A 2.5D simulation of the filling and post-filling stages of the resin infusion process
The Resin Infusion process (RI, also known as VARTM) is a subclass of the Liquid Composite Moulding (LCM) collective, which is increasingly applied in industry. As opposed to the other LCM processes, RI utilises only one rigid mould half, the upper mould half of the mould being a flexible plastic ba...
Gespeichert in:
Hauptverfasser: | , , , , |
---|---|
Format: | Tagungsbericht |
Sprache: | eng |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | The Resin Infusion process (RI, also known as VARTM) is a subclass of the
Liquid Composite Moulding (LCM) collective, which is increasingly applied in industry. As
opposed to the other LCM processes, RI utilises only one rigid mould half, the upper mould
half of the mould being a flexible plastic bag. This greatly reduces tooling costs, and makes
the process suitable for medium to very large sized parts. However, the interaction between a
flexible bag and the infusion of the laminate within, presents a significant challenge to model
and understand. The University of Auckland LCM research group is developing SimLCM as
a generic LCM mould filling simulation. SimLCM has recently been extended to simulate RI,
focusing on resin flow and laminate thickness predictions throughout the process. To
accurately predict filling times, and the evolution of fluid pressure and laminate thickness
during filling and post-filling phases, a detailed knowledge is required of the complex
compaction response of the fibre reinforcement. While significant research has been
published on modelling of the filling in RI, the post-filling period has received much less
attention. This phase is, however, significant as spatial variations in laminate thickness are
removed, preferably before the infused resin gels. Extending on previous work on rectilinear
filling, this paper will present a program of RI experiments in a range of 2D flow geometries
and the results will be compared to the predictions made using SimLCM. Special attention is
given to the post-filling stage, and the validation of the new models developed for SimLCM.
A selection of radial, peripheral and more complex filling situations have been addressed. |
---|