An improved convolutional neural network approach for damage localization in composite materials based on modal analysis

Abstract

Abstract Damage detection of composite materials using modal parameters has limitations in terms of sensitivity to small or localized damage and limited accuracy in damage localization. To address this issue, an enhanced channel attention residual network (ECARNet) damage detection model for composite laminates is proposed. First, finite element analysis is used to obtain training samples, which are processed as two-dimensional data to take full advantage of the convolutional neural network. Then, the residual module uses a multilayer perceptron instead of the traditional convolutional layers to learn the correlation between channels to enhance the generalization ability of the model, and uses the tanh activation function to retain negative information. Finally, a channel focus mechanism is introduced to enable the network to learn key features adaptively. Experimental results on two datasets with different levels of damage demonstrate the superior detection performance of ECARNet, achieving average detection accuracies of 98.13% and 97.94% respectively. A comparison with other methods verifies the effectiveness and reliability of the proposed approach. Furthermore, the effectiveness of the new method is validated on real-world test data.