Influence of yarn count yarn twist and yarn

Influence of yarn count, yarn twist and yarn technology production on yarn hairiness Gabriela Krupincová Department of Textile Technology, Faculty of Textile Engineering, Technical University of Liberec Yarn hairiness is very important parameter of yarn which is related to other parameters of yarn (influence post-spinning operation) and textile (porosity, permeability, comfort, aesthetic properties and hand). The concept of hairiness as a quantitative yarn parameter was firstly mentioned about 1952. Since the time a lot of instruments and technique for yarn hairiness evaluation was developed [1]. Hairiness - characterizes the amount of free fibers (fiber loops) protrudes from the compact yarn body towards the outer yarn surface (fabric, knitting, etc. ). [1] Barella, A. : Yarn Hairiness, Textile Progress, Vo. 13. Nr. 1, The Textile Institute 1983.

Approaches to the yarn hairiness determination The first type of testing instruments: The Uster Tester , The Premier Tester 7000, Kaisokki Laserspot LST, New methodology The second type of testing instruments: The Zweigle hairiness tester, Criter Dum II, Shirley hairiness Meter, Hairiness Counter and Toray F index tester. [1] Barella, A. : Yarn Hairiness, Textile Progress, Vo. 13. Nr. 1, The Textile Institute 1983.

Yarn hairiness - Image Analysis IN‑ 22‑ 108‑ 01/01 Yarn hairiness sphere consists of: free ends of segment 1, the loops of segment 2, the reversal ends of segment 4 (neglect), and the reversal loops of segment 5 (neglect). Double exponential model of fibre distribution in the hairiness sphere: Thanks to Neckar's model can be obtain the information about two significantly different components of hairiness. The “dense” component of hairiness is formed from short fibre ends and the loops near the yarn body. The “loose” component of hairiness is composed from long flying fibres (“wild” fibres). The “dense” component influences the comfort properties of textile materials whereas the “loose” component creates troubles during post-spinning operations and the latter reduces the aesthetic property of textile materials. [3] Neckář, B. : Yarn Hairiness, Part 1: Theoretical model of yarn hairiness, 7. th National conference Strutex, Technical University of Liberec, Czech Republic 2000.

Experimental technique for yarn hairiness determination original 1 and innovative 2 OE yarn 72 tex am 85 ktex 2/3 m-1 1. yarn bobbin 2. disk tensioning device 3. yarn guide 4. microscope or macro-scope 5. camera 6. PC 4, 7 mm 1 pxl =def. 1, 5 mm 5, 5 pxl=def Resolution of images 548 pxl x 704 pxl TV camera 960 pxl x 1280 pxl Digital camera 12 mm 0, 7 pxl=def.

Factors influencing hairiness • various approach to yarn detection, various characteristics for yarn hairiness description, differ information about hairiness sphere, differ precision – connected by using instrument, • type of fibres (fineness, diameter, shape factor, length, flexural rigidity, torsion rigidity, tenacity, extension to break, friction, for wool – crimp, compression resistance), • yarn twist, yarn count, blending ratio (migration effect), • technology of production, • measurement condition (temperature, humidity, test speed). [3] Neckář, B. : Yarn Hairiness, Part 1: Theoretical model of yarn hairiness, 7. th National conference Strutex, Technical University of Liberec, Czech Republic 2000.

Experimental results – influence of yarn count and yarn twist • Resolution of images: 548 pxl x 704 pxl, calibration: 2, 24 mm = 1 pxl (microscope) • Experimental material: Classical ring yarns - five levels of yarn count (14, 5 tex, 19, 5 tex, 29, 5 tex, and 37 tex) and three levels of T. M. twist coefficient (3, 7 Ne 1/2 in-1, 4 Ne 1/2 in-1 and 4, 3 Ne 1/2 in-1) in two variants – combed and carded yarns. Open-end yarn - five levels of yarn count (14, 5 tex, 20 tex, 35, 5 tex, 50 tex and 72 tex) and three levels of Phrix twist coefficient (70 ktex 2/3 m-1, 85 ktex 2/3 m-1 and 100 ktex 2/3 m-1). Novaspin technology - five levels of yarn count (7, 4 tex, 10 tex, 12 tex, 16 tex and 20 tex) and three levels of Phrix twist coefficient (38 ktex 2/3 m-1, 56 ktex 2/3 m-1 and 81 ktex 2/3 m-1) combed yarns.

