Investigation on optimal microstructure of dual-phase steel with high strength and ductility by machine learning
<|reference_start|>Investigation on optimal microstructure of dual-phase steel with high strength and ductility by machine learning: 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.<|reference_end|>
@article{suzuki2024investigation,
    title={Investigation on optimal microstructure of dual-phase steel with high
  strength and ductility by machine learning},
    author={Misato Suzuki, Kazuyuki Shizawa and Mayu Muramatsu},
    journal={Mater. Today Commun., Volume 41, 110557, 2024},
    year={2024},
    doi={10.1016/j.mtcomm.2024.110557},
    archivePrefix={arXiv},
    eprint={2405.01689},
    primaryClass={cs.CE}
}