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Full Description
Industry 4.0 and Industry 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency.
Computational Intelligence for Analysis of Trends in Industry 4.0 and 5.0 examines the trends in applications that merge three key disciplines: Computational Intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective.
Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, Internet of Things (IoT), IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industry 4.0 and Industry 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These trends are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face technical challenges such as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these issues, this book examines trends such as CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and Industry 5.0 systems.
Contents
1. Fundamentals of AI, ML, and Their Relevance to Industry 4.0 and 5.0 2. Deep Learning Architectures in Industrial Automation and Robotics 3. Advancements in Computational Intelligence for Enhanced Big Data Analytics in Industry 4.0 and 5.0: Challenges, Advances, and Future Perspectives 4. Integration of AI- and ML-based Algorithms in Cyber-Physical Systems for Predictive Maintenance 5. Applications and Innovations of Edge Computing in Industry 4.0 and 5.0 6. Integration of Deep Learning and Optimization Techniques for Enhanced Efficiency and Decision-Making in Industry 4.0 and 5.0 7. Fuzzy Logic and Expert Systems for Decision Support in Cyber-Physical Production Systems 8. Industry 4.0: A Review on Energy Conservation in MEMS 9. Enabling Technologies for Seamless Human-Machine Interaction in Industry 5.0 10. Face Recognition Access Control System Using Hidden Markov Model 11. Human-Centric Design Principles in Industry 4.0 and 5.0 12. Failure Prediction Using Machine Learning and Deep Learning in Industry 4.0 and 5.0: A Multi-Instance-Based Case Study 13. The Protection of Industry 4.0 and 5.0: Cybersecurity Strategies and Innovations 14. Privacy and Security Challenges of AI Integration in Industry 4.0 and 5.0 15. Optical and Quantum Computing in Industry 4.0 and 5.0 16. Blockchain Technologies in Industry 5.0: A Review of the Applications, Challenges and Future Prospects 17. Industry 5.0: A New Industrial Revolution and Technological Applications
