Gallardo-Basile, F.-J.; Roters, F.; Jentner, R.; Best, J. P.; Kirchlechner, C.; Srivastava, K.; Scholl, S.; Diehl, M.: Application of a nanoindentation-based approach for parameter identification to a crystal plasticity model for bcc metals. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 881, 145373 (2023)
Cantergiani, E.; Weißensteiner, I.; Grasserbauer, J.; Falkinger, G.; Pogatscher, S.; Roters, F.: Influence of Hot Band Annealing on Cold-Rolled Microstructure and Recrystallization in AA 6016. Metallurgical and Materials Transactions A 54, pp. 75 - 96 (2023)
Cantergiani, E.; Falkinger, G.; Roters, F.: Crystal plasticity simulations of Cube in-grain fragmentation in aluminium: Influence of crystal neighbor orientation. International Journal of Solids and Structures 252, 111801 (2022)
Shah, V.; Sedighiani, K.; Van Dokkum, J. S.; Bos, C.; Roters, F.; Diehl, M.: Coupling crystal plasticity and cellular automaton models to study meta- dynamic recrystallization during hot rolling at high strain rates. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 849, 143471 (2022)
Cantergiani, E.; Falkinger, G.; Mitsche, S.; Theissing, M.; Klitschke, S.; Roters, F.: Influence of Strain Rate Sensitivity on Cube Texture Evolution in Aluminium Alloys. Metallurgical and Materials Transactions A 53, pp. 2832 - 2860 (2022)
Sedighiani, K.; Diehl, M.; Traka, K.; Roters, F.; Sietsma, J.; Raabe, D.: An efficient and robust approach to determine material parameters of crystal plasticity constitutive laws from macro-scale stress-strain curves. International Journal of Plasticity 134, 102779 (2020)
Kasemer, M.; Falkinger, G.; Roters, F.: A numerical study of the influence of crystal plasticity modeling parameters on the plastic anisotropy of rolled aluminum sheet. Modelling and Simulation in Materials Science and Engineering 28 (8), 085005 (2020)
Kühbach, M. T.; Roters, F.: Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations. Modelling and Simulation in Materials Science and Engineering 28, 055005 (2020)
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
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
Decarbonisation of the steel production to a hydrogen-based metallurgy is one of the key steps towards a sustainable economy. While still at the beginning of this transformation process, with multiple possible processing routes on different technological readiness, we conduct research into the related fundamental scientific questions at the MPIE.
In this project, we aim to enhance the mechanical properties of an equiatomic CoCrNi medium-entropy alloy (MEA) by interstitial alloying. Carbon and nitrogen with varying contents have been added into the face-centred cubic structured CoCrNi MEA.
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.