Tool Wear Monitoring Algorithm Based on SWT-DCNN and SST-DCNN

Abstract

The accurate monitoring of tool condition is of great significance to improve the machining quality and efficiency of parts, prolong the service life of tools and machine tools, and reduce the harm of manufacturing environment. In this dissertation, two methods based on synchronous compressed continuous wavelet transform and deep convolution neural network (SWT-DCNN) and synchronous compressed continuous wavelet transform and deep convolution neural network (SST-DCNN) are proposed to monitor tool wear. It is found that the recognition accuracy of SWT-DCNN method is 99.96%, and that of SST-DCNN method is 99.86%. The reason is that SWT method has good time-frequency energy aggregation. Compared with the SST-DCNN method, the recognition accuracy of the SWT-DCNN method is more stable. At the same time, it is found that the recognition rate of the SST-DCNN method in the process of normal tool monitoring is only 93.3%, which is easy to classify normal tools into the initial wear category. The experimental results show that the two methods proposed in this paper can effectively monitor the tool wear state.