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
The key to the design and construction of advanced materials with tailored mechanical properties is nano- and micro-scale plasticity. Significant influence also exists in shaping the mechanical behavior of materials on small length scales.
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…
This project endeavours to offer comprehensive insights into GB phases and their mechanical responses within both pure Ni and Ni-X (X=Cu, Au, Nb) solid solutions. The outcomes of this research will contribute to the development of mechanism-property diagrams, guiding material design and optimization strategies for various applications.
By using the DAMASK simulation package we developed a new approach to predict the evolution of anisotropic yield functions by coupling large scale forming simulations directly with crystal plasticity-spectral based virtual experiments, realizing a multi-scale model for metal forming.