Optimization method of CNC milling parameters based on deep reinforcement learning

被引：1
|
作者：
Deng Q.-L. [1 ]
Lu J. [2 ]
Chen Y.-H. [1 ]
Feng J. [1 ]
Liao X.-P. [1 ,3 ]
Ma J.-Y. [1 ,3 ]
机构：
[1] College of Mechanical Engineering, Guangxi University, Nanning
[2] Department of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou
[3] Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology, Guangxi University, Nanning
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2022年 / 56卷 / 11期
关键词：
back propagation neural network; deep reinforcement learning; milling; multi-objective optimization; processing parameter;
D O I：
10.3785/j.issn.1008-973X.2022.11.005
中图分类号：
学科分类号：
摘要：
A deep reinforcement learning-based optimization method for CNC milling machining parameters was proposed to improve the machine tool effectiveness and the machining efficiency in CNC machining, and the applicability of deep reinforcement learning to machining parameters optimization problems was explored. The combined cutting force and material removal rate were selected as the optimization objectives of effectiveness and efficiency. The optimization function of combined cutting force and milling parameters were constructed using genetic algorithm optimization back propagation neural network (GA-BPNN) and the optimization function of material removal rate was established using empirical formulas. The competing network architecture (Dueling DQN) algorithm was applied to obtain Pareto frontier for combined cutting force and material removal rate multi-objective optimization and the decision solution was selected from Pareto frontier by combining the superior-inferior solution distance method and the entropy value method. The effectiveness of the Dueling DQN algorithm for machining parameter optimization was verified based on milling tests on 45 steel. Compared with the empirically selected machining parameters, the machining solution obtained by Dueling DQN optimization resulted in 8.29% reduction of combined cutting force and 4.95% improvement of machining efficiency, which provided guidance for the multi-objective optimization method of machining parameters and the selection of machining parameters. © 2022 Zhejiang University. All rights reserved.
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页码：2145 / 2155
页数：10
相关论文
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