Abstract
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.
基金项目
Civil Aircraft Project(MJZ4-1N22)
National Natural Science Foundation of China(51975053)
Inversion and Application Project of Outcome(D44F9A65)
Inversion and Application Project of Outcome(2B0188E1)
Key R&D Program of Inner Mongolia(2022YFHH0121)
Basic Research Fund of Beijing Institute of Technology(2021CX01023)
NETL
NSTL
万方数据