LAUSR

Abstract Chatter is one of the most critical errors in the grinding process, the occurrence of which will directly degrade the ultimate geometrical surface quality of the workpiece. Most of the present grinding chatter detection methods are based on the energy level monitoring of indicators that constructed from time domain statistics or fault characteristic frequencies. However, the grinding parameters, e.g., grinding force, feed rate, speed, etc. are time varying during the grinding process, and traditional indicators relying on energy level suffer degradation in the grinding chatter detection. In order to overcome this problem, a new grinding chatter detection method under non-stationary working conditions is proposed in this paper. With the proposed method, the kurtosis possibility density function (KPDF) is first utilized to assess the impact characteristics of the vibration signal, and a larger central moment and long tail of KPDF is probably observed in case of grinding chatter. Then two similarity evaluation indicators, i.e., intersection area and correlation distance are newly constructed to measure the variation of KPDF by taking the average of healthy KPDFs as benchmark. After that, the k-means clustering method is applied to detect the grinding chatter fault. The effectiveness of the proposed method is successfully verified by both simulation and experiment under non-stationary working conditions. The results show that the proposed method achieves higher grinding chatter detection accuracy and more compact clustering than traditional methods.