Winning, M.; Brahme, A.; Raabe, D.: Prediction of cold rolling textures of steels using an artificial neural network. Computational Materials Science 46, pp. 800 - 804 (2009)
Winning, M.; Raabe, D.; Brahme, A.: A texture component model for predicting recrystallization textures. Materials Science Forum 558 / 559, pp. 1035 - 1042 (2007)
Brahme, A.; Winning, M.; Raabe, D.: Texture Component Model for Predicting Recrystallization Textures. 15th International Conference on the Texture of Materials (ICOTOM 15), Pittsburgh, PA, USA (2008)
Brahme, A.: Brief Introduction to Cellular Automaton and Monte Carlo Method. MPIE inter-departmental tutorial day(s) 2008, MPI für Eisenforschung GmbH, Düsseldorf, Germany (2008)
Winning, M.; Raabe, D.; Brahme, A.: A texture component model for predicting recrystallization textures. The Third International Conference on Recrystallization and Grain Growth, Jeju Island, South Korea (2007)
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.