Skip to main content
SpringerLink
Log in

Grain size sensitive–MTS model for Ti-6Al-4V machining force and residual stress prediction

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Material properties are significantly influenced by the parameters of the machining process. The accurate prediction of machining force and residual stress reduces power consumption, enhances material properties, and improves dimensional accuracy of the finished product. Traditional method using the finite element analysis (FEA) costs a significant amount of time, and the archived mechanical threshold stress (MTS) model without consideration of microstructure of the material yields inaccurate result. In this paper, a grain size–sensitive MTS model is introduced for the machining process of Ti-6Al-4V. A grain size–sensitive term is introduced to the modified MTS model to account for evolution of the grain size. The grain size–sensitive MTS model takes the microstructure of the Ti-6Al-4V into consideration for the calculation of machining force and residual stress. The grain size–sensitive term is introduced into the athermal stress component using the initial yield stress, strain hardening coefficient, and the Hall-Petch coefficient. The analytical result is compared with those of experimental studies and the traditional Johnson-Cook model to prove the validity in the prediction of machining force and residual stress. The proposed model explores a new area for calculating cutting forces and residual stress.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. 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(11):943–960

    Article  Google Scholar 

  2. Fang N (2003) Slip-line modeling of machining with a rounded-edge tool—part I: new model and theory. J Mech Phys Solids 51(4):715–742

    Article  MATH  Google Scholar 

  3. Fang N, Jawahir I, Oxley P (2001) A universal slip-line model with non-unique solutions for machining with curled chip formation and a restricted contact tool. Int J Mech Sci 43(2):557–580

    Article  MATH  Google Scholar 

  4. Roth R, Oxley P (1972) Slip-line field analysis for orthogonal machining based upon experimental flow fields. J Mech Eng Sci 14(2):85–97

    Article  Google Scholar 

  5. Oxley P (1998) Development and application of a predictive machining theory. Mach Sci Technol 2(2):165–189

    Article  Google Scholar 

  6. Pawade R, Joshi SS, Brahmankar P (2008) Effect of machining parameters and cutting edge geometry on surface integrity of high-speed turned Inconel 718. Int J Mach Tools Manuf 48(1):15–28

    Article  Google Scholar 

  7. Son SM, Lim HS, Ahn JH (2005) Effects of the friction coefficient on the minimum cutting thickness in micro cutting. Int J Mach Tools Manuf 45(4–5):529–535

    Article  Google Scholar 

  8. Yun HT, Heo S, Lee MK, Min BK, Lee SJ (2011) Ploughing detection in micromilling processes using the cutting force signal. Int J Mach Tools Manuf 51(5):377–382

    Article  Google Scholar 

  9. Wang Y et al (2018) The micro-cutting performance of cermet and coated WC micro-mills in machining of TC4 alloy micro-grooves. Int J Adv Manuf Technol 96(1–4):1403–1414

    Article  Google Scholar 

  10. Campbell CE, Bendersky LA, Boettinger WJ, Ivester R (2006) Microstructural characterization of Al-7075-T651 chips and work pieces produced by high-speed machining. Mater Sci Eng A 430(1–2):15–26

    Article  Google Scholar 

  11. Calamaz M, Coupard D, Nouari M, Girot F (2011) Numerical analysis of chip formation and shear localisation processes in machining the Ti-6Al-4V titanium alloy. Int J Adv Manuf Technol 52(9):887–895

    Article  Google Scholar 

  12. Bäker M, Rösler J, Siemers C (2002) A finite element model of high speed metal cutting with adiabatic shearing. Comput Struct 80(5):495–513

    Article  Google Scholar 

  13. Kassner M et al (2002) Large-strain softening of aluminum in shear at elevated temperature. Metall Mater Trans A 33(10):3145–3153

    Article  Google Scholar 

  14. Wang Z, Rahman M, Wong Y (2005) Tool wear characteristics of binderless CBN tools used in high-speed milling of titanium alloys. Wear 258(5–6):752–758

