递归特征消除与极端随机树在铣刀磨损监测中的研究  

Application Research on Recursive Feature Elimination and Extra Trees in Milling Cutter Wear Monitoring

作　　者：刘献礼[1] 秦怡源 岳彩旭[1] 魏旭东[1] 孙艳明 郭斌[1] LIU Xianli;QIN Yiyuan;YUE Caixu;WEI Xudong;SUN Yanming;GUO Bin(Key Laboratory of Advanced Manufacturing and Intelligent Technology,Ministry of Education,Harbin University of Science and Technology,Harbin 150080,China)

机构地区：[1]哈尔滨理工大学先进制造智能化技术教育部重点实验室,哈尔滨150080

出　　处：《机械科学与技术》2023年第6期821-828,共8页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家重点研发项目(2019YFB1704800);黑龙江省优秀青年基金项目(YQ2019E029)。

摘　　要：针对金属铣削过程中刀具磨损监测问题,本文提出了一种基于递归特征消除和极端随机树相结合的刀具磨损监测模型。首先对力、振动和声发射信号的时域、频域特征进行提取,分别采用逻辑回归、分类与回归树、线性回归、线性判别分析作为递归特征消除的基模型进行特征降维。再利用处理后的特征对K近邻、支持向量回归、极端随机树模型进行训练,得出多种监测模型。通过对比刀具磨损拟合曲线图和分析评估结果的标准差,可得出基模型为分类与回归树的递归特征消除,与极端随机树算法相结合模型拟合度达到99.74%,评估结果的标准差为4.04。结果表明该方法能够实现对铣刀磨损的有效监测,从而提高零件加工质量。To solve the problem of tool wear monitoring in metal milling,a tool wear monitoring model is proposed based on recursive feature elimination and extra trees algorithm.Firstly,the time and frequency domain features of force,vibration and acoustic emission signals are extracted.Logistic regression,classification and regression tree,linear regression and linear discriminant analysis are used as base model of recursive feature elimination for feature dimension reduction.Then the processed features are used to train the K nearest neighbor,support vector regression and extra trees models,and a variety of monitoring models are obtained.By comparing the tool wear fitting curve and analyzing the standard deviation of the evaluation results,it can be concluded that the base model is the recursive feature elimination of classification and regression tree.The fitting degree of the model combined with the extreme random tree algorithm reaches 99.74%,and the standard deviation of the evaluation result is 4.04.The results show that this method can effectively monitor the wear of milling cutter,so as to improve the machining quality of parts.

关 键 词：递归特征消除 基模型 特征降维 极端随机树 刀具磨损监测 

分 类 号：TG714[金属学及工艺—刀具与模具] TP181[自动化与计算机技术—控制理论与控制工程]