Summary
Over the last decade, the steadily increasing research on gradient-structured metals and alloys has demonstrated great successes of this biological and nature-inspired concept to evade the strength-ductility trade-off dictating in regu-lar engineering materials. However, given the intrinsic limitations of conventional manufacturing methods, the currently engineered structural gradient materials are all featuring a linear pattern, usually from exterior to interior, with only simple structures of grain sizes, twin spaces, lamellae spaces, or combinations. A recent preliminary study of my research team accidentally discovered that additive manufacturing could produce periodic and 3D gradient microstructures with not only simple microstructure features, but also hierarchical ones, from grain size to sub-grain boundaries and even lattice distortions. The hierarchical gradient microstructure keeps the same level of high strength but doubles the failure strain compared to the conventional microstructures. This inspires us to systematically investigate the possibilities and boundaries of these new hierarchical gradient microstructures by additive manufacturing. Due to its extreme complexity across multiple scales and physics laws in correlating process, microstructure, and property of such new materials, we aim to develop a systematic approach for designing hierarchical gradient microstructures by using design of experiments, in-depth and multiscale characterization methods, multiphysics and multiscale numerical models, and data informatics. The intelligent integration of the physics-based and data-driven models will eventually boost the dimensionality, efficiency, and accuracy of the modeling approach for the design of the complicated hierarchical gradient microstructure in 3D. It will provide a powerful, digital and sustainable way for the design of new materials and/or processes and evaluation of material performance.
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Web resources: | https://cordis.europa.eu/project/id/101078829 |
Start date: | 01-01-2024 |
End date: | 31-12-2028 |
Total budget - Public funding: | 1 492 751,00 Euro - 1 492 751,00 Euro |
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Original description
Over the last decade, the steadily increasing research on gradient-structured metals and alloys has demonstrated great successes of this biological and nature-inspired concept to evade the strength-ductility trade-off dictating in regu-lar engineering materials. However, given the intrinsic limitations of conventional manufacturing methods, the currently engineered structural gradient materials are all featuring a linear pattern, usually from exterior to interior, with only simple structures of grain sizes, twin spaces, lamellae spaces, or combinations. A recent preliminary study of my research team accidentally discovered that additive manufacturing could produce periodic and 3D gradient microstructures with not only simple microstructure features, but also hierarchical ones, from grain size to sub-grain boundaries and even lattice distortions. The hierarchical gradient microstructure keeps the same level of high strength but doubles the failure strain compared to the conventional microstructures. This inspires us to systematically investigate the possibilities and boundaries of these new hierarchical gradient microstructures by additive manufacturing. Due to its extreme complexity across multiple scales and physics laws in correlating process, microstructure, and property of such new materials, we aim to develop a systematic approach for designing hierarchical gradient microstructures by using design of experiments, in-depth and multiscale characterization methods, multiphysics and multiscale numerical models, and data informatics. The intelligent integration of the physics-based and data-driven models will eventually boost the dimensionality, efficiency, and accuracy of the modeling approach for the design of the complicated hierarchical gradient microstructure in 3D. It will provide a powerful, digital and sustainable way for the design of new materials and/or processes and evaluation of material performance.Status
SIGNEDCall topic
ERC-2022-STGUpdate Date
31-07-2023
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