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.
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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