From simple digital twin to complex digital twin part II: Multi-scenario applications of digital twin shop floor |
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| Affiliation: | 1. School of Software Engineering, Huazhong University of Science and Technology, Hubei, PR China;2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Hubei, PR China;1. Sino-French Engineering School, Beihang University, China;2. School of Mechanical Engineering and Automation, Beihang University, China;3. Beige Institute, China;4. Jingdezhen Research Institute of Beihang University, China;5. Ningbo Research Institute of Beihang University, China;1. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, United States;2. School of Industrial and Systems Engineering, Atlanta, GA 30332, United States;1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China;2. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China;3. Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China |
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| Abstract: | The shop floor has always been an important application object for the digital twin. It is well known that production, process, and product are the core business of the shop floor. Therefore, the digital twin shop floor covers multi-dimensional information and multi-scale application scenarios. In this paper, the digital twin shop floor is constructed according to the modeling method of the complex digital twin proposed in Part I. The digital twin shop floor is firstly divided into several simple digital twins that focus on scenarios of different scales. Two simple application scenarios are constructed, including tool wear prediction and spindle temperature prediction. Main functions in different application scenarios, such as data acquisition, data processing, and data visualization, are implemented and encapsulated as components to construct simple digital twins. Secondly, ontology models, knowledge graphs, and message queues are used to assemble these simple digital twins into the complex digital twin shop floor. And two complex application scenarios are constructed, including machining geometry simulation considering spindle temperature and production scheduling considering tool wear. The implementation of the complex digital twin shop floor demonstrates the feasibility of the proposed modeling method. |
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| Keywords: | Digital twin Shop floor Intelligent manufacturing Deep learning |
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