A data and knowledge-driven cutting parameter adaptive optimization method considering dynamic tool wear |
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| Affiliation: | 1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China;2. College of Engineering and Technology, Southwest University, Chongqing, 400715, China;1. School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, Shandong China;2. School of Mechanical Engineering, Shandong University, Jinan, Shandong, China;3. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China;1. State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China;2. Wuhu Robot Industry Technology Research Institute, Harbin Institute of Technology, Wuhu 241000, China;1. Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China;2. National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), China;3. Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education,Tianjin University, Tianjin 300072, China;1. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China;2. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Abstract: | Tool wear prediction is of significance to reduce energy consumption through cutting parameter optimization. However, the current studies ignore the effect of machine aging on the tool wear prediction model, and their cutting parameter optimization methods cannot cope with the dynamic change of tool wear in the machining process. Thus, a reinforcement learning-enabled integrated method of tool wear prediction and cutting parameter optimization is proposed for minimizing energy consumption and production time. Specifically, the multi-source heterogeneous data fusion-based (MHDF) tool wear prediction model considering machine aging is first proposed to obtain the tool wear of the cutting tool. Then, a Markov Decision Process is designed to model the cutting parameter optimization process, which can be utilized to determine the proper cutting parameters adapted to the dynamic change of tool wear. Finally, the proposed method is demonstrated by extensive comparative experiments, and the results show that: 1) The proposed tool wear prediction model eliminates the influence of machine aging on prediction accuracy and has better generalizability for the machining data under different machine aging conditions, and its testing accuracy reaches 96.09%. 2) The proposed optimization method can adapt to the dynamic change of tool wear and further reduce the energy consumption and production time by 6.72% and 8.60% compared to that of not considering tool wear. The computation time of the proposed method is reduced by an average of 71.80%. |
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