On-line Chatter Detection Using an Improved Support Vector Machine
On-line Chatter Detection Using an Improved Support Vector Machine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.