Kusampudi, N.; Diehl, M.: Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization. International Journal of Plasticity 171, 103776 (2023)
Nascimento, A.; Roongta, S.; Diehl, M.; Beyerlein, I. J.: A machine learning model to predict yield surfaces from crystal plasticity simulations. International Journal of Plasticity 161, 103507 (2023)
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)
Fujita, N.; Yasuda, K.; Ishikawa, N.; Diehl, M.; Roters, F.; Raabe, D.: Characterizing Localized Microstructural Deformation of Multiphase Steel by Crystal Plasticity Simulation with Multi-Constitutive Law. Journal of the Japan Society for Technology of Plasticity 63 (732), pp. 1 - 8 (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)
Han, F.; Diehl, M.; Roters, F.; Raabe, D.: Using spectral-based representative volume element crystal plasticity simulations to predict yield surface evolution during large scale forming simulations. Journal of Materials Processing Technology 277, 116449 (2020)
Diehl, M.; Niehuesbernd, J.; Bruder, E.: Quantifying the Contribution of Crystallographic Texture and Grain Morphology on the Elastic and Plastic Anisotropy of bcc Steel. Metals 9 (12), 1252 (2019)
Diehl, M.; Kühbach, M.: Coupled experimental-computational analysis of primary static recrystallization in low carbon steel. Modelling and Simulation in Materials Science and Engineering 28 (1), 014001 (2019)
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
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].
The aim of the Additive micromanufacturing (AMMicro) project is to fabricate advanced multimaterial/multiphase MEMS devices with superior impact-resistance and self-damage sensing mechanisms.
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.