Zaafarani, N.; Singh, R.; Zaefferer, S.; Roters, F.; Raabe, D.: 3D experimental investigation and crystal plasticity FEM simulation of the texture and microstructure below a nanoindent in a Cu-single crystal. 6th European Symposium on nano-mechanical Testing (Nanomech 6), Hückelhoven, Germany (2005)
Konrad, J.; Raabe, D.; Zaefferer, S.: Deformation Behavior of a Fe3Al Alloy During Thermomechanical Treatment. MRS Fall Meeting, Boston, MA, USA (2004)
Thomas, I.; Zaefferer, S.; Friedel, F.; Raabe, D.: Orientation dependent growth behaviour of subgrain structures in IF steel. 2nd International Joint Conference on Recrystallization and Grain Growth, Annecy, France (2004)
Konrad, J.; Raabe, D.; Zaefferer, S.: Nucleation Mechanisms of Recrystallization in Warm Rolled Fe3Al Base Alloys. Discussion Meeting on the Development of Innovative Iron Aluminium Alloys, MPIE, Düsseldorf, Germany (2004)
Wöllmer, S.; Zaefferer, S.; Göken, M.; Mack, T.; Glatzel, U.: Characterization of phases of aluminized nickel base superalloys. Intern. Conf. on Strength of Materials (ICSMA 13), Budapest, Hungary (2003)
Zaefferer, S.; Motaman, S. A. H.: Metallic Materials (Microstructure, Microscopy, Modelling). Lecture: SS 2021, RWTH Aachen University, April 12, 2021 - July 23, 2021
Zaefferer, S.: Fundamentals and practical aspects of texture and microstructure measurements using EBSD-based orientation microscopy and related techniques. Lecture: January 2020, IIT Madras, India, 2020-01
Zaefferer, S.: Fundamentals and practical aspects of texture and microstructure measurements using EBSD-based orientation microscopy and related techniques. Lecture: Febuary and March 2020, MPIE Düsseldorf, 2020-01
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…