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Efficient thermal finite element modeling of selective laser melting of Inconel 718

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Abstract

In the powder bed fusion process, an accurate prediction of the transient temperature field of a part is essential to calculate the subsequent thermal stress evolution and microstructure propagation in that part. The experimental method is time-consuming and expensive since the temperature field is controlled by many process parameters. Numerical heat transfer models can be used to estimate the temperature field at any time point. However, traditional numerical simulation schemes are not suitable for the layer-wised fabrication process due to the extremely high computational cost. The computational cost mainly relies on the element number and time step size. This research provides a new efficient and part-level simulation scheme based on an open-source finite element library, which is able to adaptively refine and coarsen the mesh and solve finite element equations with multiple processors in a parallel way. Here, a new mesh strategy that aims to reduce the element number while keeping the solution accuracy is developed. The simulation speed is 12× to 18× faster compared with the traditional simulation scheme depending on the scale of the simulated domain and number of processors. Simulation results have been compared with the experimental results of an Inconel 718 component. It is shown that the testing point in the simulation experiences the same thermal cycles of the same point in the experiment. This simulation scheme can also be used to optimize the process parameters such as scanning pattern, scan velocity, and layer thickness and can be easily extended to other additive manufacturing processes.

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References

  1. DebRoy T et al (2018) Additive manufacturing of metallic components—process, structure and properties. Prog Mater Sci 92(Supplement C):112–224

    Google Scholar 

  2. A. C. F. o. A. M. Technologies and A. C. F. o. A. M. T. S. F. o. Terminology (2012) Standard terminology for additive manufacturing technologies. ASTM International

  3. Averyanova M, Bertrand P, Verquin B (2011) Manufacture of Co–Cr dental crowns and bridges by selective laser Melting technology: this paper presents the successful application of the selective laser melting technology in dental frameworks manufacturing from Co–Cr alloy using Phenix PM 100T Dental Machine over a production period of 14 months. Virtual Phys Prototyp 6(3):179–185

    Google Scholar 

  4. Vandenbroucke B, Kruth J-P (2007) Selective laser melting of biocompatible metals for rapid manufacturing of medical parts. Rapid Prototyp J 13(4):196–203

    Google Scholar 

  5. Rochus P, Plesseria J-Y, Van Elsen M, Kruth J-P, Carrus R, Dormal T (2007) New applications of rapid prototyping and rapid manufacturing (RP/RM) technologies for space instrumentation. Acta Astronaut 61(1):352–359

    Google Scholar 

  6. Dalgarno K, Stewart T (2001) Manufacture of production injection mould tooling incorporating conformal cooling channels via indirect selective laser sintering. Proc Inst Mech Eng, Part B: J Eng Manuf 215(10):1323–1332

    Google Scholar 

  7. Mani M, Lane B, Donmez A, Feng S, Moylan S, Fesperman R (2015) Measurement science needs for real-time control of additive manufacturing powder bed fusion processes. https://doi.org/10.6028/NIST.IR.8036

  8. Yan W et al (2018) Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing. Comput Mech 61(5):521–541

    MATH  Google Scholar 

  9. Denlinger ER, Heigel JC, Michaleris P (2015) Residual stress and distortion modeling of electron beam direct manufacturing Ti-6Al-4 V. Proc Inst Mech Eng, Part B: J Eng Manuf 229(10):1803–1813

    Google Scholar 

  10. Mackwood A, Crafer RC (2005) Thermal modelling of laser welding and related processes: a literature review. Opt Laser Technol 37(2):99–115

    Google Scholar 

  11. Lavery NP, Brown SG, Sienz J, Cherry J, Belblidia F (2014) A review of computational modelling of additive layer manufacturing–multi-scale and multi-physics. Sustain Des Manuf, pp 668–690

  12. Li C, Liu ZY, Fang XY, Guo YB (2018) On the simulation scalability of predicting residual stress and distortion in selective laser melting. J Manuf Sci Eng 140(4):041013-041013-10

    Google Scholar 

  13. Papadakis L, Loizou A, Risse J, Bremen S, Schrage J (2014) A computational reduction model for appraising structural effects in selective laser melting manufacturing: a methodical model reduction proposed for time-efficient finite element analysis of larger components in Selective Laser Melting. Virtual Phys Prototyp 9(1):17–25

    Google Scholar 

  14. Markl M, Körner C (2016) Multi-scale modeling of powder-bed-based additive manufacturing. Annu Rev Mater Res 46:1–34

    Google Scholar 

  15. Li C, Fu CH, Guo YB, Fang FZ (2016) A multiscale modeling approach for fast prediction of part distortion in selective laser melting. J Mater Process Technol 229:703–712

