Descrizione del progetto
Nuovi componenti per una migliore resistenza alla frattura
L’industria aerospaziale e dei trasporti europea sta utilizzando sempre più componenti di produzione additiva (AMC, Additively Manufactured Component) in compositi rinforzati con fibre e realizzati tramite modellizzazione a deposizione fusa. Non è una sorpresa, considerando i loro vantaggi (meno costi di macchinari, materiale e lavoro, meno scarti di produzione e uso di materiali più efficienti). Tuttavia, presentano uno svantaggio: i componenti AMC hanno una geometria complessa e, in alcuni casi, tassellata, che sfocia in meccanismi di danno combinati e quasi fragili. Il progetto AI2AM, finanziato dall’UE, svilupperà in questo contesto componenti di maggiore resistenza alla frattura. Nello specifico, addestrerà modelli surrogati che verranno impiegati all’interno di un nuovo quadro di ottimizzazione della topologia per fornire geometrie ottimali stampate in 3D.
Obiettivo
"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."
Campo scientifico
- engineering and technologymaterials engineeringcomposites
- natural sciencesmathematicspure mathematicstopology
- natural sciencesmathematicspure mathematicsgeometry
- engineering and technologymechanical engineeringmanufacturing engineeringadditive manufacturing
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
157 80 ATHINA
Grecia