基于切削声发射信号测量的刀具磨损状态判别

针对刀具磨损状态判别方法在变化的加工条件下判别正确率低的问题,通过实时采集刀具的切削声发射信号,提出了一种自适应获取声发射信号中刀具磨损状态特征的方法和基于磨损状态特征数据序列之间灰色关联分析结果的刀具磨损状态判别方法。以4把WNMG080408-TM T9125型号车刀在ZCK20数控车床上进行了车刀的切削磨损实验和磨损状态判别,实验结果表明:该方法能够自适应获取车刀的磨损状态特征,车刀的磨损状态判别结果与实际相符,具有较高的判别正确率。

Tool Wear State Recognition Based on Cutting Acoustic Emission Signal Measurement

Aiming at the low recognition rate under changing processing conditions of the existing tool wear state recognition methods, according to real-time acquisition of acoustic emission signal, an adaptive tool wear state features extraction method from acoustic emission signal and a tool wear state recognition method based on grey relational analysis between wear state feature data sequences are proposed. Experiment with four WNMG080408-TM T9125 type turning tools on ZCK20 digital controlled lathe was conducted and tool wear state recognition was implemented, the results show that the proposed methods are able to acquire the turning tools’ wear state feature effectively and adaptively, and the tools wear state recognition results are consistent with the actual condition, and a high recognition rate is achieved.