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A novel data-driven digital reconstruction method for polycrystalline microstructures

Bingbing Chen, Dongfeng Li, Liyuan Wang, Xiangyun Ge, Chenfeng Li Orcid Logo

Computer Methods in Applied Mechanics and Engineering, Volume: 441, Start page: 117980

Swansea University Authors: Bingbing Chen, Liyuan Wang, Xiangyun Ge, Chenfeng Li Orcid Logo

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DOI (Published version): 10.1016/j.cma.2025.117980

Abstract

Data-driven digital reconstruction is a power tool for building digital microstructures for such heterogeneous materials as porous media and composites. It uses scanned images as reference and generates digital microstructures through optimisation procedures or computer vision methods. However, data...

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Published in: Computer Methods in Applied Mechanics and Engineering
ISSN: 0045-7825 1879-2138
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69241
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The method includes experimental acquisition of microstructural data (such as phase map, lattice symmetry, and lattice orientation), conversion of experimental data to RGB image formats for continuous and symmetry-conserved visualisation, image generation from continuous and symmetry-conserved orientation colouring, and reconstruction of grain data from synthesised RGB images. The results demonstrate that this method enables efficient microstructure reconstructions with high fidelity to actual microstructural characteristics, addressing the limitations of traditional methods. 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spelling 2025-04-08T12:33:39.3475015 v2 69241 2025-04-08 A novel data-driven digital reconstruction method for polycrystalline microstructures 5b2828673b7414494f067b458092725c Bingbing Chen Bingbing Chen true false e981deb019f9fab37365829d00f4008d Liyuan Wang Liyuan Wang true false 5cadab762ba1ba8bbf8916234da59f0f Xiangyun Ge Xiangyun Ge true false 82fe170d5ae2c840e538a36209e5a3ac 0000-0003-0441-211X Chenfeng Li Chenfeng Li true false 2025-04-08 ACEM Data-driven digital reconstruction is a power tool for building digital microstructures for such heterogeneous materials as porous media and composites. It uses scanned images as reference and generates digital microstructures through optimisation procedures or computer vision methods. However, data-driven digital reconstruction methods do not apply to polycrystalline microstructures because their raw measurement data (lattice orientation, grain structure, and phase distribution) do not naturally correspond to RGB images. It faces challenges such as discontinuities and ambiguities in orientation colouring, as well as a lack of algorithms for extracting orientation data from RGB images. This paper introduces a novel data-driven digital reconstruction method for polycrystalline microstructures. The method includes experimental acquisition of microstructural data (such as phase map, lattice symmetry, and lattice orientation), conversion of experimental data to RGB image formats for continuous and symmetry-conserved visualisation, image generation from continuous and symmetry-conserved orientation colouring, and reconstruction of grain data from synthesised RGB images. The results demonstrate that this method enables efficient microstructure reconstructions with high fidelity to actual microstructural characteristics, addressing the limitations of traditional methods. Furthermore, by offering realistic digital microstructure models, this novel data-driven reconstruction scheme can be readily integrated with simulation tools to improve the study of structure–property linkages in polycrystalline materials. Journal Article Computer Methods in Applied Mechanics and Engineering 441 117980 Elsevier BV 0045-7825 1879-2138 Alloy material; Crystal plasticity finite element; Microstructural reconstruction; Representative volume element; Structure–property linkage; Electron backscatter diffraction 1 6 2025 2025-06-01 10.1016/j.cma.2025.117980 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) The authors would like to thank the supports from Chinese Scholarship Council, Swansea University, and the Royal Society, United Kingdom (IF\R2\23200112, IEC\NSFC\191628). 2025-04-08T12:33:39.3475015 2025-04-08T12:23:48.5718732 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Bingbing Chen 1 Dongfeng Li 2 Liyuan Wang 3 Xiangyun Ge 4 Chenfeng Li 0000-0003-0441-211X 5 69241__33966__31364b7d0ef84e89b5230e5e195c8da0.pdf 69241.VOR.pdf 2025-04-08T12:31:49.7066284 Output 8024604 application/pdf Version of Record true © 2025 The Authors. This is an open access article distributed under the terms of the Creative Commons CC-BY license. true eng http://creativecommons.org/licenses/by/4.0/
title A novel data-driven digital reconstruction method for polycrystalline microstructures
spellingShingle A novel data-driven digital reconstruction method for polycrystalline microstructures
Bingbing Chen
Liyuan Wang
Xiangyun Ge
Chenfeng Li
title_short A novel data-driven digital reconstruction method for polycrystalline microstructures
title_full A novel data-driven digital reconstruction method for polycrystalline microstructures
title_fullStr A novel data-driven digital reconstruction method for polycrystalline microstructures
title_full_unstemmed A novel data-driven digital reconstruction method for polycrystalline microstructures
title_sort A novel data-driven digital reconstruction method for polycrystalline microstructures
author_id_str_mv 5b2828673b7414494f067b458092725c
e981deb019f9fab37365829d00f4008d
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author_id_fullname_str_mv 5b2828673b7414494f067b458092725c_***_Bingbing Chen
e981deb019f9fab37365829d00f4008d_***_Liyuan Wang
5cadab762ba1ba8bbf8916234da59f0f_***_Xiangyun Ge
82fe170d5ae2c840e538a36209e5a3ac_***_Chenfeng Li
author Bingbing Chen
Liyuan Wang
Xiangyun Ge
Chenfeng Li
author2 Bingbing Chen
Dongfeng Li
Liyuan Wang
Xiangyun Ge
Chenfeng Li
format Journal article
container_title Computer Methods in Applied Mechanics and Engineering
container_volume 441
container_start_page 117980
publishDate 2025
institution Swansea University
issn 0045-7825
1879-2138
doi_str_mv 10.1016/j.cma.2025.117980
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
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description Data-driven digital reconstruction is a power tool for building digital microstructures for such heterogeneous materials as porous media and composites. It uses scanned images as reference and generates digital microstructures through optimisation procedures or computer vision methods. However, data-driven digital reconstruction methods do not apply to polycrystalline microstructures because their raw measurement data (lattice orientation, grain structure, and phase distribution) do not naturally correspond to RGB images. It faces challenges such as discontinuities and ambiguities in orientation colouring, as well as a lack of algorithms for extracting orientation data from RGB images. This paper introduces a novel data-driven digital reconstruction method for polycrystalline microstructures. The method includes experimental acquisition of microstructural data (such as phase map, lattice symmetry, and lattice orientation), conversion of experimental data to RGB image formats for continuous and symmetry-conserved visualisation, image generation from continuous and symmetry-conserved orientation colouring, and reconstruction of grain data from synthesised RGB images. The results demonstrate that this method enables efficient microstructure reconstructions with high fidelity to actual microstructural characteristics, addressing the limitations of traditional methods. Furthermore, by offering realistic digital microstructure models, this novel data-driven reconstruction scheme can be readily integrated with simulation tools to improve the study of structure–property linkages in polycrystalline materials.
published_date 2025-06-01T17:53:15Z
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