    Google Scholar 

  16. Smith J et al (2016) Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support. Comput Mech 57(4):583–610

    MathSciNet  MATH  Google Scholar 

  17. Hodge NE, Ferencz RM, Solberg JM (2014) Implementation of a thermomechanical model for the simulation of selective laser melting. Comput Mech 54(1):33–51

    MathSciNet  Google Scholar 

  18. Song B et al (2015) Differences in microstructure and properties between selective laser melting and traditional manufacturing for fabrication of metal parts: a review. Front Mech Eng 10(2):111–125

    Google Scholar 

  19. Chen S, Xu Y, Jiao Y (2018) A hybrid finite-element and cellular-automaton framework for modeling 3D microstructure of Ti–6Al–4 V alloy during solid-solid phase transformation in additive manufacturing. Model Simul Mater Sci Eng 26(4):045011

    Google Scholar 

  20. Keller N, Ploshikhin V (2014) New method for fast predictions of residual stress and distortion of AM parts. In: Solid freeform fabrication symposium, Austin, Texas, pp 1229–1237

  21. Ueda Y, Kim YC, Yuan MG (1989) A predicting method of welding residual stress using source of residual stress (report I): characteristics of inherent strain (source of residual stress) (mechanics, strength and structural design). Trans JWRI 18(1):135–141

    Google Scholar 

  22. Afazov S, Denmark WAD, Lazaro Toralles B, Holloway A, Yaghi A (2017) Distortion prediction and compensation in selective laser melting. Addit Manuf 17(Supplement C):15–22

    Google Scholar 

  23. Gan Z, Lian Y, Lin SE, Jones KK, Liu WK, Wagner GJ (2019) Benchmark study of thermal behavior, surface topography, and dendritic microstructure in selective laser melting of Inconel 625. Integr Mater Manuf Innov 8(2):178–193

    Google Scholar 

  24. Manvatkar V, De A, DebRoy T (2014) Heat transfer and material flow during laser assisted multi-layer additive manufacturing. J Appl Phys 116(12):124905

    Google Scholar 

  25. Denlinger ER, Irwin J, Michaleris P (2014) Thermomechanical modeling of additive manufacturing large parts. J Manuf Sci Eng 136(6):061007

    Google Scholar 

  26. Michaleris P (2014) Modeling metal deposition in heat transfer analyses of additive manufacturing processes. Finite Elements Anal Des 86:51–60

    Google Scholar 

  27. Zhang Y, Guillemot G, Bernacki M, Bellet M (2018) Macroscopic thermal finite element modeling of additive metal manufacturing by selective laser melting process. Comput Methods Appl Mech Eng 331:514–535

    MathSciNet  Google Scholar 

  28. Cheng B, Shrestha S, Chou K (2016) Stress and deformation evaluations of scanning strategy effect in selective laser melting. Addit Manuf 12:240–251

    Google Scholar 

  29. Tran H-C, Lo Y-L (2018) Heat transfer simulations of selective laser melting process based on volumetric heat source with powder size consideration. J Mater Process Technol 255:411–425

    Google Scholar 

  30. Mozaffar M, Ndip-Agbor E, Lin S, Wagner GJ, Ehmann K, Cao J (2019) Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units. Comput Mech 64(3):879–894

    MATH  Google Scholar 

  31. Bangerth W, Hartmann R, Kanschat G (2007) Deal. II—a general-purpose object-oriented finite element library. ACM Trans Math Softw (TOMS) 33(4):24

    MathSciNet  MATH  Google Scholar 

  32. Mukherjee T, Wei HL, De A, DebRoy T (2018) Heat and fluid flow in additive manufacturing—Part I: modeling of powder bed fusion. Comput Mater Sci 150:304–313

    Google Scholar 

  33. Dong L, Correia J, Barth N, Ahzi S (2017) Finite element simulations of temperature distribution and of densification of a titanium powder during metal laser sintering. Addit Manuf 13:37–48

    Google Scholar 

  34. Wolff SJ, Lin S, Faierson EJ, Liu WK, Wagner GJ, Cao J (2017) A framework to link localized cooling and properties of directed energy deposition (DED)-processed Ti–6Al–4V. Acta Mater 132:106–117

    Google Scholar 

  35. Gan Z, Liu H, Li S, He X, Yu G (2017) Modeling of thermal behavior and mass transport in multi-layer laser additive manufacturing of Ni-based alloy on cast iron. Int J Heat Mass Transf 111:709–722

    Google Scholar 

  36. Gan Z, Yu G, He X, Li S (2017) Numerical simulation of thermal behavior and multicomponent mass transfer in direct laser deposition of Co-base alloy on steel. Int J Heat Mass Transf 104:28–38

