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E-raamat: Digital Twin Driven Smart Manufacturing

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(PhD student, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China), (Professor of Manufacturing En), (Professor, School of Automation Science and Electrical Engineering, Beihang University, China)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Feb-2019
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128176313
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Digital Twin Driven Smart ManufacturingSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Feb-2019
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128176313
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Digital Twin Driven Smart Manufacturing examines the background, latest research, and application models for digital twin technology, and shows how it can be central to a smart manufacturing process. The interest in digital twin in manufacturing is driven by a need for excellent product reliability, and an overall trend towards intelligent, and connected manufacturing systems. This book provides an ideal entry point to this subject for readers in industry and academia, as it answers the questions: (a) What is a digital twin? (b) How to construct a digital twin? (c) How to use a digital twin to improve manufacturing efficiency? (d) What are the essential activities in the implementation of a digital twin? (e) What are the most important obstacles to overcome for the successful deployment of a digital twin? (f) What are the relations between digital twin and New Technologies? (g) How to combine digital twin with the New Technologies to achieve high efficiency and smartness in manufacturing?

This book focuses on these problems as it aims to help readers make the best use of digital twin technology towards smart manufacturing.

  • Analyzes the differences, synergies and possibilities for integration between digital twin technology and other technologies, such as big data, service and Internet of Things
  • Discuss new requirements for a traditional three-dimension digital twin and proposes a methodology for a five-dimension version
  • Investigates new models for optimized manufacturing, prognostics and health management, and cyber-physical fusion based on the digital twin
Preface ix
Part 1 Background and Connotation
1(82)
1 Background and Concept of Digital Twin
3(26)
1.1 Background of the Development of Digital Twin
3(2)
1.2 History of Digital Twin
5(2)
1.3 Concept of Digital Twin
7(8)
1.4 Digital Twin and Related Concepts
15(7)
1.5 Value of Digital Twin
22(2)
1.6 Summary
24(5)
References
24(5)
2 Applications of Digital Twin
29(34)
2.1 Digital Twin in Product Lifecycle
29(12)
2.2 Digital Twin in Industrial Applications
41(12)
2.3 Future Market for Digital Twin
53(1)
2.4 Challenges of Digital Twin Applications
53(2)
2.5 Summary
55(8)
References
56(7)
3 Five-Dimension Digital Twin Modeling and Its Key Technologies
63(20)
3.1 Traditional Three-Dimension Digital Twin
63(3)
3.2 New Requirements on Digital Twin
66(2)
3.3 Extended Five-Dimension Digital Twin
68(3)
3.4 Application-Oriented Three-Level Digital Twins
71(2)
3.5 Key Technologies for Digital Twin Modeling
73(4)
3.6 Eight Rules for Digital Twin Modeling
77(2)
3.7 Summary
79(4)
References
79(4)
Part 2 Digital Twin Driven Smart Manufacturing
83(86)
4 Digital Twin Shop-Floor
85(26)
4.1 Evolution Path of Shop-Floor
85(5)
4.2 Related Works
90(2)
4.3 Concept of Digital Twin Shop-Floor
92(6)
4.4 Implementation of Digital Twin Shop-Floor
98(6)
4.5 Characteristics of Digital Twin Shop-Floor
104(2)
4.6 Key Technologies for Digital Twin Shop-Floor
106(1)
4.7 Challenges for Digital Twin Shop-Floor
107(1)
4.8 Summary
108(3)
References
108(3)
5 Equipment Energy Consumption Management in Digital Twin Shop-Floor
111(14)
5.1 Introduction
111(2)
5.2 Framework of EECM in Digital Twin Shop-Floor
113(1)
5.3 Implementation of EECM in Digital Twin Shop-Floor
114(5)
5.4 Potential Advantages of EECM in Digital Twin Shop-Floor
119(3)
5.5 Summary
122(3)
References
122(3)
6 Cyber--Physical Fusion in Digital Twin Shop-Floor
125(16)
6.1 Introduction
125(2)
6.2 Reference Architecture for Digital Twin Shop-Floor
127(2)
6.3 Physical Elements Fusion
129(2)
6.4 Models Fusion
131(3)
6.5 Data Fusion
134(2)
6.6 Services Fusion
136(1)
6.7 Summary
137(4)
References
137(4)
7 Digital Twin-Driven Prognostics and Health Management
141(28)
7.1 Introduction
141(4)
7.2 Digital Twin for Complex Equipment
145(3)
7.3 Digital Twin-Driven PHM Method
148(8)
7.4 Case Study
156(8)
7.5 Summary
164(5)
References
165(4)
Part 3 Digital Twin and New Technologies
169(88)
8 Digital Twin and Cloud, Fog, Edge Computing
171(12)
8.1 Introduction
171(2)
8.2 Three-Level Digital Twins in Manufacturing
173(1)
8.3 From Cloud Computing to Fog Computing and Edge Computing
174(2)
8.4 Three-Level Digital Twins Based on Edge Computing, Fog Computing, and Cloud Computing
176(4)
8.5 Summary
180(3)
References
180(3)
9 Digital Twin and Big Data
183(20)
9.1 Introduction
183(2)
9.2 Big Data
185(2)
9.3 Lifecycle of Big Data in Manufacturing
187(4)
9.4 360° Comparison of Digital Twin and Big Data in Manufacturing
191(5)
9.5 Complementarity Between Big Data and Digital Twin
196(2)
9.6 Fusion of Digital Twin and Big Data in Manufacturing
198(2)
9.7 Summary
200(3)
References
200(3)
10 Digital Twin and Services
203(16)
10.1 Introduction
203(1)
10.2 Services in Manufacturing
204(3)
10.3 Services in Digital Twin
207(1)
10.4 Digital Twin Service Generation
208(3)
10.5 Digital Twin Service Management
211(1)
10.6 Digital Twin Service Application
211(4)
10.7 Summary
215(4)
References
215(4)
11 Digital Twin and Virtual Reality and Augmented Reality/Mixed Reality
219(24)
11.1 Introduction
219(2)
11.2 VR in Design, Manufacturing, and Service
221(3)
11.3 AR in Design, Manufacturing, and Service
224(4)
11.4 Comparison Between VR and AR
228(2)
11.5 Digital Twin and VR and AR
230(3)
11.6 Digital Twin-Driven Assembly Combining VR and AR
233(3)
11.7 Summary
236(7)
References
236(7)
12 Digital Twin, Cyber--Physical System, and Internet of Things
243(14)
12.1 Introduction
243(1)
12.2 CPS in Manufacturing
244(3)
12.3 IoT in Manufacturing
247(2)
12.4 Digital Twin and CPS
249(3)
12.5 IoT in Digital Twin-Based CPS
252(2)
12.6 Summary
254(3)
References
254(3)
Index 257
Fei Tao is a Professor at the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. His research interests are service-oriented smart manufacturing, manufacturing service management and optimization, digital twin driven product design/manufacturing/service, green and sustainable manufacturing. Meng Zhang is a PhDd student at the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. Her research is focused on digital twin technology in manufacturing and sustainable manufacturing. A.Y.C. Nee is Professor Emeritus in Manufacturing Engineering at the National University of Singapore. His research interests include the use of AI, virtual and augmented reality applications in manufacturing, sustainable product design and life cycle engineering, and computer aided manufacturing design. He is Fellow of CIRP, Fellow of SME and Fellow of the Academy of Engineering Singapore.
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