Predicting the tensile strength of polyester/cotton blended woven fabrics using feed forward back propagation artificial neural networks

Tensile strength plays a vital role in determining the mechanical behavior of woven fabrics. In this study, two artificial neural networks have been designed to predict the warp and weft wise tensile strength of polyester cotton blended fabrics. Various process and material related parameters have been considered for selection of vital few input parameters that significantly affect fabric tensile strength. A total of 270 fabric samples are woven with varying constructions. Application of nonlinear modeling technique and appreciable volume of data sets for training, testing and validating both prediction models resulted in best fitting of data and minimization of prediction error. Sensitivity analysis has been carried out for both models to determine the contribution percentage of input parameters and evaluating the most impacting variable on fabric strength.     

Predicting the air permeability of polyester/cotton blended woven fabrics

The aim of this study was to model the air permeability of polyester cotton blended woven fabrics. Fabrics of varying construction parameters i.e. yarn linear densities and thread densities were selected and tested for air permeability, fabric areal density and fabric thickness. A total of 135 different fabric constructions were tested among which 117 were allocated for development of prediction model while the remaining were utilized for its validation. Four variables were selected as input parameters on basis of statistical analysis i.e. warp yarn linear density, weft yarn linear density, ends per 25 mm and picks per 25 mm. Response surface regression was applied on the collected data set in order to develop the prediction model of the selected variables. The model showed satisfactory predictability when applied on unseen data and yielded an absolute average error of 5.1 %. The developed model can be effectively used for prediction of air permeability of the woven fabrics.     

Empirical modelling of tensile strength of woven fabrics

Aesthetic properties of fabrics have been considered as the most important fabric attribute for years. However, recently there has been a paradigm shift in the domain of textile material applications and consequently more emphasis is now being given on the mechanical and functional properties of fabrics rather than its aesthetic appeal. Moreover, in certain woven fabrics used for technical applications, strength is a decisive quality parameter. In this work, tensile strength of plain woven fabrics has been predicted by using two empirical modelling methods namely artificial neural network (ANN) and linear regression. Warp yarn strength, warp yarn elongation, ends per inch (EPI), picks per inch (PPI) and weft count (Ne) were used as input parameters. Both the models were able to predict the fabric strength with reasonably good precision although ANN model demonstrated higher prediction accuracy and generalization ability than the regression model. The warp yarn strength and EPI were found to be the two most significant factors influencing fabric strength in warp direction.     

Low stress mechanical properties of fabrics woven from bamboo viscose blended yarns

This paper presents the low stress mechanical properties of plain fabrics woven from cotton, bamboo viscose and cotton-bamboo viscose blended yarns. Three blends (100 % cotton, 50:50 cotton-bamboo and 100 % bamboo) were used to produce three yarn counts (20, 25 and 30 Ne). Each of these yarns was used to make fabrics with different pick densities (50, 60 and 70 picks per inch). It was found that bending rigidity, bending hysteresis, shear rigidity, shear hysteresis and compressibility is lower for bamboo fabrics as compared to those of 100 % cotton fabrics. On the other hand, extensibility, tensile energy and compressional resilience are higher for 100 % bamboo fabrics than 100 % cotton fabrics. Higher pick density increases linearity of load-elongation curve, bending rigidity, shear rigidity and compressional resilience. Shear and bending rigidities show very good correlation with the respective hysteresis values.     

Selection of yarn for the predefined tensile strength of cotton woven fabrics

In order to meet the required strength of a fabric, selection of yarn is difficult because tensile strength of woven fabric depends upon a number of factors. Still, the manufacturers have to use hit and trial method in order to select the yarn for the required tensile strength of fabric. This study was carried out to develop regression equations for the prediction of yarn tensile strength suitable for the predefined strength of cotton woven fabrics. These equations were developed by using empirical data obtained from two hundred and thirty four fabric samples prepared under a systematic plan with different constructions. Prediction proficiency and precision of these regression equations were evaluated by correlation analysis of the predicted and actual warp and weft yarn strength values of another set of thirty six fabric samples. The results show a very strong prediction precision of the equations.     

