Tasan, C. C.; Jeannin, O.; Barbier, D.; Morsdorf, L.; Wang, M.; Ponge, D.; Raabe, D.: In-situ characterization of martensite plasticity by high resolution microstructure and microstrain mapping. ICOMAT 2014, International Conference on Martensitic Transformations 2014, Bilbao, Spain (2014)
Wang, M.; Tasan, C. C.; Ponge, D.; Kostka, A.; Raabe, D.: Deformation micro-mechanisms in medium-Mn TRIP-maraging steel. 2nd International Conference on High Manganese Steel, HMnS 2014, Aachen, Germany (2014)
Tasan, C. C.; Wang, M.; Ponge, D.; Kostka, A.; Raabe, D.: Size effects on austenite stability investigated by in-situ EBSD. BSSM 9th Int. Conf. on Advances in Experimental Mechanics, Cardiff, UK (2013)
Wang, M.; Tasan, C. C.; Ponge, D.; Kostka, A.; Raabe, D.: Size effects on mechanical stability of metastable austenite. GDRi CNRS MECANO General Meeting on the Mechanics of Nano-Objects, MPIE, Düsseldorf, Germany (2013)
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.
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.