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Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields
The development of high-fidelity mechanical property prediction models for the design of polycrystalline materials relies on large volumes of microstructural feature data. Concurrently, at these same scales, the deformation fields that develop during mechanical loading can be highly heterogeneous. S...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343453/ https://www.ncbi.nlm.nih.gov/pubmed/35915100 http://dx.doi.org/10.1038/s41597-022-01525-w |
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author | Stinville, J. C. Hestroffer, J. M. Charpagne, M. A. Polonsky, A. T. Echlin, M. P. Torbet, C. J. Valle, V. Nygren, K. E. Miller, M. P. Klaas, O. Loghin, A. Beyerlein, I. J. Pollock, T. M. |
author_facet | Stinville, J. C. Hestroffer, J. M. Charpagne, M. A. Polonsky, A. T. Echlin, M. P. Torbet, C. J. Valle, V. Nygren, K. E. Miller, M. P. Klaas, O. Loghin, A. Beyerlein, I. J. Pollock, T. M. |
author_sort | Stinville, J. C. |
collection | PubMed |
description | The development of high-fidelity mechanical property prediction models for the design of polycrystalline materials relies on large volumes of microstructural feature data. Concurrently, at these same scales, the deformation fields that develop during mechanical loading can be highly heterogeneous. Spatially correlated measurements of 3D microstructure and the ensuing deformation fields at the micro-scale would provide highly valuable insight into the relationship between microstructure and macroscopic mechanical response. They would also provide direct validation for numerical simulations that can guide and speed up the design of new materials and microstructures. However, to date, such data have been rare. Here, a one-of-a-kind, multi-modal dataset is presented that combines recent state-of-the-art experimental developments in 3D tomography and high-resolution deformation field measurements. |
format | Online Article Text |
id | pubmed-9343453 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93434532022-08-03 Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields Stinville, J. C. Hestroffer, J. M. Charpagne, M. A. Polonsky, A. T. Echlin, M. P. Torbet, C. J. Valle, V. Nygren, K. E. Miller, M. P. Klaas, O. Loghin, A. Beyerlein, I. J. Pollock, T. M. Sci Data Data Descriptor The development of high-fidelity mechanical property prediction models for the design of polycrystalline materials relies on large volumes of microstructural feature data. Concurrently, at these same scales, the deformation fields that develop during mechanical loading can be highly heterogeneous. Spatially correlated measurements of 3D microstructure and the ensuing deformation fields at the micro-scale would provide highly valuable insight into the relationship between microstructure and macroscopic mechanical response. They would also provide direct validation for numerical simulations that can guide and speed up the design of new materials and microstructures. However, to date, such data have been rare. Here, a one-of-a-kind, multi-modal dataset is presented that combines recent state-of-the-art experimental developments in 3D tomography and high-resolution deformation field measurements. Nature Publishing Group UK 2022-08-01 /pmc/articles/PMC9343453/ /pubmed/35915100 http://dx.doi.org/10.1038/s41597-022-01525-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Stinville, J. C. Hestroffer, J. M. Charpagne, M. A. Polonsky, A. T. Echlin, M. P. Torbet, C. J. Valle, V. Nygren, K. E. Miller, M. P. Klaas, O. Loghin, A. Beyerlein, I. J. Pollock, T. M. Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields |
title | Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields |
title_full | Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields |
title_fullStr | Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields |
title_full_unstemmed | Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields |
title_short | Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields |
title_sort | multi-modal dataset of a polycrystalline metallic material: 3d microstructure and deformation fields |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343453/ https://www.ncbi.nlm.nih.gov/pubmed/35915100 http://dx.doi.org/10.1038/s41597-022-01525-w |
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