manufacturing with Zero Defects

1.4 Conclusion

Appendix 1.A ‐ Abbreviation List

References

2 Data Acquisition and Preprocessing

2.1 Introduction

2.2 Data Acquisition

2.3 Data Preprocessing

2.4 Case Studies

2.5 Conclusion

Appendix 2.A ‐ Abbreviation List

Appendix 2.B ‐ List of Symbols in Equations

References

3 Communication Standards

3.1 Introduction

3.2 Communication Standards of the Semiconductor Equipment

3.3 Communication Standards of the Industrial Devices and Systems

3.4 Conclusion

Appendix 3.A ‐ Abbreviation List

References

4 Cloud Computing, Internet of Things (IoT), Edge Computing, and Big Data Infrastructure

4.1 Introduction

4.2 Cloud Computing

4.3 IoT and Edge Computing

4.4 Big Data Infrastructure

4.5 Conclusion

Appendix 4.A ‐ Abbreviation List

Appendix 4.B ‐ Abbreviation List

References

5 Docker and Kubernetes

5.1 Introduction

5.2 Fundamentals of Docker

5.3 Fundamentals of Kubernetes

5.4 Conclusion

Appendix 5.A ‐ Abbreviation List

References

6 Intelligent Factory Automation (iFA) System Platform

6.1 Introduction

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

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

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

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

6.6 The iFA System Platform

6.7 Conclusion

Appendix 6.A ‐ Abbreviation List

Appendix 6.B ‐ List of Symbols

References

7 Advanced Manufacturing Cloud of Things (AMCoT) Framework

7.1 Introduction

7.2 Key Components of AMCoT Framework

7.3 Framework Design of Cyber‐Physical Agent (CPA)

7.4 Rapid Construction Scheme of CPAs (RCS

CPA

) Based on Docker and Kubernetes

7.5 Big Data Analytics Application Platform

7.6 Manufacturing Services Automated Construction Scheme (MSACS)

7.7 Containerized MSACS (MSACS

C

)

7.8 Conclusion

Appendix 7.A ‐ Abbreviation List

Appendix 7.B ‐ Patents (AMCoT + CPA)

References

8 Automatic Virtual Metrology (AVM)

8.1 Introduction

8.2 Evolution of VM and Invention of AVM

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

8.4 Applying AVM for Workpiece‐to‐Workpiece (W2W) Control

8.5 AVM System Deployment

8.6 Conclusion

Appendix 8.A – Abbreviation List

Appendix 8.B – List of Symbols in Equations

Appendix 8.C – Patents (AVM)

References

9 Intelligent Predictive Maintenance (IPM)

9.1 Introduction

9.2 BPM

9.3 Time‐Series‐Prediction (TSP) Algorithm for Calculating RUL

9.4 Factory‐Wide IPM Management Framework

9.5 IPM System Implementation Architecture

9.6 IPM System Deployment

9.7 Conclusion

Appendix 9.A ‐ Abbreviation List

Appendix 9.B – List of Symbols in Equations

Appendix 9.C – Patents (IPM)

References

10 Intelligent Yield Management (IYM)

10.1 Introduction

10.2 KSA Scheme

10.3 IYM System Deployment

10.4 Conclusion

Appendix 10.A ‐ Abbreviation List

Appendix 10.B ‐ List of Symbols in Equations

Appendix 10.C ‐ Patents (IYM)

References

11 Application Cases of Intelligent Manufacturing

11.1 Introduction

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

11.3 Application Case II: Solar Cell Industry

11.4 Application Case III: Semiconductor Industry

11.5 Application Case IV: Automotive Industry

11.6 Application Case V: Aerospace Industry

11.7 Application Case VI: Chemical Industry

11.8 Application Case VII: Bottle Industry

Appendix 11.A ‐ Abbreviation List

Appendix 11.B ‐ List of Symbols in Equations

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Sensor comparison.

Table 2.2 Illustrative example of applying M codes to segment the “X44Y50” ...

Table 2.3 Definition of time‐domain SFs.

Table 2.4 Tool diagnosis example: results of using an RF model.

Chapter 3

Table 3.1 Handshake codes (bytes).

Table 3.2 Header composition.

Table 3.3 Timeout parameters (in seconds).

Table 3.4 Streams.

Table 3.5 Item header.

Table 3.6 Item format codes.

Table 3.7 SECS notations.

Table 3.8 HSMS timeouts.

Table 3.9 HSMS‐SS configuration.

Table 3.10 Comparison of HSMS and SECS‐I.

Table 3.11 Required parameter list of Coater.

Chapter 5

Table 5.1 Some distinctions between Docker containers and virtual machines.

Chapter 7

Table 7.1 Time analysis of deploying two PAM

C

’s into a CPA

C

.

Table 7.2 Query times using four types of data format in experiments 1 and ...

Table 7.3 Comparisons of execution times in processing different sizes of d...

Table 7.4 Annotated keywords‐SI tags lookup table.

Table 7.5 Comparisons of development times of Novice, Skilled Professional,...

Chapter 8

Table 8.1 Simulation‐parameter definitions and setting values.

Table 8.2 Cpk values of 5‐cases APC methods (

α

1

= 0.35).

Table 8.3 MAPE

P

values of 5‐cases APC methods (

α

1

= 0.35, Unit: %)....

Table 8.4 Simulation parameters and setting values (Normal and Weibull dist...

Table 8.5 MAPE

P

values of 5‐Cases APC methods for A. Normal with RI; B. Wei...

Chapter 9

Table 9.1 Scenario of HIH. (a) Before repair of throttle valve. (b) After r...

Chapter 11

Table 11.1 Total number of devices in the TFT process.

Table 11.2 Cpk and MAPE

P

values of Cases 1 and 2.

Table 11.3 ACF and PACF.

Table 11.4 Significance of predictors.

Table 11.5 ACF and PACF.

Table 11.6 Significance of predictors.

Table 11.7 WIP tracking of Spin‐1 sizing percentage with the PDT mechanism.

List of Illustrations

Chapter 1

Figure 1.1 ISMT e‐Manufacturing hierarchy.

Figure 1.2 Four key components for the advanced e‐Manufacturing model.

Figure 1.3 MES operation procedures.

Figure 1.4 Functional architecture of the ISMT CIM framework.

Figure 1.5 The HMES framework.

Figure 1.6 ESCM architecture and key processes.

Figure 1.7 Functional‐block diagram of the holonic supply‐chain system.

Figure 1.8 The ISMT EES framework.

Figure 1.9 The proposed EES framework.

Figure 1.10 Comparison of SC and EC.

