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Automatic Texture Alignment by Optimization Method.

Alois C OttIrmgard WeißensteinerAurel R ArnoldtJohannes A ÖsterreicherNikolaus P Papenberg
Published in: Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada (2024)
Microstructure analysis via electron backscatter diffraction has become an indispensable tool in materials science and engineering. In order to interpret or predict the anisotropy in crystalline materials, the texture is assessed, e.g. via pole figure diagrams. To ensure a correct characterization, it is crucial to align the measured sample axes as closely as possible with the manufacturing process directions. However, deviations are inevitable due to sample preparation and manual measurement setup. Postprocessing is mostly done manually, which is tedious and operator-dependent. In this work, it is shown that the deviation can be calculated using the contour of the crystal orientations. This can also be utilized to define the axis symmetry of pole figure diagrams through an objective function, allowing for symmetric alignment by minimization. Experimental textures of extruded profiles and synthetically generated textures were used to demonstrate the general applicability of the method. It has proven to work excellently for deviations of up to 5∘, which are typical for careful manual sample preparation and mounting. While the performance of the algorithm is reduced with increasing misalignment, good results have also been obtained for deviations up to 15∘.
Keyphrases
  • deep learning
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