Project description
New components of improved fracture toughness
The European aerospace and transport industries are increasingly utilising additively manufactured fibre reinforced composite (AMC) components that have been made via fused deposition modelling. This is not a surprise, considering their advantages (fewer machine, material and labour costs, less manufacturing waste, and usage of more efficient materials). However, there is one drawback: AMC components have complex and, in some cases, tessellated geometry, which results in combined and quasi-brittle damage mechanisms. In this context, the EU-funded AI2AM project will develop components of increased fracture toughness. Specifically, it will train surrogate models that will be deployed within a new topology optimisation framework to deliver optimal 3D printed geometries.
Objective
"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."
Fields of science
- 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)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
157 80 ATHINA
Greece