重庆理工大学学报2024，Vol.38Issue(5)：240-249,10.DOI:10.3969/j.issn.1674-8425(z).2024.03.026
数控铣削能耗预测及切削参数多目标优化研究
Research on energy consumption prediction and multi-objective optimization of cutting parameters in CNC milling
摘要
Abstract
To study the energy-saving optimization of CNC milling machine, this paper firstly designs the experimental scheme of CNC milling with 316L stainless steel as the machining object, and the experimental data analysis is made. Then, the experimental data are employed as the samples, and BP neural network is applied to build the prediction model of the energy consumption of CNC machine tools, and the structure of the BP neural network is optimized by using dung-beetle-roach optimization ( DBO ) algorithm to build the DBO-BP neural network based on the CNC machine tool energy consumption prediction model. By comparing the two models before and after optimization, the DBO-BP neural network model with higher prediction accuracy and stability and the machining cost are selected to build a multi-objective optimization model of milling parameters, and NSGA-Ⅱ is applied to solve the multi-objective optimization model of milling parameters to obtain the optimal solution set, and finally entropy right TOPSIS is applied to determine the optimal solution set. By comparing the specific energy consumption and machining cost before and after optimization, the optimized cutting parameters reduce the specific energy consumption and machining cost by 33.84％ and 5％ respectively. Our study shows the optimized cutting parameters achieve higher energy efficiency and save machining cost.关键词
数控铣削/DBO-BP神经网络/能耗预测模型/加工成本/NSGA-ⅡKey words
CNC milling/DBO-BP neural network/energy consumption prediction model/machining cost/NSGA-Ⅱ分类
机械制造引用本文复制引用
易望远,尹瑞雪,田应权,欧丽..数控铣削能耗预测及切削参数多目标优化研究[J].重庆理工大学学报,2024,38(5):240-249,10.基金项目
国家自然科学基金项目(51765010) （51765010）