    Article  Google Scholar 

  15. Che-Haron C, Jawaid A (2005) The effect of machining on surface integrity of titanium alloy Ti–6% Al–4% V. J Mater Process Technol 166(2):188–192

    Article  Google Scholar 

  16. Ahmed T, Rack H (1998) Phase transformations during cooling in α+ β titanium alloys. Mater Sci Eng A 243(1–2):206–211

    Article  Google Scholar 

  17. Ulutan D, Ozel T (2011) Machining induced surface integrity in titanium and nickel alloys: a review. Int J Mach Tools Manuf 51(3):250–280

    Article  Google Scholar 

  18. 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(1–4):859–866

    Article  Google Scholar 

  19. Sun S, Brandt M, Dargusch M (2009) Characteristics of cutting forces and chip formation in machining of titanium alloys. Int J Mach Tools Manuf 49(7–8):561–568

    Article  Google Scholar 

  20. Wang Q, Liu Z, Wang B, Song Q, Wan Y (2016) Evolutions of grain size and micro-hardness during chip formation and machined surface generation for Ti-6Al-4V in high-speed machining. Int J Adv Manuf Technol 82(9):1725–1736

    Article  Google Scholar 

  21. Follansbee P, Gray G (1989) An analysis of the low temperature, low and high strain-rate deformation of Ti−6Al−4V. Metall Trans A 20(5):863–874

    Article  Google Scholar 

  22. Pan Z, Shih DS, Tabei A, Garmestani H, Liang SY (2017) Modeling of Ti-6Al-4V machining force considering material microstructure evolution. Int J Adv Manuf Technol 91(5):2673–2680

    Article  Google Scholar 

  23. Estrin Y, Tóth LS, Molinari A, Bréchet Y (1998) A dislocation-based model for all hardening stages in large strain deformation. Acta Mater 46(15):5509–5522

    Article  Google Scholar 

  24. 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 118:77–90

    Article  Google Scholar 

  25. Pan Z, et al. (2017) Residual stress prediction for turning of Ti-6Al-4V considering the microstructure evolution. Proc Inst Mech Eng B J Eng Manuf p. 0954405417712551

  26. Follansbee P, Kocks U (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(1):81–93

    Article  Google Scholar 

  27. Pan Z, Liang SY, and Garmestani H (2018) Finite element simulation of residual stress in machining of Ti-6Al-4V with a microstructural consideration. Proc Inst Mech Eng B J Eng Manuf p. 0954405418769927

  28. Banerjee B (2007) The mechanical threshold stress model for various tempers of AISI 4340 steel. Int J Solids Struct 44(3–4):834–859

    Article  Google Scholar 

  29. Wanying L, Yuanhua L, Yuhai C, Taihe S, Singh A (2017) Effect of different heat treatments on microstructure and mechanical properties of Ti6Al4V titanium alloy. Rare Metal Mater Eng 46(3):634–639

    Article  Google Scholar 

  30. Bogucki R, Mosór K, Nykiel M (2014) Effect of heat treatment conditions on the morphology of α phase and mechanical properties in Ti-10V-2Fe-3Al titanium alloy/Wpływ Warunków Obróbki Cieplnej Na Morfologie Fazy α I Własności Mechaniczne W Stopie Tytanu Ti-10V-2Fe-3Al. Arch Metall Mater 59(4):1269–1273

    Article  Google Scholar 

  31. MacDougall D and Maudlin P (2000) The proportion of plastic work converted to heat in ti6al4v: mts model prediction and experimental data Los Alamos National Lab., NM (US)

  32. Peng F et al (2016) Analytical modeling and experimental validation of residual stress in micro-end-milling. Int J Adv Manuf Technol 87(9–12):3411–3424

    Article  Google Scholar 

  33. Bao W, Tansel I (2000) Modeling micro-end-milling operations. Part I: analytical cutting force model. Int J Mach Tools Manuf 40(15):2155–2173

