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
Grain boundaries (GBs) affect many macroscopic properties of materials. In the case of metals grain growth, Hall–Petch hardening, diffusion, and electrical conductivity, for example, are influenced or caused by GBs. The goal of this project is to investigate the different GB phases (also called complexions) that can occur in tilt boundaries of fcc…
In order to develop more efficient catalysts for energy conversion, the relationship between the surface composition of MXene-based electrode materials and its behavior has to be understood in operando. Our group will demonstrate how APT combined with scanning photoemission electron microscopy can advance the understanding of complex relationships…
This project studies the mechanical properties and microstructural evolution of a transformation-induced plasticity (TRIP)-assisted interstitial high-entropy alloy (iHEA) with a nominal composition of Fe49.5Mn30Co10Cr10C0.5 (at. %) at cryogenic temperature (77 K). We aim to understand the hardening behavior of the iHEA at 77 K, and hence guide the future design of advanced HEA for cryogenic applications.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization as in micropillar compression. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one.…