Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Fractography plays a critical role in failure analysis of engineering components and has a considerable importance for safety investigations. Usually, the interpretation of fracture surfaces is done by experts with the help of literature and experimental data, that requires a lot of experience. The use of deep learning (DL) with neural networks in failure analysis becomes more and more relevant with the rapidly developing possibilities. Especially, the modern network architectures can assist fractographers in determining various fracture features on SEM images of the fracture surfaces. The basis for the best possible evaluation is the understanding of the influence of the input data used for training deep neural networks (DNN). Therefore, this study discusses the influence of the selection of the input data used for the prediction quality of these networks in order to take this into account for future data acquisition. Specimens of various metallic materials were subjected to fatigue cracking experiment under laboratory conditions. The fractured surfaces were then imaged using various modes or detectors (such as SE, BSE and topography) in SEM, and those captured images were used to create a training data set. The relevance of the individual data for the quality of the prediction is determined by a specific combination of the different detector data. For the training, the well-established architecture of a UNet-ResNet34 with a fixed set of hyperparameters is used. It has been found in this present study that the combination of all input data significantly increases the prediction accuracy, whereby even the combination of SE and BSE data provides considerable advantages over the exclusive use of SE images.
Many microstructural features exhibit non-trivial geometries, which can only be derived to a limited extent from two-dimensional images. E.g., graphite arrangements in lamellar gray cast iron have complex geometries, and the same is true for additively manufactured materials and three-dimensional conductive path structures. Some can be visualized using tomographic methods, but some cannot be due to weak contrast and/or lack of resolution when analyzing macroscopic objects. Classic metallography can help but must be expanded to the third dimension. The method of reconstructing three-dimensional structures from serial metallographic sections surely is not new. However, the effort required to manually assemble many individual sections into image stacks is very high and stands in the way of frequent application. For this reason, an automated, robot-supported 3D metallography system is being developed at BAM, which carries out the steps of repeated preparation and image acquisition on polished specimen.
Preparation includes grinding, polishing and optionally etching of the polished surface. Image acquisition comprises autofocused light microscopic imaging at several magnification levels. The image stacks obtained are then pre-processed, segmented, and converted into 3D models, which in the result appear like microtomographic models, but with high resolution at large volume. Contrasting by classical chemical etching reveals structures that cannot be resolved using tomographic methods. The integration of further imaging and measuring methods into this system is underway. Some examples will be discussed in the presentation.