On-line Chatter Detection Using an Improved Support Vector Machine

作     者：Changfu LIU Wenxiang ZHANG 

作者机构：School of Mechanical Engineering and AutomationNortheastern UniwersityShenyang 110819China Shanghai Yu Chen Industrial Co.LtdShanghai 201306China 

出 版 物：《Instrumentation》 (仪器仪表学报（英文版）)

年 卷 期：2019年第6卷第2期

页      面：2-7页

学科分类：080503[工学-材料加工工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

主　　题：On-line Chatter Detection Support Vector Machine Parameter Optimization Genetic Algorithms 

摘      要：On-line chatter detection can avoid unstable cutting through monitoring the machining *** order to identify chatter in a timely manner,an improved Support Vector Machine(SVM)is developed in this paper,based on extracted *** the SVM model,the penalty factor(e)and the core parameter(g)have important influence on the classification,more than from Kernel Functions(KFs).Hence,first the classification results are conducted using different *** two methods are presented for exploring the best *** chatter identification results show that the Genetic Algorithm(GA)approach is more suitable for deciding the parameters than the Grid Explore(GE)approach.