Kuo, J. C.; Zaefferer, S.; Raabe, D.: Experimental investigation of the deformation behavior of aluminium-bicrystals. MPI für Eisenforschung GmbH, Düsseldorf, Germany (2004)
Ma, A.; Roters, F.; Raabe, D.: Simulation of textures and Lankford values for face centered cubic polycrystaline metals by using a modified Taylor model. (2004)
Raabe, D.: A 3D probabilistic cellular automaton for the simulation of recrystallization and grain growth phenomena. Max-Planck-Society, München, Germany (2004)
Raabe, D.; Bréchet, Y.; Gottstein, G.; de Hosson, J.; Van Houtte, P.; Vitek, V.: Recommendations for Future Basic Research on Metallic Alloys and Composites in the 6th EU Framework Program - Metals and composites: Basis for growth, safety, and ecology. (2004)
Raabe, D.; Pramono, A.: Report on copper–niob research at the Max-Planck-Institut, Düsseldorf – Simulations and experiments. MPI für Eisenforschung, Düsseldorf, Germany (2004)
Sachtleber, M.; Raabe, D.: Theoretische und experimentelle Untersuchung der Kornwechselwirkung in Aluminium. MPI für Eisenforschung GmbH, Düsseldorf, Germany (2004)
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
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.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. 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. With this project, we aim to…