Robot grinding chatter monitoring based on improved EMD and GA-BPNN

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (9) : 131-138.

LIU Wei, LIU Wang, CAO Dahu, GE Jimin, WAN Linlin, CHEN Jia

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Abstract

Industrial robots are widely used in robotic weld seam grinding tasks due to their flexibility. However, due to the weak rigidity of the robot, the system is prone to chattering in the process of weld grinding, so the monitoring of chattering in the machining process is the basis for ensuring the machining quality. Aiming at the phenomenon of modal aliasing in the processing of vibration signals, an improved empirical modal decomposition method based on the arrangement entropy algorithm was proposed, and the abnormal signals in the vibration signals were detected and eliminated by the arrangement entropy algorithm. The energy entropy of the intrinsic mode function with the largest correlation was extracted as the eigenvalue by the correlation coefficient method, and the four time-domain features of variance, peak-to-peak, root-mean-square, and crag were extracted at the same time. Using genetic algorithm to optimize the BP neural network to establish the chatter recognition model, and finally the five extracted feature parameters were substituted into the recognition model as feature vectors to monitor the machining state. The experimental results show that the proposed improved empirical modal decomposition algorithm combined with the BP neural network model optimized by genetic algorithm can effectively monitor the chatter in robotic weld grinding.

Key words

robotic grinding / chatter monitoring / improved empirical mode decomposition / genetic algorithms / BP neural networks

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LIU Wei, LIU Wang, CAO Dahu, GE Jimin, WAN Linlin, CHEN Jia. Robot grinding chatter monitoring based on improved EMD and GA-BPNN[J]. Journal of Vibration and Shock, 2024, 43(9): 131-138

References

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