Advancing structural health monitoring: Deep learning-enhanced quantitative analysis of damage in composite laminates using surface strain field

Abstract

Composite materials have been widely used as critical components in aerospace applications due to their excellent performance characteristics. The real-time accurate identification and quantification of various types of damage within composite material structures pose a significant challenge. This study introduces an innovative damage detection method based on strain fields, which centrally employs deep learning techniques. Utilizing the Res-Mask R–CNN, this study accurately detects and categorizes various forms of damage within composite laminates, including open holes, subsurface holes, and delamination. Moreover, this method also enables precise localization and quantification of damaged areas. A series of experiments and simulations have validated the accuracy and robustness of the network model. Damage inversion experiments demonstrate that the area error of the damaged regions has been reduced to 7.4 %, and the positional error does not exceed 3.31 mm. In simulated scenarios, the shape context distance for complex damage contours does not exceed 0.21, indicating that the critical geometric features of the damage have been successfully preserved. This study provides an effective new approach for damage detection and real-time structural health monitoring of composite laminates.