Ardehali Barani, A.; Ponge, D.; Kaspar, R.: Improvement of Mechanical Properties of Spring Steels through Application of Thermomechanical Treatment. Steels for Cars and Trucks, Wiesbaden, Germany (2005)
Ardehali Barani, A.; Ponge, D.: Morphology of Martensite Formed From Recrystallized or Work-Hardened Austenite. Solid-Solid Phase Transformations in Inorganic Materials 2005 (PTM 2005), Phoenix, AZ, USA (2005)
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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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…