Grinding Monitoring through Thermal Acoustic Emission Signatures

Xun Chen; Arif Mohamed; Janet Folkes

doi:10.4028/www.scientific.net/AMR.325.287

Paper Titles

Study on Electrolytic Re-Insulation Grinding of Soft Magnetic Powder Cores
p.264

Research on the Chemical-Mechanical Grinding (CMG) Tools for Al₂O₃Ceramic
p.270

Effect of Complex Electrodischarge Grinding for Electrically Conductive PCD
p.276

A Quick-Test Method to Determine the Grinding Wheel Topography Based on Acoustic Emission
p.282

Grinding Monitoring through Thermal Acoustic Emission Signatures
p.287

Development of Three-Dimensional Measurement System of a Grinding Wheel Surface with Image Processing
p.294

Evaluation of Vibration Finished Surfaces Using Image Processing Method
p.300

Experimental Study on the Cutting Performance of TiC-Based Nanocomposite Ceramic Tools in Turning 40Cr Steel
p.309

Effect of Ground Surface Roughness of Tool on Adhesion Characteristics by PVD Coating
p.315

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 325Grinding Monitoring through Thermal Acoustic...

Grinding Monitoring through Thermal Acoustic Emission Signatures

Abstract:

This paper presents recent development in acoustic emission (AE) technique for grinding process monitoring. It demonstrated the similarity of thermal acoustic emission feature existing in grinding processes and laser irradiation tests. An innovative concept that grinding process can be monitored by using thermal AE signatures from laser irradiation tests has been proposed. Based on such idea, an artificial neural network (ANN) was built and the results showed that grinding performance variation due to wheel wear can be identified by using the ANN. This development could bring great benefits by reducing experimental works in the preparation of an ANN for grinding monitoring.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 325)

Pages:

287-293

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.325.287

Citation:

Cite this paper

Online since:

August 2011

Authors:

Xun Chen, Arif Mohamed, Janet Folkes

Keywords:

Acoustic Emission (AE), Artificial Neural Network (ANN), Grinding Monitoring

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Tönshoff, H.K., T. Friemuth, and J.C. Becker, CIRP Annals - Manufacturing Technology, 2002. 51(2): pp.551-571.

DOI: 10.1016/s0007-8506(07)61700-4

Google Scholar

[2] Tawakoli, T., et al., Advances in Abrasive Technology Xii, 2009. 76-78: pp.21-26.

Google Scholar

[3] Malkin, S., Grinding Technology Theory and Application of Machining with Abrasives 1989, Dearborn, Michigan: Society of Manufacturing Engineers.

Google Scholar

[4] X. Chen, the Journal of Engineering Manufacture, Proceedings of IMechE, 2002, Vol. 216, B2, pp.829-832.

Google Scholar

[5] Jackson, M.J., Modelling of fracture wear in vitrified cBN grinding wheels. 2007, World Academy of Materials and Manufacturing Engineering.

Google Scholar

[6] E. Paul DeGarmo, J.T. Black, and R.A. Kohser, Materials and processes in manufacturing. 10th ed. 2007: Wiley.

Google Scholar

[7] Wang, Z., et al., International Journal of Machine Tools and Manufacture, 2001. 41(2): pp.283-309.

Google Scholar

[8] Liu, Q., X. Chen, and N. Gindy, International Journal of Machine Tools and Manufacture, 2006. 46(3-4): pp.284-292.

Google Scholar

[9] Dornfeld, D.A. and E. Kannateyasibu, International Journal of Mechanical Sciences, 1980. 22(5): pp.285-296.

Google Scholar

[10] Xun Chen, James Griffin, Qiang Liu, International Journal of Manufacturing Technology and Management, 2007, v. 12 (1-3), pp.184-199.

Google Scholar

[11] J. Griffin, Thitikorn Limchimchol and X. Chen, Proceedings of the 12th Chinese Automation & Computing Society Conference in the UK, 16 September 2006, pp.211-216.

Google Scholar

[12] Steen, W.M., Laser Material Processing. 3rd ed. 2003: Springer.

Google Scholar

[13] Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, 2nd Edition. 2001: John Wiely & Sons, INC.

Google Scholar