Gap and Overlap Detection in Automated Fiber Placement
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Defect DetectionSimilar Papers 제목 키워드 기반
End-to-End Defect Detection in Automated Fiber Placement Based on Artificially Generated Data
Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and fast inline control methods of such parts…
Defect DetectionImage SegmentationSegmentationSemantic SegmentationFully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data
Anatomic tracer studies are critical for validating and improving diffusion MRI (dMRI) tractography. However, large-scale analysis of data from such studies is hampered by the labor-intensive process of annotating fiber …
Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data
Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detection of fiber bundles on tracing data is ch…
AnatomyDiffusion MRIMultipeak Wavelength Detection of Spectrally Overlapped Fiber Bragg Grating Sensors Through a CNN-Based Autoencoder
This article presents a machine learning solution to identify the peak wavelengths of fiber Bragg grating (FBG) sensors multiplexed in a network with high spectral overlapping. Machine learning solutions generally requir…
Segmentation and Characterization of Macerated Fibers and Vessels Using Deep Learning
Wood comprises different cell types, such as fibers, tracheids and vessels, defining its properties. Studying cells' shape, size, and arrangement in microscopy images is crucial for understanding wood characteristics. Ty…
Cell DetectionSegmentation