Summary
Additive Manufacturing (AM) by Laser Powder Bed Fusion (LPBF) has the potential to revolutionize future product development, design and supply chains. Since the underlying multi-scale physics are not well understood, its potential can presently not be exploited. Sub-optimal process conditions lead to severe defects on different scales, rendering parts unsuitable for use. Critically, known regimes of stable processing go along with very low built rates, i.e., very high costs compared to other processes. This limits LPBF to selected high value applications such as medical devices but prohibits applications in mass production where it otherwise could allow for entirely new technologies.
ExcelAM aims at the digital discovery of novel high-throughput process regimes in LPBF, to increase build rates by at least one order of magnitude. Computational modeling would be perfectly suited for this purpose since it allows to observe physics that are not accessible to measurement and to study novel process technologies that are not feasible with existing hardware. Unfortunately, existing computational tools are by far not powerful enough, given the complexity of LPBF. Therefore, ExcelAM will develop novel game-changing methodologies, grouped into two main classes: First, novel high-fidelity multi-physics models will be developed, capturing the complex multi-scale nature of LPBF. These are combined with cutting-edge high performance computing schemes, allowing for predictions on unprecedented time spans and system sizes. Second, novel data-based learning approaches will be developed to enrich the physical models with process data, while exploiting the manifold of existing data as effective as possible.
Based on these cutting-edge tools, ExcelAM will push the limits of LPBF. Moreover, by making them publicly available, ExcelAM will help scientists and practitioners in the field of production engineering and beyond to face the technological challenges of the 21st century.
ExcelAM aims at the digital discovery of novel high-throughput process regimes in LPBF, to increase build rates by at least one order of magnitude. Computational modeling would be perfectly suited for this purpose since it allows to observe physics that are not accessible to measurement and to study novel process technologies that are not feasible with existing hardware. Unfortunately, existing computational tools are by far not powerful enough, given the complexity of LPBF. Therefore, ExcelAM will develop novel game-changing methodologies, grouped into two main classes: First, novel high-fidelity multi-physics models will be developed, capturing the complex multi-scale nature of LPBF. These are combined with cutting-edge high performance computing schemes, allowing for predictions on unprecedented time spans and system sizes. Second, novel data-based learning approaches will be developed to enrich the physical models with process data, while exploiting the manifold of existing data as effective as possible.
Based on these cutting-edge tools, ExcelAM will push the limits of LPBF. Moreover, by making them publicly available, ExcelAM will help scientists and practitioners in the field of production engineering and beyond to face the technological challenges of the 21st century.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101117579 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2028 |
| Total budget - Public funding: | 1 484 926,00 Euro - 1 484 926,00 Euro |
Cordis data
Original description
Additive Manufacturing (AM) by Laser Powder Bed Fusion (LPBF) has the potential to revolutionize future product development, design and supply chains. Since the underlying multi-scale physics are not well understood, its potential can presently not be exploited. Sub-optimal process conditions lead to severe defects on different scales, rendering parts unsuitable for use. Critically, known regimes of stable processing go along with very low built rates, i.e., very high costs compared to other processes. This limits LPBF to selected high value applications such as medical devices but prohibits applications in mass production where it otherwise could allow for entirely new technologies.ExcelAM aims at the digital discovery of novel high-throughput process regimes in LPBF, to increase build rates by at least one order of magnitude. Computational modeling would be perfectly suited for this purpose since it allows to observe physics that are not accessible to measurement and to study novel process technologies that are not feasible with existing hardware. Unfortunately, existing computational tools are by far not powerful enough, given the complexity of LPBF. Therefore, ExcelAM will develop novel game-changing methodologies, grouped into two main classes: First, novel high-fidelity multi-physics models will be developed, capturing the complex multi-scale nature of LPBF. These are combined with cutting-edge high performance computing schemes, allowing for predictions on unprecedented time spans and system sizes. Second, novel data-based learning approaches will be developed to enrich the physical models with process data, while exploiting the manifold of existing data as effective as possible.
Based on these cutting-edge tools, ExcelAM will push the limits of LPBF. Moreover, by making them publicly available, ExcelAM will help scientists and practitioners in the field of production engineering and beyond to face the technological challenges of the 21st century.
Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
12-03-2024
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