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
This project aims to develop a testing methodology for the nano-scale samples inside an SEM using a high-speed nanomechanical low-load sensor (nano-Newton load resolution) and high-speed dark-field differential phase contrast imaging-based scanning transmission electron microscopy (STEM) sensor.
Understanding hydrogen-microstructure interactions in metallic alloys and composites is a key issue in the development of low-carbon-emission energy by e.g. fuel cells, or the prevention of detrimental phenomena such as hydrogen embrittlement. We develop and test infrastructure, through in-situ nanoindentation and related techniques, to study…
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…
In this project we developed a phase-field model capable of describing multi-component and multi-sublattice ordered phases, by directly incorporating the compound energy CALPHAD formalism based on chemical potentials. We investigated the complex compositional pathway for the formation of the η-phase in Al-Zn-Mg-Cu alloys during commercial…