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Proportional hazard model for cutting tool recovery in machining

Author

Listed:
  • Abdoulaye Diamoutene
  • Farid Noureddine
  • Rachid Noureddine
  • Bernard Kamsu-Foguem
  • Diakarya Barro

Abstract

The proportional hazard model is a statistical method capable of including information on environmental and operating conditions. In machining, in the reliability field of a cutting tool, the interest of using proportional hazard model is highlighted. On one hand, the environmental and operating conditions are described and taken into account as explanatory variables. Three covariates are considered, namely, the vibration signal, the hardness material, and the lubrication/cooling. On the other hand, a new baseline hazard function is designed according to phenomena of tiny tool breakage followed by a self-sharpening process. This latter phenomenon, which can be considered as a rare event, prompted us to study extreme value theory to propose an adequate baseline hazard function. The newly obtained baseline hazard function will be named generalized extreme value proportional hazard model. This function is obtained thanks to the Gumbel function and has the property to be non-monotonic, an increasing then decreasing function. An alternative option as a baseline hazard function, based on the flexible Weibull distributions, is also proposed. Results produced in this article show the impact of all these variables on the surface roughness of the machined parts. According to reliability studies, the premature replacement of the cutting tool implying financial losses can be delayed. This may be of particular significance and benefit, in terms of sustainable development, in the case of mass production, by limiting the frequency of tool replacement.

Suggested Citation

  • Abdoulaye Diamoutene & Farid Noureddine & Rachid Noureddine & Bernard Kamsu-Foguem & Diakarya Barro, 2020. "Proportional hazard model for cutting tool recovery in machining," Journal of Risk and Reliability, , vol. 234(2), pages 322-332, April.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:2:p:322-332
    DOI: 10.1177/1748006X19884211
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    References listed on IDEAS

    as
    1. Diamoutene, Abdoulaye & Kamsu-Foguem, Bernard & Noureddine, Farid & Barro, Diakarya, 2018. "Prediction of U.S. General Aviation fatalities from extreme value approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 65-75.
    2. Almalki, Saad J. & Nadarajah, Saralees, 2014. "Modifications of the Weibull distribution: A review," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 32-55.
    3. Barker, Kash & Baroud, Hiba, 2014. "Proportional hazards models of infrastructure system recovery," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 201-206.
    4. Sangun Park & Jiwhan Park, 2018. "A general class of flexible Weibull distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(4), pages 767-778, February.
    5. Almalki, Saad J. & Yuan, Jingsong, 2013. "A new modified Weibull distribution," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 164-170.
    6. Bobrowski, Sebastian & Chen, Hong & Döring, Maik & Jensen, Uwe & Schinköthe, Wolfgang, 2015. "Estimation of the lifetime distribution of mechatronic systems in the presence of a covariate: A comparison among parametric, semiparametric and nonparametric models," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 105-112.
    7. Vikas Upadhyay & P.K. Jain & N.K. Mehta, 2013. "Prediction of surface roughness using cutting parameters and vibration signals in minimum quantity coolant assisted turning of Ti-6Al-4V alloy," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 27(1/2/3), pages 33-46.
    8. Zio, Enrico, 2016. "Challenges in the vulnerability and risk analysis of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 137-150.
    9. Patiño Rodriguez, Carmen Elena & Francisco Martha de Souza, Gilberto, 2010. "Reliability concepts applied to cutting tool change time," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 866-873.
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