Aghajani, A.: Evolution of microstructure during long-term creep of a tempered martensite ferritic steel. Dissertation, Ruhr-University Bochum, Bochum (2009)
Huynh, N. N.: Modelling of Microstructure Evolution and Crack Opening in FCC Materials under Tension. Dissertation, Wollongong University, Wollongong New South Wales [Australia] (2009)
Liu, T.: High Resolution Investigation of Texture Formation Process in Diamond Films and the Related Macro-Stresses. Dissertation, Ruhr-University Bochum, Bochum [Germany] (2009)
Thomas, I.: Untersuchung metallphysikalischer und messtechnischer Grundlagen zur Rekristallisation und Erholung mikrolegierter IF Stähle. Dissertation, RWTH Aachen, Aachen, Germany (2008)
Cedat, D.: Modeling and Experiment on Mo-based high temperature composites. Dissertation, Ecole Centrale Paris, Laboratoire for Materials, Paris [France] (2008)
Sachs, C.: Microstructure and mechanical properties of the exoskeleton of the lobster Homarus americanus as an example of a biological composite material. Dissertation, RWTH Aachen, Aachen, Germany (2008)
Tjahjanto, D.: Micromechanical Modeling and Simulations of Tranformation-Induced Plasticity in Multiphase Carbon Steels. Dissertation, TU Delft, Delft, The Netherlands (2008)
Klüber, C.: Korrelation von mechanischen Eigenschaften und Kristallorientierung auf mikroskopischer und nanoskopischer Ebene. Dissertation, RWTH Aachen, Aachen, Germany (2008)
Bastos da Silva, A. F.: Characterization of the Microstructure, Grain Boundaries and Texture of Nanostructured Electrodeposited CoNi by use of EBSD. Dissertation, RWTH Aachen, Aachen, Germany (2007)
Goerdeler, M.: Application of a dislocation density based flow stress model in the integrative through-process modeling of Aluminium production. Dissertation, RWTH Aachen, Aachen, Germany (2007)
Wolff, C.: Der tribologisch asymmetrische Flachstauchversuch - Eine neue Methode zur Analyse von Reibungsvorgängen bei Umformprozessen. Dissertation, RWTH Aachen, Aachen, Germany (2001)
Kaushal, C.: Untersuchung der Abhängigkeit des Ölaustrags von der Oberflächenfeinstruktur beim Auswalzen gedoppelter Aluminiumfolien. Diploma, HS Niederrhein, Krefeld, Germany (2003)
Tranchant, J.: Deformation of Semi-Brittle Intermetallic Material under Superimposed Hydrostatic Pressure. Diploma, Ecole Centrale de Nantes, Nantes, France (2002)
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
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.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
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…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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.