论文标题
谷物边界滑移转移分类和人工神经网络选择
Grain boundary slip transfer classification and metric selection with artificial neural networks
论文作者
论文摘要
人工神经网络用于评估六个指标及其组合的有效性，以评估在粗粒寡晶体中跨晶界的滑移转移是否会在粗粒寡晶体边界上进行\ citep {bieler_etal_etal2019_2，alizadeh_etal202020202020}。 这种方法扩展了以前应用于分析滑移传输的一维预测。当同时考虑两个或更多指标时，该二进制分类的准确性适用于最佳单个度量标准的\ pcnt {87}。 结果表明滑动转移主要取决于晶粒之间的几何关系。 训练一个具有\ num {10}节点的双层网络，其每个隐藏层的节点具有\ num {40}的测量足以呈现最大的精度。
An artificial neural network is used to evaluate the effectiveness of six metrics and their combinations to assess whether slip transfers across grain boundaries in coarse-grained oligocrystalline Al foils \citep{Bieler_etal2019_2,Alizadeh_etal2020}. This approach extends the one- or two-dimensional projections formerly applied to analyze slip transfer. The accuracy of this binary classification reaches around \pcnt{87} for the best single metric and around \pcnt{90} when considering two or more metrics simultaneously. The results suggest slip transfer mostly depends on the geometric relationship between grains. Training a double-layer network having \num{10} nodes per hidden layer with \num{40} measurements is sufficient to render the maximum accuracy.
