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INTRELLIGENT MANUFACTURING MANAGEMENT SYSTEMS
The book explores the latest manufacturing techniques in relation to AI and evolutionary algorithms that can monitor and control the manufacturing environment.
The concepts that pertain to the application of digital evolutionary technologies in the sphere of industrial engineering and manufacturing are presented in this book. A few chapters demonstrate stepwise discussion, case studies, structured literature review, rigorous experimentation results, and applications. Further chapters address the challenges encountered by industries in integrating these digital technologies into their operational activities, as well as the opportunities for this integration.
In addition, the reader will find:
* Systemic explanations of the unique characteristics of big data, cloud computing, and AI used for decision-making in intelligent production systems;
* Highlights of the current and highly relevant topics in manufacturing management;
* Structured presentations resolving the issues being faced by many real-world applications in a broad range of areas such as smart supply chains, knowledge management, intelligent inventory management, IoT adoption in manufacturing management, and more;
* Intelligent techniques for sustainable practices in industrial waste management.
Audience
The book will be used by researchers, industry engineers, and data scientists/AI specialists working in industrial engineering, mechanical engineering, production engineering, manufacturing engineering, and operations and supply chain management. The book will also be valuable to the service sector industry, such as logistics and those implementing smart cities.
Table des matières
Preface xvii
Part I: Smart Technologies in Manufacturing 1
1 Smart Manufacturing Systems for Industry 4.0 3
Gaijinliu Gangmei and Polash Pratim Dutta
Abbreviations 3
1.1 Introduction 4
1.2 Research Methodology 5
1.3 Pillars of Smart Manufacturing 6
1.3.1 Manufacturing Technology and Processes 6
1.3.2 Materials 7
1.3.3 Data 8
1.3.4 Sustainability 8
1.3.5 Resource Sharing and Networking 9
1.3.6 Predictive Engineering 9
1.3.7 Stakeholders 10
1.3.8 Standardization 10
1.4 Enablers and Their Applications 11
1.4.1 Smart Design 12
1.4.2 Smart Machining 12
1.4.3 Smart Monitoring 13
1.4.4 Smart Control 13
1.4.5 Smart Scheduling 14
1.5 Assessment of Smart Manufacturing Systems 14
1.6 Challenges in Implementation of Smart Manufacturing Systems 15
1.6.1 Technological Issue 16
1.6.2 Methodological Issue 16
1.7 Implications of the Study for Academicians and Practitioners 17
1.8 Conclusion 17
References 18
2 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities 23
S. Deepak Kumar, G. Arun Manohar, R. Surya Teja, P. S. V. Ramana Rao, A. Mandal, Ajit Behera and P. Srinivasa Rao
Abbreviations 24
2.1 Introduction to Smart Manufacturing 24
2.1.1 Background of SM 24
2.1.2 Traditional Manufacturing versus Smart Manufacturing 25
2.1.3 Concept and Evolution of Industry 4.0 25
2.1.4 Motivations for Research in Smart Manufacturing 28
2.1.5 Objectives and Need of Industry 4.0 29
2.1.6 Research Methodology 30
2.1.7 Principles of I4. 0 30
2.1.8 Benefits/Advantages of Industry 4.0 31
2.2 Technology Pillars of Industry 4.0 31
2.2.1 Automation in Industry 4.0 33
2.2.1.1 Need of Automation 33
2.2.1.2 Components of Automation 33
2.2.1.3 Applications of Automation 34
2.2.2 Robots in Industry 4.0 34
2.2.2.1 Need of Robots 35
2.2.2.2 Advantages of Robots 35
2.2.2.3 Applications of Robots 37
2.2.2.4 Advances Robotics 37
2.2.3 Additive Manufacturing (AM) 38
2.2.3.1 Additive Manufacturing's Potential Applications 39
2.2.4 Big Data Analytics 40
2.2.5 Cloud Computing 41
2.2.6 Cyber Security 43
2.2.6.1 Cyber-Security Challenges in Industry 4.0 43
2.2.7 Augmented Reality and Virtual Reality 44
2.2.8 Simulation 46
2.2.8.1 Need of Simulation in Smart Manufacturing 46
2.2.8.2 Advantages of Simulation 47
2.2.8.3 Simulation and Digital Twin 47
2.2.9 Digital Twins 47
2.2.9.1 Integration of Horizontal and Vertical Systems 48
2.2.10 IoT and IIoT in Industry 4.0 48
2.2.11 Artificial Intelligence in Industry 4.0 49
2.2.12 Implications of the Study for Academicians and Practitioners 51
2.3 Summary and Conclusions 51
2.3.1 Benefits of Industry 4.0 51
2.3.2 Challenges in Industry 4.0 52
2.3.3 Future Directions 52
Acknowledgement 53
References 53
3 IoT-Based Intelligent Manufacturing System: A Review 59
Hiranmoy Samanta, Pradip Kumar Talapatra, Kamal Golui and Pritam Chakraborty
3.1 Introduction 60
3.2 Literature Review 60
3.3 Research Procedure 64
3.3.1 The Beginning and Advancement of SM/IM 64
3.3.2 Beginning of SM/IM 64
3.3.3 Defining SM/IM 65
3.3.4 Pote
A propos de l'auteur
Kamalakanta Muduli,
PhD, is an associate professor in the Department of Mechanical Engineering, Papua New Guinea University of Technology, Papua New Guinea. He has over 15 years of academic and research experience and has published 40 papers in peer-reviewed international journals.
V. P. Kommula, PhD, is an associate professor in the Department of Mechanical Engineering, University of Botswana. He has over 21 years of teaching experience and served in various positions with different universities in many countries. Kommula's research is in the area of lean manufacturing and productivity improvement by adopting digital technologies. He has published 42 research articles in peer-reviewed international journals.
Devendra K. Yadav, PhD, is an assistant professor in the Department of Mechanical Engineering, National Institute of Technology Calicut, Kerala, India. His current research interests include supply chain management, logistics performance measurement, and Industry 4.0 applications in supply chain domains.
Chithirai Pon Selvan, PhD, is an associate professor at Curtin University, Dubai. He has over 21 years of experience in teaching and has published more than 100 research articles in journals. His research interests are in the areas of machine design, optimization techniques, and manufacturing practices.
Jayakrishna Kandasamy, PhD, is an associate professor in the School of Mechanical Engineering, Vellore Institute of Technology University, India. He has published 47 journal articles in leading SCI journals, 22 book chapters, 85 contributions to refereed conference proceedings, and one edited book. Dr. Jayakrishna's research is focused on the design and management of manufacturing systems and supply chains to enhance efficiency, productivity, and sustainability performance.
