An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/131916 |
| _version_ | 1811089779842875392 |
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| author | Ashton, Tristan N Guillen, Donna P Harris, William H |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Ashton, Tristan N Guillen, Donna P Harris, William H |
| author_sort | Ashton, Tristan N |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:24:36Z |
| format | Article |
| id | mit-1721.1/131916 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:24:36Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1319162023-03-15T19:06:45Z An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars Ashton, Tristan N Guillen, Donna P Harris, William H Massachusetts Institute of Technology. Department of Materials Science and Engineering 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. 2021-09-20T17:30:56Z 2021-09-20T17:30:56Z 2019-10-15 2020-09-24T21:43:54Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131916 en https://doi.org/10.1007/s11837-019-03825-w Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection application/pdf Springer US Springer US |
| spellingShingle | Ashton, Tristan N Guillen, Donna P Harris, William H An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars |
| title | An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars |
| title_full | An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars |
| title_fullStr | An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars |
| title_full_unstemmed | An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars |
| title_short | An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars |
| title_sort | algorithm to generate synthetic 3d microstructures from 2d exemplars |
| url | https://hdl.handle.net/1721.1/131916 |
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