Dimensional and mechanical characterization of newly developed denim fabrics based on experimentally determined property-structural pattern relations for upholstery applications

The aim of this study was to develop new pattern denim fabrics and characterize the dimensional, the abrasion and pilling properties of these fabrics. Furthermore, tensile and tear strengths of these fabrics were determined. The potential enduses of pattern denim fabrics were evaluated by comparing the test results with traditional denim fabrics. The produced fabrics were classified as ‘Design group I’ and ‘Design group II’. In design group I, the fabrics had small structural patterns whereas the structural patterns of the fabrics of design group II were large. The dimensional properties and weights of developed pattern denim fabrics in both of the design groups were different in terms of weft densities, structural pattern sizes which influenced the numbers, directions and distributions of warp-weft interlacement. The abrasion behaviours of the traditional denim fabrics and the fabrics with large-small structural patterns were similar. However, it was determined that the fabrics with large and small patterns were abraded on the earlier abrasion cycles compared to the traditional denim fabrics. The pilling resistances of the fabrics not only depended on the hairiness levels of the yarns used during weaving, but also on the pattern sizes of the fabrics. The small structural pattern fabrics showed more resistance to pilling than those of the large structural pattern fabrics. There was a decrease on the warp and weft tensile strengths of the large structural pattern fabrics in comparison with the traditional denim fabrics. The average tear strengths of the large structural pattern denim fabrics on the warp course were higher than those of the traditional denim fabrics while the tear strengths of the large pattern and traditional denim fabrics on the weft course were similar to each other. The end-uses of the newly developed structural pattern denim fabrics were recommended as home textile.     

Mechanical characterization of flocked fabric for automobile seat cover

In this study, the tensile and tearing properties of substrate, substrate with adhesive and flocked fabric were studied with developed regression model which explains the relationships between fabric forms and tensile and tearing strength of the flocked fabrics. Warp and weft tensile strengths of wet flocked fabric are generally higher than the warp and weft tensile strengths of dry flocked fabric due to the high wet properties of cotton yarn. Weft tensile elongation of the flocked fabric is generally higher than warp tensile elongation due to the higher crimp ratio of the flocked fabric in the weft direction. Warp and weft tearing strengths of wet form substrate with adhesive and flocked fabric are higher than those of dry forms of substrate with adhesive and flocked fabric. One of the reasons could be the decrease of inter-yarn frictional forces due to the lubrication effect of the wet form of acrylic adhesive in substrate with adhesive fabric. Generally, tearing strength of flocked fabric is low compared with substrate. It was concluded that the regression model used in this study could be viable and reliable tools and flocked fabric could be considered as an alternative seat cover material to use in automotive industry.     

Assessment of color strength and color difference values of polyester fabrics containing continuous weft yarns after abrasion

This paper assesses the color difference and color strength values (K/S) obtained for eight disperse-dyed polyester fabric samples with different fabric construction parameters (weft yarn type, weft yarn count, weft density and fabric weave) after four sets of abrasion cycles. Warp yarn type and count, warp density, and warp yarn twist are the same for all fabrics. Fabric samples are dyed in a commercial red disperse dye (C.I. Disperse Red 74:1) and four different abrasion cycles (2500, 5000, 7500, 10000) are used. TheK/S values of the abraided fabrics and color difference values between the control fabric (dyed but not abraided) and abraded fabrics are calculated. The main differences in theK/S and color difference values are observed between 0–2500 abrasion cycles. The high tenacity of the polyester fibers and continuous polyester yarns causes some fuzz but no pilling formation on the fabric surface that lead to increasedK/S values and color differences. Fiber dullness, yarn thickness, yarn density and fabric weave are concluded to have different effects on the appearance after abrasion.     

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.     

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.     

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.     

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.     

Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric

In this paper, artificial neural network (ANN) model was used for predicting colour properties of 100 % cotton fabrics, including colour yield (in terms of K/S value) and CIE L, a, and b values, under the influence of laser engraving process with various combination of laser processing parameters. Variables examined in the ANN model included fibre composition, fabric density (warp and weft direction), mass of fabric, fabric thickness and linear density of yarn (warp and weft direction). The ANN model was compared with a linear regression model where the ANN model produced superior results in prediction of colour properties of laser engraved 100 % cotton fabrics. The relative importance of the examined factors influencing colour properties was also investigated. The analysis revealed that laser processing parameters played an important role in affecting the colour properties of the treated 100 % cotton fabrics.     

