Descripción del proyecto
Mejora de la eficacia de la fabricación aditiva para obtener altos índices de producción
La fabricación aditiva (AM, por sus siglas en inglés) es una solución crucial que podría ser enormemente beneficiosa para alcanzar objetivos medioambientales y mejorar la logística en muchos sectores. La fusión por láser en lecho de polvo (LPBF, por sus siglas en inglés) es un avance prometedor para la AM, que podría mejorar mucho el diseño, el desarrollo y el suministro de productos. Lamentablemente, la falta de investigación genera que la tecnología sea ineficaz para altas tasas de construcción, lo cual limita sus usos. El equipo del proyecto ExcelAM, financiado por el Consejo Europeo de Investigación, pretende abordar esta limitación desarrollando regímenes de proceso innovadores de alto rendimiento para la LPBF. Para ello, desarrollará nuevas metodologías de modelización computacional fundamentales para el desarrollo de estos nuevos regímenes de procesos. Con estas iniciativas, el equipo de ExcelAM pretende aprovechar todo el potencial de la LPBF en la fabricación aditiva.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
80333 Muenchen
Alemania