Modelling the effects of textile preform architecture on permeability
Liquid Composite Moulding (LCM) processes are identified as one of the most potentially advantageous manufacturing routes. The challenge currently is to increase their reliability and expand their applicability. To that end, it was perceived that there was a lack of an advanced integrated simulation tool for the manufacture of three-dimensional, multi-layer textile composites. The tools for the analyses of fabric forming and subsequent flow during LCM processes were simple and immature, with the latter suitable to describe flow in thin structures only. Another noted deficiency was that the simulations provided a single answer to any given problem. Industrial experience has shown that during mould filling, due to the nature of statistical variation in the material properties, the filling patterns and arising cycle times are rarely the same between a given set of identical mouldings. This thesis focuses on permeability prediction of textile reinforcements for LCM processes. The issue of textile variability was also explored through the use of the permeability models' predictive capability. Two novel and efficient numerical approaches were developed to predict textile permeability based on the fabric architecture. The objective was to reduce the complexity of the flow domain and hence provide a faster method to fully characterise the permeability of a textile. Within a wider context, these models were implemented within an integrated modelling framework encompassing draping, compaction and impregnation, based on the TexGen textile schema. TexGen is a generic geometric textile modeller that can be used to create a wide range of textile models. Several validation studies were performed using a range of reinforcements including woven and non-crimp fabrics. A stochastic analysis technique was developed to account for the effect of material variability on permeability. The study based on this technique provided important insights into permeability variations. It was shown that the permeability distribution is a strong function of the textile architecture. The permeability models developed from this work can be used to account for the effects of fabric shear/compaction and statistical variations on permeability. These predicted permeability data can complement experimental data in order to enhance flow simulations at the component scale.