Zhang, J.; Tasan, C. C.; Lai, M.; Springer, H.; Raabe, D.: Microstructural and Mechanical Characterization of Cold Work Effects in GUM Metal. 9th International Conference on Advances in Experimental Mechanics, Cardiff, UK (2013)
Springer, H.; Kostka, A.: Verbinden von hochfestem Stahl mit einer Aluminiumlegierung durch Rührreibschweißen. 4. GKSS Workshop, Geesthacht, Germany (2009)
Belde, M. M.; Springer, H.; Inden, G.; Raabe, D.: Tailoring multi-phase steel microstructures by controlling local chemical gradients. MSE 2014, Darmstadt, Germany (2014)
Lai, M.; Tasan, C. C.; Zhang, J.; Grabowski, B.; Huang, L.; Springer, H.; Raabe, D.: ω phase accommodated nano-twinning mechanism in Gum Metal: An ab initio study. 3rd International Workshop on Physics Based Material Models and Experimental Observations: Plasticity and Creep, Cesme/Izmir, Turkey (2014)
Springer, H.: A novel roll bonding methodology for the cross-scale analysis of phase properties and interac-tions in multiphase structural materials. MSE 2014, Darmstadt, Germany (2014)
Springer, H.; Kostka, A.: Verbinden von hochfestem Stahl mit einer Aluminiumlegierung durch Rührreibschweißen. 4. GKSS Workshop, Geesthacht, Germany (2009)
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…