Analysis of tool wear condition based on logarithm energy entropy and wavelet packet transformation

This paper has a research on relationship between the degree of tool wear condition and the characteristic parameters of acoustic emission (AE) signal. First, through wavelet packet transformation, AE signals observed from different tool cutting stages are analyzed at different time-frequency scale. The main energy frequency scales are found. Then, the logarithmic energy entropy of tool data is calculated based on wavelet coefficients of the main frequency scale. The logarithmic energy entropy is a characteristic parameter which can represent the complexity of signal behaviors. In this paper, the degree of tool wear can be analyzed in details. The simulation results show that from comparison between tool AE signals measured from three different cutting procedure, it can be concluded that the logarithmic energy entropy increased significantly.