Schemmann, L.; Zaefferer, S.: First experiences using a low-energy WDX spectrometer (LEXS) on a FEG-SEM for carbon determination on a martensitic steel. EMAS 2013, Porto, Portugal (2013)
Schemmann, L.; Zaefferer, S.; Raabe, D.: Influence of the inheritance of chemical elements on the transformation behaviour during intercritical annealing of DP steel strips. Euromat 2013, Sevilla, Spain (2013)
Zaefferer, S.: Techniques and application of 3D orientation microscopy based on EBSD tomography. GN-MEBA (groupement nationale de microscopie electronique a balayage) 2013, Paris, France (2013)
Zaefferer, S.: Combined Application of EBSD and ECCI for Crystal Defect Observation in Bulk Samples. GN-MEBA (groupement nationale de microscopie electronique a balayage) 2013, Paris, France (2013)
Zaefferer, S.; Elhami, N. N.: Theory and application of electron channelling contrast imaging (ECCI) of defects in metals. RMS EBSD 2013, Oxford, UK (2013)
Sandlöbes, S.; Friák, M.; Dick, A.; Zaefferer, S.; Pei, Z.; Zhu, L.-F.; Sha, G.; Ringer, S.; Neugebauer, J.; Raabe, D.: Combining ab initio calculations and high resolution experiments to improve the understanding of advanced Mg-Y and Mg-RE alloys. 7th Annual Conference of the ARC Centre of Excellence for Design in Light Metals, Melbourne, VIC, Australia (2012)
Konijnenberg, P. J.; Zaefferer, S.; Raabe, D.: Advanced analysis of 3D EBSD data obtained by FIB tomography. NVvM 2012 Materials Science Meeting, Eindhoven, The Netherlands (2012)
Tasan, C. C.; Zaefferer, S.; Raabe, D.: In-situ investigations of small strain plasticity in dual-phase steel. 23rd International Congress of Theoretical and Applied Mechanics (ICTAM), Beijing, China (2012)
Zaefferer, S.; Chen, J.; Konijnenberg, P.: A study on origin and nature of shear bands in cold rolled Mg-3Y alloy using 3D EBSD. 9th Intern. Conference on Magnesium alloys and their applications, Vancouver, Canada (2012)
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.