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IJAT Vol.16 No.3 pp. 280-285
doi: 10.20965/ijat.2022.p0280
(2022)

Technical Paper:

On-Machine Tool Condition Monitoring System Using Image Processing

Kenta Kanto, Junichi Kubota, Makoto Fujishima, and Masahiko Mori

DMG MORI Co., Ltd.
16 Meieki, Nakamura-ku, Nagoya City, Aichi 450-0002, Japan

Corresponding author

Received:
October 29, 2021
Accepted:
March 28, 2022
Published:
May 5, 2022
Keywords:
tool, measuring instrument, automation intelligence
Abstract

An automation system of machine tools can free operators from simple and hard labor by the automatic loading and unloading of workpieces, and allowance of unmanned operation during nights and holidays. To achieve complete shop floor operations, various works that are manually performed by operators need to be automated as well. It is also crucial to accurately monitor tools to detect tool wear and chip winding during machining. In this study, we propose an on-machine tool condition monitoring system that measures tool length and diameter using images to detect tool breakage and winding chips.

Cite this article as:
Kenta Kanto, Junichi Kubota, Makoto Fujishima, and Masahiko Mori, “On-Machine Tool Condition Monitoring System Using Image Processing,” Int. J. Automation Technol., Vol.16, No.3, pp. 280-285, 2022.
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References
  1. [1] F. Jovane, Y. Koren, and C. R. Boer, “Present and future of flexible automation: towards new paradigms,” CIRP Annals, Vol.52, No.2, pp. 543-560, 2003.
  2. [2] K. D. Thoben, S. Wiesner, and T. Wuest, “Industrie 4.0 and smart manufacturing-a review of research issues and application examples,” Int. J. Automation Technol., Vol.11, No.1, pp. 4-16, 2017.
  3. [3] DMG MORI website, “FY2019 First quarter result and outlook.” https://www.dmgmori.co.jp/corporate/en/ir/ir_library/pdf/fy2019_1shihanki_ex_e.pdf [Accessed May 8, 2019]
  4. [4] J. Tlusty, and G. C. Andrews, “A critical review of sensors for unmanned machining,” CIRP Annals, Vol.32, No.2, pp. 563-572, 1983.
  5. [5] M. Fujishima, K. Ohno, S. Nishikawa, K. Nishimura, M. Sakamoto, and K. Kawai, “Study of sensing technologies for machine tools,” CIRP J. of Manufacturing Science and Technology, Vol.14, pp. 71-75, 2016.
  6. [6] G. Byrne, D. Dornfeld, I. Inasaki, G. Ketteler, W. König, and R. Teti, “Tool condition monitoring (TCM) – the status of research and industrial application,” CIRP Annals, Vol.44, No.2, pp. 541-567, 1995.
  7. [7] K. Iwata, T. Moriwaki, and N. Takenaka, “An application of acoustic emission measurement to in-process sensing of tool wear,” CIRP Annals, Vol.26, No.1, pp. 21-24, 1977.
  8. [8] P. Souquet, N. Gsib, M. Deschamps, J. Roget, J. C. Tanguy, and R. Geslot, “Tool monitoring with acoustic emission industrial results and future prospects,” CIRP Annals, Vol.36, No.1, pp. 57-60, 1987.
  9. [9] D. A. Dornfeld and M. F. DeVries, “Neural network sensor fusion for tool condition monitoring,” CIRP Annals, Vol.39, No.1, pp. 101-105, 1990.
  10. [10] A. Caggiano and L. Nele, “Artificial neural networks for tool wear prediction based on sensor fusion monitoring of CFRP/CFRP stack drilling,” Int. J. Automation Technol., Vol.12, No.3, pp. 275-281, 2018.
  11. [11] C. Y. Jiang and Y. Z. Zhang, “In-process monitoring of tool wear stage by the frequency range-energy method,” CIRP Annals, Vol.36, No.1, pp. 45-48, 1987.
  12. [12] J. Herwan, S. Kano, R. Oleg, H. Sawada, and M. Watanabe, “Comparing vibration sensor positions in CNC turning for a feasible application in smart manufacturing system,” Int. J. Automation Technol., Vol.12, No.3, pp. 282-289, 2018.
  13. [13] S. Kurada and C. Bradley, “A review of machine vision sensors for tool condition monitoring,” Computers in Industry, Vol.34, No.1, pp. 55-72, 1997.
  14. [14] V. A. Novak, “New generation laser inspection system “Blum-Novotest”,” RITM of Machine-Building Industry, Vol.9, No.7, 2017.
  15. [15] M. Xu, K. Nakamoto, and Y. Takeuchi, “Compensation Method for Tool Setting Errors Based on Non-Contact On-Machine Measurement,” Int. J. Automation Technol., Vol.14, No.1, pp. 66-72, 2020.
  16. [16] S. Yoshimitsu, D. Iwashita, K. Shimana, Y. Kobaru, and S. Yamashita, “Monitoring of Cutting State in End-Milling Based on Measurement of Tool Behavior Using CCD Image,” Int. J. Automation Technol., Vol.13, No.1, pp. 133-140, 2019.
  17. [17] W. Gao, T. Asai, and Y. Arai, “Precision and fast measurement of 3D cutting edge profiles of single point diamond micro-tools,” CIRP Annals, Vol.58, No.1, pp. 451-454, 2009.
  18. [18] S. Yoshimitsu, D. Iwashita, K. Shimana, Y. Kobaru, and S. Yamashita, “Monitoring of Cutting State in End-Milling Based on Measurement of Tool Behavior Using CCD Image,” Int. J. Automation Technol., Vol.13, No.1, pp. 133-140, 2019.
  19. [19] Y. Takaya, K. Maruno, M. Michihata, and Y. Mizutani, “Measurement of a tool wear profile using confocal fluorescence microscopy of the cutting fluid layer,” CIRP Annals, Vol.65, No.1, pp. 467-470, 2016.
  20. [20] A. Weckenmann and K. Nalbantic, “Precision measurement of Cutting Tools with Two Matched Optical 3D-Sensors,” CIRP Annals, Vol.52, No.1, pp. 443-446, 2003.
  21. [21] T. Moriwaki, “Multi-functional machine tool,” CIRP Annals, Vol.57, No.2, pp. 736-749, 2008.

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Last updated on May. 20, 2022