Descrizione del progetto
Migliorare l’efficienza della produzione additiva per ottenere alti tassi di costruzione
La produzione additiva (AM) è una soluzione cruciale che potrebbe essere di grande utilità per raggiungere gli obiettivi ambientali e migliorare la logistica in molti settori. La fusione laser a letto di polvere (LPBF, laser powder bed fusion) è uno sviluppo promettente per la produzione additiva, che potrebbe portare a un miglioramento della progettazione, dello sviluppo e della fornitura dei prodotti. Purtroppo, la mancanza di ricerche fa sì che la tecnologia sia inefficiente per alti tassi di costruzione, limitandone gli usi. Il progetto ExcelAM, finanziato dal CER, intende risolvere questa limitazione sviluppando regimi di processo innovativi ad alta produttività per la LPBF. A tal fine, svilupperà nuove metodologie di modellazione computazionale fondamentali per lo sviluppo di questi nuovi regimi di processo. Attraverso questi sforzi, ExcelAM intende sbloccare il pieno potenziale della LPBF nella produzione additiva.
Obiettivo
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.
Campo scientifico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwaresupercomputers
- natural sciencescomputer and information sciencescomputational sciencemultiphysics
- engineering and technologymechanical engineeringmanufacturing engineeringadditive manufacturing
- natural sciencesphysical sciencesopticslaser physics
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
80333 Muenchen
Germania