Digital image correlation (DIC) has become a valuable tool to monitor and evaluate mechanical experiments of cracked specimen, but the automatic detection of cracks is often difficult due to inherent noise and artefacts. Machine learning models have been extremely successful in detecting crack paths and crack tips using DIC-measured, interpolated full-field displacements as input to a convolution-based segmentation model. Still, big data is needed to train such models. However, scientific data is often scarce as experiments are expensive and time-consuming. In this work, we present a method to directly generate large amounts of artificial displacement data of cracked specimen resembling real interpolated DIC displacements. The approach is based on generative adversarial networks (GANs). During training, the discriminator receives physical domain knowledge in the form of 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 when compared to a classical unguided GAN approach.

翻译：数字图像相关（DIC）已成为监测和评估裂纹试件力学实验的重要工具，但由于固有噪声和伪影，裂纹的自动检测往往十分困难。机器学习模型在利用DIC测量并插值得到的全场位移作为卷积分割模型输入时，已成功实现了裂纹路径和裂纹尖端的检测。然而，训练此类模型需要大量数据。鉴于科学实验成本高昂且耗时，科学数据往往稀缺。本文提出了一种方法，可直接生成大量与真实插值DIC位移相似的裂纹试件人工位移数据。该方法基于生成对抗网络（GANs）。在训练过程中，判别器会接收以派生出的冯·米塞斯等效应变形式呈现的物理领域知识。研究表明，与经典的无引导GAN方法相比，这种物理引导方法在样本视觉质量、切片沃瑟斯坦距离和几何评分方面均取得了更优的结果。