Gap and Overlap Detection in Automated Fiber Placement
This addresses the need for automated defect detection in composite manufacturing to replace manual inspection, though it appears incremental as it applies known techniques to a specific domain.
The paper tackles the problem of detecting gaps and overlaps in composite parts made via Automated Fiber Placement by introducing a method using Optical Coherence Tomography and computer vision, achieving high accuracy and efficiency in segmentation as validated against expert annotations.
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.