Mr. Samuel J. Vidourek Profile

Samuel J. Vidourek

at Rose-Hulman Institute of Technology

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Proceedings Article | 10 August 2021 Paper

Deep-learning-based machine vision system for defect detection of fiber interlock cable

Zhaowei Chen, Michael Holtz, Samuel Vidourek, Hossein Alisafaee

Proceedings Volume 11843, 1184317 (2021) https://doi.org/10.1117/12.2595076

KEYWORDS: Machine vision, Image processing, Defect detection, Imaging systems

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We have designed and built a machine vision system for the inspection and monitoring at the production line of fiber interlock armor cables. Fiber interlock armor cable is a highly reflective continuous cylindrical product, making the inspection task optically challenging. Our economical solution for the design approach has been to utilize a vision system based on a single camera and a tunnel source of uniform illumination in conjunction with two flat mirrors to obtain a 360-degree view of the product. The resolution of imaging system was determined based on the smallest features sizes expected on the cables, such that it allows the detection of defects and imperfections as well as geometrical measurements on the order of tens of microns. The measurement and imperfection methods utilize a deep learning algorithm to intelligently detect manufacturing defects in the cable in-line with the production. Our optical system can detect imperfections in real time during the manufacturing process and alert operators while marking the defective region on the cable, which reduces wasted product and ultimately cost on the production line. Our vision system is able to inspect a variety of interlock armor cables with different sizes and shapes, making it uniquely versatile. Our deep-learning system is 78.9% accurate with an initial training size of 10,000 samples. Our machine vision solution is highly replicable, maximizing the use of off-the-shelf parts for ease of replication to serve and operate on multiple manufacturing lines.

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