Description du projet
De nouveaux composants de résistance à la rupture améliorée
Les industries européennes de l’aérospatiale et des transports utilisent de plus en plus d’éléments en composites renforcés de fibres (AMC) fabriqués de manière additive via la modélisation par dépôt fondu. Cela n’est pas une surprise, compte tenu de leurs avantages (moins de coûts en machine, matériel et main-d’œuvre, moins de déchets de fabrication et l’utilisation de matériaux plus efficaces). Toutefois, un inconvénient subsiste: les composants AMC présentent une géométrie complexe et, dans certains cas, tessellée, ce qui entraîne des mécanismes d’endommagement combinés et quasi-fragiles. Dans ce contexte, le projet AI2AM, financé par l’UE, développera des composants dont la résistance à la rupture sera accrue. Plus précisément, il formera des modèles de substitution qui seront déployés dans un nouveau cadre d’optimisation de la topologie pour fournir des géométries imprimées en 3D optimales.
Objectif
"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."
Champ scientifique
- engineering and technologymaterials engineeringcomposites
- natural sciencesmathematicspure mathematicstopology
- natural sciencesmathematicspure mathematicsgeometry
- engineering and technologymechanical engineeringmanufacturing engineeringadditive manufacturing
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
157 80 ATHINA
Grèce