Milling chatter identification by optimized variational mode decomposition and fuzzy entropy

Chatter is a self-excited vibration that reduces the quality of the workpiece surface and accelerates tool wear, it is a phenomenon that is difficult to monitor and control. In order to achieve high-performance manufacturing, a reliable online monitoring system for chatter needs to be developed. This paper proposes a chatter identification method based on optimized variational modal decomposition (OVMD) and fuzzy entropy to achieve online monitoring of chatter accurately. The method embeds the whale optimization algorithm (WOA) into the VMD method, and the chattering features are extracted for both simulated and experimental signals to achieve the optimal decomposition of the force signal in a rapid way. In order to solve the problem that sample entropy and approximate entropy are not sensitive to chatter detection, fuzzy entropy is introduced to monitor the chatter feature components. Experimental results show that the fuzzy entropy is particularly sensitive to the occurrence of chatter in specific embedding dimensions, and this method can monitor the dynamics of milling with higher sensitivity and stability.