Top > IJAT > Grey Prediction of CBN Grinding Process

single-au.php

IJAT Vol.5 No.3 pp. 420-426
doi: 10.20965/ijat.2011.p0420
(2011)

Paper:

Views over last 60 days: 419

Grey Prediction of CBN Grinding Process

Neng-Hsin Chiu and Jie-Wei Lee

National Kaohsiung First University of Science and Technology, No2 Jhuoyue Rd., Nanzih, Kaohsiung City, 881, Taiwan, R.O.C

Received:
December 22, 2010
Accepted:
April 7, 2011
Published:
May 5, 2011
Creative Commons License
Keywords:
grinding, monitoring, acoustic emission, grey prediction, wavelet transform
Abstract
Surface grinding is a machining process with unstable quality which is usually deteriorated as the process proceeds. If grinding can be forecast to alarm before unsatisfactory, the process could be controlled better. The purpose of this paper is to construct a grey model for CBN grinding based upon acoustic emission (AE) energy extracted from the AE grinding signal to reflect ground roughness variation. A grey model from the conducted experiment was found to be well correlated with the grinding trends. The prediction accuracy, inor out- of- sample, exceeds 90%, making grey prediction suitable for prognostic monitoring of grinding.
Cite this article as:
N. Chiu and J. Lee, “Grey Prediction of CBN Grinding Process,” Int. J. Automation Technol., Vol.5 No.3, pp. 420-426, 2011.
Data files:
References
  1. [1] Andrew K. S. Jardine, Daming Lin, and Dragan Banjevic, Mechanical Systems and Signal Processing, Vol.20, pp. 1483-1510, 2006.
  2. [2] Jae-Seob Kwak, Sung-Bo Sim, and Yeong-Deug Jeong, International Journal of Machine Tools and Manufacture, Vol.46, pp. 304-312, 2005.
  3. [3] H. J. Chen, “Master Thesis,” National Kaohsiung First University of Science and Technology, 2002.
  4. [4] Jeremiah A. Couey, Eric R. Marsh, Byron R. Knapp, and R. Ryan Vallance, Precision Engineering, No.29, pp. 307-314, 2005.
  5. [5] H. K. Tönshoff, M. Jung, S. Männel, and W. Rietz, Ultrasonics, Vol.37, pp. 681-686, 2000.
  6. [6] D. Dornfield and He Gao Cai, Transaction of the ASME, Vol.106, pp. 28-33, 1984.
  7. [7] L. Xiaoli and Y. Zhejun, Wear, Vol.219, pp. 145-154, 1998.
  8. [8] T. Lundholm, Annals of CIRP, Vol.40, No.1, pp. 441-444, 1991.
  9. [9] S. Gu, J. Ni, and J. Yuan, International Journal of Machine Tools and Manufacture, Vol.42, pp. 41-51, 2002.
  10. [10] J. D. Hwang, Master Thesis, National Ping-Dong University of Science, 2003.
  11. [11] Y. R. Shih, Master Thesis, National Kaohsiung 1st University of Science, 2007.
  12. [12] Y. Tie and X. L. Bi, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, pp. 523-527, 2008.
  13. [13] S. E. Chen, Master Thesis, Tunghai University, 2004.
  14. [14] B. C. Sindney, G. Ramesh, and G. Haitao, Introduction to Wavelet Transform, Prentice Hall, New Jersey, 1998.
  15. [15] M. Misiti, “Wavelet Toolbox for the Use with MATLAB,” Matlab user’s guide, USA, 2000.
  16. [16] Jae-Seob Kwak and Man-Kyung Ha, International Journal of Advanced Manufacturing Technology, Vol.23, p.87, 2002.
  17. [17] J. L. Deng, The Journal of Grey System, Vol.1, pp. 1-24, 1989.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Sep. 09, 2024