Nellessen, J.; Sandlöbes, S.; Raabe, D.: Low cycle fatigue in aluminum single and bi-crystals: On the influence of crystal orientation. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 668, pp. 166 - 179 (2016)
Nellessen, J.; Sandlöbes, S.; Raabe, D.: Effects of strain amplitude, cycle number and orientation on low cycle fatigue microstructures in austenitic stainless steel studied by electron channelling contrast imaging. Acta Materialia 87, pp. 86 - 99 (2015)
Nellessen, J.; Sandlöbes, S.; Raabe, D.: Effects of strain amplitude, cycle number and orientation on low cycle fatigue microstructures in fcc materials studied by Electron Channeling Contrast Imaging. TMS 2015 - 144th Annual Meeting & Exhibition, Orlando, FL, USA (2015)
Nellessen, J.; Sandlöbes, S.; Raabe, D.: Systematic Investigation of the Influence of Strain Amplitude, Orientation and Cycle Number on the Dislocation Structures Formed during Low Cycle Fatigue. MSE 2014, Darmstadt, Germany (2014)
Nellessen, J.; Sandlöbes, S.; Raabe, D.: Systematic and efficient investigation of the influences on the dislocation structures formed during low cycle fatigue in austenitic stainless steel. Euromat 2013, Sevilla, Spain (2013)
Nellessen, J.: Effects of strain amplitude, cycle number and orientation on low cycle fatigue microstructures in austenitic stainless steel and aluminum. Dissertation, RWTH Aachen, Aachen, Germany (2015)
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
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.
In this ongoing project, we investigate spinodal fluctuations at crystal defects such as grain boundaries and dislocations in Fe-Mn alloys using atom probe tomography, electron microscopy and thermodynamic modeling [1,2].