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
Advanced microscopy and spectroscopy offer unique opportunities to study the structure, composition, and bonding state of individual atoms from within complex, engineering materials. Such information can be collected at a spatial resolution of as small as 0.1 nm with the help of aberration correction.
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.