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Author:   Rajesh Kumar Dhanaraj (Symbiosis International (Deemed University), Pune, India) ,  Balamurugan Balusamy (Shiv Nadar Institute of Eminence, Delhi, India) ,  Prithi Samuel (SRM Institute of Science and Technology, Chennai, India) ,  Ali Kashif Bashir (Manchester Metropolitan University, UK)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-IEEE Press
ISBN:  

9781394195305

ISBN 10:   1394195303
Pages:   448
Publication Date:   24 September 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Chapter 1. Digital twin and digital twin–driven manufacturing Abstract 1.1 Origin of digital twin References Chapter 2. Knowledge-Driven Digital Twin Manufacturing Abstract 2.1 Introduction 2.2 A service-oriented model for the Industrial Internet 2.3 A technological architecture of a service-oriented digital twin 2.4 A flexible ontology-based knowledge structure 2.5 A technological solution for advanced ubiquitous knowledge fruition 2.6 Conclusions and further research References Chapter 3. Digital twin and artificial intelligence in industries Abstract 3.1 Introduction 3.2 Methodology 3.3 Conclusion References Chapter 4. Artificial intelligence–driven digital twins in Industry 4.0 Abstract 4.1 Introduction 4.2 Digital twin and artificial intelligence in smart manufacturing 4.3 Digital twins in the intelligentization of automotive autonomous driving 4.4 Digital twins in intelligent urban transportation 4.5 Conclusion References Chapter 5. Industry 4.0: survey of digital twin in smart manufacturing and smart cities Abstract 5.1 Introduction 5.2 Digital twin—an introduction 5.3 Types of digital twin 5.4 Digital twin and base technologies 5.5 Smart cities—an introduction 5.6 Smart manufacturing—an introduction 5.7 Digital twin—use cases in additive manufacturing 5.8 Digital twin—use cases in ship-building industry 5.9 Digital twin—use cases in automotive industry 5.10 Applications of digital twin in smart manufacturing 5.11 Applications of digital twin technology in smart city projects 5.12 Summary References Chapter 6. Digital twins and artificial intelligence: transforming industrial operations Abstract 6.1 Introduction 6.2 Digital twin in industry 6.3 Data acquisition in digital twin using Internet of Things 6.4 Manufacturing process digital twin model 6.5 Industrial machinery maintenance 6.6 Case study JSC 120A digital twin in artificial intelligence manufacturing 6.7 Challenges and future work 6.8 Future work 6.9 Conclusion References Chapter 7. The convergence of digital twin, Internet of Things, and artificial intelligence: digital smart farming Abstract 7.1 Introduction 7.2 Internet of Things in agriculture 7.3 Digital twin smart farming 7.4 Digital smart farming system 7.5 First result and analysis 7.6 Conclusion References Chapter 8. Digital twin meets artificial intelligence: AI-augmented industrial automation systems using intelligent digital twins Abstract 8.1 Introduction 8.2 Artificial intelligence in industrial automation (IA) 8.3 Digital twin with intelligence 8.4 Artificial intelligence agents—digital twin 8.5 Digital twin for smart manufacturing 8.6 Conclusion References Chapter 9. Digital twin technologies for automated vehicles in smart healthcare systems Abstract 9.1 Introduction 9.2 Autonomous vehicles 9.3 Industry 4.0 in the healthcare industry 9.4 Significant technologies related to Industry 4.0 in the healthcare sector 9.5 Cloud computing 9.6 Importance of artificial intelligence in industry 4.0 9.7 Additive manufacturing 9.8 Benefits of additive manufacturing (AM) in Industry 4.0 9.9 Advanced robotics 9.10 Conclusions References Chapter 10. Impact of internet of things and digital twin on manufacturing era Abstract 10.1 Internet of Things 10.2 Importance of Internet of Things 10.3 Norms and framework for the Internet of Things 10.4 The following are the Internet of Things frameworks 10.5 Benefits of Internet of Things 10.6 What is the Internet of Things in the workplace? 10.7 Internet of Things device management 10.8 Internet of Things connectivity and networking 10.9 Application of Internet of Things 10.10 Qualities of Internet of Things References Chapter 11. Fault diagnosis in digital twin manufacturing Abstract 11.1 Introduction 11.2 Fault diagnosis in digital twin 11.3 Assistive technologies in digital twin 11.4 Cyber physical system and digital twin 11.5 Digital twin in fault diagnosis References Chapter 12. Potential applications of digital twin technology in virtual factory Abstract 12.1 Introduction 12.2 Digital model 12.3 Digital shadow 12.4 Digital twin 12.5 Foundational concept 12.6 Crucial technical components of VR 12.7 Types of digital twin 12.8 Working of digital twin 12.9 Components of digital twin 12.10 Simulation 12.11 Simulation versus digital twin 12.12 Literature review 12.13 Potential applications of digital twin 12.14 Incorporating technologies 12.15 The general architecture of Internet of Things 12.16 Pros and cons of digital twin technology 12.17 Future scope of digital twin 12.18 Conclusion Further reading Chapter 13. Digital twins in precision agriculture monitoring using artificial intelligence Abstract 13.1 Introduction 13.2 Background 13.3 Existing methodology 13.4 Proposed methodology 13.5 Conclusion References Chapter 14. Digital twins and cyber-physical system architecture for smart factory Abstract 14.1 Introduction to digital twins and cyber-physical system 14.2 Digital twin-based smart factory cyber-physical system modeling 14.3 Digital twin in cyber-physical system-based production systems 14.4 Case studies Index

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Rajesh Kumar Dhanaraj, PhD, is a Full Professor at Symbiosis International (Deemed University), Pune, India. Balamurugan Balusamy, PhD, is an Associate Dean Student in Shiv Nadar University, Delhi-NCR. Prithi Samuel, PhD, is an Assistant Professor in the Department of Computational Intelligence at SRM Institute of Science and Technology, Kattankulathur Campus, Chennai. Ali Kashif Bashir, PhD, is a Chair Professor of Networks and Security at the Manchester Metropolitan University, UK. Seifedine Kadry, PhD, is a Full Professor of Data Science with the Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon; Department of Applied Data Science, Noroff University College, Kristiansand, Norway.

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