Haghighat, S. M. H.; Welsch, E. D.; Gutiérrez-Urrutia, I.; Roters, F.; Raabe, D.: Mesoscale modeling of dislocation mechanisms and the effect of nano-sized carbide morphology on the strengthening of advanced lightweight high-Mn steels. MMM2014, 7th International Conference on Multiscale Materials Modeling
, Berkeley, CA, USA (2014)
Haghighat, S. M. H.; Welsch, E. D.; Gutiérrez-Urrutia, I.; Raabe, D.: Alloy design of advanced lightweight high-Mn steels by combined TEM and discrete dislocation dynamics simulations. 2nd International Conference on High Manganese Steels, Aachen, Germany (2014)
Welsch, E. D.; Haghighat, S. M. H.; Gutiérrez-Urrutia, I.; Raabe, D.: Investigation of nano-sized kappa carbide distribution in advanced austenitic lightweight high-Mn steels by coupled TEM and DDD simulations: Strengthening and dislocation-based mechanisms. 2nd International Conference on High Manganese Steels, Aachen, Germany (2014)
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.