Ponge, D.: Warmumformbarkeit von Stahl. Lecture: Kontaktstudium Werkstofftechnik Stahl, Teil III, Technologische Eigenschaften, Werkstoffausschuss im Stahlinstitut VDEh, Technische Universität Dortmund, June 22, 2008
Calcagnotto, M.; Ponge, D.; Raabe, D.: Fabrication of ultrafine grained dual phase steels. Lecture: National Institute for Materials Science (NIMS), Tsukuba, Japan, October 22, 2007
Storojeva, L.; Ponge, D.; Raabe, D.: Halbwarmwalzen als ein neues Produktionskonzept für Kohlenstoffstähle. Lecture: Max-Planck Hot Forming Conference, MPI für Eisenforschung GmbH, Düsseldorf, Germany, December 05, 2002
Sam, H. C.: Role of microstructure and environment on delayed fracture in a novel lightweight medium manganese steel. Master, Augsburg University (2019)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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.