Method of Controlling Cutting Tool Wear Based on Signal Analysis of Acoustic Emission for Milling

In this paper, a tool wear diagnostic system based on the information contained in the signal of acoustic emission (AE) is considered. In the process, experiments on milling of steel billets 1035 were carried out, with the reference values of cutting force being dynamically monitored with the help of a Kistler 9257B multi-component dynamometer. Registration of the AE signal is performed by an LTR22 modular data acquisition system equipped with an OKTAFON 110 sensor. The method of useful signal filtering from the entire spectrum of AE is carried out using wavelet decomposition. Detection of necessary time periods from the decomposed milling AE signal is suggested for further analysis based on Fourier analysis.