Hybrid Deterministic/Statistical Multi-scale Modelling Techniques for 3D Woven Composites

Today, composite materials are at the forefront of an engineering revolution targeting lighter, more reliable, and more fuel-efficient aerospace structures. Advanced composites are made from layers of long fibres bound together using a matrix to form the structure. The most common fibre type used in aerospace applications are Carbon Fibres combined with an Epoxy matrix. More recently other types of fibres/matrix are being introduced, such as: ceramics matrix composites for high temperature applications and metal matrix composites for abrasion/ impact resistance. However, something common between all types of composites is that they are based on fibre layers. By definition, layers are 2D. As a result, all conventional composite materials struggle with direct loading in the third direction. While 2D composites provide designers with clear advantages coming from the superior properties of the fibres and the flexibility of tailoring fibre directions or combining different fibre types, through thickness performance remains an Achilles heel that have limited their full potential.

3D Composites is a viable solution to these issues as they are made from fibres woven in all three dimensions. These materials show a lot of promise as they can carry direct load through thickness and can resist impact events. However, there are a set of modelling challenges that come with using 3D composites, which have prevented engineers from taking full advantage of these materials. Traditionally, to understand a new material behaviour, engineers and scientists test samples of the material to characterise its behaviour. Then this characteristic behaviour is included in the mathematical models that can predict the behaviour of structures made from this material. These structure models are what is used as design tool. This conventional approach does not work for 3D composites. During manufacturing, the 3D network of woven fibres deforms around corners and other structural features to conform to the structure geometry. This in turn means that the fibre network will have a different architecture for each part of the structure and consequently will have its own characteristic behaviour. As a result, simple material testing is no longer descriptive of the material behaviour and an alternative approach is needed.

This project aims to train models to detected repeating patterns that exist in a 3D woven network of fibres across a structure. These repeatable patterns will be characterised using highly detailed models to understand how each pattern behaves under different loading conditions and as part of multiple structures. Using this approach, a parameterised database containing thousands of these repeatable patterns and their behaviour will be built using unsupervised machine learning. On the structure scale, the behaviour of a full structure can be assembled from the behaviour of the repeating patterns forming it regardless of its geometry. This approach will allow engineers, for the first time, to design both the structure and the 3D fibre network forming it simultaneously. Achieving this goal allows us to build aerospace structures that are lighter, consume less fuel to fly, cheaper and faster to produce. The concept of using statistical models for describing structural behaviour have been around for some time. However, these approaches have always been proposed as a black box solution that can give an answer regarding what will happen to a structural/material but not why it happened. In this project, a hybrid approach is used, which combines statistical models with physically based deterministic models. The hybrid approach provides information about the mechanical performance, as well as the underlying physical reasons regarding why a given behaviour happens. This will allow engineers and scientist to understand 3D composites behaviour at a much deeper level than is currently possible by the statistical or deterministic models alone.