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.
The mechanical properties of bulk CrFeCoNi compositionally complex alloys (CCA) or high entropy alloys (HEA) are widely studied in literature [1]. Notably, these alloys show mechanical properties similar to the well studied quinary CrMnFeCoNi [2] . Nevertheless, little is known about the deformation mechanisms and the thermal behavior of these…
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.