An experimental study of process variables in turning operations of Ti–6Al–4V and Cr–Co spherical prostheses

  • J. V. Abellán-Nebot
  • H. R. SillerEmail author
  • C. Vila
  • C. A. Rodríguez


Ti–6Al–4V and Cr–Co alloys are extensively used in manufacturing prostheses due to their biocompatibility, high strength-to-weight ratio and high resistance to corrosion and wear. However, machining operations involving Ti–6Al–4V and Cr–Co alloys face a series of difficulties related to their low machinability which complicate the process of controlling the quality levels required in these parts. The main objective of this paper is to study the influence of cutting parameters, machine tool control accuracy and metrology procedures on surface roughness parameters and form errors in contouring operations of Ti–6Al–4V and Cr–Co workpieces. The machining performance of the two biocompatible materials is compared, focusing the study on part quality at low feed per revolution and the stochastic nature of plastic deformations at this regime. The results showed a better surface roughness control for Ti–6Al–4V, whereas for Cr–Co alloys, the performance presents high variability. In the case of form errors (sphericity), contouring errors and metrology procedures are important factors to be considered for quality assurance. In addition, the study analyses the correlation of the machining performance with different sensor signals acquired from a low cost non-intrusive multi-sensor, showing a high correlation of signals from acoustic emission sensors and accelerometers in the machining of spherical features on Ti–6Al–4V parts. The findings of this research work can be taken into account when designing prostheses components and planning their manufacturing processes.


