ABSTRACT An enhanced technique using image processing has been developed for automated ultrasonic inspection of composite materials, such as glass/carbon-fibre-reinforced polymer (GFRP or CFRP), to ascertain their structural healthiness. The proposed technique is capable of identifying the abnormality features buried in the composite by image filtering and segmentation applied to ultrasonic C-Scan images. This work presents results performed on two composite samples with simulated delamination defects. A local gating scheme is applied to raw A-Scan data for improved contrast between defective and healthy regions in the produced C-Scan image. In this test campaign, different filtering and thresholding algorithms are evaluated and compared in terms of their effectiveness on defect identification. The accuracies of less than 3 mm and 1.11 mm were attained for the defect size and depth, respectively. The results demonstrates the applicability of the proposed technique for accurate defect localization and characterization of composite materials.

中文翻译：

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使用混合超声成像技术对复合材料进行自动质量表征

摘要 已经开发了一种使用图像处理的增强技术，用于复合材料的自动超声波检测，例如玻璃/碳纤维增强聚合物（GFRP 或 CFRP），以确定它们的结构健康。所提出的技术能够通过应用于超声 C 扫描图像的图像过滤和分割来识别隐藏在复合材料中的异常特征。这项工作展示了在具有模拟分层缺陷的两个复合样品上进行的结果。局部选通方案应用于原始 A 扫描数据，以提高生成的 C 扫描图像中缺陷和健康区域之间的对比度。在此测试活动中，评估并比较了不同的过滤和阈值算法在缺陷识别方面的有效性。精度小于 3 mm 和 1。缺陷尺寸和深度分别达到 11 mm。结果证明了所提出的技术对于复合材料的精确缺陷定位和表征的适用性。