Design of Infrared Thermal Wave Image Defect Detection System Based on LabVIEW
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摘要: 碳纤维增强复合材料大量应用于航空领域，对其质量提出了更高的要求。但是传统人工检测方法工作强度高、效率低。为了提高碳纤维复合材料的缺陷检测效率，本文基于LabVIEW软件开发平台设计CFRP（carbon fiber reinforced plastics）缺陷检测系统，提取缺陷边缘并进行数量统计。本研究采用主动式红外热成像无损检测技术，通过红外热像仪获取激光扫描的损伤试样表面热图像。针对红外图像对比度及均匀性差的特点，使用HSL（hue, saturation, luminance）进行颜色平面提取，灰度变换，选用适用于处理光照分布不均匀图像的Niback局部阈值分割处理算法进行感兴趣区域图像阈值分割处理。最后通过形态学处理增强图像并实现缺陷特征提取和缺陷数量统计。本文通过搭建红外热成像缺陷检测实验平台完成红外热波缺陷图像的采集、处理，设计软件平台及用户界面以实现缺陷特征的提取。相比于人工检测，该系统的设计极大地减少了检测用时，有助于实现缺陷检测的自动化。Abstract: Carbon fiber reinforced composites are widely used in aviation field, which requires higher quality. But the traditional manual detection method has high work intensity and low efficiency. In order to improve the defect detection efficiency of carbon fiber reinforced plastics (CFRP), this study designed a defect detection system based on LabVIEW software development platform, extracted defect edges and performed quantitative statistics. In this study, active infrared thermal imaging non-destructive testing technology is used to obtain the surface thermal images of damaged samples scanned by laser through infrared thermal imager. In view of the poor contrast and uniformity of infrared images, HSL(Hue, Saturation, Luminance) is used to carry out color plane extraction and gray transform, and Niback local threshold segmentation algorithm suitable for processing images with uneven illumination distribution is selected to carry out threshold segmentation processing of image of the region of interest. Finally, morphological processing is used to enhance the image and realize defect feature extraction and defect number statistics. In this study, an infrared thermal imaging defect detection experiment platform is built to complete the acquisition and processing of infrared thermal wave defect images, and the software platform and user interface are designed to realize the extraction of the defect features. Compared with manual detection, the design of this system significantly reduces the detection time and is helpful in realizing the automation of defect detection.
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Key words:
- LabVIEW /
- CFRP /
- infrared thermal imaging /
- defect detection
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图 2 红外热成像缺陷检测系统组成示意图
Figure 2. Schematic diagram of infrared thermal imaging defect detection system
图 4 直径为3 mm的圆形平底孔缺陷热序列
Figure 4. Defect heat sequence of circular flat bottom hole with diameter of 3 mm
图 5 圆形及矩形平底孔缺陷的平面热图像
Figure 5. Plane thermal images of circular and rectangular flat bottom hole defects
图 11 形态学处理增强子系统用户界面
Figure 11. User interface of morphological processing enhancement subsystem
图 12 0°矩形平底孔缺陷形态学增强过程：(a) 二值图；(b) 自动中值；(c) 去除边界；(d) 过滤颗粒；(e) 孔洞填充；(f) 标记颗粒；(g) 缺陷可视化
Figure 12. Morphological enhancement process of 0° rectangular flat bottom hole: (a) Binary graph; (b) Automatic median; (c) Boundary removal; (d) Filter particle; (e) Hole filling; (e) Label particle; (f) Defect visualization
图 14 缺陷边界提取结果：(a) 3mm圆形；(b) 5 mm圆形；(c) 0°矩形；(d) 90°矩形
Figure 14. Defect boundary extraction results: (a) 3 mm circle; (b) 5 mm circle; (c) 0° rectangle; (d) 90° rectangle
表 1 圆形缺陷几何尺寸
Table 1. Circular defect geometry
No. Diameter D/mm Depth H/mm #1 3 0.5 #2 3 1.0 #3 3 1.5 #4 3 2.0 #5 5 0.5 #6 5 1.0 #7 5 1.5 #8 5 2.0 
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表 2 矩形缺陷几何尺寸及方位
Table 2. Rectangular defect geometry and orientation
No. Length L/mm Width W/mm Depth H/mm 长轴
方向#9 10 1 0.5 0° #10 10 1 1.0 0° #11 10 1 1.5 0° #12 10 1 2.0 0° #13 10 1 0.5 90° #14 10 1 1.0 90° #15 10 1 1.5 90° #16 10 1 2.0 90°
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