Computer-aided textile design LibTex

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1 Indian Journal of Fibre & Textile Research Vol. 33, ecember 2008, pp Computer-aided textile design LibTex ana Křemenáková a, Iva Mertová & Brigita Kolčavová-Sirková epartment of Textile Materials, Textile Faculty, Technical University of Liberec, Liberec, Czech Republic Received 26 June 2008; accepted 18 August 2008 The system LibTex has been used for the prediction of structure, parameters and properties in the line fibre yarn fabric. The system contains databases of fibre properties & fabric weaves, and the prediction is based on the complex of theoretical and regression models. The material and technological parameters for different materials, yarns and fabrics are included. The main use of this system is for optimal fabric design based on virtually created fabric. System can be used for the prediction of grey cotton dobby fabric properties for technical and clothing applications. Keywords: Computer-aided design, Cotton, Fabric, Fibre, LibTex, Yarn IPC Code : Int. Cl. 8 G06F17/00 1 Introduction Majority of known CA systems for fabric design, such as EAT GbmH (esignscope Comp. Germany) 1 and ALC Computertechnik GmbH (Germany) 2, are used for the 2 and 3 visualization of yarns and fabrics appearance. System WiseTex (Lomov, Katholic University Leuven Belgium) 3 is oriented to the design of multi-layered composites. Computer system Fibre Rope Modeller 4 is oriented to the design of high-strength mooring ropes. One of the more complex systems is TexEng 4 composed from following programs: Yarn Modeller, Weave GeoModeller, Weave Mechanics, Knit GeoModeller, Weave Engineer. There exist specialized programs for the solution of partial problems of textile mechanics or for the simulation of textile materials response to the external fields. One example is the program for drape simulation developed by Govindraj. 5 The main shortcoming of these programs is no connection with raw material properties and characteristics of production technology (fibre yarn fabric). System LibTex, used in this study, is based on the stepwise computation of properties of raw materials, intermediates and fabrics according to the following steps: (i) selection of fibre type, fineness, length, strength, and deformation at break from database, a To whom all the correspondence should be addressed. dana.kremenakova@tul.cz (ii) selection of yarn fineness, twist & production technology, and calculation of packing density, diameter, hairiness, strength, deformation at break, bending rigidity, etc. (Fig. 1), (iii) selection of fabric weave, warp set & weft set or density, and use of yarn properties for calculation of fabric binding point geometry (shortening, yarn length in weave repeat, and waviness) and other geometrical, mechanical & end-use properties (Fig. 2). 2 Fibre Module Fibre module contains databases of fibre properties, i.e. fineness, volume density, length, (HVI parameters for cotton fibres), strength, deformation at break, initial modulus, friction coefficient and moisture regain. Regression relations especially for Fig. 1 Prediction of yarn properties

2 KREMENAKOVA et al.: COMPUTER-AIE TEXTILE ESIGN LIBTEX 401 calculation of cotton fibres bundle strength measured on HVI or Pressley, and single fibre strength measured on vibroscope are also included. For evaluation of fibre quality regression relations based on Uster Statistics are used. For fibre blends the mean values of properties according to mixing ratio and properties of individual components are calculated. 3 Yarn Module Yarn module is the core of the system LibTex. Based on the type of fibres, yarn fineness, twist and technology, the yarn packing density, diameter, hairiness, breaking strength, deformation at break and bending rigidity are predicted (Fig. 1). Input parameters are shown on the top and calculated parameters are at bottom. It is possible to predict yarn properties for compact combed and carded technology, ring compact and carded technology, rotor technology and new technology Novaspin combed and carded. 6,7 Spinning system Novaspin was created in the Cotton Research Institute, Czech Republic. Properties of this yarn are comparable with yarns produced by classical ring spinning machines but production rates are approximately two times higher. The basic step of yarn properties prediction is the calculation of packing density (µ) using the following relationship: µ µ m µ 1 µ m 3 Fig. 2 Prediction of fabric properties M = 2µ 5 2 m π ZT ρ (1) Fig. 3 Radial yarn packing density (95% confidence interval) and yarn diameter [rotor yarn, 100% cotton 16.5 tex] where T is the yarn fineness; Z, the yarn twist; ρ, the fibre density; M, the material and technology characteristic; and µ m, the limit packing density. This relationship was derived by Neckar. 8 A suitable value of characteristic M for ring and rotor cotton yarns was observed in the study by Neckar 8 and for compact and Novaspin yarns in study by Kremenakova. 9 Yarn packing density can be evaluated from yarn cross-sections by using image analysis. irect method working with real fibre cross-section areas and the Secant method based on reconstruction of fibre sections around their centers can be used Result from both methods is the radial course of packing density trace from yarn axis to yarn surface (Fig.3). For prediction of other yarn properties, it is useful to replace radial packing density by constant value based on the following well-known relationship for the calculation of yarn diameter () : ( πµρ ) = 4T / (2) Yarn diameter separates yarn core from yarn hairiness. For prediction of packing density which is in correlation with yarn strength, we must define diameter of yarn core and packing density of yarn core. In this experiment, we used yarn diameter on radial packing density of 0.15 (Fig. 3). The yarn diameter can be directly measured on optical sensor of Uster Tester 4 (ref. 11). The differences between predicted and experimental data from Uster Statistics (2007) are shown in Figs 4 and 5. In these graphs, the influence of yarn technology and fineness (squeezing with optimal yarn twist) on packing density and yarn

