Monitoring of tool wear states in turning based on wavelet analysis

In this paper, a novel signal processing tool, the wavelet transform, was applied to monitor the flank wear states in turning. The wavelet transforms were implemented by FWT (fast wavelet transform) based on a QMF(quadrature mirror filter). The expansion coefficients d(j, k) with time-frequency feature obtained by FWT were used as recognition parameters of the flank wear states. The dynamic characteristics of the cutting force signals were analyzed separately using the wavelet transform and the Fourier transform. The abilities of these two transforms for analyzing and recognizing the flank wear states were compared. The experimental and analytical results show that when monitoring the flank wear slates during turning, the wavelet analysis is more sensitive, more reliable and faster than the Fourier analysis.