ABSTRACT Cloud manufacturing (CMfg), as a manufacturing mode to realize resource collaboration and service sharing, can help enterprises reduce costs, increase efficiency and enhance competitiveness. Existing CMfg relies on historical service evaluation results as the basis for task matching and thus lacks effective methods for the precise assessment of the adaptability to current tasks and the sustainability for future endeavors; throughout the service process, deficiencies occur including a lack of real-time assessment, prediction, and optimization capabilities for service execution outcomes. Motivated by the shortcomings of existing CMfg, this article focuses on how to enhance the dynamism and timeliness of the CMfg service process. A novel digital twin (DT)-driven CMfg service model is proposed. Specifically, a framework and an operation mechanism of DT-driven CMfg service are proposed; three key technologies related to DT in CMfg service are proposed, including model migration based matching modeling technology, multi-agent reinforcement learning based CMfg service composition optimization, and DT data-driven CMfg service performance prediction, which provide a theory and method for the DT and CMfg integration construction. The experiment demonstrates that the proposed methods have good performance for the production of cast aluminum engine fan bracket and provide a holistic understanding of DT in CMfg service.
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