一种基于模式识别的刀具磨损监测方法

Monitoring of Cutting-Tool Wear Based on Pattern Recognition

将刀具磨损状态的在线监测作为模式识别中的两类模式分类问题,从切削振动信号中抽取特征向量;根据投影原理构造了最佳特征平面.在此基础上提出了一种具有自学习功能的G(D)判别函数,对车削试验的磨损状态进行分类,确诊率达95％,漏诊率小于0.6％,判别时间少于15s,适用于在线监测。

The study of on-line tool wear monitoring is transformed into a problem of 2-class classifier design in statistical pattern recognition. Feature vector is extracted from vibration signal in the cutting process. The optimizing feature plane is formed according to projection theory. On the basis of this a discrimination function the G(D), having self-learning characteristics, has been proposed. It classifies situations of tool wear as the G(D). The results indicate that the recognition rate is 95%, the leak away rate less than 0.6%, discrimination time less than 15s on a microcomputer PC 286. The proposed method can provide an on-line monitoring of tool wear in the cutting process.