On-Line Grinding Chatter Detection Based on Minimum Entropy Deconvolution and Autocorrelation Function

On-line detection of chatter is one of the key techniques to avoid the harmful effects caused by chatter in grinding process. The key to chatter detection is to capture reliable chatter features and thresholds. To achieve this, it is important to make clear and extract the essential characteristics of the grinding chatter signal, which has not yet been well studied. In this paper, we are going to investigate the essential characteristics of the grinding chatter signal and propose a new approach for on-line detection of grinding chatter. The proposed approach for on-line detection of grinding chatter is based on minimum entropy deconvolution and autocorrelation function, in which the minimum entropy deconvolution is employed to deconvolve the effect of transmission path, and further to restore the essential characteristics of the chatter signals. To eliminate the interference of the non-periodic impulse signals in the measured vibration signals, an autocorrelation function is introduced. Kurtosis is employed to indicate chatter according to the changes of the processed signal. The validity of the proposed method is demonstrated through the measured vibration signals obtained from grinding processes and the presented chatter detection index is independent from the grinding conditions with excellent detection accuracy and permissible computational efficiency. This demonstrates the effectiveness of proposed method in on-line implementation.