论文标题
使用模拟和测量的显微镜数据的晶尺度各向异性弹性行为的图形神经网络建模
Graph Neural Network Modeling of Grain-scale Anisotropic Elastic Behavior using Simulated and Measured Microscale Data
论文作者
论文摘要
在这里，我们评估了图神经网络（GNN）在预测多晶金属合金的晶尺度弹性响应方面的适用性。使用GNN替代模型，在低溶剂高耐火等上单轴弹性张力（LSHR）Ni Superaly和Ti 7wt％AL（TI-7AL）中的单轴弹性张力期间的晶粒平均应力（例如面对面部居中的立方体和六边形封闭的合金）。采用了转移学习方法，其中使用晶体弹性有限元法（CEFEM）模拟对GNN替代模型进行训练，然后使用训练有素的替代模型来预测使用高能X射线衍射显微镜（HEDM）测量的微观结构的机械响应。通过与传统的平均场理论预测，保留的全场CEFEM数据和测量远场HEDM数据进行比较，探索了使用各种微观结构和微机械描述符为GNN的输入节点特征的性能。讨论了弹性各向异性对GNN模型性能的影响和扩展框架的前景。
Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in Low Solvus High Refractory (LSHR) Ni Superalloy and Ti 7wt%Al (Ti-7Al), as example face centered cubic and hexagonal closed packed alloys, are predicted. A transfer learning approach is taken in which GNN surrogate models are trained using crystal elasticity finite element method (CEFEM) simulations and then the trained surrogate models are used to predict the mechanical response of microstructures measured using high-energy X-ray diffraction microscopy (HEDM). The performance of using various microstructural and micromechanical descriptors for input nodal features to the GNNs is explored through comparisons to traditional mean-field theory predictions, reserved full-field CEFEM data, and measured far-field HEDM data. The effects of elastic anisotropy on GNN model performance and outlooks for extension of the framework are discussed.
