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
Hydrogen embrittlement is one of the most substantial issues as we strive for a greener future by transitioning to a hydrogen-based economy. The mechanisms behind material degradation caused by hydrogen embrittlement are poorly understood owing to the elusive nature of hydrogen. Therefore, in the project "In situ Hydrogen Platform for…
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.