Plasticity Encoding and Mapping during Elementary Loading for Accelerated Mechanical Properties Prediction
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912293342674944 |
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| author | Calvat, Mathieu Bean, Chris Anjaria, Dhruv Wang, Haoren Vecchio, Kenneth Stinville, J. C. |
| author_facet | Calvat, Mathieu Bean, Chris Anjaria, Dhruv Wang, Haoren Vecchio, Kenneth Stinville, J. C. |
| contents | Encoding metal plasticity captured from high-resolution digital image correlation (DIC) can be leveraged to predict a wide range of monotonic and cyclic macroscopic properties of metallic materials. To capture the spatial heterogeneity of plasticity that develops in metals, latent space features describing plasticity of a small region are spatially mapped across a large field of view while maintaining the same spatial relationships as the experimental measurements. Latent space feature maps capture the complexity and heterogeneity of metal plasticity as a low-dimensional representation. These feature maps are then used to train a convolutional neural network-based model to predict monotonic and cyclic macroscopic properties. The approach is demonstrated on a large set of face-centered cubic metals, enabling rapid and accurate property prediction. The effects of hyperparameters and training strategies are analyzed, and the extension of the proposed approach to a broader range of metallic materials and loading conditions is discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Plasticity Encoding and Mapping during Elementary Loading for Accelerated Mechanical Properties Prediction Calvat, Mathieu Bean, Chris Anjaria, Dhruv Wang, Haoren Vecchio, Kenneth Stinville, J. C. Materials Science Encoding metal plasticity captured from high-resolution digital image correlation (DIC) can be leveraged to predict a wide range of monotonic and cyclic macroscopic properties of metallic materials. To capture the spatial heterogeneity of plasticity that develops in metals, latent space features describing plasticity of a small region are spatially mapped across a large field of view while maintaining the same spatial relationships as the experimental measurements. Latent space feature maps capture the complexity and heterogeneity of metal plasticity as a low-dimensional representation. These feature maps are then used to train a convolutional neural network-based model to predict monotonic and cyclic macroscopic properties. The approach is demonstrated on a large set of face-centered cubic metals, enabling rapid and accurate property prediction. The effects of hyperparameters and training strategies are analyzed, and the extension of the proposed approach to a broader range of metallic materials and loading conditions is discussed. |
| title | Plasticity Encoding and Mapping during Elementary Loading for Accelerated Mechanical Properties Prediction |
| topic | Materials Science |
| url | https://arxiv.org/abs/2503.19799 |