Damage Detection in Composite Materials Using Tap Test Technique and Neural Networks

The wind energy sources promote the generation of electricity, being environmentally safe, clean and its use has been growing in Brazil, especially in the northeastern states with the implementation of new wind farms. Wind turbines, however, are subject to weather, risk of animal shocks, materials carried by the wind, and the vibrations of the system itself. The blade is the most important component of a wind turbine and is the one with the greatest risk of failure. They are usually made of a composite fiber-reinforced polymer matrix and balsa wood as structural reinforcement. As the material composing this blade has heterogeneous and anisotropic characteristics, conventional inspection techniques are not considered effective. The non-destructive Tap Test technique can be a safe option in composite materials because it does not suffer from these limitations. The objective of this work is to inspect composite plates of polymeric resin used in wind turbines with discontinuities using the non-destructive Tap Test technique, where regions with and without defect were analyzed. The collected signals from an accelerometer and a microphone were processed, to allow the extraction of features and the recognition of discontinuities with the aid of a neural network.