Ponge, D.: The formation of ultrafine grained microstructure in a plain C-Mn steel. International Symposium of Ultrafine Grained Steels ISUGS-2007, Kitakyushu, Japan (2007)
Ponge, D.: Warmumformbarkeit von Stahl. Kontaktstudium Werkstofftechnik Stahl, Teil III, Technologische Eigenschaften, Institut für Umformtechnik und Umformmaschinen (IFUM), Universität Hannover (2006)
Ponge, D.: Modern high strength steels for automotive applications. Robust Processes with Modern Steels, INPRO Innovationsgesellschaft für fortgeschrittene Produktionssysteme in der Fahrzeugindustrie mbH, Berlin, Germany (2006)
Romano, P.; Barani, A.; Ponge, D.; Raabe, D.: Design of High-Strength Steels by microalloying and thermomechanical treatment. TMS 2006, San Antonio, TX, USA (2006)
Ponge, D.; Song, R.; Ardehali Barani, A.; Raabe, D.: Thermomechanical Processing Research at the Max Planck Institute for Iron Research. FORTY FIRST SEMIANNUAL TECHNICAL PROGRAM REVIEW, Golden, CO, Colorado School of Mines, Advanced Steel Processing and Products Research Center (2005)
Ponge, D.; Detroy, S.: Quantitative Phase Determination of Bainitic/Martensitic Steels. EUROMAT 2005, European Congress and Exhibition on Advanced Materials and Processes, Czech Technical University in Prague (2005)
Song, R.; Ponge, D.; Kaspar, R.: Review of the properties and methods for production of ultrafine grained steels. Lecture at the SMEA Conference 2003, Sheffield (2004)
Ponge, D.: Bericht aus der Arbeitsgruppe Weiterentwicklung Umformdilatometer. Lecture at the Sitzung des Werkstoffausschusses (Arbeitskreis Umformdilatometrie), Stahlinstitut VDEh, Düsseldorf, Germany (2004)
Ponge, D.: Warmumformbarkeit von Stahl. Lecture at the Seminar 15/04, Kontaktstudium Werkstofftechnik Stahl, Teil III, Technologische Eigenschaften, Institut für Bildung im Stahl-Zentrum, Stahlinstitut VDEh (2004)
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
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…
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…
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…