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 and experimental analysis of the effect of volumetric energy absorption in powder layer on thermal-fluidic transport in selective laser melting of Ti6Al4V
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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.