Artificial Intelligence for Smart Manufacturing

This book provides readers with a comprehensive overview of the latest developments in the field of smart manufacturing, exploring theoretical research, technological advancements, and practical applications of AI approaches. With Industry 4.0 paving the way for intelligent systems and innovative technologies to enhance productivity and quality, the transition to Industry 5.0 has introduced a new concept known as augmented intelligence (AuI), combining artificial intelligence (AI) with human intelligence (HI).

As the demand for smart manufacturing continues to grow, this book serves as a valuable resource for professionals and practitioners looking to stay up-to-date with the latest advancements in Industry 5.0. Covering a range of important topics such as product design, predictive maintenance, quality control, digital twin, wearable technology, quantum, and machine learning, the book also features insightful case studies that demonstrate the practical applicationof these tools in real-world scenarios.

Overall, this book provides a comprehensive and up-to-date account of the latest advancements in smart manufacturing, offering readers a valuable resource for navigating the challenges and opportunities presented by Industry 5.0.

Introduces the latest knowledge of the application of AI on smart manufacturing Includes ready-to-use algorithms for smart manufacturing applications based on AI techniques Helps readers and practitioners easily apply AI tools to real-world smart manufacturing processes

Auteur

Kim Phuc Tran is a distinguished Senior Associate Professor (Maître de Conférences HDR, equivalent to UK Reader) of Artificial Intelligence and Data Science at the ENSAIT and the GEMTEX laboratory, University of Lille, France. He holds an Engineer's degree and a Master of Engineering degree in Automated Manufacturing, a Ph.D. in Automation and Applied Informatics from the University of Nantes, and an HDR (Doctor of Science or Dr. habil.) in Computer Science and Automation from the University of Lille, France.

Dr. Tran's research is focused on real-time anomaly detection using machine learning techniques with applications in decision support systems utilizing artificial intelligence, as well as enabling smart manufacturing with IIoT, federated learning, and edge computing. He has published over 75 papers in peer-reviewed international journals and proceedings at international conferences, and edited three books with Springer Nature and Taylor & Francis.

In addition to his academic achievements, Dr. Tran has supervised 9 Ph.D. students and 3 postdocs, and is currently conducting a regional research project on Healthcare Systems with Federated Learning as the Project Coordinator (PI). He has been involved in 8 national and European projects and serves as an Expert and Evaluator for the Public Service of Wallonia (SPW-EER), Belgium, and the Natural Sciences and Engineering Research Council of Canada.

Dr. Tran's contributions to the field of artificial intelligence and data science have been recognized with numerous accolades, including the Award for Scientific Excellence (Prime d'Encadrement Doctoral et de Recherche) from the French Ministry of Higher Education, Research and Innovation for the period of 2021 to 2025 in recognition of his outstanding scientific achievements.

Since 2017, Dr. Tran has also held the position of Senior Scientific Advisor at Dong A University and the International Research Institute for Artificial Intelligence and Data Science (IAD), Danang, Vietnam, where he has been the International Chair in Data Science and Explainable Artificial Intelligence.

Contenu
Chapter 1: Introduction to Artificial Intelligence for Smart Manufacturing.- Chapter 2: Articial Intelligence for Smart Manufacturing in Industry 5.0: Methods, Applications, and Challenges.- Chapter 3: Quality control for Smart Manufacturing in Industry 5.0.- Chapter 4: Dynamic Process Monitoring Using Machine Learning Control Charts.- Chapter 5: Fault Prediction of Papermaking Process Based on Gaussian Mixture Model and Mahalanobis Distance.- Chapter 6: Multi-objective optimization of exible ow-shop intelligent scheduling based on a hybrid intelligent algorithm.- Chapter 7: Personalized pattern recommendation system of men's shirts.- Chapter 8: Ecient and Trustworthy Federated Learning-based Explainable Anomaly Detection: Challenges, Methods, and Future Directions.- Chapter 9: Multimodal machine learning in prognostics and health management of manufacturing systems.- Chapter 10: Explainable Articial Intelligence for Cybersecurity in Smart Manufacturing.- Chapter 11: Wearable technology for Smart Manufacturing in Industry 5.0.- Chapter 12: Benets of using Digital Twin for online fault diagnosis of a manufacturing system.