A Tool Wear Predictive Model Based on SVM

摘要：

Tool wear monitoring is an integral part of modern CNC machine control. This paper presents a new tool wear predictive model by combination of workpiece surface texture analysis and support vector machine with genetic algorithm (SVMG). Firstly, the column projection method and the Gabor filter method are proposed to extract texture features of machined surfaces. Then, SVMG-based tool wear predictive model is constructed by learning correlation between extracted texture features and actual tool wear. The effectiveness of the proposed predictive model and corresponding tool wear monitoring system is demonstrated by experimental results from turning trials. After simulated and compared with the predictive model based on BP neural networks, the method shows much better performance on the predictive precision and the intelligent adjusting parameters.

关键词： Support Vector Machine Cutting Tool Wear Texture Analysis Genetic Algorithm

作者: Yiqiu Qian Jia Tian Libing Liu Yu Zhang Yingshu Chen

作者单位: Tianjin Sino-German Vocational Technical College, Tianjin, 300190 School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130 NANCHANG UNIVERSITY, 330031, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 1213-1217

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）