宇航材料工艺2024，Vol.54Issue(5)：24-30,7.DOI:10.12044/j.issn.1007-2330.2024.05.003
基于GWO-ELM的高速铣削力预测模型研究
Research on Prediction Model of High-Speed Milling Force Based on GWO-ELM
摘要
Abstract
Aiming at the problem of high-speed milling force prediction of aerospace materials such as TC4 titanium alloy,7574 aluminum alloy,AISI304 stainless steel,and 45# steel in the process of high-speed milling,this paper introduced the grey wolf algorithm(GWO)to improve the extreme learning machine(ELM)model to build the high-speed milling force prediction model,the second-order multiple regression model was used to analyze and determine the number of hidden layer nodes,the prediction results were compared with seven prediction models and experimental results,such as BP,RBF,ELM,etc.The research results show that the number of hidden layer nodes of the high-speed milling force prediction model based on GWO-ELM can be determined by the second-order multiple regression model,the accuracy of the prediction model is 98.8％,and the determination coefficient of 0.988 71 is better than other prediction models.Therefore,the high-speed milling force prediction model based on GWO-ELM is feasible and accurate.The research results of this paper provide a reference for the determination of the number of hidden layer nodes of the GWO-ELM model and the selection of the high-speed milling force prediction model.关键词
宇航材料/高速铣削力/灰狼算法(GWO)/极限学习机(ELM)Key words
Aerospace materials/High-speed milling force/Grey wolf algorithm(GWO)/Extreme learning machine(ELM)分类
机械制造引用本文复制引用
仵景岳,尹凝霞,吕亮亮,麦青群..基于GWO-ELM的高速铣削力预测模型研究[J].宇航材料工艺,2024,54(5):24-30,7.基金项目
国家自然科学基金资助项目(51375099) （51375099）
广东省教育厅特色创新类项目(2017KTSCX086) （2017KTSCX086）
广东海洋大学科研启动费资助项目(E15168) （E15168）
