This study explores the use of supervised machine learning (ML) to predict mechanical properties a family two-phase materials using their microstructural images. Random microstructures with diversity inclusion volume fractions, size distributions, and/or shapes are input into finite element analysis program determine elastic modulus, Poisson’s ratio, and phase stresses. The results establish “g...
