Hairiness is one of the most important parameters affecting fabric quality in textile industries. Up to now, many researchers have focused their studies on the hairiness and its related concepts. It is well known that fiber properties affect hairiness, nevertheless, spinning machine parameters which are also effective on hairiness are not well studied before. In this study, the prediction ability of hairiness by taking account of the variables including rotor type, rotor diameter, doffing-tube nozzle and torque-stop was studied using support vector machine (SVM) and adaptive neuro fuzzy interface system models. Moreover, the genetic algorithm was applied to ensure that the model parameters were optimized correctly. Then, the obtained results were compared with those provided by artificial neural network (ANN) and it was revealed that all models had the great potential to be used in hairiness prediction (mean absolute percentage error = 3. 8-3. 9). The performances of SVM and ANN models were almost the same, however, they were better than that of fuzzy model.

Enzymatic hydrolysis of cotton fabrics with different weft yarns has been studied. The weft yarns used were commercial cotton yarns produced by the carded, combed, open-end, air-jet and core-spun systems. The effects of cellulases hydrolysis on different fabrics were investigated by measuring the weight loss, the amount of glucose released and the breaking load of weft yarns. The results showed that the weight loss and concentration of glucose in hydrolysis vary with the increase of yarn hairiness. Loss of breaking load in shorter treatment times was related to hairiness of weft yarns but in longer enzymatic hydrolysis times, strength loss was affected by the amount of glucose released.

One way to improve the properties of staple yarns is to employ core– compact yarn spinning system. This type of yarn is used in a wide range of applications and up to now many researchers have studied its production process and properties. However, there is a lack of researches regarding the optimization of the properties of rotorcraft compact spinning (RoCos) core-spun yarns based on the spinning parameters. Therefore, in this paper, the influence of some spinning parameters including the pre-tension of filament, yarn count and type of sheath fiber on the properties of RoCos core-spun yarns was investigated. To achieve the goals of this research, the physical and mechanical properties of RoCos core-spun yarns including the tenacity, hairiness and abrasion resistance were measured, and then modeled by artificial neural network (ANN). Finally, to optimize all measured properties at the same time the ANN models and non-dominated sorting genetic algorithm (NSGAII) method were applied as a hybrid model. The results showed that the presented method could be successfully used to determine the spinning parameters to produce RoCos yarns with desired properties. The optimized values of hairiness, tenacity and abrasion resistance for an ideal yarn were observed at yarn count of 41. 5 tex, filament pre-tension of 125 g and for sheath fiber of viscous/polyester.

Core yarn was spun by introducing a PVA multifilament as the core through the yarn forming zone of a ring spinning frame and viscose fibers as the sheath containing tracer fibers. The water soluble PVA multifilament was extracted from the yarn structure. Then internal structure and properties of the hollow ring yarn was assessed and compared with those of typical ring yarns. Also, two plain fabric samples with hollow and typical weft ring yarns were produced to investigate the effect of hollowness and fibers collapse on the structural parameters of the fabrics. Bending behavior and air permeability of these fabrics were consequently studied. The results showed the mean value of fiber spinning-in-coefficient (Kf) and strength of hollow and typical ring yarns were very close, but elongation at break, unevenness and hairiness of hollow ring yarn is more than those of typical ring yarn. Results indicated that the bending rigidity and air permeability of the fabrics constructed from hollow ring weft yarn were improved due to the collapse of the fibers in the center of yarn and better flattenin.

In this research, the compact-core spun yarns have been produced using RoCoS roller and the effects of filament pre-tension, yarn count and type of sheath fibers were investigated on the physical and mechanical properties of produced yarns such as strength, elongation percentage, hairiness, and abrasion resistance. After statistically analysis on the obtained results, for modeling the core-compact yarn properties, the regression and artificial neural network (ANN) were used to predict the physical and mechanical properties. Trial and error method was considered for determining the best of ANN topology. For this aim, 1110 topologies of ANN (with different hidden layers and neurons in each hidden layer) were investigated for each property. Moreover, to evaluate the accuracy of the created ANN three indexes were used, namely mean absolute percentage error (MAPE), mean square error (MSE), and correlation coefficient (R-value). It was observed that the most accurate results were obtained based on MAPE and the best topology for predicting all properties is a two-hidden layer ANN (maximum MAPE<0.10) except for the abrasion resistance which is a three-hidden layer ANN (MAPE<0.17).