Multi-objective Optimization Method of Stable Milling Process Parameters Based on BP Neural Network and Composite Cotes Integration

doi:10.3901/JME.2023.17.279

Abstract

Abstract: The regenerative chatter in the milling process seriously affects the surface quality and production efficiency of the workpiece. Accurately and efficiently identifying the stable region of the milling chatter and selecting the process parameters reasonably are the key steps to suppress the chatter and improve the production efficiency. At present, there is still a lack of systematic and complete solutions for the optimization of multi-objective milling process parameters considering stability constraints. Therefore, a multi-objective optimization method of stable milling process parameters is established based on composite Cotes integral and neural network combined with NSGA-Ⅱ genetic algorithm. Among them, a new milling stability region prediction method is proposed based on the composite Cotes integration method to obtain a two-dimensional stability lobe diagram (SLD). Convergence analysis shows that the new approach has a faster convergence rate. Considering the uncertainty of radial depth of cut, a neural network prediction model of milling stability region constructed by discrete three-dimensional SLD surfaces is obtained. Finally, taking the material removal rate and tool life as the efficiency and cost targets respectively, the NSGA-Ⅱ genetic algorithm is used to establish the optimization model of stable milling process parameters. Compared with the empirical method, the optimized milling scheme can improve the material removal rate by 8.4% and the tool life by 16.3%. The results show that not only can the machining quality be ensured by judging the milling stability more accurately, but also scientific theoretical guidance can be provided for high-efficiency and low-cost milling to determine the process parameters.

Key words: milling chatter, multi-objective parameter optimization, stability lobe diagram, neural network, genetic algorithm

CLC Number:

TH161

LOU Weida, QIN Guohua, WANG Huamin, WU Zhuxi. Multi-objective Optimization Method of Stable Milling Process Parameters Based on BP Neural Network and Composite Cotes Integration[J]. Journal of Mechanical Engineering, 2023, 59(17): 279-290.

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