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.     

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.     

Predicting tensile strength of yarns required for producing PET/Cotton blended woven fabrics of a pre-defined tensile strength

This study was aimed at developing statistical models for the prediction of tensile strength of warp and weft yarns required for attaining a pre-defined strength of PET/Cotton blended woven fabrics. The models were developed based on the empirical data obtained from carefully developed 234 fabric samples with different constructions using 15, 20, and 25 tex yarns in warp and weft directions. The prediction ability and accuracy of the developed models were assessed by correlation analyses of the predicted and actual warp and weft yarn strength values of another set of 36 fabric samples. The analyses showed a very strong ability and accuracy of the developed statistical prediction models.     

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.     

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.     

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.     

Analysis and construction of a quality prediction system for needle-punched non-woven fabrics

In this study, polyester and polypropylene staple fibers were selected as the raw material, and then processed through roller-carder, cross-lapper and needle-punching machine to produce needle-punched non-woven fabrics. First, the experiment was planned using the Taguchi method to select processing parameters that affect the quality of the needle-punched non-woven fabric to act as the control factors for this experiment. The quality characteristics were the longitudinal and transverse tensile strength of the non-woven fabric as well as longitudinal and transverse tear strength. The L₁₈ (2¹×3⁷) orthogonal array was selected for the experiment as it offered an improvement on the traditional method that wastes a lot of time, effort and cost. By using the analysis of variance (ANOVA) technique at the same time, the effect of significant factors on the production process of needle-punched non-woven fabrics could be determined. Finally, the processing parameters were set as the input parameters of a back-propagation neural network (BPNN). The BPNN consists of an input layer, a hidden layer and an output layer where the longitudinal/transverse tensile and tear strength of the non-woven fabric were set as the output parameters. This was used to construct a quality prediction system for needle-punched non-woven fabrics. The experimental results indicated that the prediction system implemented in this study provided accurate predictions.     

Comparison of regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics

The aim of this study was to compare the response surface regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics. The prediction models are based on the experimental data comprising yarn tenacity, knitting stitch length and fabric GSM as input variables and fabric bursting strength as output/response variable. The models quantitatively characterize the non-linear relationship and interactions between the input and output variables exhibiting very good prediction ability and accuracy, with ANFIS model being slightly better in performance than the regression model.     

Enhanced visualization of simulated woven fabrics

The woven fabric graphics designed with available computer aided design (CAD) systems using different colored warp and weft yarns look quite different from the appearance of their actual fabrics. To enhance the visual effects of designed woven fabric graphics, this paper reports a modified CAD woven fabric system, which allows users to design a fabric using parameters including fabric weaves, yarn number, yarn material, fabric count, crimp shape of interwoven yarns, and illumination. This enhanced system can design both yarns and fabrics, and consider the transitional color effect around interweaving points of warp and weft yarns. Its simulation image quality of woven fabrics has been greatly improved, and several textile mills and universities are currently using this woven fabric design system.     

An experimental investigation into the mechanical behaviour of 3D woven fabrics for structural composites

3D woven composites provide efficient delamination suppression, enhanced damage tolerance, superior impact, ballistic and blast performance characteristics over 2D fabric laminates. At the same time, this type of composites, having practically straight in-plane fibers, show significantly better in-plane stiffness and strength properties than respective properties of conventional composites. But, how the in-plane elastic and strength characteristics of this type of fabrics compare with respective in-plane properties of equivalent 2D woven fabrics. This paper presents a comprehensive experimental study of the comparison of in-plane tensile, bending, crimp interchange properties of UD, 2D, 3D orthogonal, 3D angle-interlock and 3D warp interlock multi-layer structures woven from E-glass tow. The results depict that the 3D woven fabrics have considerably superior mechanical properties with much lesser crimp compared to 2D fabrics.     

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.     

Development of an objective fabric pilling evaluation method. II. Fabric pilling grading using artificial neural network

An objective pilling evaluation method has been developed using image analysis and artificial neural network. Pilling parameters obtained in the previous study were used as the input values for neural network. A total of 9 data sets including 5 standard grades and 4 interpolated intermediate grades were used for training the network. Nine samples were prepared to verify the validity of the trained network in comparison with the subjective evaluation results. 18 woven and 12 knitted samples were tested to investigate the effect of the fabric structure on the performance of the network. Finally, 55 woven fabric samples were tested to evaluate the performance of the newly developed method and it was proven to be suitable for the evaluation of pilling grade especially for woven fabrics.     

Modelling tearing behavior of durable press finished woven fabric

Tearing strength is one of the most important and critical properties related to durable press finished cotton woven fabric. In the past, modelling of tearing strength of cotton woven fabrics was based on untreated cotton woven fabric but not in durable pressed finished fabric. In this paper, a mathematical model was established to demonstrate the tearing strength mechanism of durable press finished cotton woven fabrics by dimensional analysis based on yarn diameter, cover factor, Young’s modulus and fabric elongation. The proposed model agreed well with experimental results and the proposed model can be used for optimizing durable press finishing process of cotton woven fabric.     

