Composite laminate damage identification via Gabor-based anisotropic filter enhanced attention ConvLSTM network

Composite materials are widely used due to their excellent properties. Accurately recognising damage to composite structures is of great significance for ensuring structural safety and extending service life. This study proposes a damage identification method that fuses deep learning with multiscale and multidirectional image phases. The Gabor filter bank, which takes into account the anisotropic properties of composite materials, is used to generate phase maps of composite structure images at different scales and directions. Anisotropic diffusion models are constructed for composite structure images and used as input to the network. A Gabor-based anisotropic filter enhanced attention convolutional long short-term memory (ConvLSTM) network is designed to detect and categorise damage states in composite structures. Cracking and delamination damage were considered in the finite element analysis used to produce the dataset for training the network model. A damage inversion experiment shows that the length and area errors do not exceed 4.5%. The detection results were compared and validated against those of the mainstream convolutional neural network, LSTM and ConvLSTM networks. Among these networks, the proposed algorithm achieves the best recognition results.