Intelligent scheduling of a feature-process-machine tool supernetwork based on digital twin workshop |
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| Affiliation: | 1. Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, 100124, China;2. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China;3. Machinery Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Technology, Beijing University of Technology, Beijing, 100124, China;1. School of Mechanical Engineering, Shandong University, Jinan, PR China;2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, Jinan, PR China;3. National Demonstration Center for Experimental Mechanical Engineering Education at Shandong University, Jinan, PR China;4. School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, PR China;5. School of Mechatronics Engineering, Shandong Jianzhu University, Jinan, PR China;1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China;2. Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China;1. LURPA, ENS Paris-Saclay, Université Paris-Saclay, 94235 Cachan, France;2. Airbus Central R&T, 92130 Issy-les-Moulineaux, France;1. Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau, China;2. Institute of Physical Internet, Jinan University (Zhuhai Campus), Zhuhai 519070, China;3. School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus), Zhuhai 519070, China;4. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China;5. Institute of the Belt and Road & Guangdong-Hong Kong-Macao Greater Bay Area, Jinan University, Guangzhou 510632, China;1. Hubei Key Laboratory of Modern Manufacturing and Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China;2. Hubei Digital Manufacturing Key Laboratory, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China;1. School of Mechanical Engineering, Southeast University, Nanjing 211198, China;2. Beijing Institute of Space Long March Vehicle, Beijing 100076, China;3. Beijing Spacecrafts Limited Company, Beijing 100094, China |
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| Abstract: | Modern manufacturing enterprises are shifting toward multi-variety and small-batch production. By optimizing scheduling, both transit and waiting times within the production process can be shortened. This study integrates the advantages of a digital twin and supernetwork to develop an intelligent scheduling method for workshops to rapidly and efficiently generate process plans. By establishing the supernetwork model of a feature-process-machine tool in the digital twin workshop, the centralized and classified management of multiple data types can be realized. A feature similarity matrix is used to cluster similar attribute data in the feature layer subnetwork to realize rapid correspondence of multi-source association information among feature-process-machine tools. Through similarity calculations of decomposed features and the mapping relationships of the supernetwork, production scheduling schemes can be rapidly and efficiently formulated. A virtual workshop is also used to simulate and optimize the scheduling scheme to realize intelligent workshop scheduling. Finally, the efficiency of the proposed intelligent scheduling strategy is verified by using a case study of an aeroengine gear production workshop. |
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| Keywords: | Supernetwork Digital twin workshop Information mapping Intelligent scheduling |
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