Skip to main content
Log in

Modeling of acoustic emission based on the experimental and theoretical methods and its application in face grinding

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Acoustic emission (AE) is widely used in the application of grinding monitoring, but few studies have been conducted on the quantitative relationship between the amplitude of AE signal and grinding parameters. In order to obtain the quantitative description and solve the problem of threshold recommendation in using AE signal to monitor grinding process, this paper proposes an amplitude model of AE signal of face grinding based on experimental and theoretical researches. The exponential relationship between grinding force and amplitude of AE signal under a certain condition of the fixed grinding wheel speed is achieved by experimental study. By establishing the theoretical model of the grinding force and cutting depth, the mathematical model between the amplitude of AE signal and the grinding parameters of face grinding is obtained indirectly. The experimental results prove the validity of the amplitude model of AE signal and its effectiveness in the automatic recommendation of the threshold for collision detection in face grinding.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Kwak JS, Ha MK (2004) Neural network approach for diagnosis of grinding operation by acoustic emission and power signals. J Mater Process Tech 147:65–71

    Article  Google Scholar 

  2. Wang Z, Willett P, DeAguiar PR, Webster J (2001) Neural network detection of grinding burn from acoustic emission. Int J Mach Tool Manu 41:283–309

    Article  Google Scholar 

  3. Nakai ME, Aguiar PR, Guillardi H, Bianchi EC, Spatti DH, D’Addona DM (2015) Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics. Expert Syst Appl 42:7026–7035

    Article  Google Scholar 

  4. Liao TW, Tang FM, Qu J, Blau PJ (2008) Grinding wheel condition monitoring with boosted minimum distance classifiers. Mech Syst Signal Process 22:217–232

    Article  Google Scholar 

  5. Yang ZS, Yu ZH (2011) Grinding wheel wear monitoring based on wavelet analysis and support vector machine. Int J Adv Manuf Technol 62:107–121

    Article  Google Scholar 

  6. Yang ZS, Yu ZH (2013) Experimental study of burn classification and prediction using indirect method in surface grinding of AISI 1045 steel. Int J Adv Manuf Technol 68:2439–1449

    Article  Google Scholar 

  7. Zhang DX, Bi G, Sun ZHJ, Guo Y (2015) Online monitoring of precision optics grinding using acoustic emission based on support vector machine. Int J Adv Manuf Technol 80(5–8):761–774

    Article  Google Scholar 

  8. Tawakoli T (2008) Developments in grinding process monitoring and evaluation of results. Int J Mech Manuf Syst 1:307–320

    Google Scholar 

  9. Jiang C, Song Q, Guo DB, Li HL (2014) Estimation algorithm of minimum dwell time in precision cylindrical plunge grinding using acoustic emission signal. Int J Precis Eng Manuf 15:601–607

    Article  Google Scholar 

  10. Han XS, Wu TY (2013) Analysis of acoustic emission in precision and high-efficiency grinding technology. Int J Adv Manuf Technol 67:1997–2006

    Article  Google Scholar 

  11. Sun J, Qin F, Chen P (2016) A predictive model of grinding force in silicon wafer self-rotating grinding. Int J Mach Tools Manuf 109:74–86

    Article  Google Scholar 

  12. Malkin S, Guo C (2008) Grinding technology: theory and applications of machining with abrasives, second edn. Industrial Press, New York

    Google Scholar 

  13. Wang D, Ge P, Bi W, Jiang J (2014) Grain trajectory and grain workpiece contact analyses for modeling of grinding force and energy partition. Int J Adv Manuf Technol 70(9–12):2111–2123

    Article  Google Scholar 

  14. Perveen A, Rahman M, Wong YS (2014) Modeling and simulation of cutting forces generated during vertical micro-grinding. Int J Adv Manuf Technol 71:1539–1548

    Article  Google Scholar 

  15. Guizhi X, Zhentao S, Xiaomin S, Yao W, Jianwu Y (2011) Grinding force modeling for high-speed deep grinding of engineering ceramics. J Mech Eng 47(11):169–176 (in Chinese)

    Article  Google Scholar 

  16. Sharp KW, Miller MH, Scattergood RO (2000) Analysis of the grain depth of cut in plunge grinding. J Int Soc Precis Eng Nanotechnol 24:220–230

    Google Scholar 

  17. Young HT, Liao HT, Huang HY (2007) Novel method to investigate the critical depth of cut of ground silicon wafer. J Mater Process Technol 182(1):157–162

    Article  Google Scholar 

  18. Park HW, Liang SY (2008) Force modeling of micro-grinding incorporating crystallographic effects. Int J Mach Tools Manuf 48(15):1658–1667

    Article  Google Scholar 

  19. Kwak JS, Song JB (2001) Trouble diagnosis of the grinding process by using acoustic emission signals. Int J Mach Tools Manuf 41(3):899–913

    Article  Google Scholar 

  20. Zylka L, Burek J, Mazur D (2017) Diagnostic of peripheral longitudinal grinding by using acoustic emission signal. Adv Prod Eng Manag 12(3)

  21. Mei YM, Yu ZH, Yang ZS (2017) Experimental investigation of correlation between attrition wear and features of acoustic emission signals in single-grit grinding. Int J Adv Manuf Technol 93(5–8):2275–2287

    Google Scholar 

  22. Wang JJ, Feng PF, Zha TJ (2017) Process monitoring in precision cylindrical traverse grinding of slender bar using acoustic emission technology. J Mech Sci Technol 31(2):859–864

    Article  Google Scholar 

Download references

Acknowledgments

Thanks are due to Shanghai Machine Tool Works Co., LTD for its technical support.

Funding

The authors would like to thank the financial support from National Science and Technology Major Project of China (No. 2016X04004003).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nanyan Shen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Wang, X., Shen, N. et al. Modeling of acoustic emission based on the experimental and theoretical methods and its application in face grinding. Int J Adv Manuf Technol 98, 2335–2346 (2018). https://doi.org/10.1007/s00170-018-2383-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-018-2383-1

Keywords

Navigation