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
Titanium and its alloys are widely used in critical applications due to their low density, high specific strength, and excellent corrosion resistance, but their poor plasticity at room temperature limits broader utilization. Introducing hydrogen as a temporary alloying element has been shown to improve plasticity during high-temperature processing…
Defects at interfaces strongly impact the properties and performance of functional materials. In functional nanostructures, they become particularly important due to the large surface to volume ratio.
This ERC-funded project aims at developing an experimentally validated multiscale modelling framework for the prediction of fracture toughness of metals.
In this project, links are being established between local chemical variation and the mechanical response of laser-processed metallic alloys and advanced materials.