Catastrophic tool failure detection based on acoustic emission signal analysis

Acoustic emission (AE) signal analysis is considered to be a very useful mean of on-line tool breakage detection. Many publications have proclaimed that catastrophic tool failure (CTF) causes an eminent peak in the AE signal. Therefore the magnitude of the AE(RMS) signal has been considered as a measure of the CTF. While strong bursts of AE signals similar to those arising from the CTF can be generated by tool engagement and disengagement in interrupted turning, this measure was found to be not always sensitive to the CTF. The aim of this paper is to present a method of the CTF detection in turning which uses symptoms other than the direct AERMS signal value taking into considerations the likely bursts that can be generated due to interruption. The method is based on the statistical analysis of the distributions of the AERMS signal. The kurtosis and the sum of the beta distribution parameters r and s were the main measures employed. They were found to be highly sensitive to tool chipping and breakage and have given promising results with regard to CTF detection.