Online chatter detection in milling process based on VMD and multiscale entropy

Chatter is a kind of self-excited vibration, which always has a negative impact on production efficiency. In this paper, a novel online chatter detection method for milling processes is proposed. In this method, firstly, the spindle revolution period component is extracted by angular synchronous averaging (ASA) the vibration signals generated in different cutting conditions. Then, the residual part related to chatter is calculated by subtracting the periodic component. Subsequently, the filtered signal is decomposed into a set of intrinsic mode functions (IMFs) using variational mode decomposition (VMD) to obtain chatter information. Finally, the multiscale permutation entropy (MPE) and multiscale power spectral entropy (MPSE) of the selected IMFs are calculated, and Laplacian score (LS) for feature selection is applied to select the optimal sensitive scale features with generalization. Online chatter detection based on selected sensitive scale features by splitting signal up into overlapping frames in milling process. The analysis results show that the proposed method can effectively detect the chatter under stable cutting conditions and variable cutting conditions.