Digital image correlation (DIC) has become a valuable tool in the evaluation of mechanical experiments, particularly fatigue crack growth experiments. The evaluation requires accurate information of the crack path and crack tip position, which is difficult to obtain due to inherent noise and artefacts. Machine learning models have been extremely successful in recognizing this relevant information. But for the training of robust models, which generalize well, big data is needed. However, data is typically scarce in the field of material science and engineering because experiments are expensive and time-consuming. We present a method to generate synthetic DIC data using generative adversarial networks with a physics-guided discriminator. To decide whether data samples are real or fake, this discriminator additionally receives the derived von Mises equivalent strain. We show that this physics-guided approach leads to improved results in terms of visual quality of samples, sliced Wasserstein distance, and geometry score.

翻译：数字图像相关（DIC）已成为力学实验评估中的重要工具，特别是在疲劳裂纹扩展实验中。由于固有噪声和伪影的存在，获取裂纹路径和裂纹尖端位置的精确信息十分困难。机器学习模型在识别这类相关信息方面取得了极大成功。然而，为了训练具有良好泛化能力的鲁棒模型，需要大量数据。但材料科学与工程领域的数据通常较为稀缺，因为实验成本高且耗时。我们提出一种利用生成对抗网络结合物理引导判别器来生成合成DIC数据的方法。该判别器在判定数据样本真实性时，额外接收衍生的冯·米塞斯等效应变信息。实验表明，这种物理引导方法在样本视觉质量、切片Wasserstein距离和几何评分方面均取得了更优的结果。