Characterizations of stick-slip stage of yarn pull-out in dry and softening treated polyester plain woven fabric

The aim of this study was to understand the stick-slip properties of polyester plain woven fabric. For this reason, pull-out test was conducted on dry and softening treated polyester woven fabrics. It was found that stick-slip force and accumulative retraction force depend on the number of pulled ends in the fabric, fabric sample dimensions and softening treatments. Stick-slip forces of polyester fabric in the multiple yarn pull-out test were higher than those of the single yarn pull-out test. Stick-slip force in single and multiple yarn pull-out tests in the dry polyester plain fabric were generally higher than those of the softening treated polyester plain fabric. On the other hand, the amount of stick-slip force was related to the number of interlacement points in the fabric whereas the amount of accumulative retraction force was related to fabric structural response.     

Determination of para-aramid single fabric shear by yarn pull-out and analysis by statistical model

The aim of this study was to determine the para-aramid fabric shear by the pull-out method. The fabric sample dimensions and the number of pull-out ends were identified as important parameters. Fabric shear depended on fabric density. Fabric shear strength increased when the number of pulled ends increased. When the fabric length increased, fabric shear strength generally increased. The number of pulled ends and the fabric sample dimensions influenced the fabric shear rigidity. Shear jamming angles were found based on the number of pulled ends. The results showed that para-aramid fabric shear could be measured by yarn pull-out test.     

Effects of sample dimensions on pull-out properties of woven fabric structures

The aim of this study was to understand the effects of fabric sample dimensions on pull-out properties of fabric weaves. Polyester woven fabrics were used to conduct the pull-out tests. A yarn pull-out fixture was developed and data generated from this research. Yarn pull-out forces depend on sample dimensions, fabric density, fabric weave, and number of pulled ends in the fabric. Results showed that multiple and single yarn pull-out forces of long samples were higher than those of short samples, and the multiple yarn pull-out force was higher than that of the single yarn pull-out force, and dense fabric has high pull-out force. Plain fabric weave showed high single and multiple pull-out forces compared to ribs and satin fabric weaves. The regression model could be used in this study as a viable and reliable tool. This research could be valuable for development of multifunctional fabrics in technical textile applications.     

Analysis and in-plane shear characterization of polyester plain fabric by yarn pull-out method

The aim of this study was to determine the in-plane shear properties of polyester fabric by the pull-out method and analytical relations were developed to calculate the shearing properties. After the yarn in the fabric was pulled from the top ravel region before the start of the crimp extension stage, it was found that fabric shear strength and rigidity increased when the number of pulled ends increased. In addition, when the fabric width and length increased, fabric shear strength and rigidity increased. On the other hand, the shear strength and rigidity values in untreated fabric were high compared to that of treated fabric due to the fabric treatments by softening agent. It was observed that fabric sample dimensions and the number of pull-out ends as well as the fabric treatments influenced fabric shear strength and rigidity. Also, the shear jamming angles were found to be based on the number of pulled ends. Fabric local shearing properties could be identified by pulling the yarn ends in various regions of the fabric. This could be important for the handling of the fabric during formation. The results generated from this study showed that polyester fabric shear could be measured by the yarn pull-out test.     

Pull-out properties of polyester woven fabrics: Effects of softening agent and interlacement on single and multiple yarn pull-out forces and analysis by statistical model

The aim of this study was to understand the effects of softening treatment on pull-out properties of plain, ribs and satin fabrics. Polyester woven fabrics were used to conduct the pull-out tests. Data generated from these tests included pullout force, crimp extension and fabric displacement. A developed yarn pull-out fixture was used to perform single and multiple pull-out tests on treated and untreated polyester fabrics. Yarn pull-out forces depend on fabric treatments, fabric density, fabric weave, and the number of pulled ends in the fabric. The results of regression model showed that multiple and single yarn pull-out forces of treated fabrics were lower than those of untreated fabrics. The multiple yarn pull-out force was higher than that of the single yarn pull-out force, and that dense fabric had a high pull-out force. Treated and untreated plain fabrics had high single and multiple pull-out forces compared to those of treated and untreated ribs and satin fabrics. Yarn crimp extension depends on directional crimp ratios in the fabric and the number of pulled yarn ends. High directional crimp ratio fabric showed high directional yarn crimp extension. Fabric displacement depends on the number of pulled yarn ends and also fabric treatments. Fabric displacement in multiple pull-out tests showed high fabric displacement compared to that of single pull-out tests. On the other hand, the regression model could be used in this study as a viable and reliable tool.     

Formability analysis of worsted woven fabrics considering fabric direction

Over years predicting fabric behaviour during garment manufacturing process was considered by researchers in order to reduce manufacturing problems and achieve high quality products. Fabric formability which is affected by bending and tensile behaviour through the application of small loads is a property which can predict fabric performance precisely. However, this property changes regarding fabric direction and is not constant. In this study, fabric formability is examined for worsted woven fabrics in a more detailed way by evaluating this property in various fabric directions. It was concluded that fabric formability could be expressed as a sinusoidal function of sample orientation towards warp direction. Moreover, studying several weave structures with different weft densities reveals that the less the firmness of fabric construction the more the value of fabric formability which indicates the better adaption of fabric to the exerted deformation.     

