Reinforcement Learning-Based Cutting Parameter Dynamic Decision Method Considering Tool Wear for a Turning Machining Process

IF 5.3 3区工程技术 Q1 ENGINEERING, MANUFACTURING International Journal of Precision Engineering and Manufacturing-Green Technology Pub Date : 2024-01-25 DOI:10.1007/s40684-023-00582-9

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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.

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考虑刀具磨损的基于强化学习的车削加工过程切削参数动态决策方法

摘要切削参数优化被认为是节约能耗的有效途径。在加工过程中，刀具的磨损会随着工件数量的增加而变化，这对切削参数的决策有很大影响。然而，现有的大多数方法都是针对单一工件进行的，无法根据刀具磨损的动态变化选择最佳切削参数。为此，我们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，以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，并制定了一个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中的切削参数决策。所开发的 RLCPDD 通过比较案例研究进行了验证。案例研究表明(1) 可针对刀具的不同磨损情况确定不同的切削参数，以及 (2) 基于刀具磨损情况的切削参数动态决策与 PSO 算法相比，可进一步降低能耗、生产时间和生产成本，降幅分别为 3.87%、6.36% 和 6.83%。

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来源期刊

International Journal of Precision Engineering and Manufacturing-Green Technology ENGINEERING, MANUFACTURING-ENGINEERING, MECHANICAL

CiteScore

10.30

自引率

9.50%

发文量

审稿时长

5.3 months

期刊介绍： Green Technology aspects of precision engineering and manufacturing are becoming ever more important in current and future technologies. New knowledge in this field will aid in the advancement of various technologies that are needed to gain industrial competitiveness. To this end IJPEM - Green Technology aims to disseminate relevant developments and applied research works of high quality to the international community through efficient and rapid publication. IJPEM - Green Technology covers novel research contributions in all aspects of "Green" precision engineering and manufacturing.