Crimp analysis of worsted fabrics in the terms of fabric extension behaviour

Inside a woven fabric structure, warp and weft yarns acquire crimp as a result of yarns interlacing according to the weave pattern. Since warp and weft yarns are oriented in two perpendicular directions, applying tensile load in one direction causes extension in the load side and fabric contraction in the opposite direction. This process was investigated in this study by using an image processing procedure and it was found that fabric’s extension is in coincidence with yarn’s de-crimping process in the same direction. After the de-crimping stage, yarns in the load direction will be extended and at the same time crimp in the other direction will be increased, until jamming phenomenon happens in the fabric structure. The crimp interchange between warp and weft yarns follows a three-order polynomial function with a turning point in which the yarns in the load direction have no crimp.     

Applying image analysis to automatic inspection of fabric density for woven fabrics

The gray line-profile method is introduced to find fabric density. Some patterned fabrics like stripe design as well as solid fabrics of basic weave structures are used to verify the efficiency and accuracy of the method. The approach is compared with Fourier transform method. Although the gray line-profile method is concise, it shows good results in both solid and patterned fabrics. In addition, it does not require a pre-processing or filtering technique in space or frequency domain to enhance the image suitable for the analysis. However, the approach is slightly influenced by the filter size for finding the local minimums of profile graph.     

Seam pucker and formability of the worsted fabrics

The purpose of this study is to objectively evaluate the seam pucker as a fabric formability and to analyze the effects of the fabric structural and sewing parameters to the seam pucker properties of the worsted and wool/polyester blend fabrics. The seam pucker of the worsted and wool/polyester blend fabrics according to the different types of fibre composition were surveyed by measuring fabric mechanical property using KES-FB and FAST systems and also seam puckering test was conducted by objective and subjective methods. In addition, the effects of fabric structural and sewing parameters to the seam pucker were investigated using statistical analysis. It was revealed that the effects of fabric structural parameters to the seam pucker were dominant than those of sewing machine conditions. It was shown that low pressor foot pressure and shuttle tension make good seam pucker, and high sewing speed and shuttle tension make bad seam pucker. And it was also shown that the precision on estimating of the seam pucker by KES-FB system was much higher than that of FAST system. And seam pucker values between objective and subjective methods showed good correlation.     

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.     

Analysis of woven fabric structure using image analysis and artificial intelligence

An integrated hardware and software system has been developed that can automate the analysis process of various woven fabric structures. Although the analysis of woven structure is one of the most important steps in the fabric design and quality control process, it has been dependent only on human skills with primitive devices. In this study, a dedicated hardware system has been developed to obtain an ultra-high resolution fabric images. Then a series of image analysis technique was applied to locate the intersecting regions of warps and wefts on those images. Finally, an artificial neural network was formed to determine the woven structure of fabric based on the two shape parameters extracted from those regions.     

Study of the near-infrared transmission of woven fabrics based on statistical analysis

47 kinds of woven fabrics were prepared with different fibers, thicknesses, weights per square meter and percentage covers. Statistical analysis method was employed to study the influence of the material and structure parameters on near-infrared transmission of these fabrics. Firstly, the effect of materials on near-infrared transmittance was determined through the contingency table analysis. Then the parameters significantly influencing near-infrared transmittance were found out and correlations among them were discussed by direct correlation and partial correlation analysis. Finally, regression equation of the near-infrared transmittance was established using factor analysis and principal component extraction. Results show that materials have little influence on near-infrared transmittance of woven fabrics. However, thickness, weight per square meter and percentage cover of the fabrics are closely related to near-infrared transmittance. Because there are correlations among thickness, weight per square meter and percentage cover, two new factors are extracted to establish the regression equation. The equation has an error rate of less than 5 % and thus can predict near-infrared transmittance precisely.     

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.     

Application of the real fabric frictional speeds to the fabric sound analysis using water repellent fabrics

This study aims to determine fabric frictional speeds between the arm and the trunk when people walk (1.3 m/s), jog (2.5 m/s) and run (4.5 m/s), and to apply the measured speeds to setting a sound generator for each motion to obtain fabric rustling sounds. By analyzing body motions captured by the Falcon motion analysis system and a camcorder, it was identified that the friction between the arm and trunk occurred within 10° of shoulder angle along the center line of the trunk in the sagittal plane and the maximum frictional speed occurred at the elbow within the shoulder friction range. The averages (SDs) of maximum frictional speed at the elbow were found 0.63 m/s (0.17) at walking, 1.1 m/s (0.25) at jogging, and 1.98 m/s (0.35) at running. The frictional sounds of three coated nylon fabrics were obtained using these predetermined speeds. We calculated sound characteristics such as the sound pressure levels (SPL) and Zwicker’s psychoacoustic parameter using 1/3 octave band analysis. The SPL values ranged from 74.2 dB at running to 79.0 dB at jogging, which was about the same noise level as in the busy street. The values of loudness (Z) at walking and jogging were higher than that at running, but the fluctuation strength (Z) increased in the order of walking, jogging, and running.     

Investigation of mechanical properties of basalt woven fabrics by theoretical and image analysis methods

This paper explains a study conducted to evaluate mechanical properties of woven structures of basalt. The mechanical properties like shear strength and tensile strength were studied. Tensile properties of basalt hybrid and non-hybrid fabrics are predicted by computational tool and verified with experimental data. The shear strength was investigated by using picture frame fixture. The tests were recorded by a CCD monochrome camera during displacement of specimen at various positions. The images were used for image analysis program developed in MATLAB. The results of image analysis were compared with the actual experimental results. The results illustrate that the mechanical properties of fabrics experience a marked improvement when hybridization of basalt with polyester and polypropylene in different sets of weaves takes place. These findings are important requirements for using such fabrics for high-tech applications and composite forming.     

