FEA-PINN combines a physics-informed neural network with corrective finite element simulations to predict melt pool dynamics in laser powder bed fusion at accuracy comparable to full FEA but lower computational cost.
Numerical modelling of in-situ alloying of Al and Cu using the laser powder bed fusion process: A study on the effect of energy density and remelting on deposited track homogeneity
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Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Melt Pool Dynamics in Laser Powder Bed Fusion
FEA-PINN combines a physics-informed neural network with corrective finite element simulations to predict melt pool dynamics in laser powder bed fusion at accuracy comparable to full FEA but lower computational cost.