A feed-forward neural-network-based model is presented to index, in real time, the diffraction spots recorded during synchrotron X-ray Laue microdiffraction experiments. Data dimensionality reduction is applied to extract physical 1D features from the 2D X-ray diffraction Laue images, thereby making it possible to train a neural network on the fly for any crystal system. The capabilities of the LaueNN model are illustrated through three examples: a two-phase nanostructure, a textured high-symmetry specimen deformed in situ and a polycrystalline low-symmetry material. This work provides a novel way to efficiently index Laue spots in simple and complex recorded images in <1 s, thereby opening up avenues for the realization of real-time analysis of synchrotron Laue diffraction data.

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Polycrystalline materials exhibit physical properties that are driven by both the interatomic crystallographic structure as well as the nature and density of structural defects. Crystallographic evolutions driven by phase transitions and associated twinning process can be observed in situ in three-dimensional (3D) using monochromatic synchrotron radiation at very high temperatures (over 1000 °C). This paper focuses on continuous measurements of the 3D-reciprocal space maps by high-resolution x-ray diffraction as a function of temperature along a phase transition process occurring between 1200 °C and room temperature. These high precision measurements allow observing the reciprocal space node splitting and the evolution of the diffuse scattering signal around that node as a function of temperature. The capability of this experimental method is illustrated by direct in situ high temperature measurements of the 3D splitting of a reciprocal space node due to phase transition recorded on dense pure zirconia polycrystals.

Deformation mechanisms of cold drawn and electropolished nickel microwires are studied by performing in-situ monotonous and cyclic tensile tests under synchrotron radiation. X-ray diffraction tests allow probing elastic strains in the different grain families and establishing a link with the deformation mechanisms taking place within the microwires. The measurements were carried out on several microwires with diameters ranging from as-drawn 100 µm down to 40 µm thinned down by electropolishing. The as-drawn wires exhibit a core-shell microstructure with <111> fiber texture dominant in core and heterogeneous dual fiber texture <111> and <100> in the shell. Reduction of specimen size by electropolishing results in a higher yield stress and tensile strength along with reduced ductility. In-situ XRD analysis revealed that these differences are linked to the global variation in microstructure induced by shell removal with electropolishing, which in turn affects the load sharing abilities of grain families. This study thus proposes a new way to increase ductility and retain strength in nickel microwires across different diameters by tuning the microstructure architecture.