An, D.; Zaefferer, S.: Observation of dislocation structure evolution under low cyclic fatigue in TWIP and TRIP steel. TMS 2017 Annual Meeting & Exhibition, San Diego, CA, USA (2017)
Diehl, M.; Naunheim, Y.; Morsdorf, L.; An, D.; Roters, F.; Raabe, D.: Crystal Plasticity Simulations on Real Data: Towards Highly Resolved 3D Microstructures. 26th International Workshop on Computational Mechanics of Materials - IWCMM 26, Tomsk, Russia (2016)
An, D.; Konijnenberg, P. J.; Zaefferer, S.; Raabe, D.: Correlation between the 5-parametric GBCD and the corrosion resistance of a 304 stainless steel by 3D-EBSD. RMS-EBSD Meeting 2016, Manchester, UK (2016)
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.