The effects of pyrrole, anthraquinone-2-sulphonic acid (AQSA) and iron(III) chloride (FeCl 3 ) concentrations, reaction time and temperature on the electrical conductivity of polypyrrole (PPy)-coated poly(ethylene terephthalate) (PET) fabrics were investigated. With an increase in both the AQSA and FeCl 3 concentrations, resistivity decreased to a point beyond which higher concentrations led to increased surface resistivity. Erosion of the polymer coating, in dynamic synthesis from continual abrasion, manifested as an exponential increase in the resistance of the coated textile substrate. This was not encountered in static synthesis conditions. Temperature affected the degree of surface and bulk polymerisation. The effect of polymerisation temperature on conductivity was negligible. Conductive polymer coating on textiles through chemical polymerisation enabled a smooth coherent film to encase individual fibres, which did not affect the tactile properties of the host substrate. The optimum FeCl 3 /pyrrole and AQSA FeCl 3 /pyrrole molar ratios were found to be 2.22 and 0.40 respectively.

Fabric pilling is affected by many interacting factors. This study uses artificial neural networks to model the multi-linear relationships between fiber, yarn and fabric properties and their effect on the pilling propensity of pure wool knitted fabrics. This tool shall enable the user to gauge the expected pilling performance of a fabric from a number of given inputs. It will also provide a means of improving current products by offering alternative material specification and/or selection. In addition to having the capability to predict pilling performance, the model will allow for clarification of major fiber, yarn and fabric attributes affecting fabric pilling.Pilling may be defined as a surface fabric fault comprising of circular accumulations of entangled fibers that cling to the fabric surface thereby affecting the appearance and handle of the fabric. The pilling of fabrics is a serious problem for the apparel industry and in particular wool knitwear fabrics [13]. It is realized that the problem of pilling is one of the biggest quality issues for the wool industry, however the problem remains.The formation of pills occurs as a consequence of mechanical action during washing or wear. Under the influence of mechanical action, loose fibers that protrude from the fabric surface entangle. Subjected to further mechanical action the entanglements develop into roughly spherical accumulation of fibers (pills) that are distinct from the fabric surface. Wear-off of pills occurs under continued abrasion from laundering, drying, etc., and during wear. For a given fabric, the degree to which pills form and wear-off is determined by the physical properties of the fiber, yarn and fabric constituents [20]. Many researchers have investigated the influence of these properties and have identified numerous factors that contribute to the pilling of wool fabrics. At the fiber level, fiber tenacity [12], diameter [6,21], length [10,19], and curvature [9,11] have been proven to impact on the rate of fuzz formation, extent of entanglement and the degree of wear off. Of the yarn parameters to affect pilling, yarn type [2] along with the degree of singles twist and/or fold twist [16] are most influential parameters. Yarn hairiness [3, 10] and yarn linear density [5] have also been shown to contribute significantly in fabric pilling. Fabric construction also plays a role in the pilling process, the tightness of the knitted construction (cover factor) impacts on the development of fuzz and pills along with the tendency for pill wear-off [11,16].Although the problem of pilling has attracted extensive research over the decades, due to the nature of pilling, the accurate modeling or prediction of the process is still elusive. Much research has gone into understanding the key factors and mechanisms responsible for pilling but failed to move beyond assessing the problem at a single particular stage of the manufacturing process. Without considering the complex interactions of the various factors at the different processing stages, the weight o...

For a given fiber spun to pre-determined yarn specifications, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills, and then generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for predicting worsted spinning performance with an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for predicting spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan Yarnspec™). The ANN is then subsequently trained with commercial mill data to assess the feasibility of the method as a mill-specific performance prediction tool. Incorporating mill-specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately.

This study focused on the hairiness of worsted wool yarns and how it affects the pilling propensity of knitted wool fabrics. Conventional worsted ring spun yarns were compared with comparable SolospunTM yarns and yarns modified with a hairiness reducing air nozzle in the winding process (JetWind). Measurements of yarn hairiness (S3) on the Zweigle G565 hairiness meter showed a reduction in the S3 value of approximately 46% was achieved using SolospunTM ring spinning attachment and a 33% reduction was achieved using the JetWind process. Interestingly, subsequent evaluation of the pilling performance of fabrics made from the SolospunTM spun yarn and JetWind modified yarn showed a half grade and full grade improvement, respectively over a similar fabric made from conventional ring spun yarns. This result suggested that a relatively large reduction in yarn hairiness was needed to achieve a moderate improvement in fabric pilling, and that the nature of yarn hairiness was also a key factor in influencing fabric pilling propensity. It is postulated that the wrapping of surface hairs by the air vortex in the JetWind process may limit the ability of those surface fibers to form fuzz and reach the critical height required for pill formation.

This study evaluated the performance of multilayer perceptron (MLP) and multivariate linear regression (MLR) models for predicting the hairiness of worsted-spun wool yarns from various top, yarn and processing parameters. The results indicated that the MLP model predicted yarn hairiness more accurately than the MLR model, and should have wide mill specific applications. On the basis of sensitivity analysis, the factors that affected yarn hairiness significantly included yarn twist, ring size, average fiber length (hauteur), fiber diameter and yarn count, with twist having the greatest impact on yarn hairiness.