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Determining optimal replacement time for metal cutting tools

Author

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  • Vagnorius, Zydrunas
  • Rausand, Marvin
  • Sørby, Knut

Abstract

Traditional tool life models do not take into account the variation inherent in metal cutting processes. As a consequence, the real tool life rarely matches the predicted values. To compensate for this uncertainty, tools are usually replaced prematurely, which leads to unnecessarily high tool costs. In some cases, however, wear-out occurs earlier than predicted, which imposes a risk of workpiece damage or rework and can lead to other extra charges. To balance these costs, this paper proposes an age replacement model. It is assumed that penalty costs are incurred each time a tool fails before the planned replacement. The probability of such an event is determined from the tool reliability function, which models the wear-out by a mixture of Weibull distributions, while failures due to external stresses are accounted for by a homogeneous Poisson process. The optimal replacement time is then determined from a total time on test (TTT) plot. The adequacy of the proposed approach for practical application is tested and confirmed in a case study on turning of Inconel 718 with cubic boron nitride (CBN) tools.

Suggested Citation

  • Vagnorius, Zydrunas & Rausand, Marvin & Sørby, Knut, 2010. "Determining optimal replacement time for metal cutting tools," European Journal of Operational Research, Elsevier, vol. 206(2), pages 407-416, October.
  • Handle: RePEc:eee:ejores:v:206:y:2010:i:2:p:407-416
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    References listed on IDEAS

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    1. Ann E. Gray & Abraham Seidmann & Kathryn E. Stecke, 1993. "A Synthesis of Decision Models for Tool Management in Automated Manufacturing," Management Science, INFORMS, vol. 39(5), pages 549-567, May.
    2. Jeang, Angus, 1998. "Reliable tool replacement policy for quality and cost," European Journal of Operational Research, Elsevier, vol. 108(2), pages 334-344, July.
    3. Iakovou, Eleftherios & Ip, Chi M. & Koulamas, Christos, 1996. "Optimal solutions for the machining economics problem with stochastically distributed tool lives," European Journal of Operational Research, Elsevier, vol. 92(1), pages 63-68, July.
    4. Pearn, W.L. & Hsu, Ya-Chen, 2007. "Optimal tool replacement for processes with low fraction defective," European Journal of Operational Research, Elsevier, vol. 180(3), pages 1116-1129, August.
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    Cited by:

    1. Si, Xiao-Sheng & Chen, Mao-Yin & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2013. "Specifying measurement errors for required lifetime estimation performance," European Journal of Operational Research, Elsevier, vol. 231(3), pages 631-644.
    2. Francisco Germán Badía & María Dolores Berrade, 2022. "On the Residual Lifetime and Inactivity Time in Mixtures," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
    3. Lu, Biao & Chen, Zhen & Zhao, Xufeng, 2021. "Data-driven dynamic predictive maintenance for a manufacturing system with quality deterioration and online sensors," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    4. Fernández, Arturo J. & Pérez-González, Carlos J. & Aslam, Muhammad & Jun, Chi-Hyuck, 2011. "Design of progressively censored group sampling plans for Weibull distributions: An optimization problem," European Journal of Operational Research, Elsevier, vol. 211(3), pages 525-532, June.

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