A Deep Learning-Based Probabilistic Approach for Non-Destructive Testing of Aircraft Components Using Laser Ultrasonic Data

Composite structures are commonly used in complex applications such as automotive and aerospace due to their high strength-to-weight ratio. Although strictly supervised and inspected, they are often subject to dynamic events during their useful life that can cause invisible failures that extend and severely compromise their performance over time. Detecting these defects preventively and repairing them could avoid dramatic accidents. Here, we present a deep learning-based method for the non-destructive detection of defects in composite samples based on a laser ultrasonic system (LUT). Laser ultrasonic technology is a very promising non-destructive testing (NDT) method for detecting inner defects in a non-contact way, as it does not require liquid coupling media. We investigated a composite laminate specimen containing six different programmed defects as a test sample. We show that training deep learning-based models as autoencoders makes it possible to extract features that can be used to discern defective areas from non-defective ones in the US C-scan maps. The results demonstrate high detection accuracies (above 90% balanced accuracy and 75% F1-score), indicating a promising and effective approach to NDT on composite materials.