Feature extraction of acoustic emission signal for diamond scratching of optical glass BK7

Guo BI; Tao-lin XU; Yun-feng Peng; Xin-qian GUO

doi:10.3788/OPE.20172504.0934

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Feature extraction of acoustic emission signal for diamond scratching of optical glass BK7

Micro/Nano Technology and Fine Mechanics | 更新时间：2020-07-07

- Feature extraction of acoustic emission signal for diamond scratching of optical glass BK7
- Optics and Precision Engineering Vol. 25, Issue 4, Pages: 934-942(2017)
- 作者机构：
  
  厦门大学航空航天学院, 福建厦门 361005
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172504.0934
  CLC： TN305.2;TQ171.734
- Received：22 July 2016，
  
  Accepted：18 October 2016，
  
  Published：25 April 2017
- 稿件说明：
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Guo BI, Tao-lin XU, Yun-feng Peng, et al. Feature extraction of acoustic emission signal for diamond scratching of optical glass BK7[J]. Optics and precision engineering, 2017, 25(4): 934-942.
DOI：

Guo BI, Tao-lin XU, Yun-feng Peng, et al. Feature extraction of acoustic emission signal for diamond scratching of optical glass BK7[J]. Optics and precision engineering, 2017, 25(4): 934-942. DOI： 10.3788/OPE.20172504.0934.

摘要

以金刚石压头划刻BK7光学玻璃为研究对象，分析了脆性材料脆性去除过程中的声发射机制，研究了声发射信号的特征提取技术。多种切深实验显示：BK7光学玻璃发生脆性去除的特征主要集中在[100，200]kHz、[300，400]kHz两个频段，对应不同的声发射机制，其中[100，200]kHz频带的滤波信号呈现明显的、时间间歇的突发式声发射现象，与脆性材料裂纹的生成与扩展密切相关。基于上述实验结果，提出了以突发式声发射事件为单位的特征监测方法。针对该带通滤波信号的均方根值（RMS），研究了基于凸优化理论的声发射事件识别算法，得到了脆性材料裂纹扩展的时刻及能量信息。得到的结果表明：以声发射事件为单位的特征监测具有明确的物理意义，能够更加客观地表征脆性材料的去除过程。

Abstract

By focusing on the diamond scratching of BK7 optical glass

the Acoustic Emission (AE) mechanism in the brittle removal of optical brittle materials was analyzed and a feature extraction technique of AE signals used in processing and monitoring of optical brittle materials was studied. Various cutting depth test results show that features of brittle removal for optical glass BK7 mainly focus on two frequency bands that are[100

200] kHz and[300

400] kHz

and they correspond to different AE mechanisms. In which

filtered signal of frequency band[100

200] kHz presents obvious burst-type AE phenomenon with a time interval

which is closely related to the production and extension of cracks for optical brittle materials. On the basis of the results mentioned above

a monitoring method that uses burst-type AE events as unit was proposed. Aimed at RMS (Root Mean Square) signals of the band-pass filtering

a recognition algorithm of AE events based on convex optimization theory was studied to get the time and energy information of crack growth for optical brittle materials. It concludes that the feature monitoring method that uses AE events as unit has specific physical meanings and represents removal process of optical brittle materials more objectively.

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references

BRECKINRIDGE J B, LILLIE C F. Prime focus architectures for large space telescopes:reduce surfaces to save cost[J]. Proceedings of SPIE, 2016, 9904:99044k.

YU T B, LI H N, WANG W S. Experimental investigation on grinding characteristics of optical glass BK7:with special emphasis on the effects of machining parameters[J]. The International Journal of Advanced Manufacturing Technology, 2016, 82(5):1405-1419.

吕东喜, 王洪祥, 黄燕华.光学材料磨削的亚表面损伤预测[J].光学精密工程, 2013, 21(3):680-686.

L D X, WANG H X, HUANG Y H. Prediction of grinding induced subsurface damage of optical materials[J]. Opt. Precision Eng., 2013, 21(3):680-686. (in Chinese)

LEE D E, HWANG I, VALENTE C M O, et al.. Precision Manufacturing Process Monitoring with Acoustic Emission[J]. International Journal of Machine Tools & Manufacture, 2006, 46(2):176-188.

MARTINS C H R, AGUIAR P R, FRECH A, et al.. Tool condition monitoring of single-point dresser using acoustic emission and neural networks models[J]. IEEE Transactions on Instrumentation & Measurement, 2014, 63(3):667-679.

YANG Z S, YU Z H. Grinding wheel wear monitoring based on wavelet analysis and support vector machine[J]. The International Journal of Advanced Manufacturing Technology, 2012, 62(1-4):107-121.

LIAO T W, TANG F M, QU J, et al.. Grinding wheel condition monitoring with boosted minimum distance classifiers[J]. Mechanical Systems & Signal Processing, 2008, 22(1):217-232.

CAESARENDRA W, KOSASIH B, TIEU A K, et al.. Acoustic emission-based condition monitoring methods:Review and application for low speed slew bearing[J]. Mechanical Systems & Signal Processing, 2016, 72-73:134-159.

CHEN P C, SU Y F, YANG S Y, et al.. Determination of Initial Crack Strength of Silicon Die Using Acoustic Emission Technique[J]. Journal of Electronic Materials, 2015, 44(7):2497-2506.

AHN B W, LEE S H. Characterization and acoustic emission monitoring of AFM nanomachining[J]. Journal of Micromechanics & Microengineering, 2009, 19(4):45028.

SADEGH H, MEHDI A N, MEHDI A. Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm[J]. Tribology International, 2015, 95:426-434.

VICUÑA C M, ACUÑA D Q. Cyclostationary processing of vibration and acoustic emissions for machine failure diagnosis[M]. CHAARI F, LES KOW J, NAPOLITANO A. Cyclostationarity:Theory and Methods. Switzerland:Springer, 2014:141-156. http://link.springer.com/chapter/10.1007/978-3-319-04187-2_10/fulltext.html

LI R Y, HE D. Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification[J]. IEEE Transactions on Instrumentation & Measurement, 2012, 61(4):990-1001.

龙宪海, 阳能军, 王汉功.基于声发射技术的30CrMnSi钢断裂机理研究[J].材料工程, 2011(1):17-22.

LONG X H, YANG N J, WANG H G. Fracture mechanism for 30CrMnSi steel based on acoustic emission technology[J]. Journal of Materials Engineering, 2011(1):17-22. (in Chinese)

BOYD S, VANDENBERGHE L. Convex Optimization[M]. Cambridge:Cambridge University Press, 2004.

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