Leveraging Vision Transformers for Detecting Porosity in Wind Energy Composites

Abstract The structural robustness and operational efficiency of wind turbine rotor blades are crucial for the overall effectiveness of wind energy systems, often constructed with fiber-reinforced polymers (FRPs) and adhesives. However, porosity within these materials poses a significant threat, weakening structural strength and effectiveness. Air pockets lead to stress concentration points, reducing load-carrying capacity and elevating the risk of blade failure, especially under dynamic wind loads. Manual detection of these air pockets is laborious, necessitating automated inspection techniques. Advanced imaging technologies, such as computed tomography (CT) scanning and deep learning, hold promise for identifying and quantifying porosity in FRPs and adhesives, reducing labor while enhancing accuracy. The study introduces a transformer-based model for porosity detection, departing from convolution-based methods, emphasizing the incorporation of global context throughout the network. Leveraging Vision Transformer (ViT) framework advances, the model is applied to porosity segmentation in wind energy blades, showing promising results with limited datasets. The prospect of using larger datasets suggests potential for a versatile solution in segmenting porosity or voids in various wind energy blade composites, including adhesives.