| Summary: | In the high-speed milling of aviation parts, due to the low stiffness of thin-walled structure, it is easy to produce chatter. Chatter leads to poor surface quality, dimensional error and reducing the service life of tools and machines.Therefore, a reliable detection method is needed to identify chatter.Aiming at the problem of chatter detection in the milling process of thin-walled structures, a chatter feature extraction method of thin-walled parts based on optimal variational mode decomposition and multi-scale sample entropy is proposed.Firstly, in order to solve the problem of parameter selection in variational modal decomposition, a parameter adaptive method based on genetic algorithm optimization and minimum permutation entropy is proposed. Then, the energy ratio of the decomposed signal is calculated as the principle of selecting IMF, so as to reconstruct the signal. In order to solve the problem that single scale sample entropy can not well reflect the characteristics of milling force signal when chatter occurs, multi-scale sample entropy is introduced to detect milling chatter.Finally, the experimental results show that the optimal variational modal decomposition algorithm can avoid the problem of difficult separation of chatter signals caused by modal aliasing. Multi-scale sample entropy is more conducive to chatter detection than single scale sample entropy. MSE of milling signal tends to decrease with the increase of scale factor, and MSE with scale factor of 10 is more conducive to chatter detection.
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