A method of recognizing tool-wear states based on a fast algorithm of wavelet transform

In this paper, a fast algorithm of wavelet transform was applied to recognize the tool wear states in automatic machining processing. The fast algorithm is based on a quadrature mirror filter (QMF) pair designed by Daubechies and can be implemented by a Mallatʹs tree algorithm. The expansion coefficients d(j, k), the output of the j times resolution, with time-frequency feature of the cutting force signals obtained by the fast algorithm of wavelet transform were used as recognizing parameters of tool wear states. The large number of experimental and analytical results have shown that the wavelet analysis based on the fast algorithm is more sensitive, more reliable and faster than the Fourier analysis for recognizing the tool wear states for the different cutting conditions in automatic turning.