Zaefferer, S.: Advanced applications of SEM-based electron diffraction techniques for the characterization of deformation structures of new steels. E-MRS 2012, Strasbourg, France, Strasbourg, France (2012)
Zaefferer, S.: Dislocations in metals: Observations from the atomic scale to macroscopic dimensions. ICMS Workshop, “Open problems between micro and macro systems of agents and particles”, Eindhoven, The Netherlands (2012)
Ram, F.; Zaefferer, S.: Kikuchi Bandlet Method: A Method to Resolve the Source Point Position of an EBSD Pattern. 20th Annual meeting of the German Crystallographic Society, München, Germany (2012)
Davut, K.; Zaefferer, S.: Improving the Reliability of EBSD-based Texture Analysis by a New Large Area Mapping Technique. International Conference on the Textures of Materials, ICOTOM 16, Mumbai, India (2011)
Konijnenberg, P.; Zaefferer, S.; Lee, S.-B.; Rollett, A. D.; Rohrer, G.; Raabe, D.: Advanced Methods and Tools for Reconstruction and Analysis of Grain Boundaries from 3D-EBSD Data Sets. International Conference on the Textures of Materials, ICOTOM 16, Bombay, India (2011)
Zaefferer, S.: Comprehensive 5-parameter grain boundary description: How to measure it, how to display it and how important is it? ICOTOM 16, Mumbai, India (2011)
Konijnenberg, P.; Zaefferer, S.; Raabe, D.: Advanced Reconstruction and Analysis of Grain Boundaries from 3D-EBSD Data Sets. MRS Fall Meeting 2011, Boston, MA, USA (2011)
Konijnenberg, P.; Zaefferer, S.; Raabe, D.: Advanced Reconstruction and Analysis of Grain Boundaries from 3D-EBSD Data Sets. 3D Microstructure Meeting 2011, Saarbrücken, Germany (2011)
Davut, K.; Zaefferer, S.: Factors influencing the strain-induced transformation of residual austenite in a low-alloyed TRIP steel. Euromat 2011 Conference, Montpellier, France (2011)
Zaefferer, S.; Jäpel, T.; Tasan, C. C.; Konijnenberg, P.: Detailed observation of martensite transformation and twinning in TRIP and TWIP steels using advanced SEM diffraction techniques. ICOMAT 2011, Osaka, Japan (2011)
Zaefferer, S.: Electron diffraction-based techniques in the SEM: Do they give you everything you ever wanted to know about your sample? XIVth ICEM, Wisła, Poland (2011)
Elhami, N.-N.; Zaefferer, S.; Thomas, I.; Hofmann, H.: Observation of the crystallographic defect structure in lightly deformed TWIP steel by means of electron channeling contrast imaging (ECCI). 1st International Conference on High Manganese Steels (HMnS2011), Seoul, South Korea (2011)
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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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.