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
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…