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)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.