A digital twin-driven approach towards traceability and dynamic control for processing quality |
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| Affiliation: | 1. School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, China;2. Jiangsu Provincial Key Laboratory of Advanced Manufacturing for Marine Mechanical Equipment, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, China;3. School of Mechanical Engineering, Southeast University, Nanjing 211189, China;1. Department of Construction Management, Louisiana State University, Baton Rouge 70803, USA;2. Department of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge 70803, USA;1. SQC & OR Unit, Indian Statistical Institute, Mumbai, 400020, India;2. Shailesh J. Mehta School of Management, IIT Bombay, 400076, India;3. Department of Mechanical Engineering, Jadavpur University, Kolkata, 700032, India;1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region;2. Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, China;3. Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, HungHom, Hong Kong Special Administrative Region;4. School of Aviation, UNSW Sydney, Kensington, NSW 2052, Australia;5. School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, China;1. School of Economics and Management, Tongji University, Shanghai 200092, PR China;2. Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, PR China;1. Key Laboratory of Industrial Engineering and Intelligent Manufacturing, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, China;2. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing 100854, China;3. Innovation Center for Liquid Rocket Engine Digital Research and Development, CNSA |
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| Abstract: | Processing quality is the basis for ensuring product quality, and reflects the development needs and application value of realizing intelligent manufacturing. Aiming at the low efficiency of quality problems traceability, poor timeliness and unpredictability of quality control in the machining process, digital twin technology can provide a new intelligent solution based on interaction and integration between physical workshop and virtual workshop. Therefore, a digital twin-driven approach towards traceability and dynamic control for processing quality is proposed in the paper. Firstly, a Bayesian network model for the analysis of factors affecting processing quality (BN_PQ) is introduced, which determines the relevance and influence weight of each factor to processing quality. Secondly, in order to integrate multi-source heterogeneous data to trace the processing quality, a multi-level scalable information model and association mechanism are established. Moreover, the construction method of the IoT system in manufacturing unit for dynamic control of processing quality are introduced, in which the collection method of real-time data is discussed. The contents of digital twin data for processing quality constraints (DTD_PQ) and the management method are elaborated. Then, the digital twin-driven dynamic control method of processing quality is proposed. The conceptual model of the digital twin database and the operating logic for dynamic control of processing quality are described in detail. Finally, the interactive operation and core technologies of DTD_PQ towards traceability and dynamic control of processing quality are analyzed. By choosing examples of machining the connecting rod of diesel engine and the prototype system that has been developed, the effectiveness of the proposed method is verified. |
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| Keywords: | Digital twin Processing quality Traceability and dynamic control IoT system Simulation optimization |
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