Behaviour of the twill weave woven fabrics during relaxation

This work looks into the behaviour of the twill weave woven fabrics during relaxation (when the weaving tension is released). Ten, 50-metre rolls of twill weave woven fabrics were produced. The fabrics were marked in a rectangular form at the weaving loom. After 48 hours of relaxation, the new shapes and sizes were recorded. The shapes of almost all of the samples were changed to parallelogram, even though they differed in size. The work showed that the manner of fabric deformation during relaxation depends upon the fabric structure. It indicates that contraction due to relaxation of the twill weave causes the woven fabric to skew. in the direction of the twill. The quantity of the skewness is related to the float length andthe twill type. Fabrics with longer float length have higher skewness.     

Simulation of the weave structural design of synthetic woven fabrics

This study surveys the basic procedure of data base system of the fabric structural design which can be linked with existing pattern design and garment design CAD systems. For this purpose, the theoretical and empirical equations related to the fabric structural design are analyzed and discussed with various fabric specimens. The fabric structural parameters such as weave density coefficient, cover factor and yarn density coefficient of various kinds of fabrics are calculated using the empirical equations. These calculated fabric structural parameters of many kinds of polyester and nylon fabrics are compared and discussed with weave pattern, and materials such as polyester and nylon. Furthermore the difference between fabric structural parameters calculated by empirical equations are analyzed with polyester and nylon fabrics as a basic study for data base system of the fabric structural design. Finally, the weave density coefficients of polyester and nylon fabrics were analysed and discussed with shrinkages of dyeing and finishing processes, and also surveyed according to the weaving company and weave structural parameters such as weave pattern and denier.     

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.     

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.     

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.     

Mechanical properties of worsted fabrics for emotional garment to the rapier loom characteristics

This study examined the mechanical properties of worsted fabrics woven using various rapier weaving looms. For this purpose, the warp and weft yarn tensions during weaving were measured on the three types of rapier looms, and the fabric mechanical property changes due to the warp and weft tension differences were measured and analyzed according to the fabric position and particular rapier loom using the KES-FB system. The warp tension variation along the loom width direction in P-GTX loom showed the lowest value compared to FAST and THEMA looms. The warp tensions on the central part of the three types of looms were much higher than those on the left and right sides of the looms. The extensibility and bending rigidity of the fabric woven by P-GTX rapier loom showed lower values than those of FAST and THEMA looms, which appears to have originated from the low warp and weft weaving tensions of P-GTX rapier loom. On the other hand, the compressional property and shear modulus showed compromised results due to lateral deformation by compression and constraint deformation of the warp and weft by shear. The friction coefficient of the fabric surface woven by FAST loom showed the lowest value due to the flatter surface by the high warp tension. The mechanical properties of the fabric loaded by a high warp tension on the central part of the loom were also affected by the high weft yarn crimp due to the wider spacing between warp yarns by the higher warp tension during weaving, which makes the surface of the central part of the fabric flatter and smoother than the edge part of the fabric.     

The effect of twist liveliness on the woven fabric distortion

This paper deals with the grey fabric deformation during relaxation. It investigates the effect of twist direction and twist liveliness on the 2/2 twill weave woven fabric distortion, during relaxation This experimental work shows that twist liveliness increases the natural tendency of the twill weave woven fabric to skew. In addition when the twist direction of the warp and weft is opposite to each other the bias curling occurs too.     

Influence of the deformation mode on seam slippage in woven fabrics

In the present study the influence of the deformation mode, of the specimen elongation deformation, of the fabrics?? weave type, of fabrics?? direction and of the position of seam allowances in respect to the stitching line on the seam slippage in the raw plain, twill and combined-twill weave fabrics was investigated. Fabrics were woven with the warp yarn of 20×2 tex 70 % cotton and 30 % PES blended 2-ply spun yarn and the weft yarn of 18 tex 100 % PES folded multifilament yarn using jacquard weaving machine ??Lindaucer DORNER GmbH??. Seam slippage of the investigated woven fabrics was determined using the new simple and compact technical device suitable to test fabrics for seam slippage property within five different deformation modes: an uniaxial tension of seams with opened seam allowances on the surface of metal table; an uniaxial tension of seams with bent to one side allowances in respect to stitching line on the surface of the metal table; an unrestricted uniaxial tension of seams; a bagging of seams with bent to one side allowances in respect to the stitching line using the plastic hollow cylinder; a bagging of seams with opened seam allowances using the plastic hollow cylinder. The results of the research had proved that seam slippage of the investigated woven fabrics was dependent on the deformation mode, on the elongation of sewn specimens, on the location of allowances in respect to the stitching line, on the fabric weave type as well as on the woven fabric direction.     

Automatic recognition of woven fabric patterns based on pattern database

In traditional method, the woven fabric pattern can not be easily recognized because of the error judgments of float type. To solve this problem, a novel method based on the pattern database was proposed to detect the woven fabric pattern in this study. FCM algorithm was used to classify the floats in the fabric image into two categories, warp floats and weft floats. The classification result then was then compared with the standard patterns in the pattern database, and the similar coefficients between them were calculated. The pattern which has the largest similar coefficient with the classification result was considered as the correct woven pattern, and its diagram and name were output in the system. The test for actual fabric images indicated that the method proposed can recognize the fabric patterns effectively, and the diagram and name of the fabric pattern can be recognized simultaneously. The experiments can also prove that the algorithm which is based on pattern database has good fault tolerant ability, and it can recognize the fabric patterns with a range of error judgments.     

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.