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)
Paiva do Nascimento, A. W.: An optimized method to determine initial parameters of advanced yield surfaces for sheet metal form-ing applications. Master, Ruhr-Universität Bochum (2021)
Kusampudi, N.: Using Machine Learning and Data-driven Approaches to Predict Damage Initiation in Dual-Phase Steels. Master, Ruhr-Universität Bochum (2020)
Soundararajan, C. K.: Recrystallization behavior and mechanical properties of interstitially alloyed CoCrFeMnNi equiatomic high entropy alloy. Master, RWTH Aachen University (2020)
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
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.
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…
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…