Summary
Superior high-performance materials and CO2-free production technologies are key enablers to solving Europe’s current and future societal challenges [1]. In this context, additive manufacturing (AM) as one of the disruptive, green production technologies of our time “is expected to become a key manufacturing technology in the sustainable society of the future” [2].
However, alloys specifically designed for AM are rarely available, which prohibits AM from reaching its full potential. In contrast to conventional alloys and processing, alloys processed by AM are highly microstructurally heterogeneous. It is the aim of HeteroGenius4D to use the process-inherent conditions of highly precise, bottom-up AM approach to tailor these heterogeneous structures (e.g. grains/phases and their boundaries and orientations, chemical gradients, etc.) locally and spatially on various length scales. This is the basis for the novel design concept of heterogeneities-guided alloy design for AM. The potential to print local microstructures and properties in AM adds a 4th dimension to the design of 3D printed components; i.e. enables 4D printing.
AM-processed metals with increasing degree of heterogeneity (from pure element over solid solutions with chemical gradients to multi-phase alloys with further phases and gradients) are studied systematically. The process-structure-properties-performance linkages are identified and quantified by combining high-throughput material synthesis (using extreme high-speed laser material deposition) and characterization with physics-based simulation tools, enabling a comprehensive integrated computational materials engineering (ICME) framework. The generated data serves as a basis for sophisticated data-driven (machine learning, ML) materials modelling and enables the establishment of an Experiments-ICME-ML optimal design approach for metal AM. Finally, the concept of heterogeneities-guided alloy design is generalised and transferred to graded components.
However, alloys specifically designed for AM are rarely available, which prohibits AM from reaching its full potential. In contrast to conventional alloys and processing, alloys processed by AM are highly microstructurally heterogeneous. It is the aim of HeteroGenius4D to use the process-inherent conditions of highly precise, bottom-up AM approach to tailor these heterogeneous structures (e.g. grains/phases and their boundaries and orientations, chemical gradients, etc.) locally and spatially on various length scales. This is the basis for the novel design concept of heterogeneities-guided alloy design for AM. The potential to print local microstructures and properties in AM adds a 4th dimension to the design of 3D printed components; i.e. enables 4D printing.
AM-processed metals with increasing degree of heterogeneity (from pure element over solid solutions with chemical gradients to multi-phase alloys with further phases and gradients) are studied systematically. The process-structure-properties-performance linkages are identified and quantified by combining high-throughput material synthesis (using extreme high-speed laser material deposition) and characterization with physics-based simulation tools, enabling a comprehensive integrated computational materials engineering (ICME) framework. The generated data serves as a basis for sophisticated data-driven (machine learning, ML) materials modelling and enables the establishment of an Experiments-ICME-ML optimal design approach for metal AM. Finally, the concept of heterogeneities-guided alloy design is generalised and transferred to graded components.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101077977 |
| Start date: | 01-07-2023 |
| End date: | 31-03-2029 |
| Total budget - Public funding: | 1 499 999,00 Euro - 1 499 999,00 Euro |
Cordis data
Original description
Superior high-performance materials and CO2-free production technologies are key enablers to solving Europe’s current and future societal challenges [1]. In this context, additive manufacturing (AM) as one of the disruptive, green production technologies of our time “is expected to become a key manufacturing technology in the sustainable society of the future” [2].However, alloys specifically designed for AM are rarely available, which prohibits AM from reaching its full potential. In contrast to conventional alloys and processing, alloys processed by AM are highly microstructurally heterogeneous. It is the aim of HeteroGenius4D to use the process-inherent conditions of highly precise, bottom-up AM approach to tailor these heterogeneous structures (e.g. grains/phases and their boundaries and orientations, chemical gradients, etc.) locally and spatially on various length scales. This is the basis for the novel design concept of heterogeneities-guided alloy design for AM. The potential to print local microstructures and properties in AM adds a 4th dimension to the design of 3D printed components; i.e. enables 4D printing.
AM-processed metals with increasing degree of heterogeneity (from pure element over solid solutions with chemical gradients to multi-phase alloys with further phases and gradients) are studied systematically. The process-structure-properties-performance linkages are identified and quantified by combining high-throughput material synthesis (using extreme high-speed laser material deposition) and characterization with physics-based simulation tools, enabling a comprehensive integrated computational materials engineering (ICME) framework. The generated data serves as a basis for sophisticated data-driven (machine learning, ML) materials modelling and enables the establishment of an Experiments-ICME-ML optimal design approach for metal AM. Finally, the concept of heterogeneities-guided alloy design is generalised and transferred to graded components.
Status
SIGNEDCall topic
ERC-2022-STGUpdate Date
09-02-2023
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