Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method

Abstract

Vibrational signals resulting from tool wear have non-linear and non-stationary features. It is also difficult to acquire large numbers of typically worn samples in practice. In this work, a method of predicting the wear of milling tools is proposed based on ensemble empirical mode decomposition (EEMD) and the use of a support vector machine (SVM). The EEMD method is used to decompose the original non-stationary vibration acceleration signals into several stationary intrinsic mode functions (IMFs). The energies of the signals in these different frequency bands change when the tool is worn. Thus, the tool wear state can be identified by calculating the EEMD energies and energy entropies of the different vibrational signals. The correlation coefficients between the IMF components and original signal were calculated and wear-sensitive IMFs chosen. A SVM is then established by considering the energy features extracted from a number of wear-sensitive IMFs that contain primary information on tool wear. These are considered as the inputs to judge the wear state of the tool. The results show that the method is capable of predicting the wear state of the milling tool to good effect. Furthermore, the predictions made using an LS-SVM based on EEMD method are more accurate than those made using FFT, Wavelet analysis and EMD methods.