A physical behavior model including dynamic recrystallization and damage mechanisms for cutting process simulation of the titanium alloy Ti-6Al-4V

  • D. Yameogo
  • B. Haddag
  • H. Makich
  • M. NouariEmail author


Titanium and its alloys are attractive materials due to their low density and resistance to high temperature and corrosion. However, these materials are also known for their low machinability that leads to poor surface finish and premature tool wear. When machining such materials, serrated chips are often generated. According to the literature, this is generally due to the material damage and microstructure transformation phenomena. A flow stress modeling that takes into account material damage and dynamic recrystallization (DRX) is proposed to obtain a more realistic cutting process simulation of the titanium alloy Ti-6Al-4V. The Johnson-Mehl-Avrami-Kolmogorov (JMAK) model is used to predict the recrystallized volume fraction involved in the proposed flow stress law. The microstructure evolution influences the material damage and the JMAK DRX initiation criterion is used to introduce this effect. A 2D Lagrangian finite element (FE) formulation is adopted to simulate the orthogonal cutting process. The cutting forces and chip morphology, obtained with the proposed behavior model, are analyzed and compared to those obtained with known tangent hyperbolic (TANH) and Johnson-Cook (JC) behavior models. A good accordance between the proposed model simulations and the experimental results is noticed. The link between recrystallization, damage and chip segmentation has been deeply analyzed.


Machining Ti-6Al-4V Physical-based model Microstructure Damage Chip morphology Cutting force FE analysis 


