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
This project will aim at developing MEMS based nanoforce sensors with capacitive sensing capabilities. The nanoforce sensors will be further incorporated with in situ SEM and TEM small scale testing systems, for allowing simultaneous visualization of the deformation process during mechanical tests
The field of micromechanics has seen a large progress in the past two decades, enabled by the development of instrumented nanoindentation. Consequently, diverse methodologies have been tested to extract fundamental properties of materials related to their plastic and elastic behaviour and fracture toughness. Established experimental protocols are…
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.
The balance between different contributions to the high-temperature heat capacity of materials can hardly be assessed experimentally. In this study, we develop computationally highly efficient ab initio methods which allow us to gain insight into the relevant physical mechanisms. Some of the results have lead to breakdown of the common…