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)
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)
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)
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)
Fujita, N.; Igi, S.; Diehl, M.; Roters, F.; Raabe, D.: The through-process texture analysis of plate rolling by coupling finite element and fast Fourier transform crystal plasticity analysis. Modelling and Simulation in Materials Science and Engineering 27, 085005 (2019)
Diehl, M.; Kertsch, L.; Traka, K.; Helm, D.; Raabe, D.: Site-specific quasi in situ investigation of primary static recrystallization in a low carbon steel. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 755, pp. 295 - 306 (2019)
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
Crystal plasticity modelling has gained considerable momentum in the past 20 years [1]. Developing this field from its original mean-field homogenization approach using viscoplastic constitutive hardening rules into an advanced multi-physics continuum field solution strategy requires a long-term initiative. The group “Theory and Simulation” of…
This project aims to correlate the localised electrical properties of ceramic materials and the defects present within their microstructure. A systematic approach has been developed to create crack-free deformation in oxides through nanoindentation, while the localised defects are probed in-situ SEM to study the electronic properties. A coupling…
In this project, links are being established between local chemical variation and the mechanical response of laser-processed metallic alloys and advanced materials.