An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars
Author(s)
Ashton, Tristan N; Guillen, Donna P; Harris, William H
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Abstract
The inverse problem of constructing 3D microstructures from 2D data is an area of active research within the materials science community. This paper presents the implementation of a robust, computationally efficient algorithm: the
Hierarchical Algorithm for the Reconstruction of Exemplars (HARE), written in Python to reconstruct 3D features in a given microstructure from up to three orthogonal 2D exemplars and using nearest-neighbor matching to reproduce feature qualities, such as shape, size, and distribution.
HARE’s feature sampling implements histogram reweighting to avoid both over- and undersampling. A neighborhood voting scheme allows each pixel to provisionally affect its neighbors according to its weight. The algorithm is presently configured for two-phase materials and is being extended to accommodate multiple phases. HARE is a convenient and robust base from which to generate statistically representative synthetic microstructures for use in multi-scale modeling or machine-learning applications to support advanced manufacturing and materials discovery.
Date issued
2019-10-15Department
Massachusetts Institute of Technology. Department of Materials Science and EngineeringPublisher
Springer US