Success in most aerospace and renewable energy projects involving composite materials depends on the reliability of those materials. Low reliability implies higher operation and maintenance costs and a reduction of asset’s life cycle. It is important to characterise failure modes in order to improve reliability of composite materials which are part of aeroplane or wind turbine’s subsystems.
The use of MISTRAS’ Acoustic Emission systems along with NOESIS software allows performing advanced signal processing based on neural networks and pattern recognition algorithms. These tools separate data acquired into classes according to the failure mechanism that causes the emissions (cracks in the matrix, fibres break, delaminations, fibres detachment, etc.).