Defect detection in composites by deep learning using solitary waves

This paper proposes a real-time non-destructive evaluation technique to detect defects in laminated composites by deep learning using highly nonlinear solitary waves (HNSWs). HNSW data are collected by conducting ex-periments using a granular crystal sensor composed of a vertical array of steel beads directly contacting an AS4/ PEEK composite plate. Using HNSW data, a deep learning algorithm based on the convolution neural networks (CNN) is trained and tested for the identification of delamination in AS4/PEEK composites. The influence of the number of hidden layers and various CNN parameters is investigated for improved classification accuracy of the deep learning algorithm. A general curve fit is presented in order to facilitate the correct choice of the input pixel and batch size. Moreover, a multiple mode testing scheme, classifying defects using multiple HNSW signals, is introduced to improve the accuracy of the algorithm. The efficiency and accuracy of using three different types of the input signal (i.e., original (without pre-processing) and time-sliced/time-sliced noise-cutting signals (with pre-processing)) are examined for the real-time detection of defects. Mathematical formulations are established to obtain time-sliced and time-sliced noise-cutting signals from the original HNSW signals. It was found that accuracy could be improved by increasing both the number of hidden layers and the input pixel size, reducing the learning rate, and by using a batch normalization process and RELU activation function. For all three input signals, accuracy levels of over 90% were achieved in identifying the existence and location of delamination in AS4/PEEK composites, highlighting the possibility of using the proposed deep learning algorithm for the real-time detection of defects in laminated composites.