Prediction of microstructure in laser powder bed fusion process

By Ranadip Acharya, J. Sharon, A. Staroselsky. 2017

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Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives (2019, background): “[7] developed a computational fluid dynamics (CFD) and phase-field framework to simulate grain structure evolution in the as-deposited state for the laser powder-bed fusion (PBF) process; Fergani et al.”
Microstructure modelling for metallic additive manufacturing: a review (2020, methodology): “…source software Yes No Yes Limited References (Cao and Choi 2006; Farzadi et al. 2008; Fallah et al. 2012; Montiel et al. 2012; Sahoo and Chou 2014; Gong and Chou 2015; Sahoo and Chou 2016; Acharya, Sharon, and Staroselsky 2017; Keller et al. 2017; Kumara et al. 2019; Kumara et al. 2019) (Wang,…”
Prediction of microstructure, residual stress, and deformation in laser powder bed fusion process (2018, methodology): “[7] used computational fluid dynamics (CFD) analysis to predict melt pool characteristics and phase field modeling to simulate microstructure evolution in the as-deposited state for L-PBF process.”
Finite interface dissipation phase field modeling of Ni–Nb under additive manufacturing conditions (2019, methodology): “For example, NiNb has been widely used as a binary approximation for Ni-based superalloys such as Inconel 718 [32, 34] and Inconel 625 [33].”