| Abstract Scope |
Alloy design for laser-based welding and laser powder bed fusion (LPBF) requires rapid prediction of resulting microstructures across large composition–processing design spaces. The Eagar–Tsai (ET) model is a classical analytical thermal model whose low computational cost makes it attractive for high-throughput screening. Thermal gradients, G, and solidification rates, R, can be extracted from ET temperature fields and coupled with classical solidification models, such as Kurz–Giovanola–Trivedi (KGT) theory, to predict microstructural evolution. However, the simplified physics of the ET model, including temperature-invariant thermophysical properties, limits its predictive fidelity relative to finite element method (FEM) thermal simulations. In this work, we combine the speed of ET modeling with the accuracy of FEM simulations by treating ET predictions as Bayesian priors and updating them using FEM-informed thermal data to improve the prediction of as-solidified microstructures within KGT theory. |