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Experimental results show that the proposed two algorithms can significantly reduce the information loss of yarn image and good robustness could be achieved.
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A custom-made application developed in LabVIEW from National Instruments with the IMAQ Vision Toolkit was used to acquire, analyze and process the yarn images.
j SEM image of TiO2@MWNT yarn.
The length and number of yarn hairiness detected by the image methods are highly correlated with those of the visual inspection method, and the CV of yarn evenness is also consistent with the traditional method.
Open image in new window Figure 4 Yarn cross-section images (a) reference yarn (b) ALPE yarn (c) ACPE yarn.
Make it some clothes from scraps of fabric, sequins, yarn, ribbon etc. See the image above for ideas on dressing your pipe cleaner friend.
h, i SEM images of Si3N4NT@MWNT biscrolled yarn.
A 2D Fast Fourier Transform is applied to local segments of the eddy current image to determine the local yarn orientation.
Open image in new window Figure 3 SEM images (a) reference yarn (b) ALPE yarn (c) ACPE yarn.
Use a small hole punch to make periodic holes in the image, so that if you string yarn around the edge it will follow the shape of the cartoon character or other image.
Open image in new window Figure 8 Effect of effect-yarn overfeed on core yarn tension (T 1 ) and textured yarn tension (T 2 ). Figure 9 illustrates that there is a slight decrease in loop instability with increasing effect-yarn overfeeds in both air-jet and steam-jet texturing.
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