Artificial Intelligence Technologies and Methods for Green Manufacturing

Dear Colleagues,

Artificial intelligence offers new technologies and pathways for green manufacturing, demonstrating significant potential in areas such as energy consumption optimization, intelligent disassembly, and low-carbon process planning. This Special Issue explores how to leverage the latest artificial intelligence (AI) technologies and methods to enhance the potential of green manufacturing, and delves into the patterns of influence that multimodal data exerts on green manufacturing processes. The focus is on key challenges for AI in green manufacturing scenarios: knowledge graphs supporting decision-making and optimization, traceability in dynamic environments, and the development of compact models tailored to specific tasks. Researchers and industry practitioners are invited to submit original research and reviews to share the latest research findings and practical experiences regarding AI in the field of green manufacturing.

The goal of this Research Topic is to explore AI methods and technologies, using both solid theoretical development and practical importance to implement green manufacturing. The central theme of the proposed Research Topic is AI Technologies for Green Manufacturing, where intelligent analysis, control, and optimization are the focus areas, and broad aspects and issues will be carefully discussed. Topics to be covered include, but are not limited to, the following:

• Green design methods driven by AI, including knowledge graphs, intelligent evaluation, and generative design algorithms, etc.;
• Low-carbon manufacturing driven by large language models or optimization algorithms;
• AI optimization of green manufacturing processes, including intelligent inspection, process optimization, and virtual manufacturing, etc.;
• Green workshop scheduling driven by big data analysis and intelligent optimization;
• Custom language models driving low-carbon remanufacturing;
• Remanufacturing process planning driven by knowledge graphs and optimization algorithms;
• Reverse supply chain driven by AI.

Prof. Dr. Zhigang Jiang
Dr. Yan Wang
Guest Editors

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