Solidification analysis by non-equilibrium phase field model using thermodynamics data estimated by machine learning

Abstract

A multi-phase field method (MPFM) using the finite interface dissipation model is applied to simulate the solidification microstructure evolution of a stainless-steel composition, including the delta-ferrite to gamma austenite peritectic transformation. The calculation is performed for a quinary system of engineering steel in a two-dimensional field. Thermodynamics calculations using the CALPHAD database in this MPFM are replaced by machine learning prediction to reduce the numerical time. Neural network methodology is introduced for machine learning in this study. The Gibbs free energy and chemical potential values estimated from the CALPHAD database coupling results are inputted into the neural network learning procedure, together with the composition and temperature values. The microstructure evaluated using the obtained neural network parameter is in good agreement with that directly coupled with the CALPHAD database. This calculation is approximately five times faster than direct CALPHAD calculation.