Figure 1.11 Engineering‐chain‐management system framework.

Figure 1.12 Changing curves of yield and cost during the product life cycle....

Figure 1.13 Five‐stage strategy for increasing yield in RD/ramp‐up and MP ph...

Figure 1.14 Production line of the bumping process.

Chapter 2

Figure 2.1 Fundamental steps for developing an intelligent application.

Figure 2.2 An external data acquisition system for acquiring process and met...

Figure 2.3 Relative motion between a cutting tool and a workpiece.

Figure 2.4 Installation of a Dynamometer.

Figure 2.5 Installation of a CT.

Figure 2.6 Installation of accelerometer by stud mounting.

Figure 2.7 Vibration data collection of Z‐axis.

Figure 2.8 Installation of thermal couple.

Figure 2.9 Distance between a thermal couple and spindle.

Figure 2.10 Installation of an AE sensor.

Figure 2.11 Sensor fusion system comprising an accelerometer and a thermal c...

Figure 2.12 Sensor fusion system using five types of sensors.

Figure 2.13 An external data acquisition system triggered by electronic rela...

Figure 2.14 An DC signal: (a) in time‐domain; (b) in frequency‐domain; (c) i...

Figure 2.15 A random signal: (a) in time‐domain; (b) in frequency‐domain; (c...

Figure 2.16 Three‐level decomposition tree of the DWT.

Figure 2.17 View of the time and frequency domains.

Figure 2.18 A vibration signal: (a) in time‐domain; and (b) in FFT spectrum....

Figure 2.19 Unchanged resolution of STFT time‐frequency plane.

Figure 2.20 Dynamic window of WPT time‐frequency plane.

Figure 2.21 WPT decomposition binary tree.

Figure 2.22 Architecture of the AEN.

Figure 2.23 Using a smart tool holder to detect tool state.

Figure 2.24 Detrending of the thermal effect in strain‐gauge data: (a) befor...

Figure 2.25 De‐noising signals to highlight differences between dry‐run and ...

Figure 2.26 Collected vibration signals (including idling and machining peri...

Figure 2.27 Comparison of the original and decoded features under four idlin...

Figure 2.28 Automated segmentation of machining signals using an AEN: (a) di...

Figure 2.29 Comparison of time‐domain signals (upper portion), WPT features ...

Figure 2.30 WPT distribution results for different cutting depths in the X a...

Figure 2.31 Comparison of four SFs extracted by using an AEN for samples of ...

Figure 2.32 A forging load (pressure)‐stroke curve.

Figure 2.33 Failure diagnosis in a forming process.

Figure 2.34 Sample validation using the single dimension feature of the midd...

Figure 2.35 AEN‐DNN architecture for failure diagnosis.

Chapter 3

Figure 3.1 SECS block.

Figure 3.2 Block transfer.

Figure 3.3 Multi‐block message (677 data bytes).

Figure 3.4 Illustration of system bytes.

Figure 3.5 Block transfer protocol in a multi‐block message.

Figure 3.6 T1, T2, and T3 timeouts.

Figure 3.7 Example of T2 timeout and retry limit.

Figure 3.8 Equipment is Master and Host is Slave.

Figure 3.9 Primary and secondary messages.

Figure 3.10 SECS‐II message detail of host sending S1F3 to equipment.

Figure 3.11 SECS‐II message detail of Equipment sending S1F4 to Host.

Figure 3.12 Message detail style.

Figure 3.13 Message structure receiving S1F3.

Figure 3.14 Message structure sending S1F4.

Figure 3.15 Scopes of GEM, SECS‐II, and other communications alternatives.

Figure 3.16 Subsidiary standards of HSMS.

Figure 3.17 SECS‐I RS‐232 connections versus HSMS TCP/IP Ethernet connection...

Figure 3.18 HSMS‐SS message format.

Figure 3.19 Connect.

Figure 3.20 Data.

Figure 3.21 Disconnect.

Figure 3.22 Linktest.

Figure 3.23 HSMS can share network with other TCP/IP protocols.

Figure 3.24 SECS‐I and HSMS‐SS protocol stacks.

Figure 3.25 Interface A Standards on equipment.

Figure 3.26 Interface A Integrated Scenario.

Figure 3.27 E132 Authentication model.

Figure 3.28 CEM example – diagram of a conveyor system.

Figure 3.29 CEM example – photo of a conveyor system.

Figure 3.30 CEM example‐conveyor CEM description diagram.

Figure 3.31 E125 Equipment metadata.

Figure 3.32 Example of CEM and EqSD – conveyor system.

Figure 3.33 E164 in CEM and EqSD.

Figure 3.34 Overview of data collection.

Figure 3.35 Data collection manager and consumer interfaces.

Figure 3.36 Definition of data collection report.

Figure 3.37 Definition and example of event report.

Figure 3.38 Definition and example of exception report.

Figure 3.39 Definition of trace report.

Figure 3.40 Example of trace data collection.

Figure 3.41 Example of DCR buffering.

Figure 3.42 Automation pyramid.

Figure 3.43 Illustrative use case of OPC clients and servers.

Figure 3.44 Foundations of OPC‐UA

Figure 3.45 OPU‐UA architecture.

Figure 3.46 Architecture overview of an OPC‐UA client and an OPC‐UA server....

Figure 3.47 OPC‐UA security model.

Figure 3.48 Sequence diagram of operating procedure between a pair of OPC‐UA...

Figure 3.49 Intelligent Manufacturing architecture in the FCCL industry.

Figure 3.50 Monitoring and control system architecture of Coater by applying...

Figure 3.51 Use cases of data manipulation.

Figure 3.52 Sequence diagram of Data Collection.

Figure 3.53 Sequence diagram of recipe download.

Chapter 4

Figure 4.1 Architecture of virtual machines on top of hypervisor and physica...

Figure 4.2 Comparison of cloud computing service models.

Figure 4.3 Architecture of the three cloud deployment models.

Figure 4.4 Traditional IT utilization of enterprises and factories using on‐...

Figure 4.5 Manufacturing company or factory utilizing cloud computing to ext...

Figure 4.6 Simplified generic system architecture of cloud manufacturing (CM...

Figure 4.7 Architecture and operational flow of the cloud‐based AVM system....

Figure 4.8 Generic three‐layer cloud‐based IoT architecture.

Figure 4.9 MQTT Publish/Subscribe architecture for the communication of IoT....

Figure 4.10 AMQP Publish/Subscribe architecture for the communication of IoT...

Figure 4.11 Cloud‐based IoT architecture with an edge computing layer.

Figure 4.12 Application of IoT and edge computing in wheel machining.

