Sandlöbes, S.; Friák, M.; Dick, A.; Zaefferer, S.; Pei, Z.; Zhu, L.-F.; Sha, G.; Ringer, S.; Neugebauer, J.; Raabe, D.: Combining ab initio calculations and high resolution experiments to improve the understanding of advanced Mg-Y and Mg-RE alloys. 7th Annual Conference of the ARC Centre of Excellence for Design in Light Metals, Melbourne, VIC, Australia (2012)
Sandlöbes, S.; Friák, M.; Dick, A.; Zaefferer, S.; Pei, Z.; Neugebauer, J.; Raabe, D.: Combining ab initio calculations and high-resolution experiments to understand advanced Mg alloys. German-Korean workshop on the “Production and industrial applications of semi-finished Mg products”, Irsee, Germany (2011)
Sandlöbes, S.; Senk, D.: Automatisierung im Stahlwerk durch in-situ Ab- und Prozessgasmessung. 8. Aachener Kolloquium für Instandhaltung, Diagnose und Anlagenüberwachung (AKIDA), Eurogress, Aachen, Germany (2010)
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.
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…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.