Stab-resistant property of the fabrics woven with the aramid/cotton core-spun yarns

Aramid fibers are mainly used for industrial applications and human body protection against ballistic threats. But they are used mostly in forms of composites. And fabrics woven with a high yarn count offer a moderate protection performance against the knife stabbing due to the low shear strength. This research is focused on investigating the effect of the aramid core-spun yarns on the stab resistance of the woven fabrics. With the aramid core-spun yarns with core to sheath weight ratio of 1 to 2.5 the armor specimens having different fabric densities were prepared and the knife edge impact test was conducted. On the impact energy of the knife at the level 1 according to the NIJ standard, the drop tower test results demonstrated that fabric density of the armor specimens affected the stab resistance significantly. The penetration depth of the impactor through the armor specimens was associated with the thickness and mass of the armor sample in different ways. Being the stab resistance introduced by considering the penetration depth of the impactor via thickness and weight per surface area, the effects of the fabric conditions on the anti-stabbing property could be systematically analyzed and turned out that there was an optimal level of the fabric density, showing the most effective stab resistance.     

Prediction of strength and weavability of blended spun yarns

In this paper, we report on predicting the strength of polyester/viscose spun yarns made on ring, rotor and air-jet spinning systems. A system has been developed to measure the weavability of yarns. Hamburger’s fibre bundle theory is modified to predict the strength of blended yarns from the strengths of single-fibre component yarns. The modified model predicts blended yarn strength more accurately than the original Hamburger’s model emphasizing the importance of yarn structure on blended yarn strength. The weavability of blended yarns is measured on a CTT instrument incorporating a shedding device which addresses the stresses viz., cycle extension, flex abrasion and beat up occur during weaving. The measured weavability compared well with that obtained on a commercial Sulzer Ruti Reutlingen Webtester. Yarn structure and strength and cohesion of fibres affect the strength and weavability of yarns.     

Electromagnetic interference shielding effectiveness of SS/PET hybrid yarn incorporated woven fabrics

A detailed study of electromagnetic shielding effectiveness (EMSE) of woven fabrics made of polyester and stainless steel/polyester blended conductive yarn was presented in this research work. Fabrics with different structures were analyzed and their shielding behavior was reported under different frequencies. Shielding efficiency of fabric was analyzed by vector network analyzer in the frequency range of 300 kHz to 1.5 GHz using coaxial transmission line holder. The effects of different fabric parameters such as weft density, proportion of conductive weft yarn, proportion of stainless steel content, grid openness, weave pattern and number of fabric layers on EMSE of fabrics were studied. The EMSE of fabric was found to be increased with increase in proportion of conductive yarn in the weft way. With increase in overall stainless-steel content in the fabric, the EMSE of fabric was increased. As such weave is considered, it did not have significant effect on EMSE of fabrics. But fabric with lower openness and aperture ratio showed better conducting network, hence better shielding. With increase in number of layers of fabric and ply yarns, EMSE of fabric was increased.     

Electromagnetic shielding characteristics of woven fabrics made of hybrid yarns containing metal wire

In this study, electromagnetic shielding characteristics of woven fabrics made of hybrid yarns are investigated. For this purpose, initially the hybrid yarns containing stainless steel wire are produced with hollow spindle covering technique, and then eight different fabric samples are produced using these hybrid yarns. Electromagnetic shielding values of fabric samples are determined by a test set up based on enclosure measurement technique. Measurements are made in the frequency range of 30 MHz-9.93 GHz. Test results show that woven fabric samples investigated in this study have 25–65 dB electromagnetic shielding effectiveness for incident frequency. It was also shown that the direction, density and settlement type of conductive hybrid yarn in fabric structure are important parameters affecting electromagnetic shielding characteristics of woven fabrics.     

