Springer, H.; Raabe, D.; Belde, M. M.: Rapid Alloy Prototyping – High Throughput Bulk Metallurgy at the MPIE. Workshop on machine learning and data analytics in advanced metals processing, RollsRoyce Institute Manchester, Manchester, UK (2017)
Springer, H.; Belde, M. M.; Raabe, D.: High throughput combinatorial design of novel high performance steels. International conference on High-throughput materials development, Ghent, Belgium (2015)
Belde, M. M.; Springer, H.; Raabe, D.: Tailoring multi-phase microstructures by control of local chemical gradients, applied to austenite in martensitic steel. Icomat 2014
, Bilbao, Spain (2014)
Zhang, J.; Tasan, C. C.; Lai, M.; Springer, H.; Raabe, D.: Influence of oxygen and cold deformation on the ω phase formation in gum metal. TMS 2014, San Diego, TX, USA (2014)
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
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…