Skip to main content

Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models

Journal of Intelligent Manufacturing volume 30, pages 1259–1270 (2019)Cite this article

Abstract

We present a novel approach to estimating the effect of control parameters on tool wear rates and related changes in the three force components in turning of medical grade Co–Cr–Mo (ASTM F75) alloy. Co–Cr–Mo is known to be a difficult to cut material which, due to a combination of mechanical and physical properties, is used for the critical structural components of implantable medical prosthetics. We run a designed experiment which enables us to estimate tool wear from feed rate and cutting speed, and constrain them using a Bayesian hierarchical Gaussian process model which enables prediction of tool wear rates for untried experimental settings. The predicted tool wear rates are non-linear and, using our models, we can identify experimental settings which optimise the life of the tool. This approach has potential in the future for realtime application of data analytics to machining processes.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  • Abouelatta, O., & Madl, J. (2001). Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. Journal of Materials Processing Technology, 118(1), 269–277.

    Article  Google Scholar 

  • Abukhshim, N., Mativenga, P., & Sheikh, M. (2006). Heat generation and temperature prediction in metal cutting: A review and implications for high speed machining. International Journal of Machine Tools and Manufacture, 46(7), 782–800.

    Article  Google Scholar 

  • Ahearne, E., Baron, S., et al. (2016). Tool wear in milling of medical grade cobalt chromium alloy-requirements for advanced process monitoring and data analytics. In Proceedings of the Machine Tool Technology Research Foundation annual meeting, San Francisco, California, 5th–7th July 2016.

  • Al Jabbari, Y. (2014). Physico-mechanical properties and prosthodontic applications of Co–Cr dental alloys: A review of the literature. The Journal of Advanced Prosthodontics, 6(2), 138–145.

    Article  Google Scholar 

  • Altintas, Y. (2012). Manufacturing automation: Metal cutting mechanics, machine tool vibrations, and CNC design (2nd ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  • ASTM. (2016). Standard specification for cobalt-28 chromium-6 molybdenum alloy castings and casting alloy for surgical implants (UNS R30075). https://www.astm.org/Standards/F75.htm. Accessed 23 May 2016.

  • Azmin, F. M., & Stobart, R. (2015). Benefiting from Sobol sequences experiment design type for model-based calibration. Technical reports, SAE technical paper.

  • Baron, S., & Ahearne, E. (2015). An investigation of force components in orthogonal cutting of medical grade cobalt chromium alloy (ASTM F1537). In CAPE (computer-aided production engineering) 23: Manufacturing research and its applications in the 21st century.

  • Baron, S., Ahearne, E., Conolly, P., Keaveney, S., & Byrne, G. (2015). An assessment of medical grade cobalt chromium alloy ASTM F1537 as a difficult to cut (DTC) material. In Proceedings of machine tool technology research foundation 2015.

  • Brooks, S., Gelman, A., Jones, G., & Meng, X. L. (2011). Handbook of Markov chain Monte Carlo. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Brooks, S. P., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434–455.

    Google Scholar 

  • Buechel, F. F, Sr., Buechel, F. F, Jr., Pappas, M. J., & D’Alessio, J. (2001). Twenty-year evaluation of meniscal bearing and rotating platform knee replacements. Clinical Orthopaedics and Related Research, 388, 41–50.

    Article  Google Scholar 

  • Byrne, G., Dornfeld, D., Inasaki, I., Ketteler, G., König, W., & Teti, R. (1995). Tool condition monitoring (TCM)—The status of research and industrial application. CIRP Annals-Manufacturing Technology, 44(2), 541–567.

    Article  Google Scholar 

  • Byrne, G., Ahearne, E., Cotterell, M., Mullany, B., ODonnell, G., & Sammler, F. (2016). High performance cutting (HPC) in the new era of digital manufacturing—A roadmap. Procedia CIRP, 46, 1–6.

    Article  Google Scholar 

  • Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., et al. (2017). Stan: A probabilistic programming language. Journal of Statistical Software. doi:10.18637/jss.v076.i01.

  • Chou, Y. K., & Song, H. (2004). Tool nose radius effects on finish hard turning. Journal of Materials Processing Technology, 148(2), 259–268.

    Article  Google Scholar 

  • Centers for Disease Control, Prevention. (2016). Cost of hospital discharges with common hospital operating room procedures in nonfederal community hospitals, by age and selected principal procedure: United states, selected years 2000–2012. http://www.cdc.gov/nchs/data/hus/2014/105.pdf. Accessed 23 May 2016.

  • Dimla, D., & Lister, P. (2000). On-line metal cutting tool condition monitoring. I: Force and vibration analyses. International Journal of Machine Tools and Manufacture, 40(5), 739–768.

    Article  Google Scholar 

  • Dudzinski, D., Devillez, A., Moufki, A., Larrouquere, D., Zerrouki, V., & Vigneau, J. (2004). A review of developments towards dry and high speed machining of inconel 718 alloy. International Journal of Machine Tools and Manufacture, 44(4), 439–456.

    Article  Google Scholar 

  • Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper). Bayesian Analysis, 1(3), 515–534.

    Article  Google Scholar 

  • Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472.

  • Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian data analysis. Chapman & Hall/CRC texts in statistical science (2nd ed.). London, Boca Raton, FL: Chapman and Hall, CRC Press.

    Google Scholar 

  • Girolami, M., & Calderhead, B. (2011). Riemann manifold langevin and hamiltonian monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(2), 123–214.

