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    PublicationAccess status: Open Access ,
    The way to a smarter community: exploring and exploiting data modeling, big data analytics, high-performance computing and artificial intelligence techniques for applications of 2D energy-dispersive detectors in the crystallography community
    Big data will be a source of new economic value and innovation. But even more is at stake. Big data’s ascendancy represents […] shifts in the way we analyze information that transforms how we understand and organize society”. This important statement by Mayer-Schönberger and Cukier highlights the crucial benefit this field offers to the research and business communities. It is opening up an entirely new horizon for economic and innovative solutions. It also sheds light on the main challenge researchers and business analytics across the world have been facing during the recent years, namely big data handling, extraction and employing. Digitization enabling the real-time data visualization for the further analysis is now a vital prerequisite for a successful and innovative scientific community. This work aims to contribute to the transformation of the crystallography community, dealing with an enormous volume of the measured data obtained due to technological advancement in radiation and detection, into a smarter scientific environment. \\ Specifically, this dissertation has a twofold aim. First, it is intended to provide an introduction and implementation guideline to scientists in the crystallography community and across the fields demonstrating how trend technologies might be applied to design cutting-edge research projects. Second, considering the Energy Dispersive Laue Diffraction (EDLD) as a case study, it introduces an innovative approach to exploit high-performance computing to develop a novel framework for data processing within the time frame of a few seconds compared to the traditional analytic systems requiring a few hours to process the same amount of data. The framework developed is based on multiple artificial intelligence algorithms, designed to perform tasks that cannot be processed by basic programming techniques. Extending this approach, data clouding has been employed to establish the communication channels between scientists and the collected data. This computing solution helps to achieve commoditization of computational resources, implementation of open-source software, data virtualization, globalization of workforce, establishing a data-sharing point. On the whole, due to these technological improvements, the crystallography community might gain maximum benefit from the collected data. As a proof of concept, the reliability, efficiency, and performance of the entire work has been verified by involving the system in a challenging task, namely: the one-shot analysis of the micro texture in polycrystalline materials.
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    Publication
    Using Energy‐Dispersive Laue Diffraction to Study Dislocation Arrangements in Materials Showing Wavy and Planar Slip Behavior
    ABSTRACT The present work shows an approach to monitor the evolution of the dislocation arrangement of a metallic material caused by cyclic plastic strain using white X‐ray radiation in combination with an energy‐dispersive detector. The method is demonstrated by single‐shot experiments performed on polycrystalline nickel and α‐brass, representing the pure wavy and the pure planar dislocation slip behavior. To correlate the resulting diffraction patterns with various dislocation arrangements of both metals, fatigue tests were carried out up to certain numbers of cycles and at predetermined plastic strain amplitudes. The differences in dislocation microstructure and internal stress distributions give rise to an appreciable change in the peak shape of Laue reflections, leading to unique characteristics in the respective diffraction patterns. Nickel reflections are elongated due to the high amount of cell structures leading to bending and misorientation of the lattice, whereas the present stacking faults in α‐brass result in powder‐like diffraction.
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