A chatter detection method in milling based on gray wolf optimization VMD and multi-entropy features

In metal cutting processing, especially in the processing of low-rigidity workpieces, chatter is a crucial factor affecting many aspects such as surface quality, processing efficiency, and tool life. In this paper, a novel online chatter detection method for milling processes is proposed. In this method, firstly, periodic signal and noise parts are filtered by a comb filter and empirical mode decomposition (EMD), respectively. Then, signal reconstruction is performed on the intrinsic mode functions (IMFs) based on the Pearson correlation coefficient. GWO is applied to reconstruct the signal to obtain optimized parameters. Subsequently, the reconstructed signal is decomposed by VMD with the optimal parameters. To obtain rich chatter information frequency bands, the energy entropy characteristics of each order IMF are calculated, and the two-order IMFs with larger energy entropy are selected for reconstruction. Finally, the multi-scale permutation entropy (MPE) and multi-scale fuzzy entropy (MFE) of the reconstructed signal are calculated. According to the value range of entropy in each processing state, the optimal scale feature is selected. The analysis results show that the proposed method can effectively detect the milling processing state based on the optimal scale.