Digital twin-based job shop anomaly detection and dynamic scheduling |
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| Affiliation: | 1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China;2. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden;3. Hubei Digital Manufacturing Key Laboratory, Wuhan University of Technology, Wuhan 430070, PR China;1. University of Chinese Academy of Sciences, Beijing 100049, PR China;2. Shenyang Institute of Computing Technology Chinese Academy of Sciences, Shenyang 110168, PR China;3. College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK;4. Institute of Software, China Industrial Control Systems Cyber Emergency Response Team, Beijing 100040, PR China;1. University of Chinese Academy of Sciences, Beijing 100049, PR China;2. Shenyang Institute of Computing Technology Chinese Academy of Sciences, Shenyang 110168, PR China;3. Shenyang CASNC Technology Co., Ltd., Shenyang 110168, PR China;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China;2. ShenYuan Hornors College, Beihang University, Beijing, 100191, China;4. Sandvik Coromant, Stockholm, 12679, Sweden;5. Digital Twin International Research Center, International Research Institute for Multidisciplinary Science, Beihang University, Beijing, 100191, China;1. Institute of Advanced Manufacturing and Intelligent Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China;2. Key Laboratory of CNC Equipment Reliability, Ministry of Education, School of Mechanical and Aerospace Engineering, Jilin University, Jilin 130012, China;3. Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Technology, Beijing University of Technology, Beijing 100124, China |
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| Abstract: | Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework. |
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