Online chatter detection in milling process based on fast iterative VMD and energy ratio difference

Abstract

Chatter is normally detected by time–frequency analysis, among which VMD has a perfect theoretical basis. However, the algorithm directly used for chatter detection may have low accuracy and efficiency due to the initial parameters that are difficult to determine. Therefore, a novel method based on fast iterative variational mode decomposition (FI-VMD) is proposed to solve these issues. The simulation process shows FI-VMD is almost equivalent to the extraction accuracy of VMD, but when the energy of chatter signal is very small, FI-VMD is better than VMD. Moreover, since the initial frequency is the true frequency in the FI-VMD iteration process, its calculation efficiency is much faster than that of VMD. Furthermore, the proposed energy ratio difference has strong robustness in stable cutting condition. When chatter occurs, it is more sensitive to chatter than energy ratio. Compared with an optimized VMD, the proposed method is more suitable for online chatter detection.