Enhanced damage imaging of CFRP laminates via nonlinear ultrasonic guided deep learning

Abstract

Deep learning with improved interpretability and generalization capability has been employed to detect defects in carbon fiber reinforced polymers (CFRPs). However, these models have not yet been effectively utilized to enhance nonlinear ultrasonic techniques. To address this limitation, a nonlinear ultrasonic-guided deep learning (NLU-DL) approach is proposed, in which linear and NLU features are balanced through a weighting function. The feature weights and the original data are fed into the network, thereby forming a DL model with dual-channel feature fusion. The proposed NLU-DL is verified and validated using a 3D finite element (FE) model and a laser scanning Doppler vibrometry (LSDV)-based system, with the datasets being numerically and experimentally collected, enabling a high-efficiency “one-transmitter-multiple-receiver” scheme. For more effective feature extraction, a principal component analysis-integrated convolutional neural network (NLU-PCA-CNN) is designed for 1D time-series signals, while a squeeze-and-excitation module-integrated convolutional neural network (NLU-SE-CNN) is constructed for 2D time–frequency images. The results demonstrate that the NLU-DL significantly outperforms nonlinear ultrasonics in imaging precision, and the NLU-SE-CNN achieves higher imaging contrast and stability for single-, irregular-, and multi-delaminations. Furthermore, compared with purely data-driven models, the NLU-DL offers enhanced interpretability, detection accuracy, and generalization capability, thereby presenting a novel intelligent solution for damage imaging in composites.