research-article

Research on the Signal De-noising Method of Acoustic Emission in Fused Silica Grinding

Authors:

Qiao XuAuthors Info & Claims

SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

Pages 26 - 32

https://doi.org/10.1145/3297067.3297071

Published: 28 November 2018 Publication History

Abstract

The ultra-precision grinding process of brittle and hard fused silica is very complex. In order to monitor the grinding process accurately, it's necessary to de-noise the acoustic emission (AE) signals generated in this process and extract useful parameters which can characterize the cutting procedures of abrasive grain. Firstly, according to the characteristics of AE signal when single diamond grain scratching, the AE signal with white Gaussian noise during grinding process was simulated, whose SNR was below -2dB. Then the simulated AE signal was de-noised by wavelet threshold de-noising method, empirical mode decomposition (EMD) threshold de-noising method and EMD-Wavelet threshold de-noising method. Taking the signal to residual noise ratio (SRNR) and the mean square error (RMSE) as the evaluation parameters, the optimal way was EMD-Wavelet threshold de-noising method. The SRNR increased to 9dB, and the RMSE reduced to 0.017. At the end, the AE signal acquired from fused silica grinding process was de-noised by the optimal method, and the cutting process of the abrasive particles can be observed accurately. Taking the number and energy of impulse oscillation per unit time as key parameters, the accurate monitoring of the grinding process of fused silica material was realized.

References

[1]

Xiurong Du, Xuefu Song, Yuancheng Sun, et al. 2014. Study of Subsurface Waves on Polished Silica Glass. J. Material. 28, 23(May 2014), 240--249.

[2]

Le Xu, Chunlei Zhang, Lei Dai, et al. 2016. Research on manufacturing method of non-rotationally symmetrical aspheric surface with high accuracy. Chinese Optics. 9, 3(June 2016), 364--370.

[3]

Zhongjun Qiu, Dacheng Zou, Guangpeng Yan. 2015. Mechanical dressing of resin bold diamond grinding wheel based on dressing force. Optics and Precision Engineering. 23, 4(April 2015), 996--1003.

[4]

Felipe Aparecido Alexandre, Wenderson Nascimento Lopes, Fábio R. Lofrano Dotto, et al. 2018. Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model. The International Journal of Advanced Manufacturing Technology. 96(October 2017), 67--79.

[5]

Gongtian Shen. 2015. Acoustic Emission Technology and Application. Science Press, Beijing, China.

[6]

Jianhui Zhu, Chaoyu Shi, Keming Feng, et al. 2017. Application Situation and Trend of Acoustic Emission Technology in Grinding Process. J. Tool Technology. 51, 9(June, 2017), 6--11.

[7]

Bor Mojškerc, Tomaz Kek, Janez Grum. 2018. Feasibility study of monitoring the steel quenching process using acoustic emission technology. Applied Acoustics. 129(2018), 335--345.

[8]

Felipe Aparecido Alexandre, Wenderson Nascimento Lopes, Fábio R. Lofrano Dotto, et al. Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model. The International Journal of Advanced Manufacturing Technology. 96(2018), 67--79.

[9]

Jeffrey Badger, Stuart Murphy, Garret E. O'Donnell. Acoustic emission in dressing of grinding wheels: AE intensity, dressing energy, and quantification of dressing sharpness and increase in diamond wear-flat size. International Journal of Machine Tools and Manufacture. 125 (2018), 11--19.

[10]

Hao Ma, Jianlong Yin, Dongsheng Li. 2016. Extraction method for acoustic emission characteristic signal of grinding machine based on wavelet decomposition. Transducer and Microsystem Technologies. 35, 7(July 2016), 13--15.

[11]

Bing Chen, Shichun Li, Zhaohui Deng, et al. 2018. On-line monitoring of ultra-precision grinding process for aspherical surfaces by rotary acoustic emission signal. Ordnance Material Science and Engineering. 41, 1(January 2018), 8--13.

[12]

Rui Zhang, Aidong Deng, Xiaodong Si, et al. 2018. A new method for acoustic emission signal de-noised and fault diagnosis. Journal of Vibration and Shock. 37, 4(May 2018), 75--81.

[13]

Xiling Liu, Zhou Liu, Xibing Li, et al. Wavelet threshold de-noising of rock acoustic emission signals subjected to dynamic loads. Journal of Geophysics and Engineering. 15 (2018), 1160--1170.

[14]

Jintao Yu. 2017. Research on acoustic emission processing algorithm. Chemical Industry Press, Beijing, China.

[15]

S. G. Chang, B. Yu, Vetterli M. 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing. 9, 9 (Mar. 2000), 1532--1546.

Digital Library

[16]

Boudraa A. O., Cexus J. C. De-noising via empirical mode decomposition. Proceedings of the IEEE International Symposium on Control Communications and Signal Processing. Marrakench, Morocco: IEEE, 2006, 4--8.

[17]

Manuel B V, Weng B W, Barner K E. 2008. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in Biology and Medicine. 38, 1 (January 2008), 1--13

Digital Library

Cited By

Xu RLiu ZZhang SKe TGuo T(2023)The measurement of liquid concentration based on Arduino UNO platform using ultrasonic wave sensorProceedings of the 2023 6th International Conference on Signal Processing and Machine Learning10.1145/3614008.3614047(253-259)Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1145/3614008.3614047
Mirifar SKadivar MAzarhoushang B(2020)First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission SensorsJournal of Manufacturing and Materials Processing10.3390/jmmp40200354:2(35)Online publication date: 25-Apr-2020
https://doi.org/10.3390/jmmp4020035

Index Terms

Research on the Signal De-noising Method of Acoustic Emission in Fused Silica Grinding
1. Information systems
  1. Data management systems
    1. Information integration
      1. Data cleaning

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cover image ACM Other conferences

SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

November 2018

177 pages

ISBN:9781450366052

DOI:10.1145/3297067

Copyright © 2018 ACM.

© 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2018

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SPML '18: 2018 International Conference on Signal Processing and Machine Learning

November 28 - 30, 2018

Shanghai, China

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Cited By

Xu RLiu ZZhang SKe TGuo T(2023)The measurement of liquid concentration based on Arduino UNO platform using ultrasonic wave sensorProceedings of the 2023 6th International Conference on Signal Processing and Machine Learning10.1145/3614008.3614047(253-259)Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1145/3614008.3614047
Mirifar SKadivar MAzarhoushang B(2020)First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission SensorsJournal of Manufacturing and Materials Processing10.3390/jmmp40200354:2(35)Online publication date: 25-Apr-2020
https://doi.org/10.3390/jmmp4020035

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