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A prediction model for the milling of thin-wall parts considering thermal-mechanical coupling and tool wear

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Abstract

In thin-wall milling processes, the interactions between cutting loads and the displacement of the thin-wall part lead to varying tool-workpiece engagement boundaries and undesired surface form errors. This unavoidable issue becomes more severe in the machining of titanium alloys due to their poor machinability caused by the low thermal conductivity, high strength and high chemical reactivity. This paper presents a new predictive model to calculate the cutting-induced thermal-mechanical loads and workpiece deflection in milling Ti-6Al-4V thin-wall components. The cutting heat sources and the development of tool flank wear were considered in the modelling process to improve the prediction accuracy. The cutting loads were modelled analytically and calculated using an efficient iterative algorithm, and the deformation of the thin-wall part was simulated through a finite element model. A series of cutting experiments were conducted under various cutting conditions to validate the predicted results. Both the cutting forces and thin-wall displacement were recorded to examine prediction accuracy, and good agreements have been achieved between the measured results and simulated outcomes. The predicted cutting forces in the radial, feed and axial directions are within errors of 14%, 10% and 5%, respectively, concerning the experimental values. Meanwhile, the maximum predicted deformation errors at the initial, middle and end portions of the workpiece are less than 20%.

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References

  1. Yi S, Li G, Ding S, Mo J (2017) Performance and mechanisms of graphene oxide suspended cutting fluid in the drilling of titanium alloy Ti-6Al-4V. J Manuf Process 29:182–193

    Article  Google Scholar 

  2. Pan W, Ding S, Mo J (2016) The prediction of cutting force in end milling titanium alloy (Ti6Al4V) with polycrystalline diamond tools. Proc Inst Mech Eng B J Eng Manuf 231(1):3–14

    Article  Google Scholar 

  3. Altintas Y (2012) Manufacturing automation metal cutting mechanics, machine tool vibrations, and CNC design, 2nd edn. Cambridge University Press. https://doi.org/10.1017/CBO9780511843723

  4. Lin B, Wang L, Guo Y, Yao J (2015) Modeling of cutting forces in end milling based on oblique cutting analysis. Int J Adv Manuf Technol 84(1–4):727–736

    Google Scholar 

  5. Luo Z, Zhao W, Jiao L, Wang T, Yan P, Wang X (2016) Cutting force prediction in end milling of curved surfaces based on oblique cutting model. Int J Adv Manuf Technol 89(1–4):1025–1038

    Google Scholar 

  6. Liu J, Cheng K, Ding H, Chen S, Zhao L (2018) Realization of ductile regime machining in micro-milling SiCp/Al composites and selection of cutting parameters. Proc Inst Mech Eng C J Mech Eng Sci 233(12):4336–4347

    Article  Google Scholar 

  7. Perez H, Diez E, Marquez J, Vizan A (2013) An enhanced method for cutting force estimation in peripheral milling. Int J Adv Manuf Technol 69(5–8):1731–1741

    Article  Google Scholar 

  8. Li B, Hu Y, Wang X, Li C, Li X (2011) An analytical model of oblique cutting with application to end milling. Mach Sci Technol 15(4):453–484

    Article  Google Scholar 

  9. Fu Z, Yang W, Wang X, Leopold J (2015) Analytical modelling of milling forces for helical end milling based on a predictive machining theory. Procedia CIRP 31:258–263

    Article  Google Scholar 

  10. Chen Y, Li H, Wang J (2016) Predictive modelling of cutting forces in end milling of titanium alloy Ti6Al4V. Proc Inst Mech Eng B J Eng Manuf 232(9):1523–1534

    Article  Google Scholar 

  11. Li G, Rahim M, Ding S, Sun S (2016) Performance and wear analysis of polycrystalline diamond (PCD) tools manufactured with different methods in turning titanium alloy Ti-6Al-4V. Int J Adv Manuf Technol 85(1–4):825–841

    Article  Google Scholar 

  12. Cheng K, Huo D (2013) Micro cutting: fundamentals and applications. John Wiley & Sons, Chichester

    Book  Google Scholar 

  13. Usui E, Shirakashi T, Kitagawa T (1984) Analytical prediction of cutting tool wear. Wear 100(1–3):129–151

    Article  Google Scholar 

  14. Smithey D, Kapoor S, DeVor R (2000) A worn tool force model for three-dimensional cutting operations. Int J Mach Tools Manuf 40(13):1929–1950

    Article  Google Scholar 

  15. Sun Y, Sun J, Li J, Li W, Feng B (2013) Modeling of cutting force under the tool flank wear effect in end milling Ti6Al4V with solid carbide tool. Int J Adv Manuf Technol 69(9–12):2545–2553

    Article  Google Scholar 

  16. Hou Y, Zhang D, Wu B, Luo M (2015) Milling force modeling of worn tool and tool flank wear recognition in end milling. IEEE-ASME T Mech 20(3):1024–1035

