Journal of Intelligent Manufacturing

, Volume 26, Issue 2, pp 213–223 | Cite as

Health assessment and life prediction of cutting tools based on support vector regression

  • T. Benkedjouh
  • K. Medjaher
  • N. Zerhouni
  • S. Rechak
Article

Abstract

The integrity of machining tools is important to maintain a high level of surface quality. The wear of the tool can lead to poor surface quality of the workpiece and even to damage of the machine. Furthermore, in some applications such as aeronautics and precision engineering, it is preferable to change the tool earlier rather than to loose the workpiece because of its high price compared to the tool’s one. Thus, to maintain a high quality of the manufactured pieces, it is necessary to assess and predict the level of wear of the cutting tool. This can be done by using condition monitoring and prognostics. The aim is then to estimate and predict the amount of wear and calculate the remaining useful life (RUL) of the cutting tool. This paper presents a method for tool condition assessment and life prediction. The method is based on nonlinear feature reduction and support vector regression. The number of original features extracted from the monitoring signals is first reduced. These features are then used to learn nonlinear regression models to estimate and predict the level of wear. The method is applied on experimental data taken from a set of cuttings and simulation results are given. These results show that the proposed method is suitable for assessing the wear evolution of the cutting tools and predicting their RUL. This information can then be used by the operators to take appropriate maintenance actions.

Keywords

Tool condition monitoring  Feature extraction and reduction Prognostics  Remaining useful life Support vector regression 

