首页 > 论文 > 光学 精密工程 > 25卷 > 4期(pp:934-942)

BK7光学玻璃金刚石划刻声发射信号的特征提取

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

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

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.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TN305.2;TQ171.734

DOI:10.3788/ope.20172504.0934

所属栏目:微纳技术与精密机械

基金项目:国家自然科学基金资助项目(No.51675453); 福建省自然科学基金资助项目(No.2016J01260)

收稿日期:2016-07-22

修改稿日期:2016-10-18

网络出版日期:--

作者单位    点击查看

毕果:厦门大学 航空航天学院, 福建 厦门 361005
许涛林:厦门大学 航空航天学院, 福建 厦门 361005
彭云峰:厦门大学 航空航天学院, 福建 厦门 361005
郭昕乾:厦门大学 航空航天学院, 福建 厦门 361005

联系人作者:毕果(guobi@xmu.edu.cn)

备注:毕果(1978-), 女, 河南南阳人, 工学博士, 副教授, 2000年于郑州工业大学获学士学位, 2003年于郑州大学获硕士学位, 2007年于上海交通大学获博士学位, 主要从事精密加工过程监测等方面的研究。

【1】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.

【2】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.

【3】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)

【4】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.

【5】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.

【6】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.

【7】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.

【8】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.

【9】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.

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

【11】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.

【12】VICUA C M, ACUA 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.

【13】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.

【14】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)

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

引用该论文

BI Guo,XU Tao-lin,Peng Yun-feng,GUO Xin-qian. Feature extraction of acoustic emission signal for diamond scratching of optical glass BK7[J]. Optics and Precision Engineering, 2017, 25(4): 934-942

毕果,许涛林,彭云峰,郭昕乾. BK7光学玻璃金刚石划刻声发射信号的特征提取[J]. 光学 精密工程, 2017, 25(4): 934-942

被引情况

【1】毕 果,王惠雪,周 炼,邵升阳. 金刚石砂轮磨削性能退化评估. 光学 精密工程, 2019, 27(7): 1508-1515

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF