The identification and correction of manufacturing defects, particularly gaps and overlaps, are crucial for ensuring high-quality composite parts produced through Automated Fiber Placement (AFP). These imperfections are the most commonly observed issues that can significantly impact the overall quality of the composite parts. Manual inspection is both time-consuming and labor-intensive, making it an inefficient approach. To overcome this challenge, the implementation of an automated defect detection system serves as the optimal solution. In this paper, we introduce a novel method that uses an Optical Coherence Tomography (OCT) sensor and computer vision techniques to detect and locate gaps and overlaps in composite parts. Our approach involves generating a depth map image of the composite surface that highlights the elevation of composite tapes (or tows) on the surface. By detecting the boundaries of each tow, our algorithm can compare consecutive tows and identify gaps or overlaps that may exist between them. Any gaps or overlaps exceeding a predefined tolerance threshold are considered manufacturing defects. To evaluate the performance of our approach, we compare the detected defects with the ground truth annotated by experts. The results demonstrate a high level of accuracy and efficiency in gap and overlap segmentation.

翻译：制造缺陷的识别与修正，尤其是间隙与重叠，对于确保通过自动化纤维铺设（AFP）生产的高质量复合材料部件至关重要。这些瑕疵是最常见的问题，会显著影响复合材料部件的整体质量。人工检测既耗时又费力，是一种低效的方法。为解决这一挑战，实施自动化缺陷检测系统是最佳方案。本文介绍了一种新颖的方法，该方法使用光学相干断层扫描（OCT）传感器和计算机视觉技术来检测并定位复合材料部件中的间隙与重叠。我们的方法包括生成复合材料表面的深度图图像，该图像突出显示了表面上复合材料带（或称丝束）的高度差。通过检测每个丝束的边界，我们的算法能够比较相邻丝束，并识别它们之间可能存在的间隙或重叠。任何超过预设公差阈值的间隙或重叠均被视为制造缺陷。为评估我们方法的性能，我们将检测到的缺陷与专家标注的真实数据进行对比。结果表明，该方法在间隙与重叠分割方面具有高精度和高效率。