We are witnessing rapid development in computational technologies and its applications in industry, leading to the 5th industrial revolution. Industry 5.0 is characterized by the synergies between machines and humans, with an aim to add value to production by creating personalized products able to meet customers' requirements. These intelligent manufacturing systems have been sought in various sectors (e.g. automobiles, power supplying, chemistry) to realize data-driven innovations for delivering highly customizable products and services faster, cheaper, better, and greener.
This book presents recent advancements in research, new methods and techniques, and applications of advanced computational technologies in intelligent manufacturing for modeling, simulating, optimization, decision making, and other typical issues in manufacturing processes. It stimulates the scientific exchange of ideas and experiences in the field of intelligent manufacturing applications. Researchers and practitioners alike will benefit from this book to enhance their understanding of Industry 5.0, which focuses on combining human creativity and craftsmanship with the speed, productivity, and consistency of AI systems. Real-world case studies in various fields and practical applications are provided in each chapter.
Introduction to Computational Techniques for Smart 1 Manufacturing in
Industry 5.0: Methods and Applications. Research and Application of Raw Paper
Quality Prediction Model for Cardboard Papermaking Process. Kriging Model
Based Greenhouse Gas Emissions Model of Papermaking Wastewater Treatment
Process. Peculiarities of BPG-Based Automatic Lossy Compression of Noisy
Images. Recommendation and Design of Personalized Garments based on
Intelligent Human-Product Interaction. A Probabilistic Neural Network-based
Approach to Garment Fit Level Evaluation in 3D Digitalized Environment.
Explainable Machine Learning based Control Charts for High-Dimensional
Non-Stationary Time Series Data in IoT Systems: Challenges, Methods, and
Future Directions. Monitoring the Ratio of Two Normal Variables and
Compositional Data: A Literature Review and Perspective. Energy Efficiency
Scheduling of Flexible Flow Shop Using Group Technology. Optimal Operation of
Wind-solar-thermal Synergy Considering Carbon Trading and Energy Storage
Systems. Adaptive Dempster-Shafer Theory for Evidence-based Trust Models in
Multiagent Systems. Optimization Model of Raw Material Selection for
Construction Material Manufacturing. Research on Fault Diagnosis of
Paper-making Industry based on Knowledge Graph. Research on the Construction
of Papermaking Process Model Based on Digital Twin. Index.
Kim Phuc Tran is a Senior Associate Professor of Artificial Intelligence and Data Science at the ENSAIT and the GEMTEX laboratory, University of Lille, France. He is an editor for several international journals such as IEEE Transactions on Intelligent Transportation Systems and Engineering Applications of Artificial Intelligence. His research interests include explainable and trustworthy Artificial Intelligence and its applications in Industry 5.0.
Zhenglei He is an Assistant Professor of Automation and Intelligent Manufacturing at the State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, China. He holds a Ph.D. degree in Computer Engineering, Automation and Signal Processing from University of Lille, France. His research focuses on digital twin, knowledge graph, modelling, simulation, and optimization via AI for sustainable manufacturing. He has published more than 30 papers in SCIE peer-reviewed international journals and conferences.
