Roters, F.; Diehl, M.; Wong, S. L.; Shanthraj, P.; Raabe, D.: DAMASK: the Düsseldorf Advanced MAterial Simulation Kit for studying multi-physics crystal plasticity phenomena. 10 Years ICAMS - International Symposium, Bochum, Germany (2018)
Baron, C.; Springer, H.; Raabe, D.: Design of cost-efficient high modulus steels as innovative lightweight materials. Advanced Composite Materials Congress, Stockholm, Sweden (2018)
Wong, S. L.; Laptyeva, G.; Brüggemann, T.; Karhausen, K.-F.; Roters, F.; Raabe, D.: An improved unified internal state variable model exploiting first principle calculations for flow stress modeling of aluminium alloys. International Conference on Aluminum Alloys (ICAA), Montreal, Canada (2018)
Niendorf, T.; Wegener, T.; Li, Z.; Raabe, D.: On the fatigue behavior of dual-phase high-entropy alloys in the low-cycle fatigue regime. Fatique 2018, Poitiers, France (2018)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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.
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…