Kumar, A.; Dutta, A.; Makineni, S. K.; Herbig, M.; Petrov, R.; Sietsma, J.: In-situ observation of strain partitioning and damage development in continuously cooled carbide-free bainitic steels using micro digital image correlation. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 757, pp. 107 - 116 (2019)
Dutta, A.; Ponge, D.; Sandlöbes, S.; Raabe, D.: Strain partitioning and strain localization in medium manganese steels measured by in situ microscopic digital image correlation. Materialia 5, 100252 (2019)
Dutta, A.; Ponge, D.; Sandlöbes, S.; Raabe, D.: Understanding hot vs. Cold rolled medium manganese steel deformation behavior using in situ microscopic digital image correlation. Materials Science Forum 941, pp. 198 - 205 (2018)
Haupt, M.; Dutta, A.; Ponge, D.; Sandlöbes, S.; Nellessen, M.; Hirt, G.: Influence of Intercritical Annealing on Microstructure and Mechanical Properties of a Medium Manganese Steel. International Conference on the Technology of Plasticity, ICTP 2017, Cambridge, UK, September 17, 2017 - September 22, 2017. Procedia Engineering 207, pp. 1803 - 1808 (2017)
Dutta, A.: Deformation behaviour and texture memory effect of multiphase nano-laminate medium manganese steels. Dissertation, RWTH Aachen University (2019)
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.
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…
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…