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)
Hariharan, A.; Lu, L.; Risse, J.; Jägle, E. A.; Raabe, D.: Mechanisms Contributing to Solidification Cracking during laser powder bed fusion of Inconel-738LC. Alloys for Additive Manufacturing Symposium 2019 (AAMS2019), Chalmers University of Technology, Gothenburg, Sweden (2019)
Sedighiani, K.; Diehl, M.; Traka, K.; Roters, F.; Sietsma, J.; Raabe, D.: On the determination of constitutive parameters for a physics-based crystal plasticity model from macro-scale behavior. Meeting Materials 2018 , M2i Conference, Noordwijkerhout, The Netherlands (2018)
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
“Smaller is stronger” is well known in micromechanics, but the properties far from the quasi-static regime and the nominal temperatures remain unexplored. This research will bridge this gap on how materials behave under the extreme conditions of strain rate and temperature, to enhance fundamental understanding of their deformation mechanisms. The…
The precipitation of intermetallic phases from a supersaturated Co(Nb) solid solution is studied in a cooperation with the Hokkaido University of Science, Sapporo.
In this project, we employ atomistic computer simulations to study grain boundaries. Primarily, molecular dynamics simulations are used to explore their energetics and mobility in Cu- and Al-based systems in close collaboration with experimental works in the GB-CORRELATE project.
This project is a joint project of the De Magnete group and the Atom Probe Tomography group, and was initiated by MPIE’s participation in the CRC TR 270 HOMMAGE. We also benefit from additional collaborations with the “Machine-learning based data extraction from APT” project and the Defect Chemistry and Spectroscopy group.