Yarn speed and length measurement using optical method in real time

Abstract. Yarn speed and length are two important parameters in the winding process. A noncontact method is proposed to measure the speed and length of the microtension moving yarn automatically in the winding process. A moving yarn could vibrate sharply in the microtension range. With a line laser illuminating the moving yarn, a linear charge-coupled diode camera was used to capture the images. Based on different laser reflection of the yarn texture, we calculated the speed and length of the moving yarn by extracting different features of yarn texture. Yarns with different materials were used to prove the validity of the proposed method under different winding speed. Experiment works have been performed and compared with a direct contact sensor, and the results proved that the proposed method is effective.

[1]  Bugao Xu,et al.  Fusing multifocus images for yarn hairiness measurement , 2014 .

[2]  Abdelfattah M. Seyam,et al.  Wireless yarn tension measurement, and control in direct cabling process , 2009 .

[3]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[4]  Jie Zhang,et al.  Automatic inspection of density in yarn-dyed fabrics by utilizing fabric light transmittance and Fourier analysis. , 2015, Applied optics.

[5]  Yoshida Kazunori,et al.  Yarn Velocity Measurement Using Optical Correlation Method , 1997 .

[6]  Qing Wang,et al.  Computer vision for yarn microtension measurement. , 2016, Applied optics.

[7]  Hua Lu,et al.  Research on precision tension control system based on neural network , 2004, IEEE Transactions on Industrial Electronics.

[8]  Amelia Carolina Sparavigna,et al.  Beyond capacitive systems with optical measurements for yarn evenness evaluation , 2004 .

[9]  Tao Hua,et al.  A continuous measurement system for yarn structures by an optical method , 2010 .

[10]  E.S. Storozhuk,et al.  Non-contact length and speed measurement , 2008, 2008 International Conference - Modern Technique and Technologies.

[11]  J J Yao,et al.  Using the Monte Carlo approach to study effects of power measurement uncertainties on six-port reflectometer performance , 2010 .

[12]  Lei Wang,et al.  Three-dimensional measurement of yarn hairiness via multiperspective images , 2018 .

[13]  Anirban Guha,et al.  Measurement of yarn hairiness by digital image processing , 2010 .

[14]  Wei Pan,et al.  Yarn break detection using an optical method in real time , 2017 .

[15]  Mats Jackson,et al.  Computer vision for textured yarn interlace (Nip) measurements at high speeds , 2001 .

[16]  Zhang Kang,et al.  Evaluation method for yarn diameter unevenness based on image sequence processing , 2015 .

[17]  Vítor H. Carvalho,et al.  Yarn Hairiness Characterization Using Two Orthogonal Directions , 2009, IEEE Transactions on Instrumentation and Measurement.

[18]  P. G. Shlyakhtenko,et al.  A diffraction method of monitoring the angular distribution of the fibers in the structure of a flat fibrous material , 2012 .

[19]  R.M. Vasconcelos,et al.  Automatic Yarn Characterization System: Design of a Prototype , 2009, IEEE Sensors Journal.