📝 SummarXiv

Abstract

Evaluating impact-induced damage in composite materials under varying temperature conditions is essential for ensuring structural integrity and reliable performance in aerospace, polar, and other extreme-environment applications. As matrix brittleness increases at low temperatures, damage mechanisms shift: impact events that produce only minor delaminations at ambient conditions can trigger extensive matrix cracking, fiber/matrix debonding, or interfacial failure under severe cold loads, thereby degrading residual strength and fatigue life. Precision detection and quantification of subsurface damage features (e.g., delamination area, crack morphology, interface separation) are critical for subsequent mechanical characterization and life prediction. In this study, infrared thermography (IRT) coupled with a newly developed frequency multiplexed photothermal correlation tomography (FM-PCT) is employed to capture three-dimensional subsurface damage signatures with depth resolution approaching that of X-ray micro-computed tomography. However, the inherent limitations of IRT, including restricted frame rate and lateral thermal diffusion, reduce spatial resolution and thus the accuracy of damage size measurement. To address this, we develop a new transfer learning-based infrared super-resolution generative adversarial network (IR-SRGAN) that enhances both lateral and depth-resolved imaging fidelity based on limited thermographic datasets.