Domain adaptive relational graph convolutional network for monitoring transverse matrix cracks across composite structures

Abstract

Transverse matrix cracking is the predominant failure mode in the early stages of progressive degradation, making its accurate identification crucial for ensuring the safety of carbon fiber reinforced plastics (CFRP) composite structures. However, when models trained on guided wave sensor data from one structure are applied to predict crack density in another structure with a different ply orientation, conventional deep learning methods face significant challenges due to the sensitivity of this regression task to feature scale and ply orientation. To address these challenges, we propose a domain adaptive relational graph convolutional network (DA-RGCN) model specifically designed for crack density prediction, leveraging deep domain adaptation to transfer identification knowledge learned from one laminate to another with completely different ply orientation. First, crack damage-related features are automatically extracted from sensor signals by capturing their temporal relationships during guided wave propagation. These features, along with geometric information from sensor networks, are embedded as node features within a graph structure, allowing for the learning of enhanced feature representations through the fusion of information from neighboring nodes. Subsequently, the fused features are utilized to identify crack density along each path by measuring its spatial distance from two reference states (baseline and saturation). Additionally, we employ a representation subspace distance based on principal angles to minimize distribution discrepancies between features without altering their scales. As a result, combined with the physical guidance from the damage index model, the extracted features achieve domain invariance, significantly enhancing the cross-structural generalization of the DA-RGCN. To validate the model’s capability for cross-structural identification of transverse matrix cracks, we designed transfer tasks between two layups using the CFRP Composites Dataset. The results indicate that the proposed DA-RGCN achieves an average root mean square error of 1.1841 in crack density identification, demonstrating the lowest error compared to other deep transfer learning-based and purely physics-based methods.