Predicting bending rigidity of woven fabrics by neuro-genetic hybrid modeling

The possibility of prediction of bending rigidity of cotton woven fabrics with the application of Neuro-genetic model has been explored. For this purpose, number of cotton grey fabrics meant for apparel end-use was desized, scoured, and relaxed. The fabrics were then conditioned and tested for bending properties. A feed-forward neural network model was first formed and trained with adaptive learning rate back-propagation with momentum. In the second model, a hybrid learning strategy was adopted. A genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. Later, a back-propagation was applied as a local search algorithm to achieve global optima. Results of hybrid neural network model were compared with that of back-propagation neural network model in terms of their prediction performance. Results show that the prediction by Neuro-genetic model is better in comparison with that of back-propagation neural model.     

Predicting bending rigidity of woven fabrics using artificial neural networks

This paper reports an investigation on the predictability of bending property of woven fabrics from their constructional parameters using artificial neural network (ANN) approach. Number of cotton grey fabrics made of plain and satin weave designs were desized, scoured, and relaxed. The fabrics were then conditioned and tested for bending properties. Thread density in fabric, yarn linear density, twist in yarn, and weave design were accounted as input parameters for the model whereas bending rigidity in warp and weft directions of fabric formed the outputs. Gradient descent with momentum and an adaptive learning rate back-propagation was employed as learning algorithm to train the network. A sensitivity analysis was carried out to study the robustness of the model.     

Dimensional and physical properties of plain knitted fabrics made from 50/50 bamboo/cotton blended yarns

In this study, the dimensional and some physical properties of plain knitted fabrics made from 50/50 bamboo/cotton blended yarns are investigated. In order to see the differences and similarities, the results are then compared with those for similar fabrics knitted from 50/50 conventional viscose/cotton and 50/50 modal/cotton blended yarns. Each fabric type was produced with three different stitch lengths. After all fabrics were dyed under identical dyeing conditions, they were subjected to dry and full relaxation treatments. For dimensional properties of fabrics, course, wale and stitch densities were measured. Then, by calculating statistically best-fit lines passing both through the experimental points and the origin, dimensional constants i.e. k values were predicted in terms of the fiber types. The result show that each fabric type knitted from bamboo/cotton, viscose/cotton and modal/cotton blended yarns behaves in a similar manner. However, in both dry and fully relaxed states, the modal/cotton knitted fabrics tend to have slightly higher k values than the bamboo/cotton and viscose/cotton knitted fabrics. For physical properties, fabric weight per unit area, thickness, bursting strength, air permeability and pilling were evaluated. The results show that the weight, thickness and air permeability values are independent of the fiber type. Plain knitted fabrics from modal/cotton blended yarns have the highest bursting strength values. Plain knitted fabrics from bamboo/cotton blended yarns tend to pill less.     

A general geometrical model for predicting color mixing of woven fabrics from colored warp and filling yarns

Despite the advances in woven fabrics, CAD systems, and weaving technologies, the process of weave/color selection for each area of a Jacquard pattern still requires the intervention of the CAD system operator and/or designer, who works from color gamut. Relying on the designer subjective assessment, multiple weaving trials may be needed to produce a fabric that matches the target artwork or sample. In this paper, a general geometric model is provided to predict the color contribution of warp and filling yarns of a given woven fabric in terms of warp and pick densities, warp and filling yarns sizes, weave, size of the color repeat of warp and filling yarns, and the number of yarns of different colors. Such geometrical modeling, combined with sound existing color mixing equations, paves the road for the automation of the process of weaves and color selection and thus dramatically reduces the production cycle.     

A comparative study of finer conventional and modified cotton yarns and their resultant woven fabrics

A modified ring spinning technique has been recently developed by incorporating false twisting devices into the conventional ring frame. Its application on the coarser yarn counts (7–32 Ne) showed notable advantages in modified yarn and fabric performance. More recently, it was noted that this technique can also be applied for producing finer cotton yarns. Thus this paper aims to carry out a systematic study of the physical properties of the finer modified yarns (80 Ne) and woven fabrics with respect to the conventional ones. Physical properties of conventional and modified single yarns were evaluated and compared. These two types of single yarn were used for the production of woven fabrics. Moreover, the above two types of single yarn were also plied and used for the production of woven fabrics under a commercial condition. All woven fabrics were assessed in terms of fabric tensile strength, tearing strength, abrasion resistance, fabric weight, and air-permeability as well as other fabric performance measured by the Kawabata Evaluation System (KES). Experimental results showed that finer modified yarns and fabrics exhibit higher strength, lower hairiness, and improved abrasion resistance, slightly better compression property, and smoother surface with relatively larger thickness.     

Investigation and modeling of air permeability of Cotton/Polyester blended double layer interlock knitted fabrics

The aim of this study was to analyze and model the effect of knitting parameters on the air permeability of Cotton/Polyester double layer interlock knitted fabrics. Fabric samples of areal densities ranging from 315–488 g/m² were knitted using yarns of three different cotton/polyester blends, each of two different linear densities by systematically varying knitting loop lengths for achieving different cover factors. It was found that by changing the polyester content in the inner and outer fabric layer from 52 to 65 % in the double layer knitted fabric did not have statistically significant effect on the fabric air permeability. Air permeability sharply increased with increase in knitting loop length owing to decrease in fabric areal density. Decrease in yarn linear density (tex) resulted in increase in air permeability due to decrease in areal density as well as the fabric thickness. It was concluded that response surface regression modeling could adequately model the effect of knitting parameters on the double layer knitted fabric air permeability. The model was validated by unseen data set and it was found that the actual and predicted values were in good agreement with each other with less than 10 % absolute error. Sensitivity analysis was also performed to find out the relative contribution of each input parameter on the air permeability of the double layer interlock knitted fabrics.     

Moisture and thermal permeability of the hollow textured PET imbedded woven fabrics for high emotional garments

This study examined the effects of the total porosity, pore size, and cover factor on the moisture and thermal permeability of woven fabrics made from DTY (draw textured yarns) and ATY (air jet textured yarns) composite yarns with hollow PET (polyethylene terephthalate) yarns. The wicking of the hollow composite yarn fabrics was found to be superior to that of the high twisted yarn fabrics, which may be due to the high porosity in the hollow composites yarns, but this was not related to the cover factor. The drying characteristics of the hollow composite yarn fabric with high porosity were inferior compared to the high twisted yarn fabrics due to the large amounts of liquid water in the large pores, which resulted in a longer drying time of the fabric. The thermal conductivity of the hollow composite yarn fabrics decreased with increasing measured pore diameter due to the bulky yarn structure. The effects of the hollowness of the yarn on the thermal conductivity were more dominant than those of the yarn structural parameters. The air permeability increased with increasing measured pore diameter but the effects of the cover factor on the air permeability were not observed in the hollow composite yarn fabrics. The effects of porosity on the moisture and thermal permeability of the woven fabrics made from the hollow composite filaments were found to be critical, i.e., wicking and air permeability increase with increasing porosity. In addition, the drying rate increased with increasing porosity and the thermal conductivity decreased with increasing pore diameter, but were independent of the cover factor.     

Predicting the unevenness of polyester/viscose blended open-end rotor spun yarns using artificial neural network and statistical models

In this study, an artificial neural network (ANN) and a statistical model are developed to predict the unevenness of polyester/viscose blended open-end rotor spun yarns. Seven different blend ratios of polyester/viscose slivers are produced and these slivers are manufactured with four different rotor speed and four different yarn counts in rotor spinning machine. A back propagation multi layer perceptron (MLP) network and a mixture process crossed regression model (simplex lattice design) with two mixture components (polyester and viscose blend ratios) and two process variables (yarn count and rotor speed) are developed to predict the unevenness of polyester/viscose blended open-end rotor spun yarns. Both ANN and simplex lattice design have given satisfactory predictions, however, the predictions of statistical models gave more reliable results than ANN.     

