  To this date the safety assessment of materials, used for example in the
nuclear power sector, commonly relies on a fracture mechanical analysis
utilizing macroscopic concepts, where a global load quantity K or J is compared
to the materials fracture toughness curve. Part of the experimental effort
involved in these concepts is dedicated to the quantitative analysis of
fracture surfaces. Within the scope of this study a methodology for the
semi-supervised training of deep learning models for fracture surface
segmentation on a macroscopic level was established. Therefore, three distinct
and unique datasets were created to analyze the influence of structural
similarity on the segmentation capability. The structural similarity differs
due to the assessed materials and specimen, as well as imaging-induced variance
due to fluctuations in image acquisition in different laboratories. The
datasets correspond to typical isolated laboratory conditions, complex
real-world circumstances, and a curated subset of the two. We implemented a
weak-to-strong consistency regularization for semi-supervised learning. On the
heterogeneous dataset we were able to train robust and well-generalizing models
that learned feature representations from images across different domains
without observing a significant drop in prediction quality. Furthermore, our
approach reduced the number of labeled images required for training by a factor
of 6. To demonstrate the success of our method and the benefit of our approach
for the fracture mechanics assessment, we utilized the models for initial crack
size measurements with the area average method. For the laboratory setting, the
deep learning assisted measurements proved to have the same quality as manual
measurements. For models trained on the heterogeneous dataset, very good
measurement accuracies with mean deviations smaller than 1 % could be
achieved...
