A study of diamond grinding wheel wear condition monitoring based on acoustic emission signals

The intelligent monitoring of the grinding wheel wear state has the potential to enhance several key aspects of grinding operations, including wheel utilization, wheel dressing, grinding efficiency, grinding quality and so on. In this paper, it is proposed as an acoustic emission signal-based monitoring method of electroplated diamond grinding wheel wear state for C/SiC composite material groove grinding. Firstly, the full-life wear experiment of electroplated grinding wheel grinding C/SiC composites was carried out, and the connection between the acoustic emission signal and the wear state of the grinding wheel was established by frequency domain and time-frequency domain characteristics. Secondly, the time domain, frequency domain and time-frequency domain features of the signals in the stable grinding stage of C/SiC composites were extracted by wavelet packet method. Finally, based on the extracted features, the Extreme Learning Machine (ELM) was optimized by Mayfly Algorithm (MA) to realize online monitoring and intelligent recognition of grinding wheel wear. The results show that the sample classification accuracy of this method is 96.67%, which can effectively identify the different states of grinding wheel wear.