Recent publications show that there is an increasing interest in the aircraft industry in monitoring the actual condition of a structure in real time and while the structure is in service. It is hoped that this Structural Health Monitoring (SHM) could make some regular inspections unnecessary and allow maintenance only when required. This is particularly important for CFRP structures for which aircraft manufacturers are increasingly interested. For these new composite structures where the experience of fatigue failure is relatively low, this technique could potentially be economical and improve the safety of the structures. Acoustic Emission is reported to be sensitive to the four failure types in composite materials, namely matrix cracking, delamination, debonding and fibre fracture. These failure modes can have different impacts on structural integrity and it is therefore of interest to identify these failure types before further maintenance steps are conducted. This report discusses different features in AE signals which can be used to identify the actual flaw type. These features were then applied to AE data collected from two different experiments on carbon fibre composite plates. These experiments were designed to induce the two different failure modes of matrix cracking and delamination. The data collected was used to train a neural network to recognise the two failure modes.
Production process monitoring and post-production strain measurement on a full-size carbon-fibre composite aircraft tail cone assembly using embedded optical fibre sensors
