AI 中文总结
本研究采用多模态深度学习框架，结合三种微观结构描述符，从三维图像预测Mg/LPSO两相合金的压缩局域应变分布，验证了方法的有效性。
AI 中文摘要
本研究提出一种方法，用于从三维微观结构图像预测铸态Mg/LPSO两相合金在压缩变形下的三维（3D）局域应变分布。三维局域应变分布通过对压缩试验前后的X射线CT图像应用数字体相关法获得。从每个应变测量点周围的三维微观结构图像中提取了三种微观结构描述符：相的体积分数、可表达相连通性的持续同调图，以及可表达相空间分布的两相空间相关性。随后构建了一个深度学习模型，用于从这三种微观结构描述符预测局域应变。由于本研究使用了数值数据和图像数据两种类型的描述符，因此采用多模态深度学习进行预测。因此，与使用单一描述符进行预测相比，使用多种微观结构描述符能够实现更高精度的预测。通过相关性分析和遮挡敏感性分析评估了描述符的特征重要性。结果表明，高应变往往发生在硬质相LPSO相存在大的、沿与加载方向成45°取向的伸长相的区域。该结果与其他先前研究一致，表明所提出的方法在阐明材料微观结构与变形行为之间的关系方面是有效的。
英文摘要
This study proposes a method for predicting three-dimensional (3D) local strain distribution under compressive deformation of as-cast Mg/LPSO two-phase alloys from 3D microstructure images. The 3D local strain distribution was obtained by applying the digital volume correlation method to X-ray CT images before and after compression tests. Three microstructure descriptors were extracted from the 3D microstructure images around each strain measurement point: volume fractions of the phases, persistent diagrams that can express the connectivity of the phases, and two-phase spatial correlation that can express the spatial distribution of the phases. A deep learning model was then constructed to predict local strain from the three microstructure descriptors. Since two types of descriptors were used in this study, numerical data and image data, multimodal deep learning was employed to make predictions. Thus, the use of multiple microstructure descriptors enabled predictions to be made with higher accuracy than when predictions were made from a single descriptor. Feature importance of the descriptors was assessed through correlation analysis and occlusion sensitivity analysis. The results revealed that high strain tended to occur in the region where the hard phase, LPSO phase, had a large elongated phase oriented at a 45° direction to the loading direction. This result is consistent with other previous studies and indicates that the proposed method is effective in elucidating the relationship between the microstructure and the deformation behavior of the material.
CommentsPublished in Acta Materialia, Volume 281, 120398 (2024)
Journal refActa Materialia 281 (2024) 120398
DOI：10.1016/j.actamat.2024.120398
