Infrared-based damage detection in thick composites via transfer learning on simulated and experimental data

Abstract

Reliable detection of subsurface defects in thick composite materials is critical for ensuring structural integrity in industrial applications such as wind turbine blades, aerospace components, and marine structures. This paper addresses dataset scarcity in AI-aided damage detection for thick composites using infrared thermography through a transfer learning framework leveraging finite element simulation data. Experimental datasets were obtained by conducting step-heating thermography experiments on glass-fiber-reinforced polymer (GFRP) and epoxy resin plates with artificial subsurface defects. Transient thermal analyses were performed on finite element models to mimic the actual step-heating thermography process, resulting in a large simulated dataset containing thermal videos representing the plate's surface thermal behavior during the heating-cooling process. Principal component thermography was used to extract features from both simulated and experimental thermal videos, compressing damage-related information in the raw data and enhancing the most informative features. Noise analysis on the experimental data revealed key differences compared to the simulated dataset. A U-Net architecture for image segmentation was implemented within the transfer learning framework, first pre-trained on simulated data and then fine-tuned with experimental data. The results revealed fundamental features shared across domains and demonstrated improved damage detectability in thick composite plates, especially for defects deeper than 15mm. This approach demonstrates the potential of transfer learning to improve damage detection in industrial applications involving thick composite structures, such as wind turbine blades.