The chatter identification in end milling based on combining EMD and WPD

Abstract

On-line detection of chatter in the cutting process can identify the chatter in time or before the chatter happens, so the initiative to change the cutting parameters can be taken to avoid chatter and improve the surface quality. At present, time-frequency analysis technology is performed to extract the time-frequency features of chatter by scholars. With respect to the modal aliasing problem in the process of empirical mode decomposition (EMD), the chatter identification method of combining EMD and wavelet packets decomposition (WPD) is proposed to eliminate the influence of modal aliasing. To fully extract the main features of signal, the intrinsic mode functions (IMFs) changing consistent with power spectrum or amplitude-frequency are selected for signal reconstruction. Then, WPD is used in the reconstructed signal. The two times reconstruction of signal is based on wavelet packet node with the maximum energy. The distribution of frequency and energy in the time domain is presented by Hilbert Huang Transform (HHT) spectrum, and the mean value and standard deviation of the HHT spectrum are extracted as the feature vectors. The chatter features can be extracted from the original simulation signal by this method. Three groups of experiments with different cutting depth which are on behalf of the three cutting conditions (stable cutting, slight chatter, and severe chatter) were carried out. More cutting tests were carried out under the same cutting condition. Experimental results show that this method can be used to effectively identify the chatter features in milling process.