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)
Kontis, P.; Raabe, D.; Gault, B.: The role of systematic characterization on the development of new nickel-based superalloys. Industrial Colloquium - SFB/TR 103 „From Atoms to Turbine Blades“ , Fürth, Germany (2018)
Kürnsteiner, P.; Wilms, M. B.; Weisheit, A.; Jägle, E. A.; Raabe, D.: Preventing the Coarsening of Al3Sc Precipitates by the Formation of a Zr-rich Shell During Laser Metal Deposition. TMS2018 Annual Meeting & Exhibition, Phoenix, AZ, USA (2018)
Kwiatkowski da Silva, A.; Inden, G.; Ponge, D.; Gault, B.; Raabe, D.: Precipitation of CFCC-TmC Carbides during Tempering at 450°C of a Medium Mn Steel: A Thermodynamic and Kinetic Study Followed by Atom Probe Tomography. TMS 2018 Annual Meeting & Exhibition, Phoenix, AZ, USA (2018)
Li, Z.; Raabe, D.: Tuning Phase Transformation in Compositionally Complex Alloys for Superior Mechanical Properties. TMS 2018 Annual Meeting & Exhibition, Phoenix, AZ, USA (2018)
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
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
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.