3 402 INIAN J. FIBRE TEXT. RES., ECEMBER 2008 Fig. 4 Yarn packing density (a) predicted by Eq. (1) and (b) based on 50% level of Uster Statistics (2007) Uster Statistics (2007) and Fig. 4b shows the packing density calculated from Uster Statistic diameter according to Eq. 2. Uster Statistics values were selected on the 50% quality level of yarn for weaving (from bobbins). The trends are the same but lower yarn diameter and higher packing density results for the predictions based on Eq. (1) and higher yarn diameter and lower packing density are for the measurement on optical sensor of Uster Tester 4. These differences are connected with various definition of diameter from radial packing density trace, as is shown in Fig. 3. This radial packing density trace was measured for 100% cotton rotor yarn with fineness of 16.5 tex. The highest packing density and the lowest yarn diameter correspond to the compact combed yarns. uring compact spinning the yarn triangle is eliminated and better fibre arrangement occurs. Lower packing density results for the ring combed, compact carded, Novaspin carded and Novaspin combed yarns. Yarn diameter for these yarns is higher. The lowest packing density and the highest yarn diameter are observed for rotor yarns due to poor fibre arrangement. The packing density can be used for the prediction of yarn strength. It is advantageous that the packing density reflects influence of spinning technology on strength prediction. Technology and material parameter M contain influence of other variables, such as fibre shape, friction properties, etc. For the prediction of cotton yarns strength σ c, the modified relationship (proposed earlier 9,12 ) is shown below: σ c = σ bφby = σ HVI µη β (3) where σ HVI is the fibrous bundle strength measured on HVI; and φ by, the factor of bundle strength utilization in the yarn. Orientation factor η β is computed from the equation derived earlier 12, as shown below: Fig. 5 Yarn diameter (a) predicted by Eqs (1) & (2), and (b) on 50% level of Uster Statistics (2007) diameter is clearly visible. Figure 4a shows the predicted cotton yarn packing density computed from the Eq. (1) and Fig. 5a shows the predicted cotton yarn diameter computed from Eq. (2). Fig. 5b shows the cotton yarn diameter from ( 1 η) + ( 1 η) 2 β + η β 4β = (4) sin 2β Orientation factor η β is the function of helix angle β and yarn Poisson ratio η (ref. 12), as shown 2 β ( 1 η) + ( 1+ η) sin 2β below: η β = (5) 4β

4 KREMENAKOVA et al.: COMPUTER-AIE TEXTILE ESIGN LIBTEX 403 Poisson ratio η can be computed from the following relationship 12 : η = ( cos β ) β sin 2β sin 5 2 β For helix angle it is valid, as shown below: 4 (6) tgβ = πz (7) Fig. 6a shows the factor of bundle strength utilization in the yarn φ by, computed from Eq. (3). Fig.6b shows the 50% level of yarn strength from Uster Statistics. The same trends from point of view of yarn technology and fineness are visible. The highest strength is for compact yarn and the lowest strength is for rotor yarn. Properties of blended yarns and two ply yarns are included in system LibTex. Blended yarn properties are calculated from mixing ratio and properties of yarns produced from individual components. 13 Two ply yarns properties are predicted from single yarn properties. Set of methods using the image analysis for measurement of two ply yarn geometry were designed. Measurement of characteristic dimension of two ply yarn has been described by Vyšanská 14, and the influence of two ply yarn twist on its shortening is discussed by Vyšanská and Martinková Fabric Module Yarn properties calculated in yarn module are used for the prediction of fabric properties. All input parameters are shown on the top of Fig. 2 and calculated parameters are at bottom. The first step is modeling of binding point geometry of fabrics. Approximation of the binding wave in the weave repeat for fabric construction is based on Fourier series approximation. 16 The core problem is deformation of yarn in binding point. Yarn thickness and widening can be measured from fabric crosssections by using image analysis. Figure 7 shows the cross-sections of cotton fabrics (plain weave) prepared with the same yarn (ring yarn, fineness 29.5 tex) and the same warp sett, but different weft sett. ifferent yarn deformation in binding point of fabric is shown in Fig.7. According to these experiments, the parameters of yarn flattening were computed. For prediction of fabric area cover, area mass, strength and elongation, the well-known relationship with parameters of yarn flattening is used. Original model for the prediction of air permeability was created by Havrdová and rašarová. 17 Prediction of roughness is shown in Fig. 8 [ref. 18]. New model for the prediction of fabric hand 19 is now extensively tested. Prediction of fabric comfort based on thermal conductivity is also studied. 20 Fig.6 (a) Predicted fibre bundle utilization in yarn, and (b) Yarn strength 50% Uster Statistics (2007) Fig. 7 Fabric cross-section of warp thread [ ring cotton yarn, 29.5 tex, plain weave, warp sett 212/10cm, and weft sett (a) 130/10cm & (b) 88/10cm]

