Jägle, E. A.; Choi, P.-P.; Van Humbeeck, J.; Raabe, D.: Precipitation and austenite reversion behavior of a maraging steel produced by selective laser melting. Journal of Materials Research 29 (17), pp. 2072 - 2079 (2014)
Haley, D.; Merzlikin, S. V.; Choi, P.-P.; Raabe, D.: Atom probe tomography observation of hydrogen in high-Mn steel and silver charged via an electrolytic route. International Journal of Hydrogen Energy 39 (23), pp. 12221 - 12229 (2014)
Gutiérrez-Urrutia, I.; Raabe, D.: High strength and ductile low density austenitic FeMnAlC steels: Simplex and alloys strengthened by nanoscale ordered carbides. Materials Science and Technology 30 (9), pp. 1099 - 1104 (2014)
Mandal, S.; Pradeep, K. G.; Zaefferer, S.; Raabe, D.: A novel approach to measure grain boundary segregation in bulk polycrystalline materials in dependence of the boundaries’ five rotational degrees of freedom. Scripta Materialia 81, pp. 16 - 19 (2014)
Reuber, J. C.; Eisenlohr, P.; Roters, F.; Raabe, D.: Dislocation density distribution around an indent in single-crystalline nickel: Comparing nonlocal crystal plasticity finite-element predictions with experiments. Acta Materialia 71, pp. 333 - 348 (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
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.