Hariharan, A.: On the interfacial defect formation mechanism during laser additive manufacturing of polycrystalline superalloys. Dissertation, Ruhr-Universität Bochum (2019)
Chang, Y.: Challenges and opportunities associated to the characterization of H/D in Ti and its alloys with atom probe tomography. Dissertation, RWTH Aachen University (2019)
Archie, F. M. F.: Microstructural influence on micro-damage initiation in ferritic-martensitic DP-steels. Dissertation, RWTH Aachen, Aachen, Germany (2018)
Archie, F. M. F.: Damage nucleation in DP-steels: experimental characterization of the contributing microstructural parameters. Dissertation, Fakultät für Georessourcen und Materialtechnik, RWTH Aachen (2018)
Choi, W. S.: Deformation mechanisms and the role of interfaces in face-centered cubic Fe-Mn-C micro-pillars. Dissertation, RWTH Aachen, Aachen, Germany (2018)
Elhami, N. N.: Influence of strain path changes during cup drawing on the twinning activity in TWIP steels investigated by ECCI. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Morsdorf, L.: Fundamentals of ferrous low-carbon lath martensite: from the as-quenched, to tempered and deformed states. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Stechmann, G.: A Study on the Microstructure Formation Mechanisms and Functional Properties of CdTe Thin Film Solar Cells Using Correlative Electron Microscopy and Atomistic Simulations. Dissertation, RWTH Aachen, Aachen, Germany (2017)
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.
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.