5 404 INIAN J. FIBRE TEXT. RES., ECEMBER 2008 Fig. 8 Modeling of surface roughness 18 5 Conclusions The system LibTex can be used for the prediction of grey cotton fabric properties. It is known that the variability of textile parameters is very high. It depends on raw material, technological equipment, spinning and weaving technological parameters and mainly on the finishing technology. For that reason different versions of system LibTex are prepared for different types of textiles, for example cotton fabrics from compact yarn (damasks), cotton fabric for beds, and fabric for ionex membranes. Industrial Importance: There exist design systems focused on the fabric visualization and data preparation for weaving loom control. These systems are not oriented to the prediction of fabrics properties. Often requirements for fabric properties are contradictory and finding of the optimal solution is difficult. The main aim of LibTex is to optimize the properties of fabrics with regard to their structure, the characteristics of yarn and appropriately selected raw materials. This system should facilitate the work in the field of the fabrics technical design. Acknowledgement The authors are thankful to the Textile Research Center for sponsoring the project (LN00B090 and 1M of Czech Ministry of Education) to carry out this study. References 1 s/products.php?itemid=74&lang=en&page=producttree. 2 ng.htm. 3 html. 4 Hearle J W S, Engineering design of textiles, Indian J Fibre Text Res, 31 (2005) F049, Project FF-P/047 Novaspin High-production spinning machine ( , MPO/FF). 7 Blažek J, Some ways of staple fibre spinning by the system draft spindle, paper presented at the 1 st Seminar Textiles on the Beginning of New Millennium, Liberec, Czech Republic, April [ 8 Neckář B, Yarn, Creation, Structure, Properties, (SNTL Praha), Křemenáková, System for Textile esign: Part 1 Fiber Yarn (Research Center Textile, Textile Faculty, Technical University of Liberec), Internal Standards (Research Center Textile, Textile Faculty, Technical University of Liberec), Krupincová G, Comparison of methods for yarn diameter and yarn packing density experimental determination, paper presented at the 4 th International Textile, Closing & esign Conference Magic World of Textiles, ubrovnik, Croatia, October Pan N, Prediction of statistical strengths of twisted fiber Structures, J Mater Sci, 28 (1993) Křemenáková, Militký J & Vozková P, Critical mixing ratio for blended yarn strength, paper presented at the Beltwide Cotton Conferences on Strategies for Success, San Antonio, Texas, USA, 3-6 January Vyšanská M, Single and two-ply yarn cross-sections evaluation, paper presented at the International Conference AU- TEX 07, Tampere, Finland, 2007 [C Rom ISBN ]. 15 Vyšanská M& Martinková Z, Characteristic proportions of two-ply yarn Experimental method and theory, paper presented at the International Conference TexSci 07, TUL, Liberec, 2007 [ ISBN, ]. 16 Sirková Kolčavová B, Mathematical model for description of the thread s interlacing in the fabrics using Fourier series, Ph. thesis, Textile Faculty Technical University of Liberec, Havrdová M & rašarová J, Prediction of woven fabric permeability based on geometrical model of porosity, paper presented at the 6th conference of Faculty of Engineering and Marketing of Textiles, Lodz. March Militký J, Characterization of micro and macro roughness of textile surfaces, paper presented at the 3 th International Textile, Clothing & esign Conference Magic World of Textiles, ubrovnik, Croatia, October Nováčková J & Militký J, Bed thicking hand evaluation, paper presented at the International Textile Research Conference, Shiuzoka, Japan Militky J & Kremenakova, Thermal conductivity of wool/pet weaves,. HEFAT 2008, paper presented at the 6 th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, 30 June to 2 July 2008, Pretoria, South Africa.