from the conferences organized by TANGER Ltd.
Textile yarn is a group of twisted fibers with diameters of a few micrometers, requiring a nano-resolution scanner to capture precise details of the single fibers perfectly to make a digital twine of the scanned yarn. Computed tomography (CT) technology can 3D digitally scan the sample and achieve a digital twin. The fine fibers' diameter requires a nano-CT to achieve a high-resolution yarn's digital twin; nano-CTs are more expensive than micro-CTs and require about eight times the scanning time compared to micro-CTs, which means more computational power to reconstruct and analyze the scanned objects. This paper introduces a systematic MATLAB algorithm to regenerate distorted yarn's micro-CT low-resolution cross-section images. The algorithm segments the distorted images' fibers, identifies them, and regenerates the clean, high-resolution fibers. The algorithm performance is compared to the optical microscopic cross-section image measurements using ImageJ. The results revealed that before processing, the mean fiber diameter measured 9.60 ± 0.78 µm, while post-processing it measured 10.33 ± 0.49 µm. Notably, the algorithm effectively decreased the dispersion of fiber diameters around the mean by 40%, maintaining a diameter close to the design diameter of the fibers of 10 µm.
Keywords: Yarn, fibers, micro-computed tomography, cross-section images, MATLAB, algorithms, ImageJ.© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.