基于改进EMD和GA-BPNN的机器人磨削颤振监测

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摘要工业机器人由于其灵活性，被广泛应用于机器人焊缝磨削任务中。但由于机器人的弱刚性，在焊缝磨削过程中系统容易发生颤振，因此对加工过程中的颤振监测是保证加工质量的基础。针对在加工振动信号处理过程中的模态混叠现象，提出了一种基于排列熵算法改进的经验模态分解方法，通过排列熵算法检测振动信号中的异常信号并剔除。通过相关系数法提取相关性最大的固有模态函数的能量熵作为特征值，同时提取方差、峰峰值、均方根和峭度四种时域特征。利用遗传算法优化BP神经网络（back propagation neural network，BPNN）建立颤振辨识模型，最后将提取的五种特征参数作为特征向量代入辨识模型中对加工状态进行监测。试验结果显示，提出的改进经验模态分解算法结合遗传算法优化的BP神经网络模型能够有效地对机器人焊缝磨削中的颤振进行监测。

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	刘伟
	刘旺
	曹大虎
	葛吉民
	万林林
	陈加

关键词 ：机器人磨削, 颤振监测, 改进经验模态分解, 遗传算法, BP神经网络

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

收稿日期: 2023-07-31 出版日期: 2024-05-15

引用本文:

刘伟，刘旺，曹大虎，葛吉民，万林林，陈加. 基于改进EMD和GA-BPNN的机器人磨削颤振监测[J]. 振动与冲击, 2024, 43(9): 131-138.
LIU Wei, LIU Wang, CAO Dahu, GE Jimin, WAN Linlin, CHEN Jia. Robot grinding chatter monitoring based on improved EMD and GA-BPNN. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(9): 131-138.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I9/131

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