基于EMD-SVM的钛合金铣削过程刀具磨损监测  

Tool Wear Monitoring Based on EMD-SVM in Milling Process of Ti-Alloy

作　　者：谢振龙 岳彩旭[1] 刘献礼[1] 严复钢[1] 刘智博[1] 穆殿方 梁越昇 XIE Zhenlong;YUE Caixu;LIU Xianli;YAN Fugang;LIU Zhibo;MU Dianfang;LIANG Yuesheng(Key Laboratory of Advanced Manufacturing and Intelligent Technology,Ministry of Education,Harbin University of Science and Technology Harbin,150080,China;The George W.Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta,30332,USA)

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 [2]佐治亚理工学院乔治·W·伍德拉夫机械工程学院,亚特兰大30332

出　　处：《振动．测试与诊断》2022年第5期988-996,1039,共10页Journal of Vibration,Measurement & Diagnosis

基　　金：国家重点研发计划资助项目(2018YFB2002201);国家自然科学基金资助项目(51720105009)。

摘　　要：以硬质合金刀具铣削Ti-6Al-4V为研究对象,提出了一种基于经验模态分解(empirical mode decomposition,简称EMD)及支持向量机(support vector machine,简称SVM)的刀具磨损阶段识别方法。首先,将原始加速度信号及力信号分解为一系列模态分量(intrinsic mode function,简称IMF),选择了有效的IMF来组合一个新的信号;其次,计算新信号的多评价指标矩阵,将得到的多指标矩阵(Ikaz^(TM)、功率谱熵及均方根)作为输入特征向量,得到了基于线性分类器的刀具磨损识别模型;最后,将检测信号输入模型中进行识别,对刀具磨损阶段的识别精度达到了99.17%。EMD-SVM相较于SVM、BP神经网络及小波包-SVM模型,运算时间减少,运算精度提高。实验结果表明,该模式对钛合金铣削过程中的刀具磨损具有良好的识别效果,为刀具磨损状态的监测提供了一种新方法。In this paper,a tool wear stage identification method based on empirical mode decomposition(EMD)and support vector machine(SVM)is proposed based on cemented carbide tool milling Ti-6Al-4V.Firstly,the original acceleration signal and force signal are decomposed into a series of intrinsic mode function(IMF),and an effective IMF is selected to combine a new signal.Then the multi evaluation index matrix of the new signal is calculated.Taking the multi index matrix as the input feature vector,a tool wear recognition model based on linear classifier is established.Finally,the detection signal is input into the model for recognition,and the recognition accuracy of tool wear stage reaches 99.17%.Compared with SVM,back propagation(BP)and wavelet packet-SVM model,EMD-SVM has less operation time and higher precision.The experimental results show that the model has a good recognition effect on tool wear in titanium alloy milling process,and provides a new method as a reference for tool wear monitoring.

关 键 词：经验模态分解 支持向量机 刀具磨损监测 切削力信号 振动信号 

分 类 号：TH164[机械工程—机械制造及自动化] V262.33[航空宇航科学与技术—航空宇航制造工程]