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.

中文翻译：

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基于仿真与实验双驱动环境的多智能体强化学习切削参数优化方法

在保证产品表面质量的同时提高生产效率是制造商始终关注的焦点。切削参数优化是保证高效率、高质量生产的重要技术。本文提出了一种将多智能体强化学习与双驱动虚拟加工环境相结合的切削参数优化新方法。首先，针对生产过程中数据采集不完整的问题，提出了切削仿真和实验数据的特征提取、融合和生成模型。其次，定义了优化切削参数的马尔可夫决策模型，构建了由仿真和实验数据驱动的虚拟加工环境。第三，提出了一种称为 Q-MIX-MATD3 的新型多智能体强化学习方法，该方法将双延迟深度确定性策略梯度、价值函数分解和教师模型相结合，通过与模型交互来探索切割参数优化策略。虚拟加工环境。最后，在换向器生产线上验证了所提出的方法。此外，结果表明，仿真和实验共同驱动的虚拟加工环境精度提高了5%以上，响应效率提高了31%，基于Q-MIX-MATD3的切削参数优化方法将时间成本降低了98% %并达到了经典优化方法的优化效果。