Figure 4.13 Software stack for a HDS server.

Figure 4.14 Programming in DRS.

Chapter 5

Figure 5.1 Comparison of virtual machines and Docker containers.

Figure 5.2 Illustration of Docker containers running on virtual machines.

Figure 5.3 Constituent components of Docker Engine.

Figure 5.4 A high‐level view of Docker architecture with some of its workflo...

Figure 5.5 Architecture of a Linux Docker host.

Figure 5.6 Architecture of a Windows Docker host.

Figure 5.7 Architecture of Windows Server Containers.

Figure 5.8 Architecture of Hyper‐V Containers.

Figure 5.9 Anatomy of a Docker container image.

Figure 5.10 An example of Dockerfile.

Figure 5.11 The process of building an image.

Figure 5.12 The history of the pythonapp:latest image.

Figure 5.13 The sizes of two Linux container images.

Figure 5.14 A shorthand Dockerfile for building a Linux container image.

Figure 5.15 The stacked layers of the Linux container image built by the Doc...

Figure 5.16 A shorthand Dockerfile for building a Windows container image.

Figure 5.17 The stacked layers of the Windows container image built by the D...

Figure 5.18 Illustration of many containers sharing the same image layers.

Figure 5.19 Architecture of the container network model (CNM) for Linux cont...

Figure 5.20 Architecture of bridge networking.

Figure 5.21 Architecture of host networking.

Figure 5.22 Architecture of none networking.

Figure 5.23 Architecture of overlay networking.

Figure 5.24 Architecture of CNM for Windows containers.

Figure 5.25 Workflow of building, shipping, and deploying a containerized ap...

Figure 5.26 An example Dockerfile for building a Linux web application image...

Figure 5.27 Building steps of the Linux web application image.

Figure 5.28 Execution results of the docker tag and docker push commands.

Figure 5.29 Screenshot showing that the “imrc/example‐linux” image has been ...

Figure 5.30 Execution result of the docker pull command.

Figure 5.31 Execution result of the docker run command.

Figure 5.32 Screenshot displaying the home page of the running containerized...

Figure 5.33 An example Dockerfile for building a Windows web application ima...

Figure 5.34 Building steps of the Windows web application image.

Figure 5.35 Execution result of the docker push command.

Figure 5.36 Screenshot showing that the “imrc/example‐windows” image has bee...

Figure 5.37 Docker pull command's execution result on a Windows Docker host....

Figure 5.38 Execution result of the docker run command.

Figure 5.39 Screenshot displaying the home page of the running containerized...

Figure 5.40 Architecture of Kubernetes.

Figure 5.41

Creation Process of a Pod.

Figure 5.42

System architecture with three Control Plane Nodes.

Figure 5.43

System architecture with the stacked etcd topology.

Figure 5.44

System architecture with the external etcd topology.

Figure 5.45

System architecture without ingress.

Figure 5.46

System architecture with ingress.

Figure 5.47

Two phases of scheduler.

Figure 5.48 Ready status of the control plane node.

Figure 5.49 Dashboard while the control plane node is ready.

Figure 5.50 Generating the join token of the control plane.

Figure 5.51 Worker node joining the cluster by the join token.

Figure 5.52 Status of the cluster shown by kubectl.

Figure 5.53 Status of the worker1 shown in dashboard.

Figure 5.54 Example of a YAML file

Figure 5.55 Deploying the httpd service by applying example.yaml.

Figure 5.56 Status of Pods shown in dashboard.

Figure 5.57 Screenshot displaying a workable httpd service.

Chapter 6

Figure 6.1 Architecture design of the AMCoT framework.

Figure 6.2 Functional block diagram of the AVM server.

Figure 6.3 Functional block diagram of the BPM scheme in the IPM server.

Figure 6.4 Functional block diagram of the KSA scheme in the IYM server.

Figure 6.5 Cloud‐based iFA system platform.

Figure 6.6 Server‐based iFA system platform.

Chapter 7

Figure 7.1 Architecture design of the AMCoT framework.

Figure 7.2 An example of intelligent manufacturing platform based on the AMC...

Figure 7.3 Framework of CPA.

Figure 7.4 Framework of CPA

C

.

Figure 7.5 System architecture of RCS

CPA

.

Figure 7.6 Horizontal auto‐scaling mechanism of PAM

C

’s in RCS

CPA

.

Figure 7.7 Load balance mechanism of PAM

C

’s in RCS

CPA

.

Figure 7.8 Failover mechanism among the Pods of a PAM

C

in RCS

CPA

.

Figure 7.9 Screenshot showing that a Kubernetes cluster has been created, an...

Figure 7.10 Screenshot showing that the BPM

C

has four Pods for load balance....

Figure 7.11 Screenshot showing that the four Pods of the BPM

C

are distribute...

Figure 7.12 Health‐gauging web GUI of the BPM

C

, which contains four Pods wor...

Figure 7.13 Framework of the big‐data‐analytics application platform.

Figure 7.14 Comparison of query times using four types of data table in expe...

Figure 7.15 Comparison of query times using four types of data format in exp...

Figure 7.16 Architecture of the proposed BEDPS.

Figure 7.17 Three‐phase workflow of MSACS.

Figure 7.18 System architecture of MSACS.

Figure 7.19 Hierarchical information of a Jar SSLP in a Java decompiler.

Figure 7.20 Hierarchical information of a C# DLL SSLP in a C# decompiler....

Figure 7.21 Generic KI extraction algorithm of SSLPs.

Figure 7.22 Illustration of the Lib. Info. Template in JSON.

Figure 7.23 Illustration of the SI Info. Template in JSON.

Figure 7.24 C# WSP template.

Figure 7.25 Example of “APIController.cs” for C# WSP template.

Figure 7.26 Flowchart of the automated source code generation.

Figure 7.27 Result of automated code generation for the WSP template in Figu...

Figure 7.28 Automated service construction mechanism designed in Server Cons...

Figure 7.29 Web GUI of MSACS.

Figure 7.30 GUI showing the method information in the AVMService.dll.

Figure 7.31 GUI showing the web API information of the AVM service.

Figure 7.32 GUI for conducting virtual metrology using the created AVM CMfg ...

Figure 7.33 Four‐phase workflow of MSACS

C

.

Chapter 8

Figure 8.1 Comparison between actual metrology and virtual metrology.

Figure 8.2 Current physical metrology operating scenarios.

Figure 8.3 Tool and process monitoring without and with VM.

Figure 8.4 Illustration of a false alarm and a missed detection.

Figure 8.5 Automatic Virtual Metrology (AVM) system.

