The aim of this study was to investigate the effect of elastane linear density and draft ratio on the physical and mechanical properties of core-spun yarns. Twenty yarn samples were prepared on industrial scale in a spinning mill with two different yarn linear densities, each with different two elastane deniers and five draft ratios. It was found that core-spun yarn's tenacity, elongation and hairiness are affected not only by the overall yarn linear density but also by the elastane linear density and the draft ratio. However, the effect of elastane linear density and draft ratio was not found to be statistically significant on the yarn mass variations and total imperfections, which are only affected by the overall yarn liner density. A statistically significant interaction for yarn elongation at break was found between the yarn liner density and the elastane linear density concluding that elastane linear density used in the core must be compatible with the overall yarn liner density for attaining the best yarn elongation.

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Elastane percentage in the core-spun cotton yarn of a specific linear density can be affected in two ways: either by changing the elastane denier or the draft ratio. The purpose of this study was to find out whether different mechanical properties of fabrics woven from such yarns simply depend upon the elastane percentage or whether the method of achieving a desired elastane percentage has specific effects. It was found through regression analyses that both the elastane denier and the draft ratio are almost equally important for fabric tear strength and stretchability, whereas the fabric tensile strength is predominantly influenced by the elastane denier while the fabric recovery after stretch is mainly influenced by the elastane draft ratio.

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Herein, the hydrophobic and self-cleaning properties of three different fabric surfaces have been evaluated after applying titanium dioxide (TiO 2 ) nanofinishes. The nanoparticles were prepared by sol-gel techniques and were characterized by using X-ray diffraction (XRD), scanning electron microscopy (SEM) and dynamic light scattering (DLS) methods. The ultra-refined particles were applied over three different fabric substrates having similar weave of Z-twill (3/1). The yarns of 100% polyester, blend of viscose with mod-acrylic and high performance polyethylene containing 16 yarn count (Ne) and 31.496 and 15.748 ends/cm and picks/cm, respectively, were used for required fabric preparation. The different fabric structures were applied with self-cleaning finish of TiO 2 nanoparticles prepared in our laboratory and the results were compared with commercially available finish Rucoguard AFR. The static contact angles, UV-protection factor, air permeability and hydrophobic activity of nanofinished fabric helped in evaluating their breathability and self-cleaning properties.

Three-dimensional multilayer woven preforms are mostly used in high-performance composite applications due to their better in-plane and out-of-plane mechanical properties. The present study aims to produce and characterize multilayer flax yarn-based three-dimensional-shaped preforms and their corresponding composites. The T-and H-shaped threedimensional woven preforms were prepared on conventional dobby loom using two types of weaving pattern, i.e., layerto-layer orthogonal and through thickness orthogonal. Composites were fabricated using open mould technique. Peel strength of T-and H-shaped structures was investigated and compared with laminated structures. Mechanical properties of layer-to-layer-interlocked structures in T and H shapes were found better than TT and laminated structure, both for reinforcement and composite.

Tensile strength has been accepted as one of the most important performance attributes of woven textiles. In this work, multiple linear regression models are developed by using empirical data for the prediction of woven fabric tensile strength manufactured from cotton yarns. Tensile strength of warp & weft yarns, warp & weft fabric density, and weave design were used as input parameters to determine warp-and weft-way tensile strength of the woven fabrics. The developed models are able to predict the fabric strength with very good accuracy. Warp yarn strength and ends per 25 mm are found to be the most dominant factors influencing fabric strength in warp direction while weft yarn strength and picks per 25 mm are most vital in weft direction.Recently, Artificial Neural Network (ANN) and regression based models for predicting the tensile strength of woven fabrics have been developed by Majumdar, based on the empirical data of 33 fabrics [18]. The prediction accuracy of these models is quite Journal of Engineered Fibers and Fabrics 47

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