Heat and fluid flow in additive manufacturing – Part II: Powder bed fusion of stainless steel, and titanium, nickel and aluminum base alloys

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Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (2020, methodology): “…[21–25]; Panwisawas et al. also employed a VoF method by using OpenFOAM to analyze the inter-layer and inter-track void formation [26]; Li et al. developed a thermal-ﬂuid model by combining level set method and Lagrangian particle tracking toinvestigatepowder-gasinteractioninLPBFprocesses[27].”
Additive manufacturing for energy: A review (2020, background): “The induced flow velocity can reach as high as 1 m/s [167], promoting strong convective heat/mass transport.”
Keyhole-induced porosities in Laser-based Powder Bed Fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation (2019, methodology): “Ti6Al4V thermophysical properties and the L-PBF machine processing conditions for the single track tests [41]–[43].”
Mitigation of lack of fusion defects in powder bed fusion additive manufacturing (2018, background): “we have shown how the molten pool dimensions vary with layers and hatches [24,29].”