IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v239y2025i2p276-288.html

Reliability analysis of PVD-coated carbide tools during high-speed machining of Inconel 800

Author

Listed:
  • Monojit Das
  • V.N.A. Naikan
  • Subhash Chandra Panja

Abstract

Predicting the cutting tool life is crucial for effectively managing machining costs, ensuring product quality, maintaining equipment availability and minimising waste in machining processes. When machining heat-resistant superalloys such as Inconel, the concern for tool life becomes even more pronounced. Cutting tool failure is a complex phenomenon that depends on several variables, including tool type and material, workpiece material, machine tool type and machining parameters. Traditional run-to-fail tests to predict tool life are costly and time-consuming. To address these challenges, accelerated degradation testing (ADT) offers a promising solution. ADT involves subjecting the component to higher levels of parameters, causing it to fail faster than under normal conditions. This approach saves time and reduces expenses associated with tool life tests for valuable workpieces. In implementing the concept of ADT, the experimental cutting speed ( V c ) values are selected much higher than the normal usage levels in the present study. The tool life tests are performed at three levels of V c , feed rate ( f ) , depth of cut ( a p ) and tool nose radius ( r ) using the Taguchi L 9 orthogonal array. Parametric statistical approaches, that is, accelerated failure time (AFT) models, are applied with distributions, namely the Weibull, lognormal and log-logistic distributions, to analyse the cutting tool’s reliability based on predictor variables. Various tool wear modes are considered criteria for tool failure. The comparison is made among the mean time to failure (MTTF) of cutting tools as predicted by various fitted models. Additionally, a favourable tool failure pattern is observed when using the middle level of r and operating at relatively higher V c values while ensuring that f and a p values fall within the recommended range. The proposed approach has the potential for diverse applications, including assessing the reliability of cutting tools and tool condition monitoring.

Suggested Citation

  • Monojit Das & V.N.A. Naikan & Subhash Chandra Panja, 2025. "Reliability analysis of PVD-coated carbide tools during high-speed machining of Inconel 800," Journal of Risk and Reliability, , vol. 239(2), pages 276-288, April.
    • Handle: RePEc:sae:risrel:v:239:y:2025:i:2:p:276-288
    DOI: 10.1177/1748006X241235979
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X241235979
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X241235979?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nanami Taketomi & Kazuki Yamamoto & Christophe Chesneau & Takeshi Emura, 2022. "Parametric Distributions for Survival and Reliability Analyses, a Review and Historical Sketch," Mathematics, MDPI, vol. 10(20), pages 1-23, October.
    2. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Patrick Zschech & Kai Heinrich & Raphael Bink & Janis S. Neufeld, 2019. "Prognostic Model Development with Missing Labels," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 327-343, June.
    2. Hu, Yang & Baraldi, Piero & Di Maio, Francesco & Zio, Enrico, 2015. "A particle filtering and kernel smoothing-based approach for new design component prognostics," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 19-31.
    3. Guillermo Martínez-Flórez & Roger Tovar-Falón & Héctor W. Gómez, 2024. "Mathematical Formalization and Applications to Data with Excess of Zeros and Ones of the Unit-Proportional Hazard Inflated Models," Mathematics, MDPI, vol. 12(22), pages 1-23, November.
    4. Muyang Liu & Yinjun Xiong & Quan Li & Mohammed Ahsan Adib Murad & Weilin Zhong, 2025. "Higher-Order Markov Chain-Based Probabilistic Power Flow Calculation Method Considering Spatio-Temporal Correlations," Energies, MDPI, vol. 18(5), pages 1-15, February.
    5. Zhang, Ao & Wang, Zhihua & Bao, Rui & Liu, Chengrui & Wu, Qiong & Cao, Shihao, 2023. "A novel failure time estimation method for degradation analysis based on general nonlinear Wiener processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Zhang, Jian-Xun & Hu, Chang-Hua & He, Xiao & Si, Xiao-Sheng & Liu, Yang & Zhou, Dong-Hua, 2017. "Lifetime prognostics for deteriorating systems with time-varying random jumps," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 338-350.
    7. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    8. KarabaÄŸ, Oktay & Eruguz, Ayse Sena & Basten, Rob, 2020. "Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    9. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    10. Jahani, Salman & Zhou, Shiyu & Veeramani, Dharmaraj, 2021. "Stochastic prognostics under multiple time-varying environmental factors," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank PA & Coolen-Maturi, Tahani, 2023. "New reliability model for complex systems based on stochastic processes and survival signature," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1349-1364.
    12. Gupta, Nitin & Misra, Neeraj & Kumar, Somesh, 2015. "Stochastic comparisons of residual lifetimes and inactivity times of coherent systems with dependent identically distributed components," European Journal of Operational Research, Elsevier, vol. 240(2), pages 425-430.
    13. Zhang, Jian-Xun & Si, Xiao-Sheng & Du, Dang-Bo & Hu, Chang-Hua & Hu, Chen, 2020. "A novel iterative approach of lifetime estimation for standby systems with deteriorating spare parts," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    14. Mo, Renpeng & Zhou, Han & Yin, Hongpeng & Si, Xiaosheng, 2025. "A survey on few-shot learning for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 257(PB).
    15. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    16. Qin, Shuidan & Wang, Bing Xing & Tsai, Tzong-Ru & Wang, Xiaofei, 2023. "The prediction of remaining useful lifetime for the Weibull k-out-of-n load-sharing system," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    17. Ondemir, Onder & Gupta, Surendra M., 2014. "A multi-criteria decision making model for advanced repair-to-order and disassembly-to-order system," European Journal of Operational Research, Elsevier, vol. 233(2), pages 408-419.
    18. Jin, Guang & Matthews, David E. & Zhou, Zhongbao, 2013. "A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 7-20.
    19. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    20. Jianxun Zhang & Xiao He & Xiaosheng Si & Changhua Hu & Donghua Zhou, 2017. "A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena," Energies, MDPI, vol. 10(11), pages 1-24, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:239:y:2025:i:2:p:276-288. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.