Zhang, J.; Tasan, C. C.; Lai, M.; Zhang, J.; Raabe, D.: Damage resistance in gum metal through cold work-induced microstructural heterogeneity. Journal of Materials Science 50 (17), pp. 5694 - 5708 (2015)
Schneider, A.; Zhang, J.: Orientation relationship between a ferritic matrix and k-phase (Fe3AlCx) precipitates formed during metal dusting of Fe–15Al. Intermetallics 13 (12), pp. 1332 - 1336 (2005)
Zhang, J.; Tasan, C. C.; Lai, M.; Zhang, J.; Raabe, D.: Damage Resistance through Hierarchical Microstructure Development on GUM Metal. Materials Science and Engineering (MSE2014), Darmstadt, Germany (2014)
Zhang, J.; Tasan, C. C.; Lai, M.; Springer, H.; Raabe, D.: Influence of oxygen and cold deformation on the ω phase formation in gum metal. TMS 2014, San Diego, TX, USA (2014)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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.