Investigation on optimal microstructure of dual-phase steel with high strength and ductility by machine learning
| Autor: | Suzuki, Misato, Shizawa, Kazuyuki, Muramatsu, Mayu |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Mater. Today Commun., Volume 41, 110557, 2024 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.mtcomm.2024.110557 |
| Popis: | In this study, we developed an inverse analysis framework that proposes a microstructure for dual-phase (DP) steel that exhibits high strength and ductility. The inverse analysis method proposed in this study involves repeated random searches on a model that combines a generative adversarial network (GAN), which generates microstructures, and a convolutional neural network (CNN), which predicts the maximum stress and working limit strain from DP steel microstructures. GAN was trained using images of DP steel microstructures generated by the phase-field method. CNN was trained using images of DP steel microstructures, the maximum stress and the working limit strain calculated by the dislocation-crystal plasticity finite element method. The constructed framework made an efficient search for microstructures possible because of a low-dimensional search space by a latent variable of GAN. The multiple deformation modes were considered in this framework, which allowed the required microstructures to be explored under complex deformation modes. A microstructure with a fine grain size was proposed by using the developed framework. Comment: 27 pages, 23 figures |
| Databáze: | arXiv |
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