Description du projet
Améliorer l’efficacité de la fabrication additive pour des taux de production élevés
La fabrication additive (FA) est une solution cruciale qui pourrait s’avérer extrêmement utile pour atteindre les objectifs environnementaux et optimiser la logistique dans de nombreux secteurs. La fusion laser sur lit de poudre (LPBF pour «laser powder bed fusion») est une avancée prometteuse pour la FA, qui pourrait considérablement améliorer la conception, le développement et la fourniture des produits. Malheureusement, le manque de recherche signifie que la technologie est inefficace pour les taux de production élevés, ce qui limite son utilisation. Le projet ExcelAM, financé par le CER, entend remédier à cette limitation en développant des régimes de traitement innovants à haut débit pour la LPBF. Pour ce faire, il mettra au point de nouvelles méthodologies de modélisation informatique essentielles à l’élaboration de ces nouveaux régimes de traitement. Grâce à ces efforts, ExcelAM espère libérer tout le potentiel de la LPBF dans la fabrication additive.
Objectif
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.
Champ scientifique
- 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
Mots‑clés
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thème(s)
Régime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
80333 Muenchen
Allemagne