Aerospace manufacturers are relying increasingly on aviation composites manufactured as multi-layer laminate materials. This allows for lighter and larger aircraft capable of moving higher volumes of passengers and cargo. But with these types of composites, there is always the risk of failure by way of delamination. Manufacturers need to account for failure risks throughout the design and production stages.
Researchers around the country are currently developing more accurate predictive tools based on AI and deep learning technologies. Their research is rooted in five key factors known to influence failure risk in aviation composites.
A composite material’s properties and microstructure play a critical role in strength. For example, fiber alignment is a key material property that directly impacts strength and stiffness. Being able to better predict stress in relation to fiber orientation and load can ultimately help reduce failure risk.
Other considerations include:
Understanding a material’s microstructure and mechanical properties is the starting point for predicting failure risk. By combining a material’s known properties with historical data, more accurate predictions are possible.
Long after manufacturing is complete, aviation composites are exposed to innumerable stresses that could ultimately lead to failure. So during the design stage, predicting failure risk means looking at those stresses in depth. They include things like:
Even fretting fatigue, which is the result of repetitive contact between composite surfaces, can lead to degradation and failure. Predicting failure risk means understanding the various types of fatigue and their implications.
Aviation composites are subject to negative influences as a result of design and manufacturing. Design flaws can increase failure risk even if a part is manufactured according to the tightest specifications.
In terms of manufacturing, the risk of defects is always present. Predicting failure risk means accounting for possible manufacturing defects that introduce inconsistencies or material weaknesses.
Effectively predicting failure risk must also account for environmental and operational factors. For example, components subjected to high levels of moisture and temperature extremes could be at higher risk of failure – especially when combined with design flaws and manufacturing defects.
Part failure can be induced by a variety of things, including:
In the aviation industry, all these things are a concern. Any reliable predictive analytic strategy would certainly account for them.
Finally, how aviation composites are analyzed determines the accuracy with which failure risk can be predicted. If the criteria used to analyze a particular material is not appropriate to the material’s design and use cases, incorrect failure theories are likely to lead to incorrect predictions.
Other analytical factors include:
In terms of data, new predictive tools relying on AI and deep learning require unfathomable amounts of data to generate accurate results. But as time goes on, data limitations should be less of a concern.
Modern aviation composites have made it possible to do things we couldn’t have dreamed of 100 years ago. As we continue to push the boundaries, our industry needs better tools for predicting failure risk. Fortunately, those tools are now being developed in conjunction with AI and deep learning.
Aerodine Composites recognizes the critical importance of predicting and managing failure risks in aviation composites, especially as the industry increasingly relies on multi-layer laminate materials for lighter, more efficient aircraft. With over 30 years of experience in aerospace, Aerodine integrates advanced composite technology to ensure the highest standards of quality and performance throughout the design and production stages.
