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Digital Twin Driven Smart Manufacturing

  • Book

  • February 2019
  • Elsevier Science and Technology
  • ID: 4612909

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.

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

Table of Contents

1. Background and Concept of Digital Twin
2. Applications of Digital Twin
3. Digital Twin Modeling and Its Key Technologies
4. Digital Twin Shop-floor (DTS)
5. Equipment Energy Consumption Management in Digital Twin Shop-floor
6. Cyber-Physical Fusion in Digital Twin Shop-floor
7. Digital Twin Driven Prognostics and Health Management (PHM)
8. Digital Twin and Cloud, Fog, Edge Computing
9. Digital Twin and Big Data
10. Digital Twin and Services
11. Digital Twin and Virtual Reality, Augmented Reality and Mixed Reality
12. Digital Twin and Internet of Things

Authors

Fei Tao Professor, School of Automation Science and Electrical Engineering, Beihang University, China. 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 PhD student, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. 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 Professor of Manufacturing Engineering, National University of SIngapore, Singapore. 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.