    Google Scholar 

  37. Denlinger ER, Gouge M, Irwin J, Michaleris P (2017) Thermomechanical model development and in situ experimental validation of the laser powder-bed fusion process. Addit Manuf 16:73–80

    Google Scholar 

  38. Luo Z, Zhao Y (2018) A survey of finite element analysis of temperature and thermal stress fields in powder bed fusion additive manufacturing. Addit Manuf 21:318–332

    Google Scholar 

  39. Ma L, Bin H (2007) Temperature and stress analysis and simulation in fractal scanning-based laser sintering. Int J Adv Manuf Technol 34(9–10):898–903

    Google Scholar 

  40. Fischer P, Romano V, Weber H-P, Karapatis N, Boillat E, Glardon R (2003) Sintering of commercially pure titanium powder with a Nd: YAG laser source. Acta Mater 51(6):1651–1662

    Google Scholar 

  41. Denlinger ER, Michaleris P (2016) Effect of stress relaxation on distortion in additive manufacturing process modeling. Addit Manuf 12(Part A):51–59

    Google Scholar 

  42. Bruna-Rosso C, Demir AG, Previtali B (2018) Selective laser melting finite element modeling: validation with high-speed imaging and lack of fusion defects prediction. Mater Des 156:143–153

    Google Scholar 

  43. Goldak J, Chakravarti A, Bibby M (1984) A new finite element model for welding heat sources. Metall Trans B 15(2):299–305

    Google Scholar 

  44. Romano J, Ladani L, Sadowski M (2016) Laser additive melting and solidification of inconel 718: finite element simulation and experiment. JOM 68(3):967–977

    Google Scholar 

  45. Cernuschi F, Ahmaniemi S, Vuoristo P, Mäntylä T (2004) Modelling of thermal conductivity of porous materials: application to thick thermal barrier coatings. J Eur Ceram Soc 24(9):2657–2667

    Google Scholar 

  46. Foteinopoulos P, Papacharalampopoulos A, Stavropoulos P (2018) On thermal modeling of Additive Manufacturing processes. CIRP J Manuf Sci Technol 20:66–83

    Google Scholar 

  47. Hu H, Argyropoulos SA (1996) Mathematical modelling of solidification and melting: a review. Modell Simul Mater Sci Eng 4(4):371

    Google Scholar 

  48. Kelly D, Gago DS, Zienkiewicz O, Babuska I (1983) A posteriori error analysis and adaptive processes in the finite element method: part I—error analysis. Int J Numer Methods Eng 19(11):1593–1619

    MATH  Google Scholar 

  49. Luo Z, Zhao Y (2019) Numerical simulation of part-level temperature fields during selective laser melting of stainless steel 316L. Int J Adv Manuf Technol 104(5–8):1615–1635

    Google Scholar 

  50. Guo B, Babuška I (1986) The hp version of the finite element method. Comput Mech 1(1):21–41

    MATH  Google Scholar 

  51. Geuzaine C, Remacle JF (2009) Gmsh: a 3-D finite element mesh generator with built-in pre-and post-processing facilities. Int J Numer Methods Eng 79(11):1309–1331

    MathSciNet  MATH  Google Scholar 

  52. Ahn DG, Byun KW, Kang MC (2010) Thermal characteristics in the cutting of inconel 718 superalloy using CW Nd: YAG laser. J Mater Sci Technol 26(4):362–366

    Google Scholar 

  53. Dunbar AJ, Denlinger ER, Gouge MF, Michaleris P (2016) Experimental validation of finite element modeling for laser powder bed fusion deformation. Addit Manuf 12(Part A):108–120

    Google Scholar 

  54. Dunbar AJ et al (2016) Development of experimental method for in situ distortion and temperature measurements during the laser powder bed fusion additive manufacturing process. Addit Manuf 12:25–30

    Google Scholar 

  55. Wolff SJ et al (2019) Experimentally validated predictions of thermal history and microhardness in laser-deposited Inconel 718 on carbon steel. Addit Manuf 27:540–551

    Google Scholar 

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Acknowledgements

This research work is supported by the National Sciences and Engineering Research Council of Canada (NSERC) Strategic Network for Holistic Innovation In Additive Manufacturing (HI-AM) with NSERC Project Number: NETGP 494158-16.

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Correspondence to Yaoyao Zhao.

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Luo, Z., Zhao, Y. Efficient thermal finite element modeling of selective laser melting of Inconel 718. Comput Mech 65, 763–787 (2020). https://doi.org/10.1007/s00466-019-01794-0

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