Investigation on Digital Twin Enterprises with an Architecture-Driven Modeling Approach in Cloud Manufacturing
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Abstract:
The cloud manufacturing required by an increasingly dynamic competitive industrial environment results in the formation of “digital twin enterprises”. In order to investigate the digital twin enterprise with an architecture-driven modeling approach, this paper first introduces the essential elements of the digital twin enterprise architecture. These elements are converged by the multi-agent technology to form digital resources that can be dynamically connected to the cloud manufacturing platform. Then, by combining the digital twin technology, the general activities of enterprises are further refined to form the main operation flow of the architecture. Finally, as the digital twin technology is the core supporting technology for cloud manufacturing, this paper conducts a detailed analysis of the process of digital twin enterprises participating in cloud manufacturing. Through the application of digital twin technology, enterprises can reasonably control their production resources, and the internal production data flow can be further utilized to optimize the production process. Meanwhile, the manufacturing resources of enterprises can be accessed to the networked manufacturing platform in real time, and personalized orders can be obtained and further exploited according to the enterprise’s real status. The digitalization degree of enterprises has been greatly improved by constructing the multi-agent operation framework and optimizing the process of enterprises participating in networked production activities.
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This work was supported in part by the National Key Research and Development Program of China (No.2018YFE0177000) and the National Natural Science Foundation of China (No.52075257).
NIE Qingwei, ZHU Haihua, TANG Dunbing, WANG Liping, SONG Jiaye, LIU Changchun. Investigation on Digital Twin Enterprises with an Architecture-Driven Modeling Approach in Cloud Manufacturing[J]. Transactions of Nanjing University of Aeronautics & Astronautics,2023,(1):106-114