Quantitative Analysis of Milling Force for Chatter Detection Based on Wavelet and Hilbert-Huang transforms

Online detection of chatter occurrence in machining is a challenging but crucial task in high performance machining. In this paper, chatter detection in milling process based on nonlinear signal analysis of milling forces is investigated. Effect of various milling conditions (i.e. axial and radial depths of cut) on chatter detection is studied. Wavelet and Hilbert-Huang transforms are employed to detect chatter in milling based on the force signal. Wavelet transform is used to de-noise the signal. The intrinsic mode functions are obtained using empirical mode decomposition method, the mean value and standard deviation of the instantaneous amplitudes are used as chatter indices, and the critical values of these two parameters are determined. It is suggested that the chatter could not be detected without pre-processing of the signal. Two-level decomposition is used in this study according to natural frequency of the cutting tool, and it is shown that increasing levels of wavelet decomposition does not affect chatter detection significantly. Critical values of the mean value and standard deviation of the instantaneous amplitudes based on the second-level detail component of the radial force are obtained. It is observed that the critical values are suitable for different radial depths of cut