Experimental Techniques to Obtain the Cross-Sectional Images of Textile Yarns.
Mohamed AbdelkaderAdnan MazariSumayya ZafarPublished in: Materials (Basel, Switzerland) (2022)
In the fabric industry, textile yarns are the fundamental building blocks. Hence, visualizing and studying yarn structure is essential to understand the structure and behavior of the fibers. Obtaining the yarn's cross-section images is crucial in the calculations of yarn's porosity; furthermore, a more precise expansion for the fiber's migration can be concluded from the cross-sectional images. In this paper, three different methods (microtome, micro-computed tomography, and epoxy grinding-polishing methods) to image and visualize the yarn's cross-section are presented. The experimental techniques are compared in terms of result useability, time of preparation, and overall outcome of the cross-sectional image. The images can be used for fiber distribution, air gap calculation, and twist analysis as well. The fiber diameter distribution of polyester yarn was measured based on the images obtained by the three different methods; the average fiber diameter measured based on the combined data from the three different methods was found to be 10.90 ± 0.30 µm.
Keyphrases
- deep learning
- cross sectional
- convolutional neural network
- optical coherence tomography
- computed tomography
- artificial intelligence
- machine learning
- wastewater treatment
- magnetic resonance imaging
- optic nerve
- big data
- positron emission tomography
- molecular dynamics simulations
- magnetic resonance
- high resolution
- electronic health record
- image quality
- living cells
- simultaneous determination
- tandem mass spectrometry