Delamination Detection in CFRP Components from Ultrasound Images Using Convolutional Neural Networks

Carbon fibre-reinforced polymer (CFRP) materials are used for various applications due to their desirable mechanical properties. Defects, such as delaminations, can occur during production or operation of CFRP components and can compromise their structural integrity. Ultrasonic imaging is a well-established and cost-effective technique for non-destructive testing (NDT) of CFRP components. We evaluate the use of supervised training of artificial neural networks (ANNs) for the automated detection of delaminations from ultrasound B-scans. After hyperparameter optimization, convolutional neural networks (CNNs) performed better than multilayer perceptrons (MLPs). Using a test specimen, we achieved a recall of 100% and a precision of 94.3% on unseen test images.