Milling chatter detection of thin-walled parts based on GA-SE-SCK-VMD and RCMDE

In the milling process, it is easy to produce chatter due to the low rigidity of the thin-walled structure, which leads to the deterioration of workpiece surface quality and reduces the service life of cutting tools and machine tools. Therefore, a new chatter detection method for thin-walled parts based on optimal variational mode decomposition (OVMD) and refined composite multi-scale dispersion entropy (RCMDE) is proposed in this paper. Firstly, to solve the problem that the decomposition effect of the variational mode decomposition (VMD) algorithm is greatly affected by its parameter, a genetic algorithm (GA) is used to iteratively optimize the parameter of the VMD algorithm, and a new index, square envelope spectral correlated kurtosis (SE-SCK), is introduced as the fitness function of the genetic algorithm. Then, the energy ratio of the decomposed signal is calculated as the principle of selecting sub-components, and the sub-components with rich chatter information are selected for signal reconstruction. To solve the problem that the multi-scale dispersion entropy (MDE) will miss some information in the multi-scale process, RCMDE is introduced to detect milling chatter. Finally, the experiment of the variable cutting depth in side milling of titanium alloy thin-walled parts is carried out. The experimental results show that the OVMD algorithm proposed can solve the problem of difficult separation of chatter frequency bands caused by mode aliasing and lay a foundation for subsequent chatter feature extraction. RCMDE is more conducive to chatter detection than the single-scale DE when the scale factor is 4. The distinguishing effect of RCMDE on the machining state is more than 50% higher than that of MDE when the scale factor is 4.