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)
Kontis, P.; Kostka, A.; Raabe, D.; Gault, B.: Influence of composition and precipitation evolution on damage at grain boundaries in a crept polycrystalline Ni-based superalloy. Acta Materialia 166, pp. 158 - 167 (2019)
Lu, X.; Zhang, X.; Shi, M.; Roters, F.; Kang, G.; Raabe, D.: Dislocation mechanism based size-dependent crystal plasticity modeling and simulation of gradient nano-grained copper. International Journal of Plasticity 113, pp. 52 - 73 (2019)
Su, J.; Raabe, D.; Li, Z.: Hierarchical microstructure design to tune the mechanical behavior of an interstitial TRIP-TWIP high-entropy alloy. Acta Materialia 163, pp. 40 - 54 (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
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.