Figure 8.6 AVM server.

Figure 8.7 Advanced dual‐phase VM algorithm.

Figure 8.8 Plugging AVM into the MES framework.

Figure 8.9 Relationships among AVM, MES components, and R2R controllers.

Figure 8.10 Operating scenarios among AVM, MES components, and R2R controlle...

Figure 8.11 Collaboration diagram for integrating AVM into MES.

Figure 8.12 Model of EWMA R2R control.

Figure 8.13 W2W control scheme utilizing VM [15, 16, 32].

Figure 8.14 AVM server with PreY input.

Figure 8.15 Schematic Diagram of Defining RI for Normal Distribution.

Figure 8.16 Schematic diagram of defining RI

W

for Weibull distribution.

Figure 8.17 W2W control scheme utilizing AVM with RI and GSI. (a) Complete W...

Figure 8.18 Simulation results of 5‐cases APC methods of Round 1.

Figure 8.19 RI and GSI exceed their thresholds at Sample 50 of Round 1.

Figure 8.20 GSI exceeds its threshold at Sample 349 of Round 1.

Figure 8.21 Simulation results of Cases 3‐5 for the first 200 samples. (a) R...

Figure 8.22 Mean MAPE

P

curves as functions of

α

1

of 5‐cases APC methods...

Figure 8.23 Automation levels of virtual metrology systems.

Chapter 9

Figure 9.1 SPC control chart of throttle‐valve angles in a PECVD tool.

Figure 9.2 BPM scheme.

Figure 9.3 State diagram of a device.

Figure 9.4 Procedure of collecting the important samples needed for creating...

Figure 9.5 Configurations of SPC control charts of DHI and BEI. (a) Converti...

Figure 9.6 Flow chart of baseline FDC execution procedure.

Figure 9.7 ECF RUL prediction model.

Figure 9.8 Flowchart for calculating ECF RUL.

Figure 9.9 Advanced BPM (ABPM) scheme.

Figure 9.10 Prediction results of the ECF model. (a) Aging feature predictio...

Figure 9.11 Flow chart of the TSP algorithm.

Figure 9.12 Flow chart of the pre‐alarm module (PreAM).

Figure 9.13 Management and equipment views of a solar‐cell manufacturing fac...

Figure 9.14 Health index hierarchy.

Figure 9.15 Intelligent predictive maintenance (IPM).

Figure 9.16 Implementation architecture of the IPM

C

(i.e. IPM

C

‐IA) based on ...

Figure 9.17 Workflow for constructing and deploying the IPM

C

in a Kubernetes...

Figure 9.18 Example IPM

C

volume YAML file.

Figure 9.19 Example IPM

C

deployment YAML file.

Figure 9.20 Example Dockerfile for creating the ABPM image.

Figure 9.21 Example IPM

C

service YAML file.

Chapter 10

Figure 10.1 Changing curves of yield and cost during the product life cycle....

Figure 10.2 Traditional root‐cause search process of a yield loss.

Figure 10.3 Intelligent yield management system.

Figure 10.4 Procedure for finding the root causes of a yield loss by applyin...

Figure 10.5 The KSA scheme.

Figure 10.6 Flowchart of ALASSO with automated λ adjusting. λ: penalty; KV...

Figure 10.7 Flow chart of the BSA module.

Figure 10.8 Rule I in the BSA module.

Figure 10.9 Rule II in the BSA module.

Figure 10.10 Flow Chart of the Regression Tree.

Figure 10.11 Description of Regression Tree Step 1 and Step 2.

Chapter 11

Figure 11.1 Process flow of TFT‐LCD manufacturing.

Figure 11.2 Semiconductor layer of the TFT process flow with deployment of A...

Figure 11.3 Thin‐film structure in CVD process.

Figure 11.4 Combination of TFT photo step.

Figure 11.5 CF manufacturing process flow with deployment of AVM servers. (a...

Figure 11.6 PS layer flow of the CF manufacturing process with deployment of...

Figure 11.7 LCD manufacturing process flow with deployment of AVM servers. (...

Figure 11.8 Dual‐stage indirect VM architecture.

Figure 11.9 Single‐stage example: Stage‐I VM results at Position 2. LCL, low...

Figure 11.10 Dual‐stage example: Stage‐II VM results at Position 2. LCL, low...

Figure 11.11 Combination example of cooperative‐tools: VM

I

result at Positio...

Figure 11.12 Illustration of an erroneous measurement.

Figure 11.13 Inline example of cooperative‐tools at Position 1: (a) NN

I

and ...

Figure 11.14 TFT manufacturing process.

Figure 11.15 PEP flow of the semiconductor layer.

Figure 11.16 Accumulated Type 2 loss results.

Figure 11.17 Procedure for finding the root causes of a yield loss by applyi...

Figure 11.18 RI

K

result of X

R

search.

Figure 11.19 KSA search results of Type 2 loss on Lot 49. (a) Top 1 Device: ...

Figure 11.20 RI

K

result of X

P

search.

Figure 11.21 Root cause analysis of control voltage on Chamber A of Equipmen...

Figure 11.22 T2T control scenario of the PECVD manufacturing process.

Figure 11.23 T2T control scheme.

Figure 11.24 T2T controller.

Figure 11.25 Scenario of Applying the AVM system to the PECVD process.

Figure 11.26 VM accuracy verification.

Figure 11.27 T2T with AM. (a) All samples. (b) APC samples.

Figure 11.28 T2T with VM. (a) All samples. (b) APC samples.

Figure 11.29 T2T+VM without RI&GSI. (a) All samples. (b) APC samples.

Figure 11.30 T2T+VM with RI&GSI. (a) All samples. (b) APC samples.

Figure 11.31 RI and GSI are lower than RI

T

and GSI

T

, respectively at Sample ...

Figure 11.32 Cycle‐time improvement by applying T2T+AVM.

Figure 11.33 Illustration of the necessity of adopting the C&H modeling samp...

Figure 11.34 Results of the FDC portion of the BPM scheme.

Figure 11.35 BPM‐related data and indexes of an entire PM period.

Figure 11.36 ECF RUL predictive results.

Figure 11.37 Throttle valve RUL predictive results of the TSP algorithm. (a)...

Figure 11.38 IPM dashboard. (a) Management view. (b) Equipment view.

Figure 11.39 Equipment status dashboard in MES.

Figure 11.40 Sequence diagram showing the interfaces between the MES and IPM...

Figure 11.41 Illustrations of the functions of RI, GSI, and dual‐phase schem...

Figure 11.42 Production line of the bumping process.

Figure 11.43 Common equipment model of the Sputter equipment.

