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Rao, Z.; Li, Y.; Zhang, H.; Colnaghi, T.; Marek, A.; Rampp, M.; Gault, B.: Direct recognition of crystal structures via three-dimensional convolutional neural networks with high accuracy and tolerance to random displacements and missing atoms. Scripta Materialia 234, 115542 (2023)
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International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
The key to the design and construction of advanced materials with tailored mechanical properties is nano- and micro-scale plasticity. Significant influence also exists in shaping the mechanical behavior of materials on small length scales.
This project aims to correlate the localised electrical properties of ceramic materials and the defects present within their microstructure. A systematic approach has been developed to create crack-free deformation in oxides through nanoindentation, while the localised defects are probed in-situ SEM to study the electronic properties. A coupling…
This project endeavours to offer comprehensive insights into GB phases and their mechanical responses within both pure Ni and Ni-X (X=Cu, Au, Nb) solid solutions. The outcomes of this research will contribute to the development of mechanism-property diagrams, guiding material design and optimization strategies for various applications.
By using the DAMASK simulation package we developed a new approach to predict the evolution of anisotropic yield functions by coupling large scale forming simulations directly with crystal plasticity-spectral based virtual experiments, realizing a multi-scale model for metal forming.