Abstract

Cutting parameter optimization is considered as an effective way for energy consumption saving. In the machining process, the tool wear of cutting tools varies with the rise of the number of workpieces, which has a significant effect on cutting parameters decisions. However, most of existing approaches are conducted for a single workpiece, and cannot select the optimal cutting parameters based on the dynamic changes in tool wear. To this end, a reinforcement learning-based cutting parameter dynamic decision (RLCPDD) method is developed for each workpiece adaptive to the change of tool wear. Specifically, the correlation between the energy consumption, cutting parameters, and tool wear is analyzed, and a multi-objective optimization model considering tool wear is formulated. A Markov Decision Process (MDP) can be used for designing the decision-making of cutting parameters for the machining process. The developed RLCPDD is validated by comparative case study. The case study indicates that: (1) the different cutting parameters can be determined for the different tool wear of cutting tool, and (2) the dynamic decision of cutting parameters based on tool wear can further reduce energy consumption, production time, and production cost by 3.87%, 6.36%, and 6.83% compared with the PSO algorithm.

中文翻译：

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基于强化学习的考虑刀具磨损的车削加工切削参数动态决策方法

摘要

切削参数优化被认为是节能的有效途径。在加工过程中，刀具的磨损随着工件数量的增加而变化，这对切削参数的决定有显着影响。然而，现有方法大多针对单个工件，无法根据刀具磨损的动态变化选择最佳切削参数。为此，针对每个工件开发了一种基于强化学习的切削参数动态决策（RLCPDD）方法，自适应刀具磨损的变化。具体来说，分析了能耗、切削参数和刀具磨损之间的相关性，并建立了考虑刀具磨损的多目标优化模型。马尔可夫决策过程（MDP）可用于设计加工过程的切削参数决策。所开发的 RLCPDD 通过比较案例研究得到了验证。案例研究表明：（1）可以针对刀具不同的刀具磨损情况确定不同的切削参数；（2）根据刀具磨损情况动态决定切削参数可以进一步降低能耗、生产时间和产量。与PSO算法相比，成本分别降低了3.87%、6.36%和6.83%。