Artificial Intelligence for Smart Manufacturing

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.

• Chapter 1: Introduction to Artificial Intelligence for Smart Manufacturing.- Chapter 2: Artificial 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 flexible flow-shop intelligent scheduling based on a hybrid intelligent algorithm.- Chapter 7: Personalized pattern recommendation system of men’s shirts.- Chapter 8: Efficient 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 Artificial Intelligence for Cybersecurity in Smart Manufacturing.- Chapter 11: Wearable technology for Smart Manufacturing in Industry 5.0.- Chapter 12: Benefits of using Digital Twin for online fault diagnosis of a manufacturing system.