Multi-agent reinforcement learning method for cutting parameters optimization based on simulation and experiment dual drive environment

• Published in: Mechanical systems and signal processing 2024-07, Vol.216, p.111473, Article 111473
• Main Authors: Li, Weiye, Hao, Caihua, He, Songping, Qiu, Chaochao, Liu, Hongqi, Xu, Yanyan, Li, Bin, Tan, Xin, Peng, Fangyu
• Format: Article
• Language: English
• Subjects: Cutting parameter optimization; Feature engineering; Multi-agent reinforcement learning; Teacher model; Virtual machining environment
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2024.111473

Improving production efficiency while ensuring product surface quality is a constant focus of manufacturers. Cutting parameter optimization is an important technique for ensuring high-efficiency and high-quality production. In this paper, a novel method for cutting parameter optimization that integrates multi-agent reinforcement learning with a dual-drive virtual machining environment is proposed. First, a feature extraction, fusion and generation model for cutting simulation and experimental data is proposed to solve the problem of incomplete data acquisition in the production process. Second, a Markov decision model for optimizing cutting parameters is defined, and a virtual machining environment driven by both simulation and experimental data is constructed. Third, a novel multi-agent reinforcement learning method called Q-MIX-MATD3, in which a twin delay deep deterministic policy gradient, value function decomposition and a teacher model are combined, is proposed to explore the cutting parameter optimization policy by interacting with the virtual machining environment. Finally, the proposed method is verified on a commutator production line. Moreover, the results show that the accuracy of the virtual machining environment driven by both simulation and experiment increases by more than 5 %, response efficiency increases by 31 %, and Q-MIX-MATD3-based cutting parameter optimization method reduces time cost by 98 % and achieves the optimization effect of the classical optimization method.