基于自适应变分模态分解与功率谱熵差的机器人铣削加工颤振类型辨识

doi:10.3901/JME.2023.09.090

机械工程学报 ›› 2023, Vol. 59 ›› Issue (9): 90-100.doi: 10.3901/JME.2023.09.090

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基于自适应变分模态分解与功率谱熵差的机器人铣削加工颤振类型辨识

孙朝阳¹, 彭芳瑜^1,2, 唐小卫¹, 闫蓉¹, 辛世豪¹, 吴嘉伟¹

1. 华中科技大学机械科学与工程学院武汉 430074;
2. 华中科技大学智能制造装备与技术全国重点实验室武汉 430074

收稿日期:2022-05-04 修回日期:2022-11-12 出版日期:2023-05-05 发布日期:2023-07-19
通讯作者: 唐小卫(通信作者),男,1985年出生,博士,副教授,硕士研究生导师。主要研究方向为机器人铣削加工动力学、误差测量和精度控制。E-mail:txwysxf@126.com E-mail:txwysxf@126.com
作者简介:孙朝阳,男,1998年出生。主要研究方向为机器人铣削加工动力学。E-mail:m202070588@hust.edu.cn彭芳瑜,男,1972年出生,博士,教授,博士研究生导师。主要研究方向为数控加工技术、机器人加工和智能制造等。E-mail:pengfy@hust.edu.cn;闫蓉,女,1973年出生,博士,教授,博士研究生导师。主要研究方向为多轴数控加工。E-mail:yanrong@hust.edu.cn
基金资助:
国家自然科学基金(52175463, U20A20294)和中央高校基本科研业务费专项资金(2020kfyXJJS066)资助项目。

Robotic Milling Chatter Types Detection Based on Adaptive VariationalMode Decomposition and Difference of Power Spectral Entropy

SUN Zhaoyang¹, PENG Fangyu^1,2, TANG Xiaowei¹, YAN Rong¹, XIN Shihao¹, WU Jiawei¹

1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074;
2. State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074

Received:2022-05-04 Revised:2022-11-12 Online:2023-05-05 Published:2023-07-19

摘要/Abstract

摘要： 机器人铣削加工存在模态耦合颤振和再生颤振现象,有效地进行机器人铣削加工颤振类型的辨识是进行颤振精准抑制和保证加工质量的基础。为此,提出一种基于自适应变分模态分解与功率谱熵差的颤振类型辨识(AVMD-ΔPSE)方法。通过分析机器人铣削加工颤振特性和主导模态,将机器人铣削颤振分为机器人结构模态主导的模态耦合颤振和刀具-主轴结构模态主导的再生颤振两种类型。为了提取颤振敏感子信号,利用自适应变分模态分解方法对原始信号进行分解,根据功率谱熵和频率消除算法设计功率谱熵差颤振类型辨识指标,结合多组试验数据采用高斯混合模型自适应地确定辨识指标最佳分类阈值。颤振辨识试验表明机床铣削加工颤振辨识方法运用于机器人铣削加工中仅能识别颤振却无法区分不同的颤振类型,而AVMD-ΔPSE方法能准确有效地辨识和区分机器人铣削加工中的模态耦合颤振和再生颤振,为机器人铣削颤振的针对性抑制提供理论指导。

关键词: 机器人铣削颤振, 颤振类型辨识, 自适应变分模态分解, 功率谱熵差

Abstract: Robotic milling exists both mode coupling chatter and regenerative chatter. Effective chatter types detection in robotic milling is the basis for accurate chatter suppression and guarantee of machining quality. Therefore, a new method for chatter types detection based on adaptive variational mode decomposition and difference of power spectral entropy (AVMD-ΔPSE) is proposed. By analyzing the chatter characteristics and dominant modes of robotic milling, the chatter of robotic milling is divided into two types:mode coupling chatter dominated by robot mode and regenerative chatter dominated by tool-spindle mode. In order to extract the sensitive chatter signals, the original signals are decomposed by adaptive variational mode decomposition method. According to the power spectral entropy and frequency elimination algorithm, the chatter types detection indicators about difference of power spectral entropy is designed, and the optimal classification thresholds of the identification indicators are determined by using gaussian mixture model adaptively combined with many experimental data. Chatter detection experiments show that milling machine chatter detection method is applied to robotic milling can only identify chatter cannot distinguish between different types of chatter, and AVMD-ΔPSE method can accurately recognize and distinguish between mode coupling chatter and regenerative chatter, which provides theoretical guidance for the suppression of chatter in robotic milling.

Key words: robotic milling chatter, chatter types detection, AVMD, difference of power spectral entropy

中图分类号:

TH113

孙朝阳, 彭芳瑜, 唐小卫, 闫蓉, 辛世豪, 吴嘉伟. 基于自适应变分模态分解与功率谱熵差的机器人铣削加工颤振类型辨识[J]. 机械工程学报, 2023, 59(9): 90-100.

SUN Zhaoyang, PENG Fangyu, TANG Xiaowei, YAN Rong, XIN Shihao, WU Jiawei. Robotic Milling Chatter Types Detection Based on Adaptive VariationalMode Decomposition and Difference of Power Spectral Entropy[J]. Journal of Mechanical Engineering, 2023, 59(9): 90-100.

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基于自适应变分模态分解与功率谱熵差的机器人铣削加工颤振类型辨识

Robotic Milling Chatter Types Detection Based on Adaptive VariationalMode Decomposition and Difference of Power Spectral Entropy

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