Analysis and tensile-tear properties of abraded denim fabrics depending on pattern relations using statistical and artificial neural network models

The aim of this study is to develop new pattern denim fabrics and characterize the mechanical properties of these fabrics after abrasion load. Furthermore, tensile and tear strengths of these fabrics have been analysed by using the Artificial Neural Network (ANN) and statistical model. All denim fabrics were first abraded and subsequently tensile and tearing tests were applied to the abraided fabrics seperately. Actual data generated from the tests were analyzed by ANN and regression model. The regression model has shown that tensile strength properties of the abraded large structural pattern denim fabrics are generally low compared to that of the small structural pattern and traditional denim fabrics. On the other hand, when the abrasion cycles are increased tensile properties of all denim fabrics are generally decreased. Tearing strength of weft and warp in the abraded large structural pattern denim fabrics are between small structural pattern and traditional denim fabric. On the other hand, when the abrasion cycles are increased tearing strength properties in the weft and warp for all denim fabrics are generally decreased. The results from ANN and regression models were also compared with the measured values. It is concluded that almost all values from ANN are accurately predicted compared with those of the regression model. Therefore, we suggest that both methods can be used in this study as viable and reliable tools.     

Reflectance prediction of colored polyester fabrics by a novel formula

This paper focuses on the reflectance prediction of colored (unicolored) fabrics considering relationship between fractional reflectance values and cover factors of fabrics woven from polyester yarns. A novel equation for the calculation of relation between fractional reflectance and cover factor was proposed and usage of the equation was assessed by reflectance measurements. 48 dyed polyester fabrics having different constructional parameters were used and fabrics differed from each other by their cover factors. Warp yarn type and count, warp density and warp yarn twist were the same but weft yarn count, weft yarn fiber count and weft density were different for the fabrics in experimental sub-groups. The reflectance measurements were conducted on the dyed fabric samples as well as on the individual yarn systems (warp and weft) of the same fabrics. The proposed equation was tested according to different fabric constructional parameters and reasonable results with the experimental data were obtained. The possibilities of general use of derived mathematical relations between theoretical and measured reflectance values were researched. The relation obtained was used to explain the effects of different constructional parameters on reflectance behavior of fabric surfaces.     

New production method for a plain weave figured fabric

A new production method for figured fabric has been developed. The figured fabric generated in this study is a plain weave piled fabric and it shows the same figure on both sides unlike those fabrics woven on dobby or jacquard looms. It is woven by a specialty yarn called the chenille yarn which is obtained by separating each warp of a base fabric woven in leno structure. The base fabric is woven by inserting different colored weft each time in a certain sequence arranged according to the target figure image. A CAD software and a computerized controller have been developed to control all the motions of a conventional rapier loom and to handle the numerous weft insertion schedule efficiently.     

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.     

Effect of weaving process on tensile characterization of single and multiple ends of air-entangled textured polyester yarns

The tensile properties of air-entangled textured polyester single and multiple yarn ends before and after weaving were analyzed. The effects of weaving process considering fabric unit cell interlacement and number of yarn ends were evaluated by regression model. For this purpose, plain, ribs and satin woven fabrics were produced. The yarns were raveled from fabrics, and the tensile tests were applied to these yarns. The developed regression model showed that the number of interlacement and crimp ratio on the warp and weft yarns influence their tensile strength. Tensile strength of raveled yarns decreased compared to that of the bobbin yarn due to the effect of weaving process. This property degradation on the ravel yarns considered process degradation. Generally, when the number of warp and weft yarn ends increases, the warp and weft yarn tensile strengths for each fabric type decrease, whereas the warp and weft yarn tensile elongations slightly increase. The results from regression model were compared with the measured values. This study confirmed that the method in the study can be a viable and reliable tool. The research finding could be useful those who work on preform fabrication.     

Comparative study of cellulase treatment on low stress mechanical properties of cotton denim fabric made by torque-free ring spun yarn

Cellulase is useful for bio-polishing cotton fabrics which enhances their aesthetic performance instead of stonewashing process. Torque-free ring spun process is a widely used technique to produce newly low-twist and balanced torque yarns with soft hand. In this paper, denim fabrics woven with torque-free ring spun yarn and conventional ring spun yarn respectively were treated with cellulase under the same condition and their fabric handle, expressed as low stress mechanical properties, such as tensile strength, bending, shearing, compression and surface performance were investigated by Kawabata Evaluation System for Fabric (KES-F). After cellulase treatment, both denim fabrics revealed better flexibility, elasticity recovery, raised shearing stiffness, fluffier and improved smoothness. While torque-free ring spun yarn made denim fabric showed a better fabric handle than conventional ring spun yarn made denim fabric.     

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.