The book highlights the importance of intelligent decision-making in advanced production systems, and optimization of process parameters using fuzzy-based multi-criteria decision-making tools. It discusses the decision-making aspects of Industry 4.0 using machine learning and optimization techniques and helps in moving toward the digitalization of manufacturing systems. It further covers several important topics including the role of digital twins in advanced manufacturing processes, machine learning-based prediction of overall equipment effectiveness, intelligent quality control tools, and life cycle assessment models in Industry 4.0.
Key features:
Presents a conceptual framework to measure the readiness of adopting Industry 4.0 in advanced manufacturing systems.
Discusses the impact of smart manufacturing on sustainable development and integration of Industry 4.0 and additive manufacturing.
Covers topics such as intelligent automation systems, machine learning-based preventive maintenance, and the Internet of Things-enabled additive manufacturing in Industry 4.0.
Explains cyber-physical system integration with Industry 4.0 technologies, cyber-physical systems in industrial robotics, and green cyber-physical systems.
Illustrates optimization of process parameters using fuzzy-based multi-criteria decision-making tools and life cycle assessment models in Industry 4.0.
This book is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of industrial engineering, production engineering, mechanical engineering, supply chain management, and manufacturing engineering.
1. Industry 4.0: transforming communication, sustainability, and collaboration in manufacturing 2. Integrating Industry 4.0 technologies in manufacturing systems 3. Smart manufacturing 4. Advanced manufacturing systems and Industry 4.0 5. Cyber-physical system for advanced manufacturing 6. Digital twins for advanced manufacturing 7. Decision-making in Industry 4.0 8. Navigating the legal landscape of sustainable Industry 4.0: challenges and considerations 9. Industry 4.0 performance measurement using key performance indicators for effective digital transformation 10. Machine learning applications in inventory management: a case study 11. Research issues in Industry 4.0
