1. Key Laboratory of Grain Information Processing and Control, Henan University of Technology, Ministry of Education, Zhengzhou 450001, China
2. Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou 450001, China
3. School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
4. School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China
Abstract：Glass Fiber Reinforced Polymer (GFRP) composites, renowned for their lightweight, impact-resistant, and high-strength properties, have extensive applications in aerospace, automotive manufacturing, and architectural structures. However, the manufacturing process of these composites is often plagued by defects such as pores and cracks, which can severely compromise the material's mechanical strength, leading to product quality degradation and even structural failure, resulting in substantial economic losses for enterprises. This study employs advanced terahertz imaging technology to address the challenge of inspecting epoxy glass fiber composites with various defects. Initially, based on the propagation principle of terahertz waves in transmission mode, a thickness measurement method utilizing time delay difference was adopted to accurately detect and calculate defects at different depths, successfully controlling the error below 0.1 mm, achieving satisfactory detection results. Subsequently, for the quantitative detection of defects with varying sizes, the study converted the original color images of epoxy glass fiber defects into grayscale images, followed by binarization processing using four threshold segmentation methods. Finally, by region labeling, the pixel count of the defective area was calculated, and the defect size was determined by the ratio of defective pixels to total pixels. The results demonstrated that after selecting an appropriate threshold using the manual threshold segmentation method, the root mean square error between the detected area and the actual area could reach 1.368, indicating a close approximation between the detected and actual areas. This experiment confirms that the combination of terahertz imaging technology and image processing methods can quantify the location and size of defects, providing a significant reference for advancing defect detection technology in composite materials. The findings offer new methods and tools for defect detection and quality supervision of other composite materials, holding substantial reference value and enlightening significance, and contributing to enhancing composite product quality. The application of terahertz imaging technology in this study improves the accuracy and reliability of GFRP defect detection and provides a more effective quality supervision approach for the composite material industry. These efforts introduce new ideas and development directions for the future of composite material manufacturing and inspection, promising to drive scientific progress and technological innovation in the field, and exerting a positive impact on industry development.
张 元，周文慧，葛宏义，蒋玉英，郭春燕，王 恒，温茜茜，王雨欣. 基于太赫兹成像技术的GFRP复合材料缺陷检测研究[J]. 光谱学与光谱分析, 2025, 45(07): 1874-1881.
ZHANG Yuan, ZHOU Wen-hui, GE Hong-yi, JIANG Yu-ying, GUO Chun-yan, WANG Heng, WEN Xi-xi, WANG Yu-xin. Research on Defect Detection of GFRP Composites Based on Terahertz Imaging Technology. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(07): 1874-1881.
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