    Article  Google Scholar 

  34. Dogra M, Sharma V, Dureja J (2011) Effect of tool geometry variation on finish turning-a review. J Eng Sci Technol Rev 4(1):1–13

    Article  Google Scholar 

  35. Karpat Y, Zeren E, and Ozel T (2000) Workpiece material model based predictions for machining processes. Society of Manufacturing Engineers

  36. Oxley P, Hastings W (1977) Predicting the strain rate in the zone of intense shear in which the chip is formed in machining from the dynamic flow stress properties of the work material and the cutting conditions. Proc R Soc Lond A 356(1686):395–410

    Article  Google Scholar 

  37. Huang Y, Liang SY (2003) Cutting forces modeling considering the effect of tool thermal property—application to CBN hard turning. Int J Mach Tools Manuf 43(3):307–315

    Article  Google Scholar 

  38. Oxley P, Hastings W (1976) Minimum work as a possible criterion for determining the frictional conditions at the tool/chip interface in machining. Philos Trans R Soc Lond A 282(1310):565–584

    Article  Google Scholar 

  39. Boothroyd G (1963) Temperatures in orthogonal metal cutting. Proc Inst Mech Eng 177(1):789–810

    Article  Google Scholar 

  40. Pan Z, Feng Y, Lu YT, Lin YF, Hung TP, Hsu FC, Liang SY (2017) Force modeling of Inconel 718 laser-assisted end milling under recrystallization effects. Int J Adv Manuf Technol 92(5–8):2965–2974

    Article  Google Scholar 

  41. Liang SY and Shih AJ (2015) Analysis of machining and machine tools Springer

  42. Su JC (2006) Residual stress modeling in machining processes Georgia Institute of Technology

  43. Budak E, Altintas Y, Armarego E (1996) Prediction of milling force coefficients from orthogonal cutting data. J Manuf Sci Eng 118(2):216–224

    Article  Google Scholar 

  44. Atmani Z, Haddag B, Nouari M, Zenasni M (2015) Multi-physics modelling in machining OFHC copper–coupling of microstructure-based flow stress and grain refinement models. Procedia CIRP 31:545–550

    Article  Google Scholar 

  45. Pathak B, Sahoo K, Mishra M (2013) Effect of machining parameters on cutting forces and surface roughness in Al-(1-2) Fe-1V-1Si alloys. Mater Manuf Process 28(4):463–469

    Article  Google Scholar 

  46. McDowell D (1997) An approximate algorithm for elastic-plastic two-dimensional rolling/sliding contact. Wear 211(2):237–246

    Article  Google Scholar 

  47. Pan Z, Feng Y, Ji X, Liang SY (2018) Turning induced residual stress prediction of AISI 4130 considering dynamic recrystallization. Mach Sci Technol 22(3):507–521

    Article  Google Scholar 

  48. Mendenhall M (2016) Powder diffraction SRMs

  49. Capello E (2005) Residual stresses in turning: part I: influence of process parameters. J Mater Process Technol 160(2):221–228

    Article  Google Scholar 

  50. Pan Z (2018) Predictive modeling for material microstructure affected machining Georgia Institute of Technology

  51. Liang SY, Su JC (2007) Residual stress modeling in orthogonal machining. CIRP Ann Manuf Technol 56(1):65–68

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Yanfei Lu, Zhipeng Pan & Steven Liang

  2. Boeing Research and Technology, Huntsville, AL, 35824, USA

    Peter Bocchini

  3. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Hamid Garmestani

  4. College of Mechanical Engineering, Donghua University, Shanghai, 201620, China

    Steven Liang

Authors
  1. Yanfei Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhipeng Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Bocchini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hamid Garmestani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Steven Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhipeng Pan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, Y., Pan, Z., Bocchini, P. et al. Grain size sensitive–MTS model for Ti-6Al-4V machining force and residual stress prediction. Int J Adv Manuf Technol 102, 2173–2181 (2019). https://doi.org/10.1007/s00170-019-03309-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-03309-w

Keywords

Search

Navigation