Automated identification of dislocation structures from experimental Laue microdiffraction patterns

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Micro-Laue diffraction allows the nondestructive three-dimensional microstructure analysis of crystalline materials and their defects. The Nye tensor is of particular interest to understand plastic deformation because it provides information about geometrically necessary dislocations. With this information one can estimate dislocation structure and understand deformation behavior. But the full and exact three-dimensional structural geometry of dislocations is not available. The current project starts here: By using virtual diffraction patterns based on discrete dislocation dynamics simulations an artificial neural network is optimized which connects diffraction patterns with dislocation structure. The main goal is a quantitative statistical understanding between experimental diffraction patterns with dislocation structures. This understanding results in a widely applicable tool. The input is a diffraction pattern, the output the underlying dislocation structure. This tool allows the automated analysis of diffraction patterns and thereby enables the compilation of meaningful statistics not accessible with current methods. Defect statistics provide an improved understanding of defect structure and behavior for materials design.