论文标题
通过深层神经网络通过分裂图的分裂构成的骨折韧性的定量预测$（k _ {{\ rm i} c}）$
Quantitative Prediction of Fracture Toughness $(K_{{\rm I}c})$ of Polymer by Fractography Using Deep Neural Networks
论文作者
论文摘要
断裂表面提供有关断裂的各种信息。可以使用裂缝表面的三维（3D）信息来估算骨折韧性$ k _ {{\ rm i} c} $，它可以估算裂缝的电阻。但是，这是耗时且昂贵的，以获取断裂表面的3D信息。因此，希望从二维（2D）图像中估算$ k _ {{\ rm i} c} $，可以轻松获得。近年来，使用深度学习从其2D图像中估算3D结构的方法已迅速开发。在这项研究中，我们提出了一个直接估算$ k _ {{\ rm i} c} $的分裂框架的框架，该框架使用深神经网络（DNNS）从2D骨折表面图像中进行了。通常，使用DNN的图像识别需要大量的图像数据，由于高实验成本，因此很难获得分裂学。为了补偿有限的数据，在这项研究中，我们使用了转移学习（TL）方法，并通过传输使用其他大型数据集训练的机器学习模型来构建高性能预测模型。我们发现，使用我们建议的框架获得的回归模型可以预测$ k _ {{\ rm i} c} $在大约1-5 [mpa $ \ sqrt {m} $]的范围内，其估计误差约为$ \ pm $ 0.37 [mpa $ \ sqrt} $ \ sqrt} $} $} $}。目前的结果表明，经过TL训练的DNN为定量分裂术打开了新的途径，即使使用小数据集，也可以从骨折表面估算断裂过程的参数。提出的框架还可以在几个小时内建立回归模型。因此，我们的框架使我们能够筛选材料科学领域可用的大量图像数据集，并找到值得昂贵的机器学习分析的候选人。
Fracture surfaces provide various types of information about fracture. The fracture toughness $K_{{\rm I}c}$, which represents the resistance to fracture, can be estimated using the three-dimensional (3D) information of a fracture surface, i.e., its roughness. However, this is time-consuming and expensive to obtain the 3D information of a fracture surface; thus, it is desirable to estimate $K_{{\rm I}c}$ from a two-dimensional (2D) image, which can be easily obtained. In recent years, methods of estimating a 3D structure from its 2D image using deep learning have been rapidly developed. In this study, we propose a framework for fractography that directly estimates $K_{{\rm I}c}$ from a 2D fracture surface image using deep neural networks (DNNs). Typically, image recognition using a DNN requires a tremendous amount of image data, which is difficult to acquire for fractography owing to the high experimental cost. To compensate for the limited data, in this study, we used the transfer learning (TL) method, and constructed high-performance prediction models even with a small dataset by transferring machine learning models trained using other large datasets. We found that the regression model obtained using our proposed framework can predict $K_{{\rm I}c}$ in the range of approximately 1-5 [MPa$\sqrt{m}$] with a standard deviation of the estimation error of approximately $\pm$0.37 [MPa$\sqrt{m}$]. The present results demonstrate that the DNN trained with TL opens a new route for quantitative fractography by which parameters of fracture process can be estimated from a fracture surface even with a small dataset. The proposed framework also enables the building of regression models in a few hours. Therefore, our framework enables us to screen a large number of image datasets available in the field of materials science and find candidates that are worth expensive machine learning analysis.
