WPD和SVM-PSO在微铣刀磨损在线监测中的应用  

Application of WPD and SVM-PSO in Online Monitoring of Micro Milling Tool Wear

作　　者：王二化[1] 刘颉 WANG Erhua;LIU Jie(Changzhou City Lab of Intelligent Technology for Advanced Manufacturing Equipment,Changzhou College of Information Technology,Changzhou 213164,Jiangsu,China;School of Hydropower and Information Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]常州信息职业技术学院常州市高端制造装备智能化技术重点实验室,江苏常州213164 [2]华中科技大学水电与数字化工程学院,武汉430074

出　　处：《机械科学与技术》2022年第7期1076-1084,共9页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家973项目(2011CB706803);常州市高端制造装备智能化技术重点实验室(CM20183004);江苏省青蓝工程中青年学术带头人;常州信息职业技术学院“1+1+1”协同培育工程建设项目;常州信息职业技术学院科技创新团队项目(CCIT2021STIT010201)。

摘　　要：为提高微铣刀磨损状态的预测精度和计算效率,本文提出了一种基于小波包分解(Wavelet packet decomposition,WPD)和支持向量机-粒子群优化(Support vector machine-particle swarm optimization,SVM-PSO)的微铣刀磨损在线监测方法。首先根据刀具使用时长和磨损程度将微铣刀磨损分为初始磨损、轻度磨损、中度磨损、重度磨损和刀具失效5种状态;接着对采集到的振动信号进行WPD变换,提取小波包关键节点的能量比和小波包系数峭度作为磨损特征,并分析了不同切削参数对这2个特征的影响;最后利用SVM-PSO模型进行微铣刀磨损状态分类与预测。研究结果表明,和网格搜索法相比,本文提出的微铣刀磨损在线监测方法在计算精度和效率方面具有综合优势,可以为其它刀具磨损监测提供必要的理论基础和实践指导。In order to improve the prediction accuracy and calculation efficiency of tool wear state in the micro milling,an online monitoring method of tool wear in the micro milling based on the wavelet packet decomposition(WPD)and support vector machine-particle swarm optimization(SVM-PSO)is put forward.Firstly,the wear of tool in the micro milling can be divided into the five states:initial wear,light wear,medium wear,heavy wear and tool failure.Secondly,the collected vibration signals are transformed by using WPD,and the energy ratio and kurtosis of key nodes of wavelet packet are extracted as the wear features,and the influence of the different cutting parameters on the two features is analyzed.Finally,SVM-PSO model is used to classify and predict the wear state of tool in the micro milling.The results show that,comparing with the grid search method,the online wear monitoring method of tool in the micro milling proposed in this paper has comprehensive advantages in the calculation accuracy and efficiency,and can provide the necessary basis and guidance for monitoring the other tool wear.

关 键 词：微铣刀磨损 振动信号 小波包分解 支持向量机 粒子群优化 

分 类 号：TG54[金属学及工艺—金属切削加工及机床]