Transactions of the Korean Society of Machine Tool Engineers (한국공작기계학회논문집)

The Korean Society of Manufacturing Technology Engineers (한국생산제조학회)
권호 표지

Sensor Fusion and Neural Network Analysis for Drill-Wear Monitoring

센서퓨젼 기반의 인공신경망을 이용한 드릴 마모 모니터링

  • ;
  • 권오양 (인하대학교 기계공학부)
  • Published : 2008.02.15
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

Keywords

References

  1. Li, X., 2002, 'A brief review: acoustic emission method for tool wear monitoring during turning,' Int. J. Mach. Tools Manufact., Vol. 42, pp. 157-165 https://doi.org/10.1016/S0890-6955(01)00108-0
  2. Li, X. and Wu, J., 2000, 'Wavelet analysis of acoustic emission signals in boring,' Proc. Instn. Mech. Engrs. Part B, Vol. 214, No. 5, pp. 421-424
  3. Jantunen E., 2004, 'The applicability of various indirect monitoring methods to tool condition monitoring in drilling,' Int. J. COMADEM, Vol. 7, No. 3, pp. 24-31
  4. Rehorn, A. G., Jiang, J. and Orban, P. E., 2005, 'Stateof- the-art methods and results in tool condition monitoring: a review,' Int. J. Adv. Manufacturing Technology, Vol. 26, pp. 693-710 https://doi.org/10.1007/s00170-004-2038-2
  5. Sun, J., Hong, G. S., Rahman, M. and Wong, Y. S., 2004, 'Identification of feature set for effective tool condition monitoring by AE sensing,' Int. J. Production Research, Vol. 42, No. 5, pp. 901-918 https://doi.org/10.1080/00207540310001626652
  6. Shin, H. G., Kim, S. I. and Kim, T. Y., 2001, 'A Study on Tool Wear in Drilling of Hot-rolled High Strength Steel,' Korean Society of Machine Tool Engineer, Vol. 10, No. 2, pp. 10-17
  7. Lim, J. S., Wang, D. H., Kim, W. I. and Lee, Y. K., 2002, 'The estimation of tool wear and fracture mechanism using sensor fusion in micro-machining,' Proceedings of the Korean Society of Machine Tool Engineers Conference, pp. 245-250
  8. Abu-Mahfouz, I., 2003, 'Drilling wear detection and classification using vibration signals and artificial neural network,' Int. J. Mach. Tools Manufact., Vol. 43, pp. 707-720 https://doi.org/10.1016/S0890-6955(03)00023-3
  9. Patra, K., Pal, S. K. and Bhattacharyya, K., 2007, 'Artificial neural network based prediction of drillflank wear from motor current signals,' Applied Soft Computing, Vol. 7, pp. 929-935 https://doi.org/10.1016/j.asoc.2006.06.001
  10. Misiti, M., Misiti, Y., Oppenheim, G. and Poggi J. M., 2000, Wavelet Toolbox User's Guide, Version 2.0, Natick, MA, USA, The Math Works, Inc
  11. Kim, J. S., Kim, N. K. and Bae, J. K., 1997, 'Monitoring of Tool Wear using AE Signal in Interrupted cutting,' Korean Society of Machine Tool Engineer, Vol. 6, No. 2, pp. 112-118
  12. Singh, A. K. and Panda, S. S., 2006, 'Predicting drill wear using an artificial neural network,' Int. J. Adv. Manuf. Technol.,Vol. 28, pp.456-462 https://doi.org/10.1007/s00170-004-2376-0
  13. Demuth, H. and Beale, M., 1998, Neural Network Toolbox User's Guide, Version 3.0, Natick, MA, USA, The Math works, Inc