Figure 11.44 Turbo Pump RUL predictive results of the TSP algorithm. (a) Agi...

Figure 11.45 Illustration of UBM bumping process variables (5 device variabl...

Figure 11.46 Analysis results with/without IESA. (a) Without IESA analysis. ...

Figure 11.47 IESA Regression Tree analysis results.

Figure 11.48 Illustration of adding new interaction‐effect variables (SD 01 ...

Figure 11.49 Conversion of the OS‐to‐SS Q‐time variable into binary form....

Figure 11.50 Trend chart of the accumulated yield loss vs. OS‐to‐SS Q‐time....

Figure 11.51 Integrating GAVM into WMA.

Figure 11.52 GAVM architecture for machine tools.

Figure 11.53 A unique QR‐code‐identification engraved on the mounting‐surfac...

Figure 11.54 One‐to‐many relationship among a vender and its customers via A...

Figure 11.55 Architecture of the existing GAVM system.

Figure 11.56 Detailed drawing of integrating WMA’s vender and customers into...

Figure 11.57 Global cyber‐physical interactions (AVM models refreshing). LCL...

Figure 11.58 Operating scenarios of modeling and running samples of the TVA ...

Figure 11.59 Flowchart of the TVA scheme.

Figure 11.60 VM results with and without the TVA scheme.

Figure 11.61 Comparison of on‐machine probing (OMP), CMM, and VM.

Figure 11.62 Using a probe to touch the outside of an EC end‐face.

Figure 11.63 Position trends and their curve‐fitting results of 10 ECs.

Figure 11.64 Actual deformed position (

D

) and ideal position (

I

) of an EC....

Figure 11.65 Approximate machining position (A) on a deformed EC.

Figure 11.66 Flowchart of generating the fittest ellipse and AC via GA.

Figure 11.67 Flowchart of integrating the on‐line probing, the DF scheme, an...

Figure 11.68 AVM results for diameter prediction.

Figure 11.69 Position prediction. (a) VM results of four cases: (1) without ...

Figure 11.70 Comparison of off‐line measurement and virtual metrology.

Figure 11.71 CPAVM scheme.

Figure 11.72 Illustration of the production‐data‐traceback (PDT) mechanism....

Figure 11.73 Information flow of the PDT mechanism.

Figure 11.74 AMCoT for carbon‐fiber manufacturing.

Figure 11.75 AVM results of sizing percentage.

Figure 11.76 Carbon‐fiber manufacturing on‐line display results.

Figure 11.77 Two‐stage PET stretch‐blow molding machine.

Figure 11.78 Implementation of AVM for blow molding machines.

Figure 11.79 Architecture of IM‐based R2R control.

Figure 11.80 Architecture of AVM‐based R2R control.

Figure 11.81 AVM‐based R2R control implementation in multiple machines.

Figure 11.82 Flow chart of AVM‐based R2R control scheme.

Figure 11.83 Experimental results of AVM‐based R2R control for Case 1‐out‐of...

Figure 11.84

C

PM

values of Case 1‐out‐of‐1 lot.

Guide

Cover Page

Series Page

Title Page

Copyright Page

Editor Biography

List of Contributors

Preface

Acknowledgments

Foreword

Table of Contents

Begin Reading

Index

Wiley End User License Agreement

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IEEE Press445 Hoes LanePiscataway, NJ 08854

IEEE Press Editorial BoardEkram Hossain, Editor in Chief

Jón Atli Benediktsson  

Xiaoou Li  

Jeffrey Reed  

Anjan Bose  

Lian Yong  

Diomidis Spinellis  

David Alan Grier  

Andreas Molisch  

Sarah Spurgeon  

Elya B. Joffe  

Saeid Nahavandi  

Ahmet Murat Tekalp  

Industry 4.1

Intelligent Manufacturing with Zero Defects

Edited by

Fan‐Tien Cheng

IEEE Press Series on Systems Science and EngineeringMengChu Zhou, Series Editor

Copyright © 2022 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

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Library of Congress Cataloging‐in‐Publication Data is applied for

Hardback: 9781119739890

Cover Design: WileyCover Image: © Fan‐Tien Cheng

Editor Biography

Soon after Fan‐Tien Cheng graduated from the department of Electrical Engineering of National Cheng Kung University (NCKU) in 1976, he got in the Chung Shan Institute of Science and Technology (CSIST), Taiwan, ROC, serving as Research Assistant at the most basic level and then got promoted to Senior Scientist in 19 years. Then he went back to NCKU to start his teaching career and devoted the knowledge and practices he had learned in CSIST to the research domains of production improvement, manufacturing automation, and e‐manufacturing for industries such as semiconductor, TFT–LCD, solar cell, machine tool, and aerospace to help achieve the goal of enhancing the industry competitiveness by successfully improving manufacturing processes and lowering production cost.

Professor Cheng has devoted himself to the academic research and industrial applications of the Intelligent Manufacturing and Industry 4.0 for the past decades and his accomplishments are eminent. Among them, the academic and applied research of Automatic Virtual Metrology (AVM) are especially unmatched worldwide. More than 40 journal papers related to VM had won him dozens of patents from Taiwan ROC, USA, Japan, China, Germany, and Korea; and 54 technology transfers had been successfully executed on several high‐tech industries such as semiconductor (TSMC, UMC, ASE, and SUMCO), TFT–LCD (Innolux and CPT), solar cell (Motech); and traditional industries like aerospace (AIDC), machine tool industry (FEMCO/FATEK), blow molding machine (ChumPower), and carbon fiber (FPC), as well as foundations, constituted as a juristic person (ITRI and MIRDC).

Some of Professor Cheng’s honors and awards include 2011 Award for Outstanding Contributions in Science and Technology, Executive Yuan, Taiwan, ROC, three times ofMinistry of Science and Technology(MoST) Outstanding Research Award (2006, 2009, 2013), three times of the National Invention and Creation Award ofMinistry of Economic Affairs(MoEA) (2011, 2012, 2018), University‐Industry Economic Contribution Award from MoEA, Industry‐University Cooperation Award for College Teachers, Ministry of Education (MoE), NCKU Chair Professor since January 2009, 17th TECO Award from TECO Technology Foundation, 2010, 2013 IEEE Inaba Technical Award for Innovation Leading to Production (for contributions to the development of the AVM System), 2014 Outstanding Research Award of Pan Wen Yuan Foundation, and 2015 20th Outstanding Achievement Award of The Phi Tau Phi Scholastic Honor Society. Moreover, Professor Cheng won IEEE Fellow since January 2008, two times of IEEE ICRA Best Automation Paper Award (1999 and 2013) as well as CASE 2017 Best Application Paper Award. He is currently in his second‐term of President of Chinese Institute of Automation Engineers (CIAE) since 2017. Besides, he is the Senior Editor of the IEEE T‐ASE since October 2017. Furthermore, he is honored to be the IEEE CASE Conference Steering Committee Chair since September 2020.

