Modelling the resin infusion under flexible tooling process : a physical and genetic approach
Kessels, Josef Franciscus Anna
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Resin Infusion under Flexible Tooling (RIFT) is a process by which resin is infused through the fibres through the application of a vacuum. Only a one-sided mould is used and the other side is covered with a flexible bag. In the two parts of this thesis a physically based flow model and a genetically based tool are presented to simulate and optimise the RIFT process in advance, both for complex 2 1/2 dimensional geometries. The flow model of Part 1 was based on Darcy's law. Due to the flexible bag, the preform compacts during the process and hence the fibre compaction flu term was taken into account and the process was modelled transient. A stabilisation method was developed, using a thickness prediction for a new time step size. Although this prediction was based on a rather crude assumption, it provided a simple and fast way to overcome stability problems. Experiments were carried out to establish and model the different wet and dry compaction behaviour of two types of preform. The different dry and wet preform properties were also taken into account. A fluid presence function was used for flow front tracking and for the pressure prediction in the partially filled cells. The model was implemented for the use of 2 1/2 dimensional unstructured meshes. The model was validated with experiments. The compaction of the preform and the flow front propagation during mould filling were measured. It was found that, in the case of highly compactable fibres, the fibre compaction flux term increased the accuracy of the calculated results significantly. The genetically based tool of Part II is capable of optimising the different process parameters such as flow pipe position and length, fill-distance and number of vents. The tool consists of a mesh distance-based model coupled with a genetic optimisation algorithm. The mesh distance-based model was based on the assumption that the resin fills the nodes closest to the inlets first. The genetic algorithm was based on the principles of natural selection and genetics and its effectiveness was improved with a variable cross-over rate. The mesh distance-based model was verified with cases known from literature and with the results from the physically based flow model. The effectiveness of the genetic algorithm was validated with a number of design cases. For the simple 2D design cases, the tool provided fast solutions which agreed very well with the obvious solutions. For the more complex cases, the algorithm proved to be a very stable and effective method for finding the optimal flow pipe arrangement on any complex geometry.
- Engineering