Abstract
Computational modeling of the laser powder bed fusion process is often restricted to the conduction dominant regime whereby advective heat transfer and fluid flow can be disregarded. Under processing conditions that result in high energy densities, fluid flow driven by Marangoni effects influences the uncertainty in solidification dynamics and melt pool dimensions which affect the as-built microstructure and scan strategies development. Using a computational fluid dynamics model for melt pool predictions of Inconel 625, and applying sparse grids and interpolation techniques, the uncertainty in input parameters including thermophysical properties and the surface tension gradient can be propagated to predictions of cooling rates and melt pool geometries. Results show the uncertainty in surface tension gradient had the largest effect on melt pool dimensions, whereas uncertainty in laser absorptivity and specific heat capacity were the most influential on the solidification dynamics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brackbill JU, Kothe DB, Zemach C (1992) A continuum method for modeling surface tension. J Comput Phys 100:335–354. https://doi.org/10.1016/0021-9991(92)90240-Y
Svenungsson J, Choquet I, Kaplan AFH (2015) Laser welding process—a review of Keyhole welding modelling. Phys Procedia 78:182–191
Guo SH, Zou JL, Xiao RS (2020) Characterizations of welding mode transformation process during 1-μm and 10-μm laser welding. AIP Adv 10. https://doi.org/10.1063/1.5132776
Assuncao E, Williams S, Yapp D (2012) Interaction time and beam diameter effects on the conduction mode limit. Opt Lasers Eng 50:823–828. https://doi.org/10.1016/j.optlaseng.2012.02.001
Coleman J, Plotkowski A, Stump B et al (2020) Sensitivity of thermal predictions to uncertain surface tension data in laser additive manufacturing.J Heat Transfer 142:122201–1–122201–13.https://doi.org/10.1115/1.4047916
Sahoo P, Debroy T, McNallan MJ (1988) Surface tension of binary metal-surface active solute systems under conditions relevant to welding metallurgy. Metall Trans B 19. https://doi.org/10.1007/BF02657748
Mills KC, Su YC (2006) Review of surface tension data for metallic elements and alloys: part 1—pure metals. Int Mater Rev 51:329–351
Hayashi M, Jakobsson A, Tanaka T, Seetharaman S (2003) Surface tension of the nickel-based superalloy CMSX-4. High Temp - High Press, 35–36.https://doi.org/10.1068/htjr123
Li Z, Mills KC, McLean M, Mukai K (2005) Measurement of the density and surface tension of Ni-based superalloys in the liquid and mushy states. Metall Mater Trans B 36:247–254.https://doi.org/10.1007/s11663-005-0026-z
Ricci E, Giuranno D, Novakovic R et al (2007) Density, surface tension, and viscosity of CMSX-4 ® superalloy. Int J Thermophys 28:1304–1321. https://doi.org/10.1007/s10765-007-0257-0
Vinet B, Schneider S, Garandet JP et al (2004) Surface tension measurements on CMSX-4 superalloy by the drop-weight and oscillating-drop methods.Int J Thermophys 25:1889–1903https://doi.org/10.1007/s10765-004-7743-4
Higuchi K, Fecht HJ, Wunderlich RK (2007) Surface tension and viscosity of the Ni-based superalloy CMSX-4 measured by the oscillating drop method in parabolic flight experiments. AdvEng Mater 9:349–354. https://doi.org/10.1002/adem.200600277
Wells S, Plotkowski A, Krane MJM (2021) Propagation of input uncertainties in numerical simulations of laser powder bed fusion. Metall Trans B 52:3016–3031
Stoyanov M (2015) User manual: Tasmanian sparse grids. Oak Ridge: National Laboratory
Stoyanov M (2018) Adaptive sparse grid construction in a context of local anisotropy and multiple hierarchical parents. In: Sparse grids and Applications - Miami 2016. Lecture Notes in Computational Science and Engineering. Springer. pp 175–199
Morrow Z, Stoyanov M (2020) A Method for dimensionally adaptive sparse trigonometric interpolation of periodic functions. SIAM J Sci Comput 42. A2436–A2460 https://doi.org/10.1137/19m1283483
Stoyanov MK, Webster CG (2016) A dynamically adaptive sparse grids method for quasi-optimal interpolation of multidimensional functions. Comput Math Appl 71: 2449–2465
Stoyanov M, Lebrun-Grandie D, Burkardt J, Munster D (2013) Tasmanian. https://github.com/ORNL/TASMANIAN
Carman PC (1939) Permeability of saturated sands, soils and clays. J Agric Sci 29:262–273.https://doi.org/10.1017/S0021859600051789
Carman PC (1997) Fluid flow through granular beds. Process Saf Environ Prot: Trans Inst Chem Eng, Part B 75:532–548
Kozeny J (1927) Ueber kapillare Leitung des Wassers im Boden. Sitzungsber Akad Wiss 136(2a), pp 271–306. Sitzungsberichte der Akademie der Wissenschaften in Wien 136
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Minerals, Metals & Materials Society
About this paper
Cite this paper
Wells, S., Krane, M.J.M. (2022). Uncertainty Quantification of Model Predictions Due to Fluid Flow in Laser Powder Bed Fusion of IN625. In: TMS 2022 151st Annual Meeting & Exhibition Supplemental Proceedings. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-030-92381-5_101
Download citation
DOI: https://doi.org/10.1007/978-3-030-92381-5_101
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92380-8
Online ISBN: 978-3-030-92381-5
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)