List of Contributors

Fan‐Tien Cheng

, Director/Chair Professor, Intelligent Manufacturing Research Center/Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan, ROC

Min‐Hsiung Hung

, Professor, Department of Computer Science and Information Engineering, Chinese Culture University, Taipei, Taiwan, ROC

Yu‐Chen Chiu

, Specialist, Intelligent Manufacturing Research Center, National Cheng Kung University, Tainan, Taiwan, ROC

Yu‐Ming Hsieh

, Associate Research Fellow, Intelligent Manufacturing Research Center, National Cheng Kung University, Tainan, Taiwan, ROC

Hao Tieng

, Associate Research Fellow, Intelligent Manufacturing Research Center, National Cheng Kung University, Tainan, Taiwan, ROC

Haw‐Ching Yang

, Professor, Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, ROC

Yu‐Chuan Lin

, Secretary General, Intelligent Manufacturing Research Center, National Cheng Kung University, Tainan, Taiwan, ROC

Chin‐Yi Lin

, Postdoctoral Research Fellow, Intelligent Manufacturing Research Center, National Cheng Kung University, Tainan, Taiwan, ROC

Chao‐Chun Chen

, Professor, Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan, ROC

Hung‐Chang Hsiao

, Professor, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC

Kuan‐Chou Lai

, Professor, Department of Computer Science, National Taichung University of Education, Taichung, Taiwan, ROC

Hsien‐Cheng Huang

, Deputy Director, e‐Manufacturing Research Center, National Cheng Kung University, Tainan, Taiwan, ROC

Yu‐Yong Li

, Postdoctoral Research Fellow, Intelligent Manufacturing Research Center, National Cheng Kung University, Tainan, Taiwan, ROC

Preface

In the era of global competition, improving productivity and increasing yield of the manufacturing industries through information and communication as well as cloud computing technologies, big data analytics, and cyber‐physical systems (CPS) are the common goals for the manufacturers worldwide. For instance, Germany proposed Industry 4.0 in hopes to construct Smart Factory so as to enhance its global competitiveness and continue to take the lead in manufacturing. The Advanced Manufacturing Partnership issued by the United States aimed to regain the leadership in international manufacturing competitiveness for attracting the manufacturing industries back to the United States. Chinese government brought out the plan called “Made in China 2025” in 2015, where the guidelines and strategies of becoming one of the countries with strong manufacturing power by 2025 were clearly stated. Facing the United States‐China trade war that began in 2018, even though Chinese government stopped to mention Made in China 2025 in public to not intensify the conflict, they claimed that their goals of enhancing Intelligent Manufacturing have not been changed. Adjustments on the strategies in accordance with the international trend would be made, which is considered as the new version of Made in China 2025.

Overview and Goals

The current Industry 4.0 related technologies emphasize on productivity improvement but not on quality enhancement; in other words, they can only keep the faith of achieving nearly Zero‐Defects (ZD) state without realizing this goal. The key reason for this inability is the lack of an affordable online and real‐time Total Inspection technology. By adopting the Automatic Virtual Metrology (AVM) that has been certified with the invention patents from six countries (Taiwan ROC, USA, Japan, Germany, China, and Korea) developed by the research team of Fan‐Tien Cheng, the Editor and main author of this book, ZD can be achieved as AVM can provide the Total Inspection data of all products online and in real time. A defective product will be discarded once it is detected by AVM; in this way, all of the deliverables will be ZD. Further, the Key‐variable Search Algorithm (KSA) of the Intelligent Yield Management (IYM) system developed by our research team can be utilized to find out the root causes of the defects for continuous improvement on those defective products. As such, ZD of all products can be achieved. Therefore, once AVM and IYM are integrated into the successfully developed Industry 4.0 platform constituting of Internet of things (IoT), CPS, big data analytics, and cloud computing, the state of ZD can be realized, which is defined as Industry 4.1 by Fan‐Tien Cheng. The concepts of Industry 4.1 were disclosed in IEEE Robotics and Automation Letters in January 2016.

To realize and promote Intelligent Manufacturing, National Cheng Kung University (NCKU) established the Intelligent Manufacturing Research Center (iMRC) with Professor Fan‐Tien Cheng being its Director. Based on the platform of Advanced Manufacturing Cloud of Things (AMCoT) that won IEEE Conference on Automation Science and Engineering (CASE) 2017 Best Application Paper Award, iMRC integrates cross‐disciplinary research resources, utilizes various Intelligent‐Manufacturing related technologies, and implements Intelligent Manufacturing services [including AVM, IYM, Intelligent Predictive Maintenance (IPM), …, etc.] to develop the so‐called Intelligent Factory Automation (iFA) System Platform. Through implementing the iFA System Platform to the manufacturing tools and production lines of high‐tech (e.g. semiconductor, TFT‐LCD, and solar cell) and traditional industries (e.g. machine tool, aerospace, blow molding machine, and carbon fiber), ZD of all products as well as highly efficient and flexible intelligence capabilities (single‐machine intelligence, production‐line intelligence, and global‐fab intelligence) can be accomplished for improving the competitiveness and profits of all Intelligent‐Manufacturing related industries.

Organization and Features

To promote Intelligent Manufacturing and carry out the vision of Industry 4.1, our research team decided to include the survey and introduction of Intelligent Manufacturing, as well as the intact concept and the core technologies of Industry 4.1, along with the successful cases of iFA implementation for Industry 4.1 in different industries all in this book. This book contains 11 chapters in total. Chapters 1–5 describe the evolution of automation and the development strategy of Intelligent Manufacturing and introduce the mandatory components and fundamental technologies for constructing Intelligent Manufacturing. Chapter 6 introduces the overall concept of iFA integrated by the AMCoT framework with pluggable modules of AVM, IPM, and IYM intelligent services. Two versions of the iFA System Platform are provided for different business models. Chapter 7 illustrates the AMCoT framework for constructing the advanced cloud manufacturing platform. Chapters 8–10 address the principles and implementation of AVM, IPM, and IYM, respectively. Finally, the actual Intelligent‐Manufacturing implementation and application cases adopting all the techniques mentioned above in seven industries, including flat panel display, solar cell, semiconductor, automobile, aerospace, carbon fiber, and blow molding, are presented in Chapter 11. Moreover, all the major patents related to AMCoT, AVM, IPM, and IYM are listed in Appendices 7.C, 8.C, 9.C, and 10.C, respectively.

