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
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.
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…