In addition, it cannot be ruled out that the complex specimen preparation does not already have an effect on the area to be examined, e.g., by stress-induced solid state transformation. Up to now, it has been difficult and time-consuming to examine such microstructures, since excellent surface qualities are required for techniques such as electron backscatter diffraction, where the size of the probed specimen is limited. Therefore, the examination of the properties of these promising materials is essential. Such microstructures can significantly affect the properties. Moreover, additive manufacturing often promotes the evolution of coarse-grained columnar microstructures with a strong texture due to the specific local temperature history at each spot of the specimen during processing. As an example, the orientation of grains has a strong influence on the behavior of shape memory alloys (SMAs), e.g., on the transformation strain of singlecrystalline SMAs 1, 2, 3, 4 or oligocrystalline SMAs 5, 6, 7, 8, 9. These properties are very interesting for many materials being promising for industrial applications. The relevance of characterizing coarse-grained structures and their orientations has increased considerably in recent years since coarse-grained structures tend to have anisotropic, i.e., direction-dependent, properties. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. X-ray diffraction crystallography allows non-destructive examination of crystal structures.
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