Kuo, J. C.; Zaefferer, S.; Raabe, D.: Experimental investigation of the deformation behavior of aluminium-bicrystals. MPI für Eisenforschung GmbH, Düsseldorf, Germany (2004)
Ma, A.; Roters, F.; Raabe, D.: Simulation of textures and Lankford values for face centered cubic polycrystaline metals by using a modified Taylor model. (2004)
Raabe, D.: A 3D probabilistic cellular automaton for the simulation of recrystallization and grain growth phenomena. Max-Planck-Society, München, Germany (2004)
Raabe, D.; Bréchet, Y.; Gottstein, G.; de Hosson, J.; Van Houtte, P.; Vitek, V.: Recommendations for Future Basic Research on Metallic Alloys and Composites in the 6th EU Framework Program - Metals and composites: Basis for growth, safety, and ecology. (2004)
Raabe, D.; Pramono, A.: Report on copper–niob research at the Max-Planck-Institut, Düsseldorf – Simulations and experiments. MPI für Eisenforschung, Düsseldorf, Germany (2004)
Sachtleber, M.; Raabe, D.: Theoretische und experimentelle Untersuchung der Kornwechselwirkung in Aluminium. MPI für Eisenforschung GmbH, Düsseldorf, Germany (2004)
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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
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…
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…