基于经验模态分解和希尔伯特-黄变换的精密孔镗削颤振特征提取

李欣; 梅德庆; 陈子辰

doi:10.3788/OPE.20111906.1291

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基于经验模态分解和希尔伯特-黄变换的精密孔镗削颤振特征提取

微纳技术与精密机械 | 更新时间：2020-08-12

- 基于经验模态分解和希尔伯特-黄变换的精密孔镗削颤振特征提取
- Feature extraction of chatter for precision hole boring processing based on EMD and HHT
- 光学精密工程 2011年19卷第6期页码：1291-1297
- 作者机构：
  
  浙江大学现代制造工程研究所浙江省先进制造技术重点实验室,浙江杭州 310027
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.50405036)();浙江省自然科学基金资助项目(No.Y104462)();中央高校基本科研业务费专项基金资助项目(No.2009Q
- DOI：10.3788/OPE.20111906.1291
  中图分类号： TG53;TP391.4
- 收稿日期：2010-07-14，
  
  修回日期：2010-10-27，
  
  网络出版日期：2011-06-25，
  
  纸质出版日期：2011-06-25
- 稿件说明：
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李欣, 梅德庆, 陈子辰. 基于经验模态分解和希尔伯特-黄变换的精密孔镗削颤振特征提取[J]. 光学精密工程, 2011,19(6): 1291-1297

LI Xin, MEI De-qing, CHEN Zi-chen. Feature extraction of chatter for precision hole boring processing based on EMD and HHT[J]. Editorial Office of Optics and Precision Engineering, 2011,19(6): 1291-1297
李欣, 梅德庆, 陈子辰. 基于经验模态分解和希尔伯特-黄变换的精密孔镗削颤振特征提取[J]. 光学精密工程, 2011,19(6): 1291-1297 DOI： 10.3788/OPE.20111906.1291.

LI Xin, MEI De-qing, CHEN Zi-chen. Feature extraction of chatter for precision hole boring processing based on EMD and HHT[J]. Editorial Office of Optics and Precision Engineering, 2011,19(6): 1291-1297 DOI： 10.3788/OPE.20111906.1291.

摘要

针对精密孔镗削加工过程中容易出现颤振现象

影响精密孔的表面质量问题

建立了镗削加工的监测系统

对镗削颤振信号进行特征提取

以实现对镗削颤振的快速预报。首先

根据颤振信号的时频特点

将经验模态分解(EMD)和希尔伯特-黄变换(HHT)引入颤振特征提取过程

并介绍了其基本原理及具体实现过程。然后

在线拾取镗杆的振动信号

并对振动信号进行EMD分解和HHT变换。最后

通过对各本征模态函数分量的Hilbert谱分析

提取出了颤振发生的征兆特征。实验结果表明

利用EMD和HHT对镗削振动信号进行特征提取

可以在颤振形成之前0.5 s得到颤振爆发的征兆

为后续的颤振抑制工作赢取时间

进而确保精密孔的表面加工质量。

Abstract

For the chatter occured frequently in precise hole boring processing and resulted in low quality of finished surface

a chatter monitoring system was established to finish the feature extraction of the chatter to predict the chatter and gain the precious time for chatter suppression. Firstly

according to the characteristics of chatter signals

Empirical Mode Decomposition(EMD) and Hilbert-Huang Transform(HHT) were introduced to extract the chatter feature

and their basic theories were investigated. Then

the vibration of boring bar was picked up online during the process

and the vibration signal was decomposed by EMD and then transformed by HHT. Finally

the feature of chatter symptom was extracted by analyzing the Hilbert spectrum of each Intrinsic Mode Function(IMF). The experimental results show that extracting chatter feature from vibration of boring bar by EMD and HHT can gain the chatter symptom by 0.5 s ahead of the chatter outbreak

which can provide sufficient guarantee for follow-up chatter suppression and can provide the high quality surface for workpieces.

关键词

Keywords

references

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