Greene Tweed’s Pragati Verma looks at how to best predict the structural performance of randomised composite components.

Aircraft manufacturers across the world are using more and more composite materials instead of traditional metals, such as steel and aluminium. What makes composites so appealing is their high strength-to-weight ratio – a comparison of their strength to how much it weighs. Composites aren’t just one of the strongest materials around, but they can be strong without being heavy — exactly what an aircraft needs.

Let’s consider one such high-performance composite material that works well for complex and intricate geometries, such as brackets, fairings, and enclosures:  Compression moulded, random, discontinuous long-fibre (DLF) thermoplastic composite. This material derives its structural properties primarily from fibre reinforcement. The distribution of the discontinuous fibres can be controlled and predicted to improve the structural performance of the moulded parts. But it’s not easy to predict performance via computer simulations, especially for discontinuous and randomised material systems.

Models that can predict future behaviour of discontinuous and randomised material are in high demand.

Travis Mease, Greene Tweed’s product manager, structural components says: “From a performance and risk reduction perspective, accurately predicting fibre orientation is crucial. It’s an important design parameter that drives the sizing of our components. Greene Tweed has invested heavily into flow modelling capabilities, so that we can apply fibre orientation tensor values to FE failure analyses.”

According to him, this validation by analysis method for DLF composite parts is a risk reduction tool that cuts time and cost associated with tool designs and re-evaluations. The prediction and validation of fibre orientation of DLF composites is complicated due to the complex behaviour of flow-induced fibre orientation with high fibre concentration, and reluctance to destructively validate orientation via sectioning and polishing for optical microscopy.

Greene Tweed (GT) understood the importance of evaluating fibre orientation in compression moulded DLF composites. GT engineers used a modified Autodesk Moldflow programme, customised to predict the fibre orientation in compression moulding simulation of DLF parts of various shapes and geometries, such as brackets, structural components, fairings, and enclosures. They then took the flow modelling fibre orientation results for structural finite element analysis to predict the performance of DLF components under different loading conditions.

According to Mease, this process generates more accurate results specific to each part, compared to the generic assumptions traditionally used.

He explains: “Every part has its own unique material introduction point and geometry, creating similarly unique flow and fibre orientation patterns. The more accurate the flow modelling, the more confidence we have in our stress and failure predictions.”

To validate the results, GT engineers went on to compare predictions from our model with computed tomography (CT) analysis of actual components. After extensive testing, they found quantitative and qualitative agreement between GT’s fibre orientation predictions and experimental CT analysis results for DLF composite parts. The modelling of fibre orientation and flow-moulding process showed good predictive capabilities for local variation and distribution of fibre orientation in compression moulded DLF thermoplastic composite parts, regardless of size, variety, and shape complexity. And that’s not all. This predictive capability means that damage initiation and failure mode of the part can be visually explained by the fibre orientation prediction.

As a result, Greene Tweed now has the capability to predict how, when, and where a component will fail, and it can also ensure a successful composite optimisation effort that includes ample weight savings, cost-cuts, and safety margins. Additionally, by having this prediction capability, we have eliminated the need to do several iterations and thus, Greene Tweed is better positioned to meet its customers’ scheduled deadlines.

www.gtweed.com