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An intelligent sensor fusion system for tool monitoring on a machining centre

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Abstract

The objective of this paper is to construct an intelligent sensor fusion monitoring system for tool breakage on a machining centre. Since none of the sensing and diagnosis techniques have proved to be completely reliable in practice, an intelligent tool-monitoring system consisting of a neural-network-based algorithm and a sensor fusion system is proposed. The dual sensing signals of cutting force and acoustic emission are used simultaneously in the proposed system owing to good correlation existing between them, and, a self-learning neural-network algorithm is used to integrate multiple sensing information to make a proper decision about tool condition. The results show good performance in tool-breakage detection by the proposed monitoring system, especially where there is high interference.

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References

  1. H. K. Tönshoff et al., “Developments and trends in monitoring and control of machining processes”,Annals CIRP,37(2), pp. 611–621, 1988.

    Google Scholar 

  2. A. G. Ulsoy and Y. Koren, “Control of machining processes”,Journal of Systems Measurement and Control,115, pp. 301–308, 1993.

    Google Scholar 

  3. J. Tlusty and G. C. Andrews, “A critical review of sensors for unmanned machining”,Annals CIRP,32, pp. 563–572, February 1983.

    Google Scholar 

  4. K. N. Lou and C. F. Wu, “On the study of the multilayer neural network and Kalman filtering for system identification”,ASME Winter Annual Meeting,DSC-55(1), pp. 547–562, 1994.

    Google Scholar 

  5. M. Shiraishi, “Scope of in-process measurement, monitoring and control techniques in machining processes Part. 1. In-process techniques for tools”,Precision Engineering,10(4), pp. 179–189, 1988.

    Google Scholar 

  6. N. H. Cook and K. Subramanian, “Sensing of drill wear and prediction of drill life”,Trans. ASME, D,99(2), pp. 295ff, 1977.

    Google Scholar 

  7. M. Martelotti, “Analysis of the milling process”,Trans. ASME,63, pp. 667ff, and67, pp. 233ff, 1941.

  8. I. Yellowley, “Observations of the mean values of forces, torques and specific power in the peripheral milling process”,International Journal of Machine Tool Design and Research,25(4), pp. 337–346, 1985.

    Google Scholar 

  9. E. Kannatey-Asibu and D. A. Dornfeld, “Quantitative relationships for acoustic emission from orthogonal metal cutting”,Journal of Engineering for Industry,103, pp. 330–340, 1981.

    Google Scholar 

  10. K. Iwata and T. Moriwaki, “An application of acoustic emission measurement to in-process sensing of tool wear”,Annals CIRP,26, pp. 21–26, 1977.

    Google Scholar 

  11. E. N. Diei and D. A. Dornfeld, “A model of tool fracture generated acoustic emission during machining”,Journal of Engineering for Industry,109, pp. 227–233, 1987.

    Google Scholar 

  12. I. Inasaki and S. Yonetsu, “In-process detection of cutting tool damage by acoustic emission measurement”,Proceedings 22nd International Machine Tool Design and Research Conference, pp. 261–268, 1981.

  13. J. B. Liu, “Monitoring the precision machining process: sensors, signal processing and information analysis”, PhD dissertation, ME, University of California at Berkeley, 1991.

  14. J. A. Freeman and D. M. Skapura,Neural Networks: Algorithm, Applications and Programming Techniques, Addison-Wesley, 1992.

  15. R. E. Kalman, “A new approach to linear filtering and prediction problems”,Journal of Basic Engineering Trans. ASME, D,82(1), pp. 35–45, 1960.

    Google Scholar 

  16. R. Ippolito, G. F. Micheletti and R. Vilenchich, “Experimental analysis of the correlation between cutting forces variation with time and cutting data”,Proceedings 14th MTDR Conference, pp. 741–745, 1973.

  17. J. Roget, P. Souquet and N. Gsib, “Use of acoustic emission for in-process monitoring of tool during turning and milling”,Progress in Acoustic Emission 3, the Japanese Society of NDI, pp. 94–101, 1986.

  18. M. S. Lan and D. A. Dornfeld, “In-process tool fracture detection”,ASME Journal of Engineering Materials and Technology,106, pp. 111–118, 1984.

    Google Scholar 

  19. T. I. Liu and K. S. Anantharaman, “On-line sensing of drill wear using neural network approach”,IEEE International Conference on Neural Networks,2, pp. 690–694, 1993.

    Google Scholar 

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Authors and Affiliations

  1. Graduate Institute of Mechanical Engineering, National Chung Cheng University, Chia-Yi, Taiwan

    Kang-Ning Lou

  2. Department of Vehicle Engineering, NYPI, 64 Wen Haw Road, 63208, Huwei, Yunlin, Taiwan

    Cheng-Jen Lin

Authors
  1. Kang-Ning Lou
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  2. Cheng-Jen Lin
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Lou, KN., Lin, CJ. An intelligent sensor fusion system for tool monitoring on a machining centre. Int J Adv Manuf Technol 13, 556–565 (1997). https://doi.org/10.1007/BF01176299

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