文献类型：期刊文献

中文题名：基于GWO-ELM的高速铣削力预测模型研究

英文题名：Research on Prediction Model of High-Speed Milling Force Based on GWOELM

作者：仵景岳[1];尹凝霞[1];吕亮亮[1];麦青群[1]

机构：[1]广东海洋大学机械与动力工程学院,湛江524088

年份：2024

卷号：54

期号：5

起止页码：24

中文期刊名：宇航材料工艺

外文期刊名：Aerospace Materials & Technology

收录：北大核心2023、CSTPCD、、CSCD2023_2024、北大核心、CSCD

基金：国家自然科学基金资助项目(51375099);广东省教育厅特色创新类项目(2017KTSCX086);广东海洋大学科研启动费资助项目(E15168)。

语种：中文

中文关键词：宇航材料;高速铣削力;灰狼算法(GWO);极限学习机(ELM)

外文关键词：Aerospace materials;High-speed milling force;Grey wolf algorithm(GWO);Extreme learning machine(ELM)

中文摘要：针对TC4钛合金、7574铝合金、AISI304不锈钢及45^(#)钢等宇航材料在高速铣削过程中的高速铣削力预测问题,引入基于灰狼算法(GWO)改进的极限学习机(ELM)模型构建高速铣削力预测模型,利用二阶多元回归模型分析确定隐含层节点数,预测结果与BP、RBF、ELM等七种预测模型和实验结果进行比较。研究结果表明:基于GWO-ELM的高速铣削力预测模型隐含层节点数可以利用二阶多元回归模型分析确定,预测模型的准确率为98.8%、决定系数达到0.98871优于其他预测模型,故基于GWO-ELM的高速铣削力预测模型具有可行性和准确性,该研究结果可为GWO-ELM模型隐含层节点数的确定及高速铣削力预测模型的选择提供参考与借鉴。

外文摘要：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 0.98871 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.