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AI2AM

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Artificial Intelligence driven topology optimisation of Additively Manufactured...

Artificial Intelligence driven topology optimisation of Additively Manufactured Composite Components "Additively Manufactured fibre reinforced composite (AMC) components manufactured via fused deposition modelling (FDM) rapidly find applications within the European aerospace and transport industry , due to their well-known advant... ver más

31/08/2023

NATIONAL TECHNICAL...

165K€

Presupuesto del proyecto: 165K€

Líder del proyecto

ETHNICON METSOVION POLYTECHNION No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-08-31

MSCA-IF-2020: Individual Fellowships

I+D

Cerrada hace 4 años

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2 Participantes

NATIONAL TECHNICAL UNIVERSIT...

165.09K€ | Lider

ROTATEK

766.89K€ | Participante

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Duración del proyecto: 28 meses Fecha Inicio: 2021-04-28
Fecha Fin: 2023-08-31

Líder del proyecto

ETHNICON METSOVION POLYTECHNION No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 165K€

Descripción del proyecto "Additively Manufactured fibre reinforced composite (AMC) components manufactured via fused deposition modelling (FDM) rapidly find applications within the European aerospace and transport industry , due to their well-known advantages mainly relating to less machine, material and labour costs, less manufacturing waste, and usage of more efficient materials. A major drawback of AMC components is their usually complex and in cases tessellated geometry; this gives rise to combined (e.g., fibre pull-outs and matrix cracking) and quasi-brittle damage mechanisms that deviate from the usual high strength and ductile metal design paradigm. Such a complexity, if controlled, can result in components of tailored mechanical properties, e.g., of increased fracture toughness and pseudo-ductile post fracture response. Unfortunately, current analysis and design methods lack the necessary level of refinement, or the underlying theoretical framework indeed, to efficiently address this critical issue. AI2AM aims to deliver a holistic approach to additively manufacture topologically optimum composite components of increased fracture toughness. It will achieve this by developing a state-of-the-art fracture simulation framework for composite structures harnessing the fidelity and computational advantages of phase field modelling for fracture and scaled boundary finite element methods. This high fidelity physics based ""continuum toolbox"" will be used to train surrogate models based on machine learning methods. The surrogates will then be deployed within a novel topology optimisation framework to deliver optimal and 3D printed geometries. The envisaged methodology crosses the boundaries of computational mechanics, optimisation, and machine learning and brings together a talented academic with world-class experts in topology optimisation, composites, and additive manufacturing."

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