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© 2017, The Author(s) 2017. Resin transfer moulding is one of several processes available for manufacturing fibre-reinforced composites from dry fibre reinforcement. Recently, dry reinforcements made with Automated Dry Fibre Placement have been introduced into the aerospace industry. Typically, the permeability of the reinforcement is assumed to be constant throughout the dry preform geometry, whereas in reality, it possesses inevitable uncertainty due to variability in geometry. This uncertainty propagates to the uncertainty of the mould filling and the fill time, one of the important variables in resin injection. It makes characterisation of the permeability and its variability an important task for design of the resin transfer moulding process. In this study, variability of the geometry of a reinforcement manufactured using Automated Dry Fibre Placement is studied. Permeability of the manufactured preforms is measured experimentally and compared to stochastic simulations based on an analytical model and a stochastic geometry model. The simulations showed that difference between the actual geometry and the designed geometry can result in 50% reduction of the permeability. The stochastic geometry model predicts results within 20% of the experimental values.

The application of 3D weaves has advantages over conventional uni-directional or 2D woven lay-ups. There is potential to produce near net-shaped preforms and to increase damage resistance due to the presence of through thickness reinforcement. Conventional 3D weaves typically consist of orthogonal yarns interwoven with through thickness binder yarns. This paper describes a feasibility study to find optimum architectures for 3D woven fabrics where some of the normal manufacturing constraints are relaxed. This will provide the basis for development of novel manufacturing methods based on optimum textile architectures. A framework has been developed for the automatic generation and analysis of 3D textile geometries, utilising the open-source pre-processor TexGen. A genetic algorithm is used to select an optimum geometry by evaluating results from finite element simulations using the commercial solver Abaqus. This paper highlights the flexibility of TexGen software to create complex 3D models by means of its Python scripting application programming interface (API). A standard layer-to-layer geometry is used as a starting point to which off-axis yarn rotations, in-plane shift of entire layers and adjustments to binder yarns can be applied. Geometric variables are selected to represent the textile architecture enabling the automation of unit cell creation and finite element analysis. A Genetic Algorithm is used to determine the optimum through thickness binder path, the number and the width of the binders, and yarn angles using a weighted objective function of the material elastic properties. The case studies show that the algorithm is efficient to converge to the optimum fibre architecture.

International audience; no abstract

Manipulation of the through-thickness yarn path enables 3D woven reinforcement to separate locally in the form of a bifurcation, creating net-shaped preforms for T- and I-beams. Preforming introduces fibre architecture deformation at the 3D woven bifurcation area. We report a geometric modelling approach to represent the realistic fibre architecture, as a preprocessing tool for finite element analyses. The study started with x-ray micro-computed tomography (µCT) of two 3D woven T-beams varying only by their yarn path at the T-junction area. Supported by the µCT image analysis, a set of mathematical formula were proposed to describe the identified features in the 3D woven T-beams. We then moved on to implement the automated modelling procedure in the open-source software TexGen. Using the weave pattern as input data, TexGen first simulates as-woven flat T-piece. Next, TexGen applies geometric transformation and refinements to simulate the preforming process of T-beams. The paper highlights an efficient approach to model the complex woven bifurcation structure at mesoscale.