Various loading conditions—cyclic, quasi-static, and dynamic—can induce transverse matrix cracks in cross-ply and woven composite structures. Identification and quantification of this damage on a composite’s surface can provide valuable information on the overall damage state of the structure. This work seeks to develop automated methods for identifying and quantifying transverse matrix crack damage on the surface of composites. To this end, model plain weave glass–epoxy composite specimens were developed that were consistent in geometry and manufacturing process and for which the loading conditions and resulting damage quantity and damage mode could be controlled. High-resolution images (80 megapixel) were captured of the model composite specimen surfaces. These images were then subjected to a manual transverse crack identification method, which established a control with known quantity and spatial location of transverse cracks. Two automated methods were developed to identify and quantify transverse cracks. The first used 8-bit (256 shades of gray) images, an ImageJ preprocessing step, and finally used MATLAB to identify the damage. The second used 16-bit (65,536 shades of gray) images processed directly by MATLAB (no ImageJ preprocessing) to identify the damage. It was found that the 8-bit method more accurately assessed the quantity of transverse cracks because the preprocessing step reduced error-causing high-contrast artifacts (e.g., reflections, composite material inconsistencies, dirt, and ink/marks). Finally, binned scatterplot maps indicating damage quantity and spatial location were created to provide at-a-glance assessment of composite damage condition.

各种加载条件（循环，准静态和动态）会在交叉层和编织复合结构中引起横向基体裂纹。识别和量化复合材料表面的这种损伤可以提供有关结构整体损伤状态的有价值的信息。这项工作旨在开发用于识别和量化复合材料表面横向基体裂纹损伤的自动化方法。为此，开发了在几何形状和制造过程上一致的模型平纹玻璃-环氧树脂复合材料试样，并可以控制其加载条件以及所造成的损害数量和损害方式。捕获了模型复合材料样本表面的高分辨率图像（80兆像素）。然后对这些图像进行手动横向裂纹识别方法，它建立了一个具有已知数量和空间位置的横向裂纹的控件。开发了两种自动方法来识别和量化横向裂纹。首先使用8位（256个灰度阴影）图像，执行ImageJ预处理步骤，最后使用MATLAB识别损坏。第二个使用由MATLAB直接处理的16位（65,536阴影灰度）图像（无ImageJ预处理）来识别损坏。发现8位方法可以更准确地评估横向裂纹的数量，因为预处理步骤减少了引起错误的高对比度伪影（例如，反射，复合材料不一致，污垢和墨水/标记）。最后，创建了指示损坏数量和空间位置的装箱散点图，以提供对复合损坏情况的快速评估。