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
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.