中国机械工程2025，Vol.36Issue(5)：1018-1027,1073,11.DOI:10.3969/j.issn.1004-132X.2025.05.013
基于变分模态滤波和注意力机制的重载机器人铣削系统颤振辨识方法
Chatter Identification Method for Heavy-duty Robotic Milling Systems Based on Variational Mode Filtering and Attention Mechanism
摘要
Abstract
A method was proposed for identifying chatters in heavy-duty robotic milling systems by integrating variational mode filtering with fixed parameters,envelope filtering and an attention mechanism network identification.Initially,variational mode filtering theory was applied to eliminate non-chatter signal components in the high-frequency ranges by optimally selecting a quadratic penalty.Then,to swiftly identify the current machining conditions,the envelope filtering method was em-ployed,leveraging signal time domain distribution and the frequency domain mapping law to remove the spindle speed-related signal components in the low-frequency ranges.Subsequently,a network identification model incorporating an attention mechanism was developed to identify preprocessed multi-temporal short-term signal segments for machining condition identification,followed by verifi-cation experiments on heavy-duty robotic milling systems.Experimental analysis results demonstrate that by eliminating non-chatter signals in the high-frequency ranges and spindle speed-related compo-nents in the low-frequency ranges,the accuracy of regenerative chatter identification is significantly enhanced,achieving an identification accuracy of 98.75%.Compared with alternative identification methods,the proposed method may effectively identify regenerative chatters during heavy-duty robot-ic milling processes,thus offering valuable technical support for future online chatter suppression of heavy-duty robotic milling.关键词
机器人铣削/颤振辨识/变分模态滤波/注意力机制Key words
robotic milling/chatter identification/variational mode filtering/attention mecha-nism分类
信息技术与安全科学引用本文复制引用
梁志强,刘志兵,陈司晨,杜宇超,刘宝隆,高子瑞,乐毅,肖玉斌,郑浩然,仇天阳..基于变分模态滤波和注意力机制的重载机器人铣削系统颤振辨识方法[J].中国机械工程,2025,36(5):1018-1027,1073,11.基金项目
国家自然科学基金(52375400) （52375400）
转化应用项目(2B0188E1,D44F9A65) （2B0188E1,D44F9A65）
