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Sensor Fusion and Neural Network Analysis for Drill-Wear Monitoring

Prasopchaichana, Kritsada  권오양    (인하대학교 기계공학부  );
  • 초록

    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.


  • 주제어

    Neural network .   Levenberg-Marquardt .   sensor fusion .   drill wear .   tool-condition monitoring.  

  • 참고문헌 (13)

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    2. 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 
    3. 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 
    4. 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 
    5. 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 
    6. Demuth, H. and Beale, M., 1998, Neural Network Toolbox User's Guide, Version 3.0, Natick, MA, USA, The Math works, Inc 
    7. 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 
    8. 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     
    9. 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 
    10. 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 
    11. 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 
    12. 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 
    13. 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 
  • 이 논문을 인용한 문헌 (3)

    1. Kim, Cho-Won ; Choi, Kook-Jin ; Jung, Sung-Hwan ; Hong, Dae-Sun 2009. "Development of a Web-Based Remote Monitoring System for Evaluating Degradation of Machine Tools Using ART2" 한국공작기계학회논문집 = Transactions of the Korean society of machine tool engineers, 18(1): 42~49     
    2. Yoo, Song-Min 2010. "A study on the exit stage quality prediction of flexible disk process using neural network" 한국공작기계학회지 = Journal of the Korean society of machine tool engineers, 19(6): 760~767     
    3. Park, Hong-Seok ; Hoang, Van-Vinh ; Song, Jun-Yeob ; Kim, Dong-Hoon ; Le, Ngoc-Tran 2013. "A Concept of Self-Optimizing Forming System" 한국생산제조시스템학회지 = Journal of the Korean Society of Manufacturing Technology Engineers, 22(2): 292~297     

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