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  1. 1.
    Donachie MJ (2000) Titanium: a technical guide. ASM International, OhioGoogle Scholar
  2. 2.
    Sun S, Brandt M, Dargusch MS (2009) Characteristics of cutting forces and chip formation in machining of titanium alloys. Int J Mach Tools Manuf 49:561–568CrossRefGoogle Scholar
  3. 3.
    Su G, Liu Z, Li L, Wang B (2015) Influences of chip serration on micro-topography of machined surface in high-speed cutting. Int J Mach Tools Manuf 89:202–207CrossRefGoogle Scholar
  4. 4.
    Altintas Y (2012) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge university press, CambridgeGoogle Scholar
  5. 5.
    Cheng K (2008) Machining dynamics: fundamentals, applications and practices. Springer Science & Business Media, BerlinGoogle Scholar
  6. 6.
    Jaspers S, Dautzenberg JH (2002) Material behaviour in metal cutting: strains, strain rates and temperatures in chip formation. J Mater Process Technol 121:123–135CrossRefGoogle Scholar
  7. 7.
    Nakayama K, Arai M, Kanda T (1988) Machining characteristics of hard materials. CIRP Ann.-Manuf. Technol. 37:89–92CrossRefGoogle Scholar
  8. 8.
    Shaw MC, Vyas A (1993) Chip formation in the machining of hardened steel. CIRP Ann.-Manuf. Technol. 42:29–33CrossRefGoogle Scholar
  9. 9.
    Umbrello D (2008) Finite element simulation of conventional and high speed machining of Ti6Al4V alloy. J Mater Process Technol 196:79–87CrossRefGoogle Scholar
  10. 10.
    Aurich JC, Bil H (2006) 3D finite element modelling of segmented chip formation. CIRP Ann.-Manuf. Technol. 55:47–50CrossRefGoogle Scholar
  11. 11.
    Zhen-Bin H, Komanduri R (1995) On a thermomechanical model of shear instability in machining. CIRP Ann-Manuf Technol 44:69–73CrossRefGoogle Scholar
  12. 12.
    Davies MA, Burns TJ, Evans CJ (1997) On the dynamics of chip formation in machining hard metals. CIRP Ann.-Manuf. Technol. 46:25–30CrossRefGoogle Scholar
  13. 13.
    Wan ZP, Zhu YE, Liu HW, Tang Y (2012) Microstructure evolution of adiabatic shear bands and mechanisms of saw-tooth chip formation in machining Ti6Al4V. Mater Sci Eng A 531:155–163CrossRefGoogle Scholar
  14. 14.
    Sagapuram D, Viswanathan K, Mahato A, Sundaram NK, M’Saoubi R, Trumble KP, Chandrasekar S (2016) Geometric flow control of shear bands by suppression of viscous sliding, in: Proc R Soc A, The Royal Society: p. 20160167Google Scholar
  15. 15.
    Nouari M, Makich H (2013) Experimental investigation on the effect of the material microstructure on tool wear when machining hard titanium alloys: Ti–6Al–4V and Ti-555. Int J Refract Met Hard Mater 41:259–269CrossRefGoogle Scholar
  16. 16.
    Rhim S-H, Oh S-I (2006) Prediction of serrated chip formation in metal cutting process with new flow stress model for AISI 1045 steel. J Mater Process Technol 171:417–422CrossRefGoogle Scholar
  17. 17.
    Calamaz M, Coupard D, Girot F (2008) A new material model for 2D numerical simulation of serrated chip formation when machining titanium alloy Ti–6Al–4V. Int J Mach Tools Manuf 48:275–288CrossRefGoogle Scholar
  18. 18.
    Liu R, Melkote S, Pucha R, Morehouse J, Man X, Marusich T (2013) An enhanced constitutive material model for machining of Ti–6Al–4V alloy. J Mater Process Technol 213:2238–2246CrossRefGoogle Scholar
  19. 19.
    Sima M, Özel T (2010) Modified material constitutive models for serrated chip formation simulations and experimental validation in machining of titanium alloy Ti–6Al–4V. Int J Mach Tools Manuf 50:943–960CrossRefGoogle Scholar
  20. 20.
    Ducobu F, Rivière-Lorphèvre E, Filippi E (2016) Material constitutive model and chip separation criterion influence on the modeling of Ti6Al4V machining with experimental validation in strictly orthogonal cutting condition. Int J Mech Sci 107:136–149CrossRefGoogle Scholar
  21. 21.
    Atmani Z, Haddag B, Nouari M, Zenasni M (2016) Combined microstructure-based flow stress and grain size evolution models for multi-physics modelling of metal machining. Int J Mech Sci 118:77–90CrossRefGoogle Scholar
  22. 22.
    Follansbee PS, Kocks UF (1988) A constitutive description of the deformation of copper based on the use of the mechanical threshold stress as an internal state variable. Acta Metall 36:81–93CrossRefGoogle Scholar
  23. 23.
    Zerilli FJ, Armstrong RW (1987) Dislocation-mechanics-based constitutive relations for material dynamics calculations. J Appl Phys 61:1816–1825CrossRefGoogle Scholar
  24. 24.
    Arısoy YM, Özel T (2015) Prediction of machining induced microstructure in Ti–6Al–4V alloy using 3-D FE-based simulations: effects of tool micro-geometry, coating and cutting conditions. J Mater Process Technol 220:1–26CrossRefGoogle Scholar
  25. 25.
    Pan Z, Liang SY, Garmestani H, Shih DS (2016) Prediction of machining-induced phase transformation and grain growth of Ti-6Al-4 V alloy. Int J Adv Manuf Technol 87:859–866CrossRefGoogle Scholar
  26. 26.
    Shang X, Cui Z, Fu MW (2017) Dynamic recrystallization based ductile fracture modeling in hot working of metallic materials. Int J Plast 95:105–122CrossRefGoogle Scholar
  27. 27.
    Kolmogorov VL, Smirnov SV (1998) The restoration of the margin of metal plasticity after cold deformation. J Mater Process Technol 74:83–88CrossRefGoogle Scholar
  28. 28.
    Smirnov SV (2013) The healing of damage after the plastic deformation of metals, Frat Ed Integrità Strutt 7Google Scholar
  29. 29.
    Wang B, Liu Z (2015) Shear localization sensitivity analysis for Johnson–Cook constitutive parameters on serrated chips in high speed machining of Ti6Al4V. Simul Model Pract Theory 55:63–76CrossRefGoogle Scholar
  30. 30.
    Kouadri S, Necib K, Atlati S, Haddag B, Nouari M (2013) Quantification of the chip segmentation in metal machining: application to machining the aeronautical aluminium alloy AA2024-T351 with cemented carbide tools WC-Co. Int J Mach Tools Manuf 64:102–113CrossRefGoogle Scholar
  31. 31.
    Atlati S, Haddag B, Nouari M, Zenasni M (2011) Analysis of a new segmentation intensity ratio “SIR” to characterize the chip segmentation process in machining ductile metals. Int J Mach Tools Manuf 51:687–700CrossRefGoogle Scholar
  32. 32.
    Ma W, Chen X, Shuang F (2017) The chip-flow behaviors and formation mechanisms in the orthogonal cutting process of Ti6Al4V alloy. J Mech Phys Solids 98:245–270MathSciNetCrossRefGoogle Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Lorraine, LEM3 UMR CNRS 7239, Institut Mines-Telecom, GIP-InSICSaint-Dié-des-VosgesFrance

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