An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars

• Published in: JOM (1989) Vol. 72; no. 1; pp. 65 - 74
• Main Authors: Ashton, Tristan N., Guillen, Donna Post, Harris, William H.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2020; Springer Nature B.V
• Subjects: 3D Materials Science; Algorithms; Chemistry/Food Science; Earth Sciences; Engineering; Environment; Histograms; Image reconstruction; Inverse problems; Machine learning; Materials science; Microstructure; Neighborhoods; Physics; Two phase materials
• ISSN: 1047-4838; 1543-1851; 1543-1851
• DOI: 10.1007/s11837-019-03825-w

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.