A Metric on the Polycrystalline Microstructure State Space
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866908589498564608 |
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| author | Miley, Dylan Suwandi, Ethan Schweinhart, Benjamin Mason, Jeremy K |
| author_facet | Miley, Dylan Suwandi, Ethan Schweinhart, Benjamin Mason, Jeremy K |
| contents | Material microstructures are traditionally compared using sets of statistical measures that are incomplete, e.g., two visually distinct microstructures can have identical grain size distributions and phase fractions. While this is not a severe concern for materials fabricated by traditional means, the microstructures produced by advanced manufacturing methods can depend sensitively and unpredictably on the processing conditions. Moreover, the advent of computational materials design has increased the frequency of synthetic microstructure generation, and there is not yet a standard approach in the literature to validate the generated microstructures with experimental ones. This work proposes an idealized distance on the space of single-phase polycrystalline microstructures such that two microstructures that are close with respect to the distance exhibit statistically similar grain geometries in all respects below a user-specified length scale. Given a pair of micrographs, the distance is approximated by sampling windows from the micrographs, defining a distance between pairs of windows, and finding a window matching that minimizes the sum of pairwise window distances. The approach is used to compare a variety of synthetic microstructures and to develop a procedure to query a proof-of-concept database suitable for general single-phase polycrystalline microstructures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18981 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Metric on the Polycrystalline Microstructure State Space Miley, Dylan Suwandi, Ethan Schweinhart, Benjamin Mason, Jeremy K Computational Physics Computational Engineering, Finance, and Science Material microstructures are traditionally compared using sets of statistical measures that are incomplete, e.g., two visually distinct microstructures can have identical grain size distributions and phase fractions. While this is not a severe concern for materials fabricated by traditional means, the microstructures produced by advanced manufacturing methods can depend sensitively and unpredictably on the processing conditions. Moreover, the advent of computational materials design has increased the frequency of synthetic microstructure generation, and there is not yet a standard approach in the literature to validate the generated microstructures with experimental ones. This work proposes an idealized distance on the space of single-phase polycrystalline microstructures such that two microstructures that are close with respect to the distance exhibit statistically similar grain geometries in all respects below a user-specified length scale. Given a pair of micrographs, the distance is approximated by sampling windows from the micrographs, defining a distance between pairs of windows, and finding a window matching that minimizes the sum of pairwise window distances. The approach is used to compare a variety of synthetic microstructures and to develop a procedure to query a proof-of-concept database suitable for general single-phase polycrystalline microstructures. |
| title | A Metric on the Polycrystalline Microstructure State Space |
| topic | Computational Physics Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2410.18981 |