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Undesirable self-excited chatter has always been a typical issue restricting the improvement of robotic milling quality and efficiency. Sensitive chatter identification based on processing signals can prompt operators to adjust the machining process and prevent chatter damage. Compared with the traditional machine tool, the uncertain multiple chatter frequency bands and the band-moving of the chatter frequency in robotic milling process make it more challenging to extract chatter information. This paper proposes a novel method of chatter identification using optimized variational mode decomposition (OVMD) with multi-band information fusion and compression technology (MT). During the robotic milling process, the number of decomposed modes k and the penalty coefficient α are optimized based on the dominant component of frequency scope partition and fitness of the mode center frequency. Moreover, the mayfly optimization algorithm (MA) is employed to obtain the global optimal parameter selection. In order to conquer information collection about the uncertain multiple chatter frequency bands and the band-moving of the chatter frequency in robotic milling process, MT is presented to reduce computation and extract signal characteristics. Finally, the cross entropy of the image (CEI) is proposed as the final chatter indicator to identify the chatter occurrence. The robotic milling experiments are carried out to verify the proposed method, and the results show that it can distinguish the robotic milling condition by extracting the uncertain multiple chatter frequency bands and overcome the band-moving of the chatter frequency in robotic milling process.
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