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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