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Digital image correlation (DIC) has become a valuable tool to monitor andevaluate mechanical experiments of cracked specimen, but the automaticdetection of cracks is often difficult due to inherent noise and artefacts.Machine learning models have been extremely successful in detecting crack pathsand crack tips using DIC-measured, interpolated full-field displacements asinput to a convolution-based segmentation model. Still, big data is needed totrain such models. However, scientific data is often scarce as experiments areexpensive and time-consuming. In this work, we present a method to directlygenerate large amounts of artificial displacement data of cracked specimenresembling real interpolated DIC displacements. The approach is based ongenerative adversarial networks (GANs). During training, the discriminatorreceives physical domain knowledge in the form of the derived von Misesequivalent strain. We show that this physics-guided approach leads to improvedresults in terms of visual quality of samples, sliced Wasserstein distance, andgeometry score when compared to a classical unguided GAN approach.

Generating artificial digital image correlation data using physics-guided adversarial networks