Damage detection and classification of carbon fiber-reinforced polymer composite materials based on acoustic emission and convolutional recurrent neural network

Abstract

Carbon fiber-reinforced polymer (CFRP) composite laminates generate acoustic emission signals during the tensile process, which can be used to identify the type of acoustic emission (AE) signal at a given moment and determine the current damage condition. However, due to the large number and complexity of AE signals generated during the tensile process of carbon fiber composite laminates, it is challenging to manually identify and annotate them. To address this issue, we propose a low-complexity classification and detection network model based on the convolutional recurrent neural network (CRNN) architecture. First, we construct a dataset of composite damage signals for a single damage category and use log-mel spectrogram features to train the model for that specific category of damage signals. Then, we utilize the trained model to recognize the AE signals of CFRP composite laminates. The proposed method achieves an accuracy of 99.02% in classifying single damage modes in the test set and effectively distinguishes the types of AE signals generated during the damage process of CFRP composite laminates. Compared with the classic classification model using AE signals, the number of parameters in our proposed low-complexity CRNN model is reduced by 94.42%. It also demonstrates that 19.4% of the AE signals during the damage process are in a superimposed state, indicating the simultaneous occurrence of multiple damage modes in CFRP composite laminates.