Journal article 170 views 63 downloads
Recent Progress of Digital Reconstruction in Polycrystalline Materials
,
XIANGYUN GE,
Chenfeng Li
Archives of Computational Methods in Engineering
Swansea University Authors:
Bingbing Chen, Pete Davies, Richard Johnston , XIANGYUN GE, Chenfeng Li
-
PDF | Version of Record
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY).
Download (23.82MB)
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...
| Published in: | Archives of Computational Methods in Engineering |
|---|---|
| ISSN: | 1134-3060 1886-1784 |
| Published: |
Springer Science and Business Media LLC
2025
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa68978 |
| 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 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. |
|---|---|
| Item Description: |
Review |
| College: |
Faculty of Science and Engineering |
| Funders: |
The authors would like to thank the supports from Chinese Scholarship Council, Swansea University, and the Royal Society (IF∖R2∖23200112, IEC∖NSFC∖191628). |