References

  1. AFNOR. (2005). Condition monitoring and diagnostics of machines–Prognostics: Part 1: General guidelines. NF ISO 13381-1.Google Scholar
  2. Akansu, A. N., Serdijn, W. A., & Selesnick, I. W. (2010). Emerging applications of wavelets: A review. Physical Communication, 3(1), 1–18.CrossRefGoogle Scholar
  3. Aliustaoglu, C., Ertunc, H. M., & Ocak, H. (2009). Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mechanical Systems and Signal Processing, 23, 539–546.CrossRefGoogle Scholar
  4. Alonso, F., & Salgado, R. (2008). Analysis of the structure of vibration signals for tool wear detection. Mechanical Systems and Signal Processing, 22, 735–748.CrossRefGoogle Scholar
  5. Bhattacharyya, P., Sengupta, D., Mukhopadhyay, S., & Chattopadhyay, A. B. (2008). Online tool condition monitoring in face milling using current and power signals. International Journal of Production Research, 46(4), 1187–1201.CrossRefGoogle Scholar
  6. Brezak, D., Majetic, D., Udiljak, T., & Kasac, J. (2012). Tool wear estimation using an analytic fuzzy classifier and support vector machines. Journal of Intelligent Manufacturing, 23, 797–809.CrossRefGoogle Scholar
  7. Chelidze, D., & Cusumano, J. (2004). A dynamical systems approach to failure prognosis. Journal of Vibration and Acoustics, 126, 2–8.CrossRefGoogle Scholar
  8. Chen, X., & Li, B. (2007). Acoustic emission method for tool condition monitoring based on wavelet analysis. International Journal of Advanced Manufacturing Technology, 33, 968–976.CrossRefGoogle Scholar
  9. Dong, M., & He, D. (2007). A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mechanical Systems and Signal Processing, 21, 2248–2266.CrossRefGoogle Scholar
  10. Gajate, A., Haber, R., Del Toro, R., Vega, P., & Bustillo, A. (2012). Tool wear monitoring using neuro-fuzzy techniques: A comparative study in a turning process. Journal of Intelligent Manufacturing, 23, 869–882.CrossRefGoogle Scholar
  11. He, D., Li, R., & Bechhoefer, E. (2012). Stochastic modeling of damage physics for mechanical component prognostics using condition indicators. Journal of Intelligent Manufacturing, 23, 221–226.CrossRefGoogle Scholar
  12. Heng, A., Tan, A. C., Mathew, J., Montgomery, N., Banjevic, D., & Jardine, A. K. (2009). Intelligent condition-based prediction of machinery reliability. Mechanical Systems and Signal Processing, 23(5), 1600–1614.CrossRefGoogle Scholar
  13. Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739.CrossRefGoogle Scholar
  14. Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.Google Scholar
  15. Jemielniak, K., & Arrazola, P. (2008). Application of ae and cutting force signals in tool condition monitoring in micro-milling. CIRP Journal of Manufacturing Science and Technology, 1, 97–102.CrossRefGoogle Scholar
  16. Kilundu, B., Dehombreux, P., & Chiementin, X. (2011). Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mechanical Systems and Signal Processing, 25, 400–415.CrossRefGoogle Scholar
  17. Kunpeng, Z., San, W. Y., & Soon, H. G. (2009). Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. International Journal of Machine Tools and Manufacture, 49, 537–553.CrossRefGoogle Scholar
  18. Lebold, M., & Thurston, M. (2001). Open standards for condition-based maintenance and prognostic systems. In Proceedings of the 5th maintenance and reliability conference (MARCON).Google Scholar
  19. Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2003). Model-based prognostic techniques applied to a suspension system. Transactions on Systems, Man, and Cybernetics, 38, 1156–1168.Google Scholar
  20. Medjaher, K., Tobon-Mejia, D. A., & Zerhouni, N. (2012). Remaining useful life estimation of critical components with application to bearings. IEEE Transactions on Reliability, 61(2), 292–302.CrossRefGoogle Scholar
  21. Oraby, S., & Hayhurst, D. (2004). Cutting tool condition monitoring using surface texture via neural network. International Journal of Mathematical and Computational Tools Manufacturing, 44, 1261–1269.Google Scholar
  22. Pal, S., Heyns, P. S., Freyer, B. H., Theron, N. J., & Pal, S. K. (2011). Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. Journal of Intelligent Manufacturing, 22, 491–504. Google Scholar
  23. PHM Society. (2010). PHM data chalelnge 2010. https://www.phmsociety.org/competition/phm/10.
  24. Purushothaman, S. (2010). Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns. Journal of Intelligent Manufacturing, 21, 717–730.CrossRefGoogle Scholar
  25. Rehorn, A., Jiang, J., & Orban, P. (2005). State of the art methods and results in tool condition monitoring: A review. International Journal of Advance Manufacturing Technology, 26, 693–710.CrossRefGoogle Scholar
  26. Roweis, T., & Saul, L. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323–2326.CrossRefGoogle Scholar
  27. Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1(1), 20.Google Scholar
  28. Schölkopf, B., & Smola, A. (2002). Learning with kernels. Cambridge: MIT Press.Google Scholar
  29. Shi, D., & Gindy, N. N. (2007). Tool wear predictive model based on least squares support vector machines. Mechanical Systems and Signal Processing, 21(4), 1799–1814.CrossRefGoogle Scholar
  30. Sikorska, J., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836.CrossRefGoogle Scholar
  31. Sun, J., Hong, G., Rahman, M., & Wong, Y. (2004). The application of nonstandard support vector machine in tool condition monitoring system. In Second IEEE international workshop on electronic design, test and applications (DELTA 2004, pp. 295–300.Google Scholar
  32. Tenenbaum, J., Silva, V. D., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319–2323.CrossRefGoogle Scholar
  33. Tobon-Mejia, D. A., Medjaher, K., & Zerhouni, N. (2012). Cnc machine tool’s wear diagnostic and prognostic by using dynamic bayesian networks. Mechanical Systems and Signal Processing, 28, 167–182.CrossRefGoogle Scholar
  34. Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. New York: Wiley.CrossRefGoogle Scholar
  35. Van Der Maaten, L. J. P., Postma, E. O., & Van Den Herik, H. J. (2009). Dimensionality reduction: A comparative review. Tech. report, Tilburg University. TiCC-TR 2009-005.Google Scholar
  36. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.CrossRefGoogle Scholar
  37. Vapnik, V., Golowich, S., & Smola, A. (1997). Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 9, 281–287.Google Scholar
  38. Yan, J. H., & Lee, J. (2005). Degradation assessment and fault modes classification using logistic regression. Journal of Manufacturing Science and Engineering Transactions of the ASME, 127(4), 912–914.Google Scholar
  39. Yan, W., Qiu, H., & Iyer, N. (2008). Feature extraction for bearing prognostics and health management (PHM)—A survey. Technical report. Air Force Research Laboratory.Google Scholar
  40. Yeo, S. H., Khoo, L. P., & Neo, S. S. (2000). Tool condition monitoring using reflectance of chip surface and neural network. Journal of Intelligent Manufacturing, 11, 507–514.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • T. Benkedjouh
    • 1
  • K. Medjaher
    • 2
  • N. Zerhouni
    • 2
  • S. Rechak
    • 3
  1. 1.Laboratoire de Mécanique des Structures (LMS)EMPAlgiersAlgeria
  2. 2.Automatic Control and Micro-Mechatronic Systems Department, FEMTO-STUniversité de Franche-Comté/CNRS/ENSMM/UTBMBesançonFrance
  3. 3.Laboratoire de Génie MécaniqueENPAlgiersAlgeria

Personalised recommendations