Premium
Characterization and reconstruction of 3D stochastic microstructures via supervised learning
Author(s) -
BOSTANABAD R.,
CHEN W.,
APLEY D.W.
Publication year - 2016
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12441
Subject(s) - computer science , characterization (materials science) , artificial intelligence , machine learning , stochastic modelling , property (philosophy) , microstructure , algorithm , pattern recognition (psychology) , mathematics , materials science , philosophy , epistemology , metallurgy , nanotechnology , statistics
Summary The need for computational characterization and reconstruction of volumetric maps of stochastic microstructures for understanding the role of material structure in the processing–structure–property chain has been highlighted in the literature. Recently, a promising characterization and reconstruction approach has been developed where the essential idea is to convert the digitized microstructure image into an appropriate training dataset to learn the stochastic nature of the morphology by fitting a supervised learning model to the dataset. This compact model can subsequently be used to efficiently reconstruct as many statistically equivalent microstructure samples as desired. The goal of this paper is to build upon the developed approach in three major directions by: (1) extending the approach to characterize 3D stochastic microstructures and efficiently reconstruct 3D samples, (2) improving the performance of the approach by incorporating user‐defined predictors into the supervised learning model, and (3) addressing potential computational issues by introducing a reduced model which can perform as effectively as the full model. We test the extended approach on three examples and show that the spatial dependencies, as evaluated via various measures, are well preserved in the reconstructed samples.