Numerical and data-driven modeling of spall failure in polycrystalline ductile materials
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915578345684992 |
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| author | Saha, Indrashish Graham-Brady, Lori |
| author_facet | Saha, Indrashish Graham-Brady, Lori |
| contents | Developing materials with tailored mechanical performance requires iteration over a large number of proposed designs. When considering dynamic fracture, experiments at every iteration are usually infeasible. While high-fidelity, physics-based simulations can potentially reduce experimental efforts, they remain computationally expensive. As a faster alternative, key dynamic properties can be predicted directly from microstructural images using deep-learning surrogate models. In this work, the spallation of ductile polycrystals under plate-impact loading at strain rates of O(10^6 s^-1) is considered. A physics-based numerical model that couples crystal plasticity and a cohesive zone model is used to generate data for the surrogate models. Three architectures - 3D U-Net, 3D Fourier Neural Operator (FNO-3D), and U-FNO were trained on the particle-velocity field data from the numerical model. The generalization of the models was evaluated using microstructures with varying grain sizes and aspect ratios. U-FNO and 3D U-Net performed significantly better than FNO-3D across all datasets. Furthermore, U-FNO and 3D U-Net exhibited comparable accuracy for every metric considered in this study. However, training the U-FNO requires almost twice the computational effort compared to the 3D U-Net, making it a desirable option for a surrogate model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03706 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Numerical and data-driven modeling of spall failure in polycrystalline ductile materials Saha, Indrashish Graham-Brady, Lori Materials Science Developing materials with tailored mechanical performance requires iteration over a large number of proposed designs. When considering dynamic fracture, experiments at every iteration are usually infeasible. While high-fidelity, physics-based simulations can potentially reduce experimental efforts, they remain computationally expensive. As a faster alternative, key dynamic properties can be predicted directly from microstructural images using deep-learning surrogate models. In this work, the spallation of ductile polycrystals under plate-impact loading at strain rates of O(10^6 s^-1) is considered. A physics-based numerical model that couples crystal plasticity and a cohesive zone model is used to generate data for the surrogate models. Three architectures - 3D U-Net, 3D Fourier Neural Operator (FNO-3D), and U-FNO were trained on the particle-velocity field data from the numerical model. The generalization of the models was evaluated using microstructures with varying grain sizes and aspect ratios. U-FNO and 3D U-Net performed significantly better than FNO-3D across all datasets. Furthermore, U-FNO and 3D U-Net exhibited comparable accuracy for every metric considered in this study. However, training the U-FNO requires almost twice the computational effort compared to the 3D U-Net, making it a desirable option for a surrogate model. |
| title | Numerical and data-driven modeling of spall failure in polycrystalline ductile materials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2507.03706 |