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Packaging containers for dangerous goods that include aggressive liquids require that any packaging material that is based on high-density polyethylene has a high degree of stability and durability. This work is focused on testing the environmental stress cracking of the high-density polyethylenes used for such containers in contact with crop protection formulations, in particular, two model liquids established in Germany as standardized test media representatives for crop protection formulations containing the various admixtures typical for such products. One of the liquids is water-based and contains mostly surface-active ingredients, while the other is solvent-based and includes some emulsifiers. Originally established for pin impression tests, these model liquids and their individual components were here used for the first time as environmental media in the Full Notch Creep Test, which addresses the resistance against environmental stress cracking. The Full Notch Creep Test was carried out on five high-density polyethylene types with both model liquids, and also on one selected material with its components. The evaluation was focused on the fracture surface structures, which were visualized by a scanning electron microscope and by optical in situ imaging of the notch opening. While the water-based model liquid and its surface-active individual components induced environmental stress cracking with the characteristic pattern for a craze-crack mechanism and so-called brittle fracture on the surface, the solvent-based model liquid and its soluble ingredients exhibited rather ductile failure behavior, caused by the plasticizing effect on the polymer that reduced the yield stress of the high-density polyethylene.
For both cases, fracture surface analysis, together with side views of the crack opening, showed a clear relation between surface pattern, notch deformation (e.g., by blunting), or crack opening due to crack growth with time to failure and the solubility of the liquids in high-density polyethylene.
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.
Die „auffälligen großen Streifen“ sind von echten Schwingstreifen nur sehr schwer zu trennen, weil sie parallel zu den Schwingstreifen verlaufen und meistens ebenfalls nicht sehr groß sind. Die im Labor getesteten Proben zeigten, dass diese großen Streifen zusammen mit Verreibungen auftraten und „tire tracks“ („Reifenspuren“) verursachten. Dies wurde durch eine hochauflösende REM-Untersuchung bestätigt, die „tire tracks“ auf den „großen Streifen“ zeigte. Ein weiterer Hinweis ist die Änderung des R-Werts von -1 auf 0,1, was zu einer Bruchfläche ohne große Streifen und „tire tracks“ führt.
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.