Abstract
Increasing demand for weight reduction and greater fuel efficiency continues to spur the use of composite
materials in commercial aircraft structures. Subsequently, as composite aerostructures become larger and
more complex, traditional autoclave manufacturing methods are becoming prohibitively expensive. This has
prompted renewed interest in out-of-autoclave processing techniques in which resins are introduced into a
reinforcing preform. However, the success of these resin infusion methods is highly dependent upon operator
skill and experience, particularly in the development of new manufacturing strategies for complex parts.
Process modeling, as a predictive computational tool, aims to address the issues of reliability and waste that
result from traditional trial-and-error approaches. Basic modeling attempts, many of which are still used in
industry, generally focus on simulating fluid flow through an isotropic porous reinforcement material. However,
recent efforts are beginning to account for the multiscale and multidisciplinary complexity of woven
materials, in simulations that can provide greater fidelity. In particular, new multi-physics process models
are able to better predict the infusion behavior through textiles by considering the effect of fabric deformation
on permeability and porosity properties within the reinforcing material. In addition to reviewing previous
research related to process modeling and the current state of the art, this paper highlights the recent
validation of a multi-physics process model against the experimental infusion of a complex double dome
component. By accounting for deformation-dependent flow behavior, the multi-physics process model was
able to predict realistic flow behavior, demonstrating considerable improvement over basic isotropic permeability
models.
Original language | English |
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Pages (from-to) | 596-607 |
Number of pages | 12 |
Journal | Engineering |
Volume | 3 |
Issue number | 5 |
Early online date | 25 Sept 2017 |
DOIs | |
Publication status | Published - Oct 2017 |