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Understanding stress distributions at grain boundaries in polycrystalline materials is crucial for predicting damaged nucleation sites. In high-purity materials, voids often nucleate at grain boundaries due to high stress from granular interactions and weakened atomic ordering. While traditional crystal plasticity models simulate grain-level mechanics, their high computational cost often prevents systematic identification of critical microstructural features and efficient forecast of extreme damage events. This paper addresses these challenges by developing a computationally efficient physics-assisted statistical modelling framework. The method starts by leveraging physical knowledge to hypothesize a broad set of microstructural factors influencing stress conditions. Causal inference is then applied to reveal the predominant features with physical explanations, leading to a parsimonious statistical model. A conditional Gaussian mixture model (CGMM) is employed when the identified relationship is utilized as a predictive model to quantify the uncertainty not readily explained by these features. Using body-centred cubic (BCC) tantalum as a representative material, a series of synthetic microstructures from single- to octu-crystal configurations are created. Results show that high-stress states strongly correlate with the elastic and plastic deformation capabilities and the directional misalignment of grain responses near boundaries. The statistical model achieves rapid and accurate forecasts, demonstrating its potential for analysing realistic polycrystalline materials.
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Grain Growth and the Effect of Different Time Scales
Many technologically useful materials are polycrystals composed of a myriad of small monocrystalline grains separated by grain boundaries. Dynamics of grain boundaries play a crucial role in determining the grain structure and defining the materials properties across multiple scales. In this work, we consider two models for the motion of grain boundaries with the dynamic lattice misorientations and the triple junctions drag, and we conduct extensive numerical study of the models, as well as present relevant experimental results of grain growth in thin films.
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- Award ID(s):
- 1905463
- PAR ID:
- 10430860
- Editor(s):
- Español, M; Lewicka, M; Scardia, L; Schlömerkemper, A
- Date Published:
- Journal Name:
- Association for Women in Mathematics series
- Volume:
- 31
- ISSN:
- 2364-5741
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/978-3-031-04496-0_2