Experimental results – influence of yarn count and yarn twist

Experimental results – influence of yarn count and yarn twist

Experimental results – creation of prediction models The standard or powerful statistical methods allow the prediction model creation. This approach is limited in case of hairiness parameter prediction because of factor mutually connection (multicolinearity), factor limited range and proper selection of technological yarn creation parameters (interdependence yarn count, yarn twist). Linear regression model is a model which is formed by a linear combination of explanatory variables or their functions. Parameters can be estimate by minimization of measure between the vector dependent variable and the hyper-plane. (finding the minimal length of the residual vector).

Experimental results – prediction models of H Hairiness=b. T+c. Z+d, Hairiness=a. ZT+ b. T +c. Z+d, Hairiness=b. T+ d, Hairiness=c. Z+ d

Experimental results – prediction models of Ic dens Hairiness=b. T+c. Z+d, Hairiness=a. ZT+ b. T +c. Z+d, Hairiness=b. T+ d, Hairiness=c. Z+ d Similar results can be found for both component of hairiness I 1 dens, I 2 dens.

Possibility of prediction The double exponential model of hair distribution – Professor Neckář Uster statistic - example for 100% cotton yarn ring yarn - combed - count < 15 tex HU 50%= 16, 5993 (590/ T)-0, 38018 HU 5%= 5, 9177 (590/ T )-0, 18277 HU 95%= 35, 4762 (590/ T )-0, 52115 compact yarn - combed HU 50%= 19, 6786 (590/ T)-0, 50769 H -0, 44966 U 5%= 13, 846 (590/ T ) H -0, 53828 U 95%= 25, 5793 (590/ T ) open end yarn - carded HU 50%= 13, 9343 * (590/ T) -0, 33833 HU 5%= 7, 4797 * (590/ T )-0, 21373 HU 95%= 38, 4026 * (590/ T )-0, 57526 IH [mm] - integral characteristic of hairiness, Pi - probability that the light beam passes without problems at the distance x, Ci [mm] - parameter of internal yarn structure, d [mm] - fibre diameter, hi [mm] - half decrease interval of number of protruding fibers, mhi [-] - packing density of hairs, T [tex] - yarn count, HU [-] - hairiness index (Uster Statistic).

Summarization – influence of yarn technology production Classical: Various researches demonstrate that the effect of the number of draw frame passages influence hairiness significantly. The greater parallelization of fibres whit a consequent reduction in the number of hooks. The way of sliver and roving preparation in relation of yarn count and number of drawing passages for obtaining giving yarn count is very important. • Open end: Fibres are better controlled in the rotor as far as possible. For this reason, in this type of yarn, short ends predominate over long ends. Hairiness is influenced by the rotor diameter, its surface and its speed. u n t , y a r n t w i s t a n d t e c h n o l o g y o f p r o d u c t i o n a r e i m p o r t a n t • • Open end yarn has characteristic closed structure with belt fibres. These yarns have smallest hairiness. Disordered internal structure leads to the smallest strength. The ring yarn has more arranged structure with higher hairiness and maximal strength. The experimental yarns have similar internal structure as ring one. The main differences are in hairiness and looser arrangements in subsurface layers. Result is slightly lower strength.

Summarization – influence of yarn geometrical parameters The hairiness sphere consists from different type of fibre segments. The occurrence of short fibre end is not directly depend to the twist, but the loops and their arranging is due to higher twist move to the yarn surface. Thanks to this reason the hairiness decrease when the yarn twist increase. • During the twisting of the yarn some fibres are further displaced from their central position to the yarn surface (fibre migration effect). When the yarn count increase the diameter increase too because of higher number of fibres in yarn cross-section. This increase cause the higher probability of occurrence hairs out of yarn core and the hairiness increase as yarn unevenness. u n t , y a r n t w i s t a n d t e c h n o l o g y o f p r o d u c t i o n a r e i m p o r t a n t • • Yarns from the same material produced by different technologies have comparable geometrical characteristics. The main differences are in hairiness, strength and elongation at break.

Conclusion • The main factors influence hairiness are: type of fibres, yarn twist, yarn count, technology of yarn production, approach to yarn hairiness observation and measurement condition. • Yarn hairiness influences: porosity, permeability, transport of moisture, comfort, aesthetic properties and hand of textile. • The aim of our future work is study of another important factors which can influence yarn hairiness (steps of production textile materials – weft winding, warp sizing, yarn dyeing, fabric hairiness, . . . ). • Why? The reason is obtaining more deeply information about yarn structure and its changes during processing. Thanks to detailed knowledge it is possible predict to yarn behaviour and design fabric and textile products precisely in the way of customers demand. • The regression model can be used for estimation hairiness parameter successfully, but its using is limited. For prediction can be used Uster Statistic as well as the probability double exponential model of professor Neckář.