5 Werkstofftechnik
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The following work deals with the quantitative fracture surface evaluation in damage analysis. So far, fracture surfaces have almost exclusively been evaluated qualitatively, i.e. the presence of fracture features is documented and their surface proportions are estimated, if necessary. Many years of experience are required, as well as an intensive comparison with defined comparative images from the literature. The aim of this work is the development of classifiers which can recognize fracture mechanisms or fracture features in scanning electron microscope images (SEM). The basis is 46 SEM images, which have been evaluated by fractography experts with regard to fracture features. The existing data set of images is expanded using augmentation methods in order to increase the variability of the data and counteract overfitting. Only convolutional neural networks (CNN) are used to create the classifiers. Various network configurations are tested, with the SegNet achieving the best results.
Although the slow crack growth in glass is dominated by stress-corrosion phenomena, it also should reflect the underlaying intrinsic fracture behavior controlled by glass chemistry and structure. To investigate such underlaying phenomena, crack growth velocity in alkali silicate glasses was measured in vacuum across 10 orders of magnitude with double cantilever beam technique. Measured and literature crack growth data were compared with calculated intrinsic fracture toughness data obtained from Young’s moduli and theoretical fracture surface energy. Data analysis reveals slight correlation with the packing density and significant deviations from the intrinsic brittle fracture behavior. These deviations do not follow simple compositional trends. Two opposing processes may explain this finding: a decrease in the apparent fracture surface energy due to stress-induced chemical changes at the crack tip and its increase due to energy dissipation during fracture.