Mayweg, D.; Morsdorf, L.; Li, Y.; Herbig, M.: Correlation between grain size and carbon content in white etching areas in bearings. Acta Materialia 215, 117048 (2021)
Wu, X.; Mayweg, D.; Ponge, D.; Li, Z.: Microstructure and deformation behavior of two TWIP/TRIP high entropy alloys upon grain refinement. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 802, 140661 (2021)
Mayweg, D.; Morsdorf, L.; Wu, X.; Herbig, M.: The role of carbon in the white etching crack phenomenon in bearing steels. Acta Materialia 203, 116480 (2021)
Morsdorf, L.; Mayweg, D.; Li, Y.; Diederichs, A.; Raabe, D.; Herbig, M.: Moving cracks form white etching areas during rolling contact fatigue in bearings. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 771, 138659 (2020)
Morsdorf, L.; Mayweg, D.; Li, Y.; Diederichs, A.; Raabe, D.; Herbig, M.: Moving cracks and missing C atoms – chasing the mysteries of white etching areas in bearings. 2nd meeting of "Metallurgical Metallurgy for Plasticity-driven Damage and Fracture" research forum 2021 (ISIJ), virtual (2021)
Qin, Y.; Mayweg, D.; Tung, P.-Y.; Pippan, R.; Herbig, M.: Mechanism of cementite decomposition in 100Cr6 bearing steels during high pressure torsion. MSE Congress 2020, virtual, Sankt Augustin, Germany (2020)
Mayweg, D.; Morsdorf, L.; Wu, X.; Herbig, M.: The role of carbon in the white etching crack phenomenon in bearing steels. MSE Congress 2020, virtual, Sankt Augustin, Germany (2020)
Mayweg, D.: Microstructural characterization of white etching cracks in 100Cr6 bearing steel with emphasis on the role of carbon. Dissertation, RWTH Aachen University (2021)
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
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…