Analysis and tensile characterization of air-entangled textured polyester woven fabrics depending on interlacement and yarn sets

The aim of this study was to understand the failure mechanism of two dimensional dry fabric structure considering yarn sets and interlacements. For this purpose, data generated on air-entangled textured polyester woven fabric under the simple tensile load and analyzed by developed regression model. The regression model showed that warp and weft directional tensile strengths of satin fabric were higher than those of plain and rib fabrics in unravel sample. This might be related to the number of interlacements of the fabrics. There was not a considerable difference between warp directional tensile strength of ravel and unravel satin fabrics, whereas weft directional tensile strength of ravel satin fabric decreased rapidly with respect to its unravel form. The satin fabric showed the highest warp directional tensile strength among the others. The lowest weft directional tensile strength was received from ribs fabric. In semi-ravel sample, all fabrics showed low warp and weft directional tensile strength values except in plain fabric. Warp directional tensile elongation of plain fabric was the highest in unravel sample. Satin fabric showed the highest warp directional tensile elongation in the ravel sample. Warp directional tensile elongations of all the fabrics in the semi-ravel sample became low. Weft directional tensile elongation of satin fabric was the highest in unravel sample. In addition, satin and plain fabrics showed the highest weft directional tensile elongations in the ravel sample. Weft directional tensile elongations of all the fabrics in the semi-ravel sample became low except in ribs fabric.     

Evaluation of bamboo charcoal/stainless steel/TPU composite woven fabrics

Composite woven fabric satisfies what people require. Bamboo charcoal (BC) has been identified as a multifunctional material that has far-infrared ray, anions, deodorization and etc. BC fibers and yarns were made of bamboo charcoal powders and have further become a pervasive materials used in textile industry. In this study, cotton yarns, stainless steel/cotton (SS/C) complex yarn, bamboo charcoal/cotton (BC/C) complex yarns were woven into the plain, twill and Dobby composite woven fabrics. The warp yarn was composed of cotton yarns, and the weft yarn was made up of BC/C and SS/C complex yarns with a picking ratio of 1:1 and 3:1. Thermoplastic polyurethanes (TPU) film was then attached to the composite woven fabrics, forming the BC/SS/TPU composite woven fabrics. Tests of electromagnetic shielding effectiveness (EMSE), far-infrared emissivity, anions, water resistance, and water vapor permeability measured the single-layer, two-layer and four-layer composite woven fabrics, obtaining a far-infrared emissivity of 0.95 by 39.8 counts per minute, an anion count of 149 amount/cc, an EMSE of −11.87 dB under frequency of 900 MHz, a surface resistivity of 8×10⁻⁶ Ω/square, a water resistance of −8219 mmH₂O, and water vapor permeability of 989 g/m²·h and 319 g/m²·24 h in accordance with JIS L 1099 A1 and ASTM E96 BW.     

Analysis and off-axis tensile characterization of air-entangled textured polyester woven fabrics depending on unit cell interlacing frequency

The aim of this study is to analyze and determine the off-axis tensile properties of air-entangled textured polyester fabrics based on unit cell interlacing frequency. For this purpose, continuous filament polyester air-entangled textured yarn was used to produce plain, ribs and satin woven fabrics. The fabrics were cut from the warp direction (0°) to weft direction (90°) at every 15° increment, and tensile tests were applied to those of the off-axis samples. The strength and elongation results were introduced to the statistical model developed, and regression analyses were carried out. Hence, the effects of off-axis loading and interlacement on the directional tensile properties of the fabric were investigated. The regression model showed that off-axis loading influences fabric tensile strength. On the other hand, interlacement frequency is the most important factor for fabric tensile elongation. The results from the regression model were compared with the measured values. This study confirmed that the method used in this study as can be a viable and reliable tool. Future research will concentrate on multiaxially directional fabric and the probability that it will result in homogeneous in-plane fabric properties.