Ti–6Al–4V alloys Cr–Co alloys Prostheses Turning Process parameters 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balazic M, Kopac J, Jackson MJ, Ahmed W (2007) Review: titanium and titanium alloy applications in medicine. Int J Nano Biomater 1:3–34CrossRefGoogle Scholar
  2. 2.
    Long M, Rack HJ (1998) Titanium alloys in total joint replacement—a materials science perspective. Biomaterials 19:1621–1639CrossRefGoogle Scholar
  3. 3.
    Ohkubo C, Watanabe I, Ford JP, Nakajima H, Hosoi T, Okabe T (2000) The machinability of cast titanium and Ti–6Al–4 V. Biomaterials 21:421–428CrossRefGoogle Scholar
  4. 4.
    Yang X, Liu CR (1999) Machining titanium and its alloys. Mater Sci Technol 3:107–139Google Scholar
  5. 5.
    Barry J, Byrne G, Lennon D (2001) Observations on chip formation and acoustic emission in machining Ti–6Al–4 V alloy. Int J Mach Tools Manuf 41:1055–1070CrossRefGoogle Scholar
  6. 6.
    Ezugwu EO (2005) Key improvements in the machining of difficult-to-cut aerospace alloys. Int J Mach Tools Manuf 45:1353–1367CrossRefGoogle Scholar
  7. 7.
    Ezugwu EO, Da Silva RB, Bonney J, Machado AR (2005) Evaluation of the performance of CBN tools when turning Ti–6Al–4 V. Int J Mach Tools Manuf 45:1009–1014CrossRefGoogle Scholar
  8. 8.
    Aspinwall DK, Dewes RC, Mantle AL (2005) The machining of gamma-TiAl intermetallic alloys. CIRP Ann 54:99–104CrossRefGoogle Scholar
  9. 9.
    López de Lacalle LN, Pérez-Bilbatua J, Sánchez JA, Llorente JI, Gutierrez A, Albóniga J (2000) Using high pressure coolant in the drilling and turning of low machinability alloys. Int J Adv Manuf Technol 16:85–91CrossRefGoogle Scholar
  10. 10.
    Aydin AK (1991) Evaluation of finishing and polishing techniques on surface roughness of chromium–cobalt castings. J Prosthet Dent 65:763–767CrossRefGoogle Scholar
  11. 11.
    Xenodimitropoulou G, Radford DR (1998) The machining of cobalt–chromium alloy in partial denture. Int J Prosthodont 11(6):565–573Google Scholar
  12. 12.
    Shi AJ (2008) Biomedical manufacturing: a new frontier of manufacturing research. J Manuf Sci Eng 130:021009-1-021009-8Google Scholar
  13. 13.
    Grill A (2003) Diamond-like carbon coatings as biocompatible materials—an overview. Diamond Relat Mater 12:166–170CrossRefGoogle Scholar
  14. 14.
    Abellan-Nebot JV, Liu J, Subiron FR, Shi J (2011) State space modeling of variation propagation in multistage machining processes considering operation-induced variations. Submitted to ASME Transactions on Manufacturing Science and Engineering, in pressGoogle Scholar
  15. 15.
    Liu J, Shi J, Hu SJ (2009) Quality assured setup planning based on the stream of variation model for multi-stage machining processes. IIE Trans, Qual Reliab Eng 41:323–334Google Scholar
  16. 16.
    Camalaz M, Coupard D, Girot F (2008) A new material model for 2D numerical simulation of serrated chip formation when machining titanium alloy Ti–6Al–4 V. Int J Mach Tools Manuf 48:275–288CrossRefGoogle Scholar
  17. 17.
    Gadelmawla ES, Koura MM, Maksoud TMA, Elewa IM, Soliman HH (2002) Roughness parameters. J Mater Process Technol 123:133–145CrossRefGoogle Scholar
  18. 18.
    Stephenson DA, Agapiou JS (1997) Metal cutting theory and practice. Marcel Dekker, New YorkGoogle Scholar
  19. 19.
    Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine-tools—a review. Part I: geometric, cutting-force induced and fixture-dependent errors. Int J Mach Tools Manuf 40:1235–1256CrossRefGoogle Scholar
  20. 20.
    Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine-tools—a review. Part II: thermal errors. Int J Mach Tools Manuf 40:1257–1284CrossRefGoogle Scholar
  21. 21.
    López de Lacalle LN, Lamikiz A (2009) Machine-tools for high performance machining. Springer, LondonCrossRefGoogle Scholar
  22. 22.
    Ramesh R, Mannan MA, Poo AN (2005) Tracking and contour error control in CNC servo systems. Int J Mach Tools Manuf 45:301–326CrossRefGoogle Scholar
  23. 23.
    Liang M, Mgwatu M, Zuo M (2001) Integration of cutting parameter selection and tool adjustment decisions for multipass turning. Int J Adv Manuf Technol 17:861–869CrossRefGoogle Scholar
  24. 24.
    Feng CXJ, Wang X (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20:348–356CrossRefGoogle Scholar
  25. 25.
    Benardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43:833–844CrossRefGoogle Scholar
  26. 26.
    Schwenke H, Knapp W, Haitjema H, Weckenmann A, Schmitt R, Delbressine F (2008) Geometric error measurement and compensation of machines—an update. CIRP Ann 57:660–675CrossRefGoogle Scholar
  27. 27.
    Siller H, Rodriguez CA, Ahuett H (2006) Cycle time prediction in high-speed milling operations for sculptured surface finishing. J Mater Process Tech 174:355–362CrossRefGoogle Scholar
  28. 28.
    Liu K, Melkote SN (2006) Effect of plastic side flow on surface roughness in micro-turning processes. Int J Mach Tools Manuf 46:1778–1785CrossRefGoogle Scholar
  29. 29.
    Grzesik W (1996) A revised model for predicting surface roughness in turning. Wear 194:143–148CrossRefGoogle Scholar
  30. 30.
    Boothroyd G, Knight WA (1989) Fundamentals of machining and machine-tools. Marcel Dekker, New YorkGoogle Scholar
  31. 31.
    Brammertz PH (1961) Die entstehung der oberflächenrauheit beim feindrehem. Industrie Anzeiger 2:25–32Google Scholar
  32. 32.
    Gass SI, Witzgall C, Harary HH (1998) Fitting circles and spheres to coordinate measuring machine data. Int J Flex Manuf Syst 10:5–25CrossRefGoogle Scholar
  33. 33.
    The Brown & Sharpe DEA Mistral programming manual (2000)Google Scholar
  34. 34.
    Montgomery D, Runger G (2007) Applied statistics and probability for engineers, 4th edn. Wiley, New Jersey, pp 273–277zbMATHGoogle Scholar
  35. 35.
    Buford A, Goswami T (2004) Review of wear mechanisms in hip implants: paper I—general. Mater Design 25:385–393CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • J. V. Abellán-Nebot
    • 1
  • H. R. Siller
    • 2
    Email author
  • C. Vila
    • 1
  • C. A. Rodríguez
    • 2
  1. 1.Department of Industrial Systems Engineering and DesignUniversitat Jaume ICastellón de la PlanaSpain
  2. 2.Centre for Innovation in Design and Technology, Tecnológico de MonterreyMonterreyMexico

Personalised recommendations