Chatter Recognition of Robotic Grinding Process Based on SABO Optimized VMD and K-means++
Due to low stiffness characteristics,the robot is susceptible to chatter vibration during machi-ning.To address the issues of feature frequency extraction and recognition of chatter vibration,a subtrac-tion-average-based optimizer(SABO)is proposed to optimize key parameters in VMD,allowing for the selection and recombination of chatter-sensitive IMF components.Furthermore,a vibration recognition index based on the power spectral entropy difference(ΔPSE)is constructed,taking into account the spectral characteristics of the vibration signal.The K-means++algorithm is employed to distinguish different types of chatter vibrations.Experimental results demonstrate SABO-VMD-K-means++method can accurately i-dentify the types of chatter vibration in robot grinding processes,providing valuable guidance for chatter vi-bration monitoring in robot grinding operations.
robot grinding chattersubtraction-average-based optimizerfeature extractionchatter type i-dentification
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