Recent Progress of Digital Reconstruction in Polycrystalline Materials

Chen, Bingbing; Li, Dongfeng; Davies, Pete; Johnston, Richard; GE, XIANGYUN; Li, Chenfeng

doi:10.1007/s11831-025-10245-4

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Recent Progress of Digital Reconstruction in Polycrystalline Materials

Bingbing Chen, Dongfeng Li, Pete Davies, Richard Johnston

, XIANGYUN GE, Chenfeng Li

Archives of Computational Methods in Engineering, Volume: 32, Issue: 6, Pages: 3447 - 3498

Swansea University Authors: Bingbing Chen, Pete Davies, Richard Johnston , XIANGYUN GE, Chenfeng Li

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DOI (Published version): 10.1007/s11831-025-10245-4

Abstract

This study comprehensively reviews recent advances in the digital reconstruction of polycrystalline materials. Digital reconstruction serves as both a representative volume element for multiscale modelling and a source of quantitative data for microstructure characterisation. Three main types of dig...

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Published in:	Archives of Computational Methods in Engineering
ISSN:	1134-3060 1886-1784
Published:	Springer Nature 2025
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa68978

first_indexed	2025-02-27T11:28:51Z
last_indexed	2025-08-07T08:09:39Z
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spelling	2025-08-06T14:36:11.7634157 v2 68978 2025-02-27 Recent Progress of Digital Reconstruction in Polycrystalline Materials 5b2828673b7414494f067b458092725c Bingbing Chen Bingbing Chen true false 38c85534a35a03aac99b687029078831 Pete Davies Pete Davies true false 23282e7acce87dd926b8a62ae410a393 0000-0003-1977-6418 Richard Johnston Richard Johnston true false c39ff918855a366c9266912315a176f9 XIANGYUN GE XIANGYUN GE true false 82fe170d5ae2c840e538a36209e5a3ac 0000-0003-0441-211X Chenfeng Li Chenfeng Li true false 2025-02-27 ACEM This study comprehensively reviews recent advances in the digital reconstruction of polycrystalline materials. Digital reconstruction serves as both a representative volume element for multiscale modelling and a source of quantitative data for microstructure characterisation. Three main types of digital reconstruction in polycrystalline materials exist: (i) experimental reconstruction, which links processing-structure-properties-performance by reconstructing actual polycrystalline microstructures using destructive or non-destructive methods; (ii) physics-based models, which replicate evolutionary processes to establish processing-structure linkages, including cellular automata, Monte Carlo, vertex/front tracking, level set, machine learning, and phase field methods; and (iii) geometry-based models, which create ensembles of statistically equivalent polycrystalline microstructures for structure-properties-performance linkages, using simplistic morphology, Voronoi tessellation, ellipsoid packing, texture synthesis, high-order, reduced-order, and machine learning methods. This work reviews the key features, procedures, advantages, and limitations of these methods, with a particular focus on their application in constructing processing-structure-properties-performance linkages. Finally, it summarises the conclusions, challenges, and future directions for digital reconstruction in polycrystalline materials within the framework of computational materials engineering. Journal Article Archives of Computational Methods in Engineering 32 6 3447 3498 Springer Nature 1134-3060 1886-1784 1 8 2025 2025-08-01 10.1007/s11831-025-10245-4 Review 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 (IF∖R2∖23200112, IEC∖NSFC∖191628). 2025-08-06T14:36:11.7634157 2025-02-27T09:28:49.8289926 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Bingbing Chen 1 Dongfeng Li 2 Pete Davies 3 Richard Johnston 0000-0003-1977-6418 4 XIANGYUN GE 5 Chenfeng Li 0000-0003-0441-211X 6 68978__33723__c49ff24051b74f019a70b116417686bb.pdf 68978.VOR.pdf 2025-03-03T10:54:28.2093080 Output 24980852 application/pdf Version of Record true © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY). true eng http://creativecommons.org/licenses/by/4.0/
title	Recent Progress of Digital Reconstruction in Polycrystalline Materials
spellingShingle	Recent Progress of Digital Reconstruction in Polycrystalline Materials Bingbing Chen Pete Davies Richard Johnston XIANGYUN GE Chenfeng Li
title_short	Recent Progress of Digital Reconstruction in Polycrystalline Materials
title_full	Recent Progress of Digital Reconstruction in Polycrystalline Materials
title_fullStr	Recent Progress of Digital Reconstruction in Polycrystalline Materials
title_full_unstemmed	Recent Progress of Digital Reconstruction in Polycrystalline Materials
title_sort	Recent Progress of Digital Reconstruction in Polycrystalline Materials
author_id_str_mv	5b2828673b7414494f067b458092725c 38c85534a35a03aac99b687029078831 23282e7acce87dd926b8a62ae410a393 c39ff918855a366c9266912315a176f9 82fe170d5ae2c840e538a36209e5a3ac
author_id_fullname_str_mv	5b2828673b7414494f067b458092725c_*_Bingbing Chen 38c85534a35a03aac99b687029078831__Pete Davies 23282e7acce87dd926b8a62ae410a393__Richard Johnston c39ff918855a366c9266912315a176f9__XIANGYUN GE 82fe170d5ae2c840e538a36209e5a3ac_**_Chenfeng Li
author	Bingbing Chen Pete Davies Richard Johnston XIANGYUN GE Chenfeng Li
author2	Bingbing Chen Dongfeng Li Pete Davies Richard Johnston XIANGYUN GE Chenfeng Li
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publisher	Springer Nature
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description	This study comprehensively reviews recent advances in the digital reconstruction of polycrystalline materials. Digital reconstruction serves as both a representative volume element for multiscale modelling and a source of quantitative data for microstructure characterisation. Three main types of digital reconstruction in polycrystalline materials exist: (i) experimental reconstruction, which links processing-structure-properties-performance by reconstructing actual polycrystalline microstructures using destructive or non-destructive methods; (ii) physics-based models, which replicate evolutionary processes to establish processing-structure linkages, including cellular automata, Monte Carlo, vertex/front tracking, level set, machine learning, and phase field methods; and (iii) geometry-based models, which create ensembles of statistically equivalent polycrystalline microstructures for structure-properties-performance linkages, using simplistic morphology, Voronoi tessellation, ellipsoid packing, texture synthesis, high-order, reduced-order, and machine learning methods. This work reviews the key features, procedures, advantages, and limitations of these methods, with a particular focus on their application in constructing processing-structure-properties-performance linkages. Finally, it summarises the conclusions, challenges, and future directions for digital reconstruction in polycrystalline materials within the framework of computational materials engineering.
published_date	2025-08-01T05:26:57Z
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Recent Progress of Digital Reconstruction in Polycrystalline Materials

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