The occurrence of fatigue cracks is an inherent part of the design of engineering structures subjected to nonconstant loads. Thus, the accurate description of cracks in terms of location and evolution during service conditions is mandatory to fulfill safety‐relevant criteria. In the present work, we implement a deep convolutional neural network to detect crack paths together with their crack tips based on displacement fields obtained using digital image correlation. To this purpose, fatigue crack propagation experiments were performed for AA2024‐T3 rolled sheets using specimens with different geometries. Several hundred datasets were acquired by digital image correlation during the experiments. A part of the displacement data from one of the specimens was then used to train the neural network. The results show that the method can accurately detect the shape and evolution of the cracks in all specimens. Adding synthetic data generated by finite element analyses to the training step improved the accuracy for cracks with stress intensity factors that exceeded the range of the original training data.

中文翻译：

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使用数字图像相关和卷积神经网络自动检测疲劳裂纹路径

疲劳裂纹的出现是承受非恒定载荷的工程结构设计的固有部分。因此，为了满足安全相关标准，必须在使用条件下准确描述裂纹的位置和演变。在当前的工作中，我们实现了一个深度卷积神经网络，以基于使用数字图像相关性获得的位移场来检测裂纹路径及其裂纹尖端。为此，使用不同几何形状的试样对AA2024-T3轧制薄板进行了疲劳裂纹扩展实验。在实验过程中通过数字图像相关性获取了数百个数据集。然后，将其中一个样本的一部分位移数据用于训练神经网络。结果表明，该方法可以准确地检测出所有试样的裂纹形状和裂纹。将有限元分析生成的合成数据添加到训练步骤中，可以提高应力强度因子超出原始训练数据范围的裂纹的准确性。