Micro-Computed Tomography Non-Destructive Testing and Defect Quantitative Analysis of Carbon Fiber-Reinforced Polymer, Glass Fiber-Reinforced Polymer and Carbon/Glass Hybrid Laminates Using Deep Learning Recognition

X-ray micro-computed tomography (Micro-CT) is an advanced technique capable of non-destructive detection of internal defects in materials. Fiber-reinforced polymer (FRP) laminates are prone to forming defects such as pores during the manufacturing process, which significantly affect their mechanical properties. In this study, Micro-CT technology was employed to conduct non-destructive testing on carbon fiber (CFRP), glass fiber (GFRP) and carbon/glass hybrid (C/G) laminates. Combined with the U-Net++ deep learning model, precise segmentation and three-dimensional reconstruction of pores were achieved. A systematic quantitative analysis was carried out on the distribution, size, volume and porosity of pores in six specimens with two layup angles (0/90 and ±45). The research results show that the pores in CFRP are mainly dispersed micro-pores and are relatively evenly distributed; the porosity of GFRP is the highest, and large interlaminar pores are prone to forming. The porosity fluctuates sharply in the thickness direction, revealing that the interlaminar interface is a defect-sensitive area. This provides a reliable quantitative basis and theoretical support for optimization and defect assessment.