中国机械工程2017，Vol.28Issue(17)：2080-2089,2108,11.DOI:10.3969/j.issn.1004-132X.2017.17.011
基于谐波小波包和BSA优化LS-SVM的铣刀磨损状态识别研究
State Recognition of Milling Tool Wears Based on Harmonic Wavelet Packet and BSA Optimization LS-SVM
摘要
Abstract
Aiming at the problems of milling tool wear state recognitions,a state recognition method was proposed based on harmonic wavelet packet and LS-SVM.To overcome the band overlap-ping problems in traditional wavelet packet decompositions,the milling force signal energies of each bands were extracted in different wear states by harmonic wavelet packet,which were brought in multi-class LS-SVM classifier after normalizing,then the classification recognition of different cutting tool states was achieved.BSA was proposed to search the optimal values of the kernel functional pa-rameters and error penalty factors which affected the precision of the LS-SVM significantly.Experi-mental results show that harmonic wavelet packet is more effective and feasible than wavelet packet, and the proposed milling tool wear recognition method has higher accuracy.关键词
刀具磨损/谐波小波包/回溯搜索算法/最小二乘支持向量机Key words
tool wear/harmonic wavelet packet/backtracking search algorithm (BSA)/least squares support vector machine(LS-SVM)分类
机械制造引用本文复制引用
董彩云,张超勇,孟磊磊,肖鹏飞,罗敏,林文文..基于谐波小波包和BSA优化LS-SVM的铣刀磨损状态识别研究[J].中国机械工程,2017,28(17):2080-2089,2108,11.基金项目
国家自然科学基金资助项目(51575211,51421062) （51575211,51421062）
国家自然科学基金国际(地区)合作与交流项目(51561125002) （地区）
湖北省自然科学基金资助项目(2014CFB348) （2014CFB348）
高等学校学科创新引智计划资助项目(B16019) （B16019）
