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)
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
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.