Ardehali Barani, A.; Ponge, D.: Effect of Austenite Deformation on the Precipitation Behaviour of Si–Cr spring Steels During Tempering. Solid-Solid Phase Transformations in Inorganic Materials 2005 (PTM 2005), Phoenix, AZ, USA (2005)
Calcagnotto, M.; Ponge, D.; Raabe, D.: Microstructure control and mechanical properties of ultrafine grained dual phase steels. Lecture: Osaka University, Osaka [Japan], December 24, 2008
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)
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
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.
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…