Li, Y. J.; Choi, P.; Goto, S.; Borchers, C.; Raabe, D.; Kirchheim, R.: Evolution of strength and microstructure during annealing of heavily cold-drawn 6.3 GPa hypereutectoid pearlitic steel wire. 53rd International Field Emission Symposium (IFES), Tascaloosa, AL, USA (2012)
Li, Y. J.; Choi, P.; Borchers, C.; Chen, Y.Z.; Goto, S.; Raabe, D.; Kirchheim, R.: Atom Probe Tomography characterization of heavily cold drawn pearlitic steel wire. 52nd International Field Emission Symposium (IFES), Sydney, Australia (2010)
Djaziri, S.; Li, Y.; Goto, S.; Kirchlechner, C.; Raabe, D.; Dehm, G.: Microstructural characterization of cold-drawn pearlitic steel wires at the nanometer scale. The Thin Film & Small Scale Mechanical Behavior Gordon Research Conference, Waltham, MA, 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
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.