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)
Otto de Mentock, D.; Roongta, S.; Shanthraj, P.; Eisenlohr, P.; Diehl, M.; Roters, F.: Challenges of Developing and Scaling up DAMASK, a Unified Large-strain Multi-physics Crystal Plasticity Simulation Software. TMS - Algorithm Development in Materials Science and Engineering, Orlando, FL, USA (2024)
Roters, F.; do Nascimento, A. W. P.; Roongta, S.; Diehl, M.: An optimized method for the simulation-based determination of initial parameters of advanced yield surfaces for sheet metal forming applications. Complas 2021, online (2021)
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
Within this project we investigate chemical fluctuations at the nanometre scale in polycrystalline Cu(In,Ga)Se2 and CuInS2 thin-flims used as absorber material in solar cells.
This project aims to investigate the dynamic hardness of B2-iron aluminides at high strain rates using an in situ nanomechanical tester capable of indentation up to constant strain rates of up to 100000 s−1 and study the microstructure evolution across strain rate range.
This project deals with the phase quantification by nanoindentation and electron back scattered diffraction (EBSD), as well as a detailed analysis of the micromechanical compression behaviour, to understand deformation processes within an industrial produced complex bainitic microstructure.
Within this project, we will use a green laser beam source based selective melting to fabricate full dense copper architectures. The focus will be on identifying the process parameter-microstructure-mechanical property relationships in 3-dimensional copper lattice architectures, under both quasi-static and dynamic loading conditions.