Establishment of structure-property linkages using a Bayesian model selection method: Application to a dual-phase metallic composite system

Abstract

We investigate the viability of establishing low-cost surrogate structure-property (S–P) linkages by introducing a Bayesian model selection method to extend the Materials Knowledge Systems (MKS) homogenization framework, which employs the n-point spatial correlation function, principal component analysis, and regression techniques. In particular, we place emphasis not only on choosing the important structural features but also on interpreting their implications for the property under consideration. First, the yield strengths of synthetic microstructures with various morphological characteristics are estimated by physics-based crystal plasticity simulation. Then, the dimension-reduced microstructural features are revealed by a combination of 2-point spatial correlations and principal component analysis. The Bayesian model selection method is further applied to establish a microstructure-to-yield-strength surrogate model. Finally, the model is validated with an independent dataset and its constituent features are interpreted with a morphology reconstruction based on a Monte Carlo algorithm. The method is found to be capable of interpreting the key microstructural features as well as modeling the mechanical response of a dual-phase metallic composite in consideration of the diverse microstructural factors.