Application of artificial neural network (ANN) for prediction of fabrics’ extensibility

In the field of clothing technology, prediction of the fabric properties is very important because the fabric is the basic element of every clothing item. Knowing the fabric properties it is possible to predict fabrics’ behaviour during process of clothing manufacturing (in phase of cutting, sewing and ironing) as well as clothing items’ behaviour during usage. According to the fabrics’ characteristics and model design it is possible to predict appearances of the clothing items and their draping which can be presented with many computer simulations. In this paper extensibility of the fabric which appears during a small forces loading on the fabrics are investigated. Loading of small forces on the fabric appears in each phases of clothing manufacturing processes and during usage of clothing items. Investigations are managed on 50 fabrics which are weaving in twill weave and 100 % wool. The basic characteristics of fabric (density of warp and weft, mass per unit area, thickness) are defined according appropriate standard methods and tensile properties in the warp and weft directions are measured using KES-FB1 measuring system. Using an artificial neural network (ANN) prediction of extensibility properties of the fabrics are done, results are compared with experimental values and deviations are determined. ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Based on the implemented investigations, minimal deviations between experimental and predicted values are obtained and can be concluded that ANN can be used for prediction of the fabrics properties.     

Mechanical properties of denim fabric reinforced poly(lactic acid)

Denim, a twilled cotton fabric, was used to enhance the mechanical and thermal properties of poly(lactic acid) (PLA). The denim fabric reinforced composites with different numbers of denim layers were fabricated by using a hand layup method. The impact, tensile, and dynamic mechanical properties of the composites were observed with increasing denim layers to examine the reinforcing effect of denim fabrics. Numerical analysis was carried out to model the elastic modulus of the composite by using a commercial software. Three-dimensional geometry of the denim fabric reinforced PLA composite was generated through a CAD program, and the elastic modulus was calculated by applying uniform deformation on one surface. The impact strength, tensile strength, and thermal properties of the composites were improved by piling denim fabrics. The denim fabric reinforced composites exhibited outstanding impact strength due to the retarded crack propagation as well as large energy dissipation. The 3 layer denim reinforced composite showed best results among all specimens, and its impact strength, tensile strength, and tensile modulus were measured to be 82 J/m, 75.76 MPa, and 4.65 GPa, respectively. The PLA/denim composites have good mechanical properties and can substitute traditional composites such as glass fiber or carbon fiber reinforced composites.     

Evaluation of the structural properties of plain fabrics woven from various fibers using Peirce’s model

Peirce’s fabric model has been widely used to predict the structural behavior of various plain woven fabrics. The structure of plain woven fabric can be defined in terms of the warp yarn number, weft yarn number, warp fabric density, weft fabric density, warp crimp, and weft crimp. The warp and weft yarn diameters are calculated from the warp and weft yarn numbers, and the effective coefficient of the yarn diameter is defined by using this model. We have investigated structural properties, such as the effective coefficient of the yarn diameter, yarn crimp, and fabric thickness for two different fabrics in which the constituent yarns are assumed to be either incompressible or compressible. This model is also applied to various plain fabrics woven from cotton, rayon, wool, linen, nylon, acetate, polyester, and silk yarns.     

New insight into compressive shrinkage finishing in a garment company: The effects on physical,mechanical and colorimetric properties of cotton woven fabrics

Compressive shrinkage or compressive shrinkage finishing is one of the most important finishing procedures in the textile industry to improve the dimensional stability of cotton fabrics. Study of the physical and mechanical properties of compressive shrinkage finished fabrics could be useful for optimizing the treatment conditions. This research was carried out in a production line of a recognized garment company on cotton woven fabrics with two different woven patterns (twill and plain). The samples were first dyed with reactive and sulfur dyes in a jigger dyeing machine and finished with a silicone softener. The dried fabrics were then processed in a compressive shrinkage machine. Several physical and mechanical properties of the samples were evaluated including area shrinkage, crimp percentage, thickness, abrasion resistance, drapeability, mechanical and colorimetric properties. The results showed that the thickness of all treated samples increased due to compressive shrinkage. The fabrics were analyzed with a Martindale Abrasion Tester to determine the abrasion resistance. Interestingly, we noted an increase in the abrasion resistance. After the compressive shrinkage process, the strength of the plain woven fabrics decreased in the warp direction, but increased for twill woven cotton fabrics. On the contrary, the strength of all samples increased in the weft direction. Colorimetric evaluation of the samples showed that the effect of compressive shrinkage on the color of all samples was negligible.     

