Flank wear estimation in turning through wavelet representation of acoustic emission signals

This paper investigates a flank wear estimation technique in turning throug h wavelet representation of acoustic emission (AE) signals. It is known tha t the power spectral density? of AE signals in turning is sensitive to grad ually increasing flank wear In previous methods, the power spectral density of AE signals is computed from Fourier transform based techniques. 70 over come some of the limitations associated with the Fourier representation of AE signals for flank wear estimation, wavelet representation of AE signals is investigated This investigation is motivated by the superiority of the w avelet transform over the Fourier transform in analyzing rapidly changing s ignals such as AE, in which high frequency components are to be studied wit h sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals fbr flank wear estimation is in vestigated by conducting a set of turning experiments on AISI 6150 steel wo rkpiece and K68 (C2) grade uncoated carbide inserts. In these experiments, flank wear is monitored through AE signals. A recurrent neural network of s imple architecture is used to relate AE features to flank wear. Using this technique, accurate flank wear estimation results are obtained for the oper ating conditions that are within in the range of those used during neural n etwork training. These results compared to those of Fourier transform repre sentation are much superior. These findings indicate that the wavelet repre sentation of AE signals is more effective in extracting the AE features sen sitive to gradually increasing flank wear than the Fourier representation.