曲面结构石英纤维增强树脂复合材料分层损伤缺陷太赫兹智能检测

Intelligent detection of delamination defect in curved structural quartz fiber reinforced polymer composites using terahertz technology

为探索先进预警机雷达罩石英纤维增强树脂复合材料中典型分层缺陷的智能化检测手段,将协作机器人与反射式太赫兹时域光谱系统相结合,搭建了一种基于光纤耦合的反射式太赫兹时域光谱扫查系统,测试所得信噪比的动态范围约60 dB。利用搭建系统对预埋模拟分层缺陷的曲面结构石英纤维增强树脂复合材料样件进行太赫兹无损检测,不同扫描区域获得的反射式太赫兹成像图中均能通过目视辨认出内部预埋缺陷。利用改进的YOLOv4算法在缺陷自动识别中获得90.18%的准确率和91.26%召回率,分别较原YOLOv4算法提高3.37%和4.01%,对小目标缺陷的检测效果良好。实验丰富了预警机雷达罩样件的设计和检测研究内容,探索了曲面结构石英纤维增强树脂复合材料缺陷太赫兹成像,为预警机雷达罩的无损检测提供了新的智能检测工艺,具备工程应用价值。

In order to explore an intelligent means of detecting delamination defects in airborne warning and control system radomes made of quartz fiber reinforced polymers(QFRP), a reflective terahertz time-domain spectroscopy system based on fiber coupling was built by combining a cooperative robot. The signal noise ratio dynamic range is about 60 dB after testing. Terahertz nondestructive testing of curved structural QFRP sample with pre-buried simulated delamination defects was performed using the build system. The internal pre-buried defects were visually identified in the reflected terahertz images obtained from different scanning areas. An improved YOLOv4algorithm was used to obtain 90.18% accuracy and 91.26% recall in automatic defect identification, which were3.37% and 4.01% higher than the original YOLOv4 algorithm, respectively. The small target defects can also be recognized well. This experiment enriches the design and detection of airborne warning and control system radome sample, explores the detection of curved structural QFRP sample using terahertz imaging method and provides a new intelligent nondestructive testing method of airborne warning and control system radomes detection, which has the value of engineering application.