Projektbeschreibung
Effizienz der additiven Fertigung für hohe Fertigungsraten erhöhen
Die additive Fertigung ist eine entscheidende Lösung, die in vielen Sektoren von großem Nutzen für die Erreichung von Umweltzielen und eine bessere Logistik sein könnte. Das pulverbettbasierte Laserstrahlschmelzen stellt eine vielversprechende Entwicklung für die additive Fertigung dar, die zu einer deutlich verbesserten Gestaltung, Entwicklung und Lieferung von Produkten führen könnte. Leider bedeutet der Mangel an Forschung, dass die Technologie für hohe Fertigungsraten ineffizient ist, was ihre Einsatzmöglichkeiten einschränkt. Ziel des ERC-finanzierten Projekts ExcelAM ist es, diese Einschränkung durch die Entwicklung innovativer Hochdurchsatzverfahren für pulverbettbasiertes Laserstrahlschmelzen zu beseitigen. Dazu werden neue Methoden für die computergestützte Modellierung erarbeitet, die für die Entwicklung dieser neuen Verfahrensführung entscheidend sind. Durch diese Bemühungen zielt ExcelAM darauf ab, das volle Potenzial des pulverbettbasierten Laserstrahlschmelzens in der additiven Fertigung erschließen.
Ziel
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.
Wissenschaftliches Gebiet
- 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
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thema/Themen
Finanzierungsplan
HORIZON-ERC - HORIZON ERC GrantsGastgebende Einrichtung
80333 Muenchen
Deutschland