Song, J.; Kostka, A.; Veehmayer, M.; Raabe, D.: Hierarchical microstructure of explosive joints: Example of titanium to steel cladding. Materials Science and Engineering A 528, pp. 2641 - 2647 (2011)
Kostka, A.; Song, J.; Raabe, D.; Veehmayer, M.: Structural characterization and analysis of interface formed by explosion cladding of titanium to low carbon steel. 19th International Symposium on Metastable, Amorphous and Nanostructured Materials (ISMANAM), Moscow, Russia (2012)
Kostka, A.; Song, J.; Raabe, D.; Veehmayer, M.: Microstructure and properties of interfaces formed by explosion cladding of Ti-Steel. XXI Conference on Applied Crystallography, Zakopane, Poland (2009)
Kostka, A.; Song, J.; Raabe, D.; Veehmayer, M.: Microstructure and properties of interfaces formed by explosion cladding of Ti-Steel. XXI Conference on Applied Crystallography, Zakopane, Poland (2009)
Song, J.: Explosive Cladding of Titanium onto Low Carbon Steel. International SurMat Workshop, Department of Material Science and Engineering, Ruhr-Universität Bochum, Bochum, Germany (2008)
Song, J.: Microstructure and properties of interfaces formed by explosion cladding of Titanium to low Carbon steel. Dissertation, Ruhr-University Bochum, Bochum, Germany (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.