Quantitative Evaluation Method for the Severity of Surface Fuzz Defects in Carbon Fiber Prepreg

Fuzz defects are prevalent surface imperfections in carbon fiber-reinforced polymer (CFRP) prepregs. Current manual inspection methods or conventional neural network-based approaches face significant limitations in achieving standardized and accurate severity assessment of such defects. In this article, a methodology comprising three key technical innovations is proposed: First, an adaptive thresholding algorithm is implemented, utilizing local average grayscale values to accurately identify fuzz defect pixels. Second, a grayscale histogram analysis is performed on the identified defect regions to quantify severity levels, effectively mitigating the influence of substrate material variations and illumination conditions on assessment accuracy. Third, a quantitative formula is defined based on the detection boxes drawn by the neural network object detection model and the effective area of defects to evaluate the severity of fuzz defects. Experimental validation shows 90% consistency with practical manual assessment in defect severity ranking tasks, proving its industrial applicability.