采用同步压缩变换和能量熵的机器人加工颤振监测方法

薛雷 1; 曾宏伟 2; 覃程锦 2; 陶建峰 2; 刘成良 2; 汤海洋 1; 黄稳 1

doi:10.7652/xjtuxb201908004

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采用同步压缩变换和能量熵的机器人加工颤振监测方法

更新时间：2025-07-09

- 采用同步压缩变换和能量熵的机器人加工颤振监测方法
- A Chatter Monitoring Method for Robotic Machining Using Synchro-Squee-ed Transform and Energy Entropy
- 西安交通大学学报 2019年53卷第8期页码：24-30+89
- 作者机构：
  
  上海飞机制造有限公司,上海,200240
- 作者简介：
- 基金信息：
  
  : 2018-12-10。作者简介: 薛雷(1985-),男,高级工程师;陶建峰(通信作者),男,副教授,博士生导师。基金项目: 国家重点研发计划资助项目(2017YFD0700602,2017YFB1300603);国家重点研发计划子课题资助项目(2016YFD0700105-02)
- DOI：10.7652/xjtuxb201908004
  中图分类号： TH17;TP181
- 网络首发：2019-08-10，
  
  纸质出版：2019
- 稿件说明：
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薛雷 1, 曾宏伟 2, 覃程锦 2, 等. 采用同步压缩变换和能量熵的机器人加工颤振监测方法[J]. 西安交通大学学报, 2019,53(8):24-30+89.

A Chatter Monitoring Method for Robotic Machining Using Synchro-Squee-ed Transform and Energy Entropy[J]. 2019, 53(8): 24-30+89.
薛雷 1, 曾宏伟 2, 覃程锦 2, 等. 采用同步压缩变换和能量熵的机器人加工颤振监测方法[J]. 西安交通大学学报, 2019,53(8):24-30+89. DOI： 10.7652/xjtuxb201908004.

A Chatter Monitoring Method for Robotic Machining Using Synchro-Squee-ed Transform and Energy Entropy[J]. 2019, 53(8): 24-30+89. DOI： 10.7652/xjtuxb201908004.

摘要

为避免机器人加工过程中颤振给加工质量及刀具寿命带来不利影响

提出了一种基于同步压缩变换和能量熵的机器人加工颤振监测方法。首先

利用同步压缩变换获取主轴振动信号高分辨率时频谱

并将获得的时频谱均分为有限个频带; 然后

对各频带对应的时域子信号进行重构并求取其能量值; 最后

采用能量熵作为表征颤振能量分布变化的特征量

通过计算所有子信号的能量熵之和对颤振进行在线监测。在库卡工业机器人加工实验平台上

开展了机器人制孔加工实验验证

实验结果表明:在不同的机器人加工条件下

所提方法均能在颤振发生前22 ms左右有效地将其检测出来; 计算一次能量熵的时长约为20 ms

能够基本满足颤振在线监测的实时性要求。

Abstract

A chatter monitoring method of robotic machining process based on synchro-squee-ed transform and energy entropy is proposed to avoid the adverse effects of chatter on machining quality and tool life during robotic machining. Firstly

the synchro-squee-ed transform is applied to obtain the high-resolution time-frequency description of the spindle vibration signal

and then the time-frequency description is equally divided into a finite number of frequency bands. Secondly

the time-domain sub-signal corresponding to each frequency band is reconstructed and the energy value of the sub-signal is calculated. Finally

the energy entropy is utili-ed to characteri-e the energy distribution of the chatter. The chatter is monitored online by calculating the sum of energy entropy of all sub-signals. Robotic drilling experiments are conducted on a KUKA industrial robot machining experimental platform to verify the proposed chatter monitoring method. Results show that the proposed algorithm effecti

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references

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