    Article  Google Scholar 

  17. Liang X, Liu Z (2017) Experimental investigations on effects of tool flank wear on surface integrity during orthogonal dry cutting of Ti-6Al-4V. Int J Adv Manuf Technol 93(5–8):1617–1626

    Article  Google Scholar 

  18. Liang X, Liu Z, Wang B, Hou X (2018) Modeling of plastic deformation induced by thermo-mechanical stresses considering tool flank wear in high-speed machining Ti-6Al-4V. Int J Mech Sci 140:1–12

    Article  Google Scholar 

  19. Li G, Li N, Wen C, Ding S (2017) Investigation and modeling of flank wear process of different PCD tools in cutting titanium alloy Ti6Al4V. Int J Adv Manuf Technol 95(1–4):719–733

    Google Scholar 

  20. Komanduri R, Hou Z (2001) Thermal modeling of the metal cutting process-part II: temperature rise distribution due to frictional heat source at the tool–chip interface. Int J Mech Sci 43(1):57–88

    Article  Google Scholar 

  21. Huo D, Cheng K, Webb D, Wardle F (2004) A novel FEA model for the integral analysis of a machine tool and machining processes. Key Eng Mater 257-258:45–50

    Article  Google Scholar 

  22. Li G, Yi S, Li N, Pan W, Wen C, Ding S (2019) Quantitative analysis of cooling and lubricating effects of graphene oxide nanofluids in machining titanium alloy Ti6Al4V. J Mater Process Technol 271:584–598

    Article  Google Scholar 

  23. Pan W, Ding S, Mo J (2014) Thermal characteristics in milling Ti6Al4V with polycrystalline diamond tools. Int J Adv Manuf Technol 75(5–8):1077–1087

    Article  Google Scholar 

  24. Yan L, Yang W, Jin H, Wang Z (2012) Analytical modeling of the effect of the tool flank wear width on the residual stress distribution. Mach Sci Technol 16(2):265–286

    Article  Google Scholar 

  25. Lin S, Peng F, Wen J, Liu Y, Yan R (2013) An investigation of workpiece temperature variation in end milling considering flank rubbing effect. Int J Mach Tools Manuf 73:71–86

    Article  Google Scholar 

  26. Sun Y, Sun J, Li J (2016) Modeling and experimental study of temperature distributions in end milling Ti6Al4V with solid carbide tool. Proc Inst Mech Eng B J Eng Manuf 231(2):217–227

    Article  Google Scholar 

  27. Budak E, Altintas Y (1995) Modeling and avoidance of static form errors in peripheral milling of plates. Int J Mach Tools Manuf 35(3):459–476

    Article  Google Scholar 

  28. Ratchev S, Liu S, Huang W, Becker A (2004) Milling error prediction and compensation in machining of low-rigidity parts. Int J Mach Tools Manuf 44(15):1629–1641

    Article  Google Scholar 

  29. Wan M, Zhang W (2006) Efficient algorithms for calculations of static form errors in peripheral milling. J Mater Process Tech 171(1):156–165

    Article  Google Scholar 

  30. Kang Y, Wang Z (2013) Two efficient iterative algorithms for error prediction in peripheral milling of thin-walled workpieces considering the in-cutting chip. Int J Mach Tools Manuf 73:55–61

    Article  Google Scholar 

  31. Sun Y, Jiang S (2018) Predictive modeling of chatter stability considering force-induced deformation effect in milling thin-walled parts. Int J Mach Tools Manuf 135:38–52

    Article  Google Scholar 

  32. Venuvinod P, Lau W (1986) Estimation of rake temperatures in free oblique cutting. Int J Mach Tool D R 26(1):1–14

    Article  Google Scholar 

  33. Wan M, Zhang W, Qiu K, Gao T, Yang Y (2005) Numerical prediction of static form errors in peripheral milling of thin-walled workpieces with irregular meshes. J Manuf Sci Eng 127(1):13–22

    Article  Google Scholar 

  34. Mills K (2004) Recommended values of thermophysical properties for selected commercial alloys. Woodhead, Cambridge

    Google Scholar 

  35. Li G, Yi S, Wen C, Ding S (2018) Wear mechanism and modeling of tribological behavior of polycrystalline diamond tools when cutting Ti6Al4V. J Manuf Sci Eng 140(12):1–15. https://doi.org/10.1115/1.4041327

  36. Ding S, Izamshah RAR, Mo J, Zhu Y (2010) Chatter detection in high speed machining of titanium alloys. Key Eng Mater 458:289–294

    Article  Google Scholar 

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Wu, G., Li, G., Pan, W. et al. A prediction model for the milling of thin-wall parts considering thermal-mechanical coupling and tool wear. Int J Adv Manuf Technol 107, 4645–4659 (2020). https://doi.org/10.1007/s00170-020-05346-2

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  • DOI: https://doi.org/10.1007/s00170-020-05346-2

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