Song, W.: Characterization and simulation of bainite transformation in high carbon bearing steel 100Cr6. Dissertation, RWTH Aachen, Aachen, Germany (2014)
Shan, Y.: Investigation on the Influence of Hydrogen on Dislocation Formation during Nanoindentation in TWIP Steels. Master, RWTH Aachen, Aachen, Germany (2018)
Qin, Y.: Effect of post-heat treatment on the microstructure and mechanical properties of SLM-produced IN738LC. Master, RWTH Aachen, Aachen, Germany (2017)
Lu, L.: Characterization of the crack formation mechanism in Ni-based superalloy Inconel 738LC produced by Selective Laser Melting (SLM). Master, Institut für Eisenhüttenkunde, RWTH Aachen, Aachen, Germany (2015)
Sheng, Z.: Characterization of the Microstructure and Mechanical Properties of Maraging Steels Produced by Laser Additive Manufacturing. Master, RWTH Aachen University, Aachen, Germany (2014)
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.
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…