Collection

Computational Techniques in Intelligent Manufacturing Systems

Submission status
Closed
In the context of Industry 4.0, intelligent manufacturing systems have attracted various industrial sectors (e.g. automobile, power supplying, chemistry) in order to realize data-driven innovations for delivering highly customizable products and services faster, cheaper, better, and greener. In an intelligent manufacturing system, computational techniques constitute a key issue for providing efficient decision support tools based on data measured through Internet of Thing and ubiquitous sensing mechanism. In complex industrial production scenarios, these computational techniques enable to enhance the quality of products and their manufacturing processes as well as sustainability (i.e. minimization of environmental and social impacts), and reduce uncertainties related to human factors. This special issue aims to collect the latest scientific contributions on computational techniques applied to intelligent manufacturing systems (concepts, methodologies, algorithms and applications) in order to offer a systematic overview of this emerging research field and provide innovative interdisciplinary and industry-oriented approaches.

Editors

  • Xianyi Zeng Xianyi Zeng , 

    Xianyi Zeng

    Xianyi Zeng received his Ph.D. degree from the Centre d’Automatique, Université des Sciences et Technologies de Lille, France, in 1992. He is currently a Full Professor of ENSAIT, Roubaix, France, and director of the GEMTEX National Laboratory. He is the Associate Editor of International Journal of Computational Intelligence System and Journal of Fiber Bioengineering and Informatics, also a Senior Member of IEEE. He has organized 12 international conferences and workshops since 1996.

  • Yi Man Yi Man  &

    Yi Man

    Yi Man received his Ph.D. degree on Chemical Engineering from South China University of Technology. He is currently an associate professor of South China University of Technology. He serves as the Associate Editor of Journal of Cleaner Production (Elsevier). He has engaged in the teaching and research work in the scientific field of artificial intelligence-based energy engineering for a long time. He has published more than 50 papers on AI-based chemical engineering with focus on methodological aspects of the algorithm’s development and application.

  • Zhenglei He Zhenglei He

    Zhenglei He

    Zhenglei He received his Ph.D. degree on automation and production from the University of Lille, France, in 2020 and serves as assistant professor at South China University of Technology. His research mainly focuses on knowledge and data collaboratively driven cleaner production and intelligent manufacturing, decision support and optimization with regard to the complicate system issues in the process industry, computational intelligence for sustainable production systems.

Articles (10 in this collection)