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)
In this project we pursue recent developments in the field of austenitic steels with up to 18% reduced mass density. The alloys are based on the Fe-Mn-Al-C system.
Local lattice distortion is one of the core effects in complex concentrated alloys (CCAs). It has been expected that the strength CCAs can be improved by inducing larger local lattice distortions. In collaboration with experimentalists, we demonstrated that VCoNi has larger local lattice distortions and indeed has much better strength than the…
The nano-structure of surfaces influences the interactions and reactions occurring on it, which has strong impacts for applications in diverse fields, such as wetting phenomena, electrochemistry or biotechnology. We study these nanoscale structures on functional interfaces by nano-spectroscopy. Furthermore we try to understand their influence on…