Digital Twin Driven Service

Digital Twin Driven Service

1st Edition - March 14, 2022

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  • Editors: Fei Tao, Qinglin Qi, A.Y.C. Nee
  • Paperback ISBN: 9780323913003
  • eBook ISBN: 9780323986151

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Description

Digital Twin Driven Smart Service draws on the latest industry practice and research to explain how to implement digital twin service in a range of scenarios. It addresses relevant theory and methodologies, including product service, prognostic health management service, energy efficient service and testing service. Other sections discuss  key enabling technologies supported by cutting-edge case studies of implementation. Drawing on the work of researchers at the forefront of this technology, this book is the ideal guide for anyone interested in product services, manufacturing services and digital twin services. This book is one part of a trilogy on digital twins, the other titles being Digital Twin Driven Smart Design and Digital Twin Driven Smart Manufacturing.

Key Features

  • Provides a wide range of applications, including tribological testing, cutting tool service and energy efficiency assessment
  • Explains everything needed to understand and implement digital twin models for service, including frameworks, theories and technologies
  • Explores future challenges for research in this area, including the ongoing standardization of digital twin technology

Readership

Researchers and postgraduates interested in engineering optimization, advanced design and manufacturing, and product lifecycle management

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • Preface
  • Chapter 1. From service to digital twin service
  • 1.1. Introduction
  • 1.2. The development and connotation of service
  • 1.3. Product service and manufacturing service
  • 1.4. Digital twin service
  • 1.5. Summary
  • Chapter 2. Digital twin-driven service collaboration
  • 2.1. Introduction
  • 2.2. The concept of digital twin-driven service collaboration
  • 2.3. Enabling digital twin service collaboration
  • 2.4. Case study
  • 2.5. Summary
  • Chapter 3. Digital twin-driven production line custom design service
  • 3.1. Introduction
  • 3.2. Framework for digital twin-driven production line custom design service system
  • 3.3. Development method of digital twin-driven production line custom design service system
  • 3.4. Case study
  • 3.5. Summary
  • Chapter 4. Digital twin-enhanced product family design and optimization service
  • 4.1. Introduction
  • 4.2. DT-enhanced services for ambient-based product family design
  • 4.3. DT-enabled services for in-context product family optimization
  • 4.4. Overall system framework
  • 4.5. Case study
  • 4.6. Summary
  • Chapter 5. Digital twin-driven fault diagnosis service of rotating machinery
  • 5.1. Introduction
  • 5.2. The related works
  • 5.3. Digital twin-driven fault diagnosis framework
  • 5.4. Case studies
  • 5.5. Summary
  • Chapter 6. Digital twin-driven energy-efficient assessment service
  • 6.1. Introduction
  • 6.2. Energy-efficient assessment for manufacturing capability
  • 6.3. Digital twin-driven assessment service
  • 6.4. Case studies
  • 6.5. Summary
  • Chapter 7. Digital twin-driven cutting tool service
  • 7.1. Introduction
  • 7.2. Related works
  • 7.3. Framework of digital twin-driven cutting tool service
  • 7.4. Enabling digital twin-driven cutting tool service
  • 7.5. Case study
  • 7.6. Summary
  • Chapter 8. Digital twin-driven prognostics and health management
  • 8.1. Introduction
  • 8.2. Digital twin-driven PHM framework
  • 8.3. Case study
  • 8.4. Summary
  • Chapter 9. Production process management for intelligent coal mining based on digital twin
  • 9.1. Introduction
  • 9.2. Production process management and control framework of coal mining face based on digital twin
  • 9.3. Key technologies
  • 9.4. Typical application case
  • 9.5. Summary
  • Chapter 10. Digital twin enhanced tribo-test service
  • 10.1. Introduction
  • 10.2. The related works
  • 10.3. Understanding of tribo-test service
  • 10.4. The framework of digital twin enhanced tribo-test service
  • 10.5. Case study
  • 10.6. Summary
  • Index

Product details

  • No. of pages: 326
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: March 14, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323913003
  • eBook ISBN: 9780323986151

About the Editors

Fei Tao

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.

Affiliations and Expertise

Professor, School of Automation Science and Electrical Engineering, Beihang University, China

Qinglin Qi

Qinglin Qi is a Hong Kong scholar supported by both the School of Automation Science and Electrical Engineering, Beihang University, and Department of Industrial Systems Engineering, The Hong Kong Polytechnic University. His research interests are service-oriented smart manufacturing, digital twin driven manufacturing and service.

Affiliations and Expertise

Postdoctor, School of Automation Science and Electrical Engineering, Beihang University, China, and Department of Industrial Systems Engineering, The Hong Kong Polytechnic University, China

A.Y.C. Nee

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.

Affiliations and Expertise

Professor of Manufacturing Engineering, National University of SIngapore, Singapore

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