Defect detection in composites by deep learning using solitary waves

•Developed the CNN-based deep learning algorithm for a real-time detection of the existence and location of delamination in laminated composites using HNSW signals generated from a granular crystal sensor in a non-destructive manner.•Investigated the influences of the hidden layer and other CNN parameters such as learning rate, activation function, dropout, input image pixel size, batch size, and filter size to improve the accuracy of the deep learning algorithm. Furthermore, a general fitting curve (see Eq. (5)) that can be used for the optimal choices of the input pixel and batch sizes was developed.•Investigated the efficiency and accuracy of three different types of the input signals, i.e., original (raw) without pre-processing and two pre-processed signals (i.e., time-sliced and time-sliced noise-cutting signals), for real-time detection of detects using HNSWs. Moreover, we provided mathematical formulations to obtain time-sliced signals in Eq. (1) and time-sliced noise-cutting signals in Eq. (2) from pre-processing of the original HNSW signals.•Developed a multiple mode testing scheme, classifying defects using multiple HNSW signals instead of using a single HNSW signal, to improve the classification accuracy of the deep learning algorithm.