首页|Early chatter identification based on optimized VMD with multi-band information fusion and compression method in robotic milling process

Early chatter identification based on optimized VMD with multi-band information fusion and compression method in robotic milling process

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Undesirable self-excited chatter has always been a typical issue restricting the improve-ment 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.Com-pared 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 a are optimized based on the dominant component of frequency scope par-tition 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 col-lection 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 indi-cator 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.

Robotic millingChatter detectionVariational mode decompo-sitionInformation fusion and compressionChatter featur

Sichen CHEN、Zhiqiang LIANG、Yuchao DU、Zirui GAO、Haoran ZHENG、Zhibing LIU、Tianyang QIU、Xibin WANG

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School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China

Beijing Institute of Technology Chongqing Innovation Center,Chongqing 401120,China

Civil Aircraft ProjectNational Natural Science Foundation of ChinaInversion and Application Project of OutcomeInversion and Application Project of OutcomeKey R&D Program of Inner MongoliaBasic Research Fund of Beijing Institute of Technology

MJZ4-1N2251975053D44F9A652B0188E12022YFHH01212021CX01023

2024

10.1016/j.cja.2023.10.009

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(6)
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