TOOL WEAR PREDICTION BASED ON WAVELET TRANSFORM AND SUPPORT VECTOR MACHINES

Dongfeng Shi, Nabil N. Gindy

Abstract

The machining quality and efficiency may be improved significantly by using appropriate tool wear prediction techniques. A new approach based on wavelet transform and support vector machine is proposed to improve the accuracy of tool wear prediction in this paper. Firstly, the wavelet transform is introduced to decompose sensory signals into different scales to reduce the dimensionality of original signals and extract features associated with different tool wear condition. Secondly, the least square support vector machine is further presented to construct predictive model due to its high convergence rate and powerful generalization ability. Thirdly, the possibility to employ power sensor rather than delicate dynamometer for the tool wear monitoring is explored. Finally, the effectiveness of proposed tool wear prediction approach is demonstrated by extensive experimental turning trials.

References

  1. B. Sick, On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research, Mechanical Systems and Signal Processing 16 (2002) 487-546
  2. D. F. Shi, D. A Axinte and N. N Gindy 'Development of an online machining process monitoring system: A case study of broaching process', International Journal of Advanced Manufacturing Technology, 2006, (in press)
  3. J. L. Stein, C. H. Wang, “Analysis of power monitoring in AC induction drive systems”, ASME Trans. on Journal of Dynamic Systems, Measurement and Control Vol. 112, pp239-248, 1990
  4. Y. Altintas, “Prediction of cutting forces and tool breakage in milling from feed drive current measurements”, ASME Trans. on Journal of Engineering for Industry, Vol. 114, pp386-392, 1992
  5. J. M. Lee, D. K. Choi, J. Kim, and C. N. Chu, “Real-time tool breakage monitoring for NC milling process,” Ann. CIRP, Vol. 44, No. 1, pp 59-62, 1995.
  6. J. Kwok, Moderating the outputs of support vector machine classifier. IEEE Trans. Neural Networks, 10(1999) 1018-1031
  7. L. J. Cao and FEH Tay, Support vector machine with adaptive parameters in financial time series forecasting, IEEE Trans. Neural Networks, 14(6) (2003) 1506-1518
  8. I. Goethals, K. Pelckmans, JAK Suykens and Bart De Moor, Subspace identification of Hammerstein systems using least squares support vector machines, IEEE Trans. on Automatical Control, 50(10) (2005) 1509-1519
  9. S. Mallat, A Wavelet Tour of Signal Processing. London, Academic Press Limited, 1997
  10. V. N. Vapnik, The nature of statistical learning theory, Springer, New York, 1999
  11. J. A. K. Suykens, T Van Gestel, J De Brabanter, B De Moor and J Vandewalle, Least squares support vector machines, World Scientific, Singapore, 2002
  12. J. A. K. Suykens, J Vandewalle, Least squares support vector machine classifiers, Neural processing letters, 9 (1999),293-300
Download


Paper Citation


in Harvard Style

Shi D. and N. Gindy N. (2011). TOOL WEAR PREDICTION BASED ON WAVELET TRANSFORM AND SUPPORT VECTOR MACHINES . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: MSIE, (ICINCO 2011) ISBN 978-989-8425-75-1, pages 479-485. DOI: 10.5220/0003647304790485


in Bibtex Style

@conference{msie11,
author={Dongfeng Shi and Nabil N. Gindy},
title={TOOL WEAR PREDICTION BASED ON WAVELET TRANSFORM AND SUPPORT VECTOR MACHINES},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: MSIE, (ICINCO 2011)},
year={2011},
pages={479-485},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003647304790485},
isbn={978-989-8425-75-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: MSIE, (ICINCO 2011)
TI - TOOL WEAR PREDICTION BASED ON WAVELET TRANSFORM AND SUPPORT VECTOR MACHINES
SN - 978-989-8425-75-1
AU - Shi D.
AU - N. Gindy N.
PY - 2011
SP - 479
EP - 485
DO - 10.5220/0003647304790485