The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.

Using composite materials in turbine blades has become common in the wind power industry due to their mechanical properties and low mass. This work aims to investigate the effectiveness of the active infrared thermography technique as a non-destructive inspection tool to identify defects in composite material structures of turbine blades. Experiments were carried out by heating the sample and capturing thermographic images using a thermal camera in four different scenarios, changing the heating strategy. Such a preliminary experiments are prodromic to build, in future, the so-called optimal experiment design for thermal property estimation. The experimental results using two heaters arranged symmetrically on the sample detected the presence of the defect through temperature curves extracted from thermal images, where temperature asymmetries of 25% between the regions with and without defect occurred. Moreover, when only a larger heater was used in transmission mode, the defect was detected based on differences between normalized excess temperatures on the side with and without the defect in the order of 20%. Additionally, numerical simulations were carried out to present solutions for improving defect detection. It was demonstrated that active infrared thermography is an efficient technique for detecting flaws in composite material structures of turbine blades. This research contributes to advancing knowledge in inspecting composite materials.