Kobayashi, S.; Zaefferer, S.; Schneider, A.; Raabe, D.; Frommeyer, G.: Slip system determination by rolling texture measurements around the strength peak temperature in a Fe3Al-based alloy. Intern. Conf. on Strength of Materials (ICSMA 13), Budapest, Hungary (2003)
Raabe, D.: Experimental and Theoretical Investigation of Grain Scale Plasticity. Colloquium lecture at the Department of Materials Science and Engineering of Northwestern University, Evanston, Chicago, USA (2002)
Raabe, D.; Helming, K.; Roters, F.; Zhao, Z.; Hirsch, J.: A Texture Component Crystal Plasticity Finite Element Method for Scalable Large Strain Anisotropy Simulations. ICOTOM 13, Seoul, South Korea (2002)
Raabe, D.: Modelling Applied to Aluminium Alloy Metallurgy. Keynote lecture at the 8th International Conference on Aluminium Alloys (ICAA-8), Cambridge, UK (2002)
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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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.
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.