Gap and Overlap Detection in Automated Fiber Placement
- URL: http://arxiv.org/abs/2309.00206v1
- Date: Fri, 1 Sep 2023 01:48:21 GMT
- Title: Gap and Overlap Detection in Automated Fiber Placement
- Authors: Assef Ghamisi and Homayoun Najjaran
- Abstract summary: The identification and correction of manufacturing defects, particularly gaps and overlaps, are crucial for ensuring high-quality composite parts.
This paper introduces 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.
The results demonstrate a high level of accuracy and efficiency in gap and overlap segmentation.
- Score: 4.0466311968093365
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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