Wan, D.; Ma, Y.; Sun, B.; Razavi, S. M. J.; Wang, D.; Lu, X.; Song, W.: Evaluation of hydrogen effect on the fatigue crack growth behavior of medium-Mn steels via in-situ hydrogen plasma charging in an environmental scanning electron microscope. Journal of Materials Science & Technology 85, pp. 30 - 43 (2021)
Sun, B.; Ma, Y.; Vanderesse, N.; Srinivas Varanasi, R.; Song, W.; Bocher, P.; Ponge, D.; Raabe, D.: Macroscopic to nanoscopic in situ investigation on yielding mechanisms in ultrafine grained medium Mn steels: Role of the austenite-ferrite interface. Acta Materialia 178, pp. 10 - 25 (2019)
Song, W.: Characterization and simulation of bainite transformation in high carbon bearing steel 100Cr6. Dissertation, RWTH Aachen, Aachen, Germany (2014)
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
“Smaller is stronger” is well known in micromechanics, but the properties far from the quasi-static regime and the nominal temperatures remain unexplored. This research will bridge this gap on how materials behave under the extreme conditions of strain rate and temperature, to enhance fundamental understanding of their deformation mechanisms. The…
The Ni- and Co-based γ/γ’ superalloys are famous for their excellent high-temperature mechanical properties that result from their fine-scaled coherent microstructure of L12-ordered precipitates (γ’ phase) in an fcc solid solution matrix (γ phase). The only binary Co-based system showing this special type of microstructure is the Co-Ti system…
In this project, we employ atomistic computer simulations to study grain boundaries. Primarily, molecular dynamics simulations are used to explore their energetics and mobility in Cu- and Al-based systems in close collaboration with experimental works in the GB-CORRELATE project.
This project is a joint project of the De Magnete group and the Atom Probe Tomography group, and was initiated by MPIE’s participation in the CRC TR 270 HOMMAGE. We also benefit from additional collaborations with the “Machine-learning based data extraction from APT” project and the Defect Chemistry and Spectroscopy group.