Deep learning approach for chemistry and processing history prediction from materials microstructure.

ABSTRACT: Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials' microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe-Cr-Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e

SUBMITTER: Farizhandi AAK 

PROVIDER: S-EPMC8927426 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature