Abstract
Fiber-reinforced polymer (FRP) composites are increasingly popular due to their superior strength to weight ratio. In contrast to significant recent advances in automating the FRP manufacturing process via 3D printing, quality inspection and defect detection remain largely manual and inefficient. In this paper, we propose a new approach to automatically detect, from microscope images, one of the major defects in 3D printed FRP parts: fiber-deficient areas (or equivalently, resin-rich areas). From cross-sectional microscope images, we detect the locations and sizes of fibers, construct their Voronoi diagram, and employ α-shape theory to determine fiber-deficient areas. Our Voronoi diagram and α-shape construction algorithms are specialized to exploit typical characteristics of 3D printed FRP parts, giving significant efficiency gains. Our algorithms robustly handle real-world inputs containing hundreds of thousands of fiber cross-sections, whether in general or non-general position.
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Acknowledgements
We thank Arevo Inc. and Danning Zhang for valuable discussions and providing the cross-sectional microscope images for testing, Sara Shonkwiler for feedback on the manuscript, the Voronoi Diagram Research Center at Hanyang University for their freely available Voronoi visualization software BetaConcept [29], and the anonymous reviewers for helpful suggestions for improving the manuscript.
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Xiang Li is currently a Ph.D. candidate in the University of California, Berkeley, where he received his master degree. He has his bachelor degrees from both Shanghai Jiao Tong University and the University of Cincinnati. His research interests include computer aided design, computer vision, and computational materials science.
Sara McMains is a professor of mechanical engineering at the University of California, Berkeley. She received her B.A. degree in computer science from Harvard and her M.S. and Ph.D. degrees in computer science both from the University of California, Berkeley. Her research interests include computational geometry, computer graphics and visualization, geometric and solid modeling, CAD/CAM, geometric design for manufacturing feedback, geometric dimensioning and tolerancing, additive manufacturing, computer vision, and machine learning.
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Li, X., McMains, S. A Voronoi diagram approach for detecting defects in 3D printed fiber-reinforced polymers from microscope images. Comp. Visual Media 9, 41–56 (2023). https://doi.org/10.1007/s41095-021-0265-1
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DOI: https://doi.org/10.1007/s41095-021-0265-1