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
Titanium and its alloys are widely used in critical applications due to their low density, high specific strength, and excellent corrosion resistance, but their poor plasticity at room temperature limits broader utilization. Introducing hydrogen as a temporary alloying element has been shown to improve plasticity during high-temperature processing…
Defects at interfaces strongly impact the properties and performance of functional materials. In functional nanostructures, they become particularly important due to the large surface to volume ratio.
This ERC-funded project aims at developing an experimentally validated multiscale modelling framework for the prediction of fracture toughness of metals.
In this project, links are being established between local chemical variation and the mechanical response of laser-processed metallic alloys and advanced materials.