Konrad, J.; Zaefferer, S.; Schneider, A.: Investigation of nucleation mechanisms of recrystallization in warm rolled Fe3Al base alloys. Materials Science Forum 467-470, pp. 75 - 80 (2004)
Zaefferer, S.; Konrad, J.; Raabe, D.: 3D-Orientation Microscopy in a Combined Focused Ion Beam (FIB) - Scanning Electron Microscope: A New Dimension of Microstructure Characterisation. Microscopy Conference 2005, Davos, Switzerland (2005)
Konrad, J.; Raabe, D.; Zaefferer, S.: Investigation of orientation gradients around particles and their influence on particle stimulated nucleation in a hot rolled Fe3Al based alloy by applying 3D EBSD. DPG Frühjahrstagung, Berlin, Germany (2005)
Konrad, J.; Raabe, D.; Zaefferer, S.: Investigation of Nucleation Mechanisms of Recrystallization in Warm Rolled Fe3Al Base Alloys. 2nd International Conference on Recrystallization and Grain Growth, Annecy, France (2004)
Konrad, J.: Hot Rolling Behaviour and Plastic Anisotropy of Fe3Al-based Alloys. Discussion Meeting on the Development of Innovative Iron Aluminium Alloys, MPIE Düsseldorf (2004)
Konrad, J.; Raabe, D.; Zaefferer, S.: Texturentwicklung beim Warmwalzen und bei der Rekristallisation von Fe3Al-Basislegierungen. Sitzung des DFG Fachausschuss Intermetallische Phasen, MPIE, Düsseldorf, Germany (2004)
Konrad, J.; Zaefferer, S.; Schneider, A.; Raabe, D.; Frommeyer, G.: Texturentwicklung beim Warmwalzen und bei der Rekristallisation von Fe3Al-Basislegierungen. Treffen des Fachausschusses Intermetallische Phasen, MPI Eisenforschung, Düsseldorf (2004)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.