Fan‐Tien Cheng

Life Fellow, IEEE

Chair Professor, NCKU

Director of Intelligent Manufacturing Research Center (iMRC), NCKU

July 2021

Acknowledgments

Firstly, I would like to thank all the contributors for sharing their precious knowledge and experience in their professional fields. Throughout numerous discussions, the outline of this book is gradually shaped. I really appreciate their time and effort devoted in writing this book.

In addition to the authors listed, I would also like to express my deepest gratitude to my secretaries and research assistants: Pei‐Ying Du, Ken‐Ying Liao, Yan‐Yu Shih, and Benny Suryajaya, for their dedication in completing the book. I want to sincerely thank them for helping with the translation, layout arrangement, proofreading, artwork illustration, and contribution in any manuscript preparation tasks. Without their effort, I, the editor and main contributor, as well as the other contributors couldn’t have made it this far.

Fan‐Tien Cheng

Life Fellow, IEEE

Chair Professor, NCKU

Director of Intelligent Manufacturing Research Center (iMRC), NCKU

July 2021

Foreword

Since the term “Industry 4.0” was coined in Germany in 2011, industries worldwide have been investing in the development of smart factories that are more efficient and better adaptive to digital transformation to enhance their service‐oriented and customized‐supply capabilities.

To take Industry 4.0 a step further, Professor Fan‐Tien Cheng proposed the upgraded version of Industry 4.1, core to which is the realization of Zero Defects, a solution taking advantage of the newly developed Intelligent Factory Automation (iFA) System Platform to address the production quality issue that has received relatively scant attention in Industry 4.0. To put into practice Zero Defects as well as in response to the Intelligent Manufacturing Industry Innovation policy of the Taiwan, ROC government, he further established the Intelligent Manufacturing Research Center (iMRC) at National Cheng Kung University (NCKU) in 2018.

As NCKU’s President, I always take great pride in the achievements of all my colleagues and students. In the latest Times Higher Education (THE) Impact Rankings 2020, NCKU is ranked first in Taiwan, ROC, second in the Asia region, and 38th globally. It excels especially in “Industry, Innovations, and Infrastructure,” one of United Nation’s 17 Sustainable Development Goals (SDGs), and earns the 10th place worldwide. Professor Cheng’s innovative research has played a critical role in this intense competition because, as I understand it, his Industry 4.1 and Intelligent Manufacturing are key to NCKU’s success in the SDG of “Industry, Innovations, and Infrastructure” and NCKU’s continuous leadership in the Engineering field.

As NCKU is accelerating its research momentum, especially in disciplines of traditional strengths like Intelligent Manufacturing and Engineering, I am glad that Professor Cheng is willing to share his valuable research and industry‐university cooperation experiences in this book, one that will become an important reference not only for students but professors and researchers alike, and not only at NCKU but in industries and higher education worldwide whose focus is Intelligent Manufacturing.

This book not only details the essentials that have paved the way from Industry 4.0 to Industry 4.1 but also provides numerous practical industrial application cases in different manufacturing industries. It thus offers readers a comprehensive perspective of what they are and will be facing in the industry. I am sure this book is fundamental – a must‐have indeed – for researchers, engineers, and focused students in the fields of, among others, Intelligent Manufacturing and Industry 4.1.

Huey‐Jen Su

President, National Cheng Kung University (NCKU)

Industry 4.0 is a confluence of trends and technologies for the fourth industrial revolution. It has been “pushed” by the digital revolution over the past many decades and the recent Internet of Things (IoT); and “pulled” by demand from customers for high quality and customized products at reasonable prices and lead times. With (i) the ubiquitous connection and interaction of machines, things, and people; (ii) the integration of cyber and physical systems; and (iii) the emerging of disruptive technologies such as big data, machine learning, artificial intelligence, 3D printing and robotics, the ways we design and manufacture products and provide services are undergoing fundamental changes.

Although much R&D progress has been made, industries have been slow to develop effective holistic Industry 4.0 strategies. From a recent survey of 2000 C‐suite executives by Deloitte (https://www2.deloitte.com/content/dam/insights/us/articles/us32959‐industry‐4‐0/DI_Industry4.0.pdf), only 10% of the executives surveyed indicated they had long‐range strategies to leverage new technologies that reach across their organizations. This is not surprising since creating and implementing holistic Industry 4.0 strategies are complicated, and require deep understanding, sharp vision, inspirational leadership, and resolute persistence. Among those with comprehensive Industry 4.0 strategies, the results have been impressive: 73% of those with a strategy report success in protecting their businesses from disruption, versus 12% of those with more scattershot approaches; 61% of those with Industry 4.0 strategies report that they have developed innovative products and services, versus 12% of those lacking strategies; and 60% of those with Industry 4.0 strategies report that they have found growth opportunities for existing products and services, versus 8% of those lacking strategies. Those companies with strategies also are growing more financially, and making more progress investing in technologies that have a positive societal impact.

Consider specifically a key area of Industry 4.0, the quality of products and processes. It is well‐known that a host of methods and processes such as Statistical Process Control (SPC), Zero Defect Manufacturing (ZDM), Six Sigma Methodologies, Preventive Maintenance (PM), Continuous Improvement (Kaizen), Total Quality Management (TQM), etc., have been around for years and are contributing to the quality of products and processes. Integrating the digital revolution, the Internet of Things, big data, machine learning, and artificial intelligence to raise the quality of products and processes to a new level and with practical and scalable implementations, however, remains a major challenge for scholars, practitioners, and C‐suite executives alike.

This book “Industry 4.1 – Intelligent Manufacturing with Zero Defects” focuses on improving the quality of products and processes, and is the culmination of the brilliant but down‐to‐the‐earth efforts of the team led by Professor Fan‐Tien Cheng over the past many years. The efforts started with Virtual Metrology. In view of the incompatible paces of fast production and slow metrology, 100% inspection is impossible, and sample inspection has been the practice. With the advancements in sensing, metrology, analytics and Industry 4.0 technologies, the team innovatively integrated physical metrology with its cyber counterparts, Virtual Metrology (VM). The resulting Automatic Virtual Metrology (AVM) system presented in this book is capable of predicting the quality of a product based on machine parameters, sensor data in the production equipment, and off‐line sampling measurements. It also provides on‐line and real‐time total inspection of all work pieces to timely detect abnormalities during production. As a result, the sampling rate of real measurements can be cut down, the production costs can be reduced, and the goal of nearly zero defects of deliverables can be achieved.

