A Multimodal Fusion-Based Autoencoder for Nondestructive Evaluation of Aircraft Structures

The effects of the lightning strike on composite aircraft structures have been an active research area in the aviation industry, given the concern over safe aircraft operations. To maintain safe operations, civil and military regulators require effective approaches to assess and quantify the severity of lightning damage. Although X-rays are commonly used to determine material damage in aircraft structures, the technique requires access to both sides of the investigated part. This paper proposes a novel autoencoder model to check the feasibility of evaluating the damage to carbon fiber reinforced polymers (CFRP) panels from the outer surface of in-service aircraft structures. Two alternative techniques to X-ray, such as ultrasonic testing (UT) and infrared thermography (IR), nondestructive evaluation methods, are employed to develop the proposed model. The fusion model uses U-net as the backbone and spatial attention fusion as the fusion strategy while combining structural similarity index (SSIM) and perceptual losses as the loss function. Also, the log-Gabor filter is used in the model to obtain high-frequency edge information for fusion. The results are then compared against five state-of-the-art fusion methods, revealing that the proposed model performs better in quantifying the lightning damage to aircraft CFRP structures.