Herrera, C.; Ponge, D.; Raabe, D.: Microstructural evolution during hot working of 1.4362 duplex stainless steel. 2nd International Symposium on Steel Science (ISSS 2009), Kyoto, Japan (2009)
Calcagnotto, M.; Ponge, D.; Raabe, D.: Experimental study on orientation gradients and GNDs in ultrafine grained dual-phase steels. International Conference on Processing & Manufacturing of Advanced Materials (THERMEC 2009), Berlin, Germany (2009)
Nnamchi, P.; Ponge, D.; Raabe, D.; Barani, A.; Bruckner, G.; Krautschik, J.: Influence of the As-Cast Microstructure on the Evolution of the Hot Rolling Textures of Ferritic Stainless Steels with Different Compositions. 15th International Conference on the Textures of Materials (ICOTOM 15), Carnegie Mellon University Center, Pittsburgh, PA, USA (2008)
Calcagnotto, M.; Ponge, D.; Raabe, D.: Fabrication of Ultrafine Grained Ferrite/Martensite Dual Phase Steel by Large Strain Warm Deformation and Subsequent Intercritical Annealing. ISUGS 2007 (International Symposium on Ultrafine Grained Steels), Kitakyushu, Japan (2007)
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
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.
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…
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…