    Article  Google Scholar 

  • Halpin, T. (2012). The wear and failure of PCBN cutting tools in interrupted hard machining. Technical reports, University College Dublin.

  • Hoffman, M. D., & Gelman, A. (2014). The No-U-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo. The Journal of Machine Learning Research, 15(1), 1593–1623.

    Google Scholar 

  • Itakura, K., Kuroda, M., Omokawa, H., Itani, H., Yamamoto, K., & Ariura, Y. (1999). Wear mechanism of coated cemented carbide tool in cutting of inconel 718 super-heat-resisting alloy. International Journal of the Japan Society for Precision Engineering, 33(4), 326–332.

    Google Scholar 

  • Jaspers, S., & Dautzenberg, J. (2002). Material behaviour in metal cutting: strains, strain rates and temperatures in chip formation. Journal of Materials Processing Technology, 121(1), 123–135.

    Article  Google Scholar 

  • Kaiser, R. (2013). Study of the effect of casting variables and heat treatment procedure on the microstructure and mechanical properties of Co–Cr–Mo biomedical alloy. Technical reports, University College Dublin.

  • Killick, R., & Eckley, I. (2014). changepoint: An R package for changepoint analysis. Journal of Statistical Software, 58(3), 1–19.

    Article  Google Scholar 

  • Kistler. (2016). Multicomponent dynamometer technical specification sheet. https://www.kistler.com/?type=669&fid=41281. Accessed 23 May 2016.

  • Klocke, F., & Krieg, T. (1999). Coated tools for metal cutting-features and applications. CIRP Annals-Manufacturing Technology, 48(2), 515–525.

    Article  Google Scholar 

  • Krzanowski, W. (2000). Principles of multivariate analysis. Oxford: OUP.

    Google Scholar 

  • Markopoulos, A. P. (2013). Cutting mechanics and analytical modeling. In Finite element method in machining processes. Springer, London, pp 11–27

  • Merchant, M. E. (1945). Mechanics of the metal cutting process. I. Orthogonal cutting and a type 2 chip. Journal of Applied Physics, 16(5), 267–275.

    Article  Google Scholar 

  • Neal, R. M., et al. (2011). Mcmc using hamiltonian dynamics. Handbook of Markov Chain Monte Carlo, 2, 113–162.

    Google Scholar 

  • Pawade, R., Joshi, S. S., Brahmankar, P., & Rahman, M. (2007). An investigation of cutting forces and surface damage in high-speed turning of inconel 718. Journal of Materials Processing Technology, 192, 139–146.

  • Rasmussen, C., & Williams, K. (2006). Principles of multivariate analysis. Cambridge: MIT Press.

    Google Scholar 

  • Scott, A. J., & Knott, M. (1974). A cluster analysis method for grouping means in the analysis of variance. Biometrics, 30(3), 507–512.

  • Settineri, L., Faga, M. G., & Lerga, B. (2008). Properties and performances of innovative coated tools for turning inconel. International Journal of Machine Tools and Manufacture, 48(7), 815–823.

    Article  Google Scholar 

  • Shaw, M. C. (2005). Metal cutting principles (2nd ed.). New York: Oxford University Press.

    Google Scholar 

  • Sobol, I. M. (1967). On the distribution of points in a cube and the approximate evaluation of integrals. Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, 7(4), 784–802.

    Google Scholar 

  • Teti, R., Jemielniak, K., ODonnell, G., & Dornfeld, D. (2010). Advanced monitoring of machining operations. CIRP Annals-Manufacturing Technology, 59(2), 717–739.

    Article  Google Scholar 

  • Tozzi, J. (2015). Older, heavier Americans fuel a $4 billion knee replacement market. http://www.bloomberg.com/news/articles/2015-09-02/older-heavier-americans-fuel-a-4-billion-knee-replacement-market. Accessed 23 May 2016.

  • Uhlmann, E., Fuentes, J. O., & Keunecke, M. (2009). Machining of high performance workpiece materials with CBN coated cutting tools. Thin Solid Films, 518(5), 1451–1454.

    Article  Google Scholar 

  • Williams, D. (2008). On the mechanisms of biocompatibility. Biomaterials, 29(20), 2941–2953.

    Article  Google Scholar 

  • Xue, C., & Chen, W. (2011). Adhering layer formation and its effect on the wear of coated carbide tools during turning of a nickel-based alloy. Wear, 270(11–12), 895–902.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank DePuy Synthes and Enterprise Ireland for supporting this research through the Innovation Partnership (IP) programme. The Innovation Partnership programme is co-funded by the European Union through the European Regional Development Fund 2014–2020.

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, University College Dublin, Dublin, Ireland

    Damien McParland & Andrew Parnell

  2. School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland

    Szymon Baron, Denis Dowling & Eamonn Ahearne

  3. Central Statistics Office of Ireland, Dublin, Ireland

    Sarah O’Rourke

  4. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

    Andrew Parnell

Authors
  1. Damien McParland
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Szymon Baron
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sarah O’Rourke
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Denis Dowling
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eamonn Ahearne
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrew Parnell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Damien McParland.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 158 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

McParland, D., Baron, S., O’Rourke, S. et al. Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models. J Intell Manuf 30, 1259–1270 (2019). https://doi.org/10.1007/s10845-017-1317-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-017-1317-3

Keywords

  • Cobalt chromium alloys
  • Orthogonal cutting
  • Forces in cutting
  • Gaussian process
  • Tool life optimisation