Effects of loom characteristics on the physical properties of PET fabrics for emotional clothing

The fabric defects complained by garment manufacturers are stop marks, streaky phenomena on the warp direction, thickness variation and color differences between edges on the right and left sides of the fabrics, which are partly due to the tension variation of warp and weft directions. It is well known that these defects are related to the difference of fabric mechanical property according to the loom characteristics and fabric position such as center and both edges parts of the fabric, which affect garment formability and wearing performance of garment. This research is focusing about which factor is dominant for the difference of fabric mechanical properties which affects garment formability and wearing performance between loom characteristic factor and fabric position factor such as center and both edges of the fabric, which is affected by warp and weft tensions. For this purpose, two kinds of looms were selected, and warp and weft tensions during weaving were measured and the mechanical properties of the fabrics woven by two kinds of looms such as tensile, bending, shear, compression and surface properties were also measured according to the positions such as center and both edge parts of the fabric. These fabric mechanical properties were examined with warp and weft tensions according to the looms and were also analyzed according to the positions of the fabric woven by two kinds of looms. The warp tension on the vicinity of center parts of the looms was much higher than those on the vicinity of both edges of looms. It revealed that the warp tension difference makes differences of fabric mechanical properties such as tensile, bending, shear and surface properties except compressional property. And the differences of these mechanical properties according to the fabric positions and looms seem to make homogeneity of the fabric hand and tailorability of garment deteriorating.     

The mechanical properties and abrasion behavior of warp knitted fabrics for footwear

The abrasion behavior of three kinds of warp knitted fabrics, which are normally used for upper sole of footwear, was evaluated. We measured the changes of mechanical and structural properties of each sample as abrasion cycle increased. Each sample showed similar trends in compression and surface properties but there were significant differences in abrasion rate among the samples. The mechanical properties showed remarkable differences with directions. The frictional coefficient (MIU) of fabric surface increased at the beginning of abrasion and decreased as abrasion cycles increased. The weight and thickness of the fabric linearly decreased with abrasion cycles. The surface roughness (SMD) and the compressional resilience (RC) decreased as abrasion cycles increased while compressional energy (WC) increased.     

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.     

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.     

Warp and weft directional tensile properties of multistitched woven fabric E-glass/polyester nano composites

The aim of this study was to understand the warp and weft directional tensile properties of the developed two dimensional (2D) multistitched multilayer E-glass/polyester woven nano composites. It was found that the warp and weft directional specific tensile strength and modulus of unstitched structure were higher than those of the machine stitched and machine stitched/nano structures due to stitching caused filament breakages. When the nano silica material in the unstitched E-glass/polyester composite structure increased, the warp and weft directional specific tensile strength and the modulus of the unstitched/nano structures increased. The failure of warp and weft directional 2D unstitched and unstitched/nano woven E-glass/polyester composite structures had a complete delamination in their cross-sections. But, the failure of warp and weft directional 2D stitched and stitched/nano woven E-glass/polyester composite structures had a local delamination in their cross-sections and the failure was confined at a narrow area. The warp and weft directional specific damaged areas of unstitched structure were higher than those of the stitched and stitched/nano structures. Also, the warp and weft directional specific damaged areas of machine stitched structure were slightly higher than those of the machine stitched/nano structure. It could be concluded that the addition of nano silica to the stitched structures improved to their damage resistance.     

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.     

Artificial neural network approach for predicting colour properties of laser-treated denim fabrics

In this study, artificial neural network (ANN) and linear regression (LR) approaches are proposed for predicting colour properties of laser-treated denim fabrics. Denim fabrics were treated under different combinations of laser processing parameters, including pixel time (μs), resolution (dot per inch) and grayscale (lightness percentage) as inputs. Colour properties, including colour yield (K/S sum value), CIE L*, a* and b* values and yellowness index were predicted as outputs in these approaches. Later, the prediction performances of two approaches were compared and the statistical findings revealed that ANN approach was able to provide more accurate prediction than LR approach, especially for L value. Moreover, among the three input variables, grayscale (lightness percentage) was found to be the most important factor affecting colour properties of laser-treated denim fabrics.     

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.