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.     

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.     

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.     

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.     

Deformation analysis of composite reinforcing fabrics through yarn pull-out, drape and shear tests

This study reveals a new method based on image processing of bias extension test results to determine shear angle that characterizes the shear properties of fabric reinforcements. This way the simple and exact determination of shear angle with conventional devices is solved. The new method was tested on fabric reinforcements made of carbon, aramid and glass fibers and the results were compared with that of two known versions of bias extension tests. The analysis of the relation between shear angle and other deformation properties that characterize spatial deformation behavior involved the comparison of shear test results with yarn pull-out and drape tests carried out with special methods.     

Evaluation of anti-stabbing performance of fabric layers woven with various hybrid yarns under different fabric conditions

Various types of special fibers are used for human body protection, mostly in the form of fiber-reinforced composites. These composites are made of special fibers and matrix resin; however, they are often not comfortable for the wearer due to the lack of flexibility and air permeability. This study focuses on an evaluation of human body protective performance against stabbing for various special fibers such as aramid, basalt, and steel fibers, being utilized in cotton hybrid forms. These hybrid forms are designed to improve wearer comfort, while maintaining adequate anti-stab resistance. Specimens prepared with various fabric densities are tested in terms of anti-stabbing performance, according to the NIJ standard. In addition, we investigate the influence of factors such as fiber type, the number of fabric layers, fabric weight, and fabric density on anti-stabbing performance. Results show that the penetration depth of the impactor, which punctures and protrudes through the specimens, decreases with the number of layers, the thickness, and the mass of the armor sample; however, these factors have different relationships according to the material type. Consequently an objective evaluation of anti-stabbing performance is needed. We suggest an anti-stabbing index that can be applied as a criterion to evaluate the antistabbing performance of various specimens woven with special fibers under different fabric conditions. Using the new index, anti-stabbing performances of various specimens can be compared and raw material and fabric conditions that offer the most efficient anti-stabbing performance can be selected.     

Dyeing and fastness properties of vat dyes on meta-aramid woven fabric

meta-Aramid fibers have an excellent heat-resistant property and are widely used for protective clothings such as fire-fighter suit and racing suit. They can also be used as military uniforms such as flight suit or army uniform. Vat dyes are specially used for military uniforms owing to outstanding fastness properties, earth tone shade, and near infrared (NIR) camouflage. In this study, 100 % meta-aramid woven fabric was dyed with three vat dyes using an exhaustion method and their dyeing and fastness properties were investigated. Color yields of the vat dyes on the meta-aramid fabric were found to be dependent upon dyeing temperature, liquor ratio, amount of reducing agent, and amount of salt. Dyeing behavior of the vat dye on the meta-aramid fiber was very similar to that on cellulose fibers. It was found that the meta-aramid fabric dyed with 1% owf of C.I. Vat Green 1 satisfied the tolerance of the reflectance spectrum of forest green color in the Korean military standard. Thermal stability and mechanical property of the meta-aramid fabric did not significantly affected by the vat dyeing process. Wash and perspiration fastness was generally good but rubbing and light fastness was unsatisfied.     

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.     

全球棉花及棉纺织品和服装零售的趋势

1全球棉价走势 全球棉价的长期平均值为每磅72美分。自1995年WTO农业协议生效以来，全球棉价持续走低，2001年跌至每磅42美分左右，是29年以来的最低水平（见图1）。仅在2003年11月棉价才在短期内突破76美分（见图2）。随后又急剧下跌，于2004年12月跌至每磅43美分。棉价上涨的主要原因是棉价触底后。全球棉花库存持续减少，供需失衡造成的。近期棉价（2005年11月17日）维持在每磅56美分左右。     

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.     

Automatic classification of woven fabric structure based on texture feature and PNN

In today’s textile industry, the classification of woven fabrics is usually manual which requires considerable human efforts and a long time. With the rapid development of computer vision, the automatic and efficient methods for woven fabric classification are desperately needed. This paper proposes an automatic and real-time classification method to analyze three woven fabrics: plain, twill and satin weave. The methodology involves two approaches to extract texture features, that is, gray-level co-occurrence matrix (GLCM) and Gabor wavelet. Then, principal component analysis (PCA) is utilized to deal with the texture feature vectors to gain minimize redundancy and maximize principal component feature vectors. Finally, in the classification phase, probabilistic neural network (PNN) is applied to classify three basic woven fabrics. With strong realtime, fault-tolerance and non-linear classification capability, PNN can be a promising tool for classification of woven fabrics. The experimental results show that PNN classifier with faster training speed can classify woven fabrics accurately and efficiently. Besides, compared with GLCM method and Gabor wavelet method, the fusion of the two feature vectors obtains the best classification result (95 %).     

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.