Effective implementation of Automatic Virtual Metrology, however, is not easy, especially if we want it to be scalable to large factories and transferrable to other companies and other industries. Major infrastructure needs to be established efficiently and flexibly. Based on the team’s successful research, development, implementation, and redeployment at many factories and across multiple industries, this book methodically presents the essential infrastructure components. The content includes data collection and management and feature extraction; communication standards; computation infrastructure of cloud, edge, Internet of Things and big data; container‐related software development, deployment, and management technologies of Docker and Kubernetes; the overall architecture of the advanced manufacturing “Cloud of Things” framework, and the specific design and implementation of key components such as cyber‐physical agents, big data analytics application platform, the automated construction scheme for manufacturing services, and AVM and other servers.

Extending the ideas, methods, and infrastructure presented above, the book then focuses on Intelligent Predictive Maintenance (IPM). Predictive maintenance, sometimes known as “condition‐based maintenance,” is to monitor the performance and conditions of equipment during operations to predict when equipment performance is deteriorating and when equipment is going to fail, followed by scheduled or corrective maintenance. Intelligent Predictive Maintenance presented in this book detects the abnormality of key components of manufacturing tools based on advanced fault detection and classification techniques and predicts their Remaining Useful Lives (RUL) using time series prediction algorithms. Factory‐wide implementation is then discussed to improve tool availability and prevent unscheduled down of manufacturing tools.

Since modern manufacturing facilities are generally capital intensive, it is critical to have consistently high yields to justify the investment and to have a positive bottom line. Intelligent Yield Management (IYM) presented in this book is a closely related cousin of Intelligent Predictive Maintenance, with the purpose to effectively detect root causes that affect the yield. It consists of data collection and management; statistical, big data, and machine learning tools for defect and yield analysis; and timely resolution of issues discovered while maintaining the requisite quality and reliability standards. The kernel of the above is the “Key‐variable Search Algorithm” (KSA), which includes new root‐cause search methods for solving the high‐dimensional variable selection problem, and modules for checking the quality of input data and for evaluating the reliability of search results.

The current Industry 4.0‐related technologies emphasize productivity improvement but not on quality enhancement. They can have the faith of achieving nearly Zero‐Defect Manufacturing but without effective methods to achieve it. By developing and implementing the novel methods, technologies, and infrastructure presented above, zero defects of products can be effectively achieved. This is what is defined as Industry 4.1 in the book. The actual deployment cases in seven industries, including flat panel display, semiconductor, solar cell, automobile, aerospace, carbon fiber, and blow molding, are presented in the final Chapter 11. The ingenuity is outstanding, the effort is tremendous, and the impact is far‐reaching and long‐lasting.

Since many acronyms are used throughout the book, readers are advised to have Abbreviation Lists handy when reading the book. Beyond this point, I sincerely hope that you enjoy reading the book, and delightfully discover the wonderful world of Industry 4.1.

Peter B. Luh

Board of Trustees Distinguished Professor

SNET Professor of Communications & Information Technologies

Dept. of Electrical & Computer Engineering

University of Connecticut

Since Germany brought up Industry 4.0 in 2012, the trend of Intelligent Manufacturing has boomed globally. By integrating the innovative information‐and‐communication technologies such as IoT, Cloud, Big Data, AI, etc., various Cyber‐Physical Systems are developed to promote factory process optimization, yield improvement, efficiency enhancement, and cost reduction. Besides, in response to changes in consumers' habits, Zero Defects, High Variety Low Volume, and Rapid Change have become mandatory indicators for Intelligent Manufacturing.

Advanced Semiconductor Engineering Inc. (ASE), is the leading provider of independent semiconductor manufacturing services in assembly and test. ASE develops and offers complete turnkey solutions in IC packaging, design and production of interconnect materials, front‐end engineering test, wafer probing, and final test. In 2011, ASE started to vigorously promote Intelligent Manufacturing and established over 15 lights‐out factories in response to changes in the global industrial environment. Moreover, ASE also collaborated with various top universities in Taiwan, ROC for R&D of IoT, Cloud, Big Data, and AI technologies, which have cultivated more than 400 professionals in the automation field via co‐hosting educational trainings and industry programs to improve the automation capability within ASE.

ASE began the industry‐university collaboration with Prof. Fan‐Tien Cheng in 2014. Initially, we implemented Automatic Virtual Metrology (AVM) to achieve total inspection in an efficient and economic way so as to reduce the measurement cost. The project was a great success, and ever since then Prof. Cheng has become one of our major collaborators. The subsequent cooperation includes Intelligent Yield Management (IYM), Intelligent Predictive Maintenance (IPM), Advanced Manufacturing Cloud of Things (AMCoT), and Scheduling, which can be said to be the practical applications of all the research essence of Prof. Cheng on the production line.

The Industry 4.1 proposed by Prof. Cheng aims at Zero Defects, it applies AVM to accomplish total inspection and utilizes IYM to find the root causes of a yield loss. In addition to enhancing production efficiency, it also improves product yield and makes products close to Zero Defects, which is a great step forward in the realm of Industry 4.0.

Although Intelligent Manufacturing is a hot subject nowadays, it is challenging for the enterprises to actually carry it out; many enterprises still struggle to realize the vision of Intelligent Manufacturing. The implementation of novel technologies isn’t the only core for Intelligent Manufacturing, the shaping of the ecological chain of the automation industry and the cultivation of talents are also important factors.

As the development of hardware like sensor, microcontroller, Automatic Material Handling System (AMHS), and robot is coming to a mature state gradually, the focus of Intelligent Manufacturing has shifted to the software. The cloud‐based technologies such as Big Data and AI application modules draw more attention to the researchers and professionals at present. The technologies introduced in this book are a series of automation technologies developed upon IoT, Cloud, Big Data, and AI. Aside from explaining through the theories in detail, it also includes hands‐on application cases in various industries. This is a book worth reading for both industrial professionals and scholars, and I highly recommend these materials for Intelligent Manufacturing education.

Michael Lee

Vice GM of ASEKH MIS Center

Former Plant Manager of ASE Testing and Wafer Bumping Plants

Former Executive Secretary of ASE Security Committee

Former Committee Member of ASE Automation Committee

By the time we established the Precision Machinery Research & Development Center (PMC) in 1993, the board of directors agreed to my suggestion of focusing our efforts on two fields of expertise, IT and total quality control, to speed up our competitiveness on machine tools made in Taiwan, ROC.

Back then, we were totally unaware that IT could even be developed outside our expertise realm to missions such as Apollo 13 by NASA through digital twins.