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.     

Automatic color separating system for printed fabric using the self-organizing map network approach

We can only use color numbers, color values and design to describe the color pattern of printed fabrics, which is different from woven fabrics with yarn disposition and texture as pattern determinants. Since most printed fabrics contain many different patterns nowadays, we need more than words and simple methods to describe the color patterns. The complication in pattern identification has made the analysis and comparison difficult and will have to be conducted manually. The automatic computer color separating system for printed fabrics proposed in this paper uses unsupervised learning network to automatically separate printed colors. The system first uses color scanner to pick the image of the printed fabrics and stores it as digital image. Then, it uses wavelet transformation to minify the fabric image to reduce the calculation load of color separation and also reserve the printing structure and color distribution of the original image. It also uses LAB color model to acquire characteristic value of the colors and the Self-Organizing Map Network (SOMN) to conduct color separation. According to our experimental results, this system can rapidly and automatically complete color separation and identify repeating patterns for printed fabrics’ images.     

Research on digital intelligent recognition method of the weave pattern of fabric based on the redundant information

Digital intelligent recognition for the weave pattern of fabric plays an important role to improve automation and artificial intelligence in textile production process. In order to improve the data processing efficiency and minimize the negative influence such as human error in the conventional methods, a rapid, automatic and accurate method for the surface structure analysis and the fabric weave pattern recognition is proposed. First of all, an imaging system was designed to obtain the double-faced images of fabric samples, and then the captured images were treated by projection algorithm in both warp and weft directions to generate a grid net which splits the image into massive nodes. In the following step, the nodes were preliminary classified based on the intensity of the node’s quadrilateral boundary and at the same time, the color of the nodes was calculated by using the color clustering method. To improve the accuracy of node classification, the types and color information of the adjacent nodes, together with double-faced image information, were utilized for error correction. At last, the node information acquired was encoded and expressed digitally by a basic matrix, two one-dimension matrices (row and column) and a color mapping table. Following the procedure above, the digital model of the weave pattern of the sample fabric is established. Experiments have been conducted and show the performance of the proposed method.     

Investigation of air permeability of core spun cotton/spandex weft knitted structures under relaxation

In this research work, air permeability variations of core spun cotton/spandex single jersey and 1×1 rib knitted structures were studied under relaxation treatments. Results are compared with similar fabrics made from 100 % cotton material. Even though cotton/spandex fabrics knitted with same stitch lengths, their structural spacing and stitch densities vary with the progression of treatments. Similar behavior was also observed with 100 % cotton knitted structures. Under higher machine set stitch lengths (i.e., lower fabric tightness factor), higher structural spacing and lower stitch densities were resulted and those variations significantly affected on the air permeability variations of knitted structures. 1×1 rib knitted structures showed significantly higher air permeability than single jersey structures and it is more prominent with cotton rib structures. However, cotton/spandex 1×1 rib and single jersey structures have not showed such significant deviations. Air permeability of cotton/spandex and 100 % cotton rib and single jersey knitted structures decreased with lower machine set stitch lengths (i.e., at higher fabric tightness factors). There was a correlation with fabric tightness, air permeability, areal density and fabric thickness such as knitted fabrics became tighter, their weight and thickness were higher, while their air permeability was lower. Thus, fabric areal density and fabric thickness are positively correlates to machine set stitch length^?1 (fabric tightness factor). Air permeability of a knitted structure depends on material type, knitted structure, stitch length, relaxation treatment, structural spacing and stitch density.     

Predicting properties of single jersey fabrics using regression and artificial neural network models

In our previous works, we had predicted cotton ring yarn properties from the fiber properties successfully by regression and ANN models. In this study both regression and artificial neural network has been applied for the prediction of the bursting strength and air permeability of single jersey knitted fabrics. Fiber properties measured by HVI instrument and yarn properties were selected as independent variables together with wales’ and courses’ number per square centimeter. Firstly conventional ring yarns were produced from six different types of cotton in four different yarn counts (Ne 20, Ne 25, Ne 30, and Ne 35) and three different twist multipliers (α _e 3.8, α _e 4.2, and α _e 4.6). All the yarns were knitted by laboratory circular knitting machine. Regression and ANN models were developed to predict the fabric properties. It was found that all models can be used to predict the single jersey fabric properties successfully. However, ANN models exhibit higher predictive power than the regression models.     

Flexural stiffness of core spun cotton/spandex weft knitted structures under relaxation and machine washing treatments

In this research work, behavior of flexural stiffness of core spun cotton spandex single jersey, 1x1 rib and interlock fabrics was studied under relaxation and machine washing treatments. Results are compared with similar fabrics made from 100 % cotton. Fabric weight density increased with the progression of treatments and it is proportionate to the fabric tightness factor (stitch length^?1). Even though both types of fabrics had same machine set stitch lengths, cotton/spandex fabrics have shown the higher fabric weight densities than that of 100 % cotton fabrics. Although 1x1 rib and single jersey fabrics knitted with the same machine set stitch lengths, rib fabrics have given higher fabric weight densities than single jersey fabrics. Among the three knitted structures, interlock fabrics with higher machine set stitch lengths gave the higher fabric weights. Fabric stiffness and flexural rigidity have given higher values under the progression of treatments and it was found that higher values of stiffness have given by cotton/spandex knitted fabrics compared to their cotton fabrics. Fabric stiffness and flexural rigidity in wale direction were higher than that in course direction, but it is only observed in single jersey fabrics. However, 1x1 rib and interlock fabrics have shown an opposite behavior. It was also observed a positive correlation between TF (i.e.: stitch length^?1) and bending length/flexural rigidity in both fabric types. Lower flexural rigidities reported with single jersey structures and highest values gave with interlock structures of cotton/spandex and cotton fabrics.     

利用无人机图像颜色与纹理特征数据在小麦生育前期对产量进行预测

为了实现基于无人机的小麦产量快速预测，通过不同种植密度、氮肥和品种的田间试验，应用无人机航拍获取小麦生育前期（越冬前期和拔节期）的RGB图像，通过图像处理获取小麦田间颜色和纹理特征指数，并在小麦收获后测定实际产量。通过分析不同颜色和纹理特征指数与小麦产量的关系，筛选出适合小麦产量预测的颜色和纹理特征指数，建立小麦产量预测模型并进行验证。结果表明，小麦生育前期图像颜色指数与产量的相关性较好，而纹理特征指数相关性较差。对越冬前期利用单一颜色指数NDI构建的产量预测模型验证时，R为0.541，RMSE为671.26 kg·hm^-2；对拔节期用单一颜色指数VARI构建的产量预测模型验证时，R为0.603，RMSE为639.78 kg·hm^-2，预测结果比较理想，但不是最优。对越冬前期颜色指数NDI和纹理特征指数ENT相结合构建的产量预测模型验证时，R和RMSE分别为0.629和611.82 kg·hm^-2，比单一颜色指数模型分别提升16.27%和减小8.85%；对拔节期颜色指数VARI和纹理特征指数COR相结合构建的产量预测模型验证时，R和RMSE分别为0.746和510.29 kg·hm^-2，较单一颜色指数模型分别提升23.71%和减小20.24%。上述结果说明，将无人机图像颜色和纹理特征指数相结合建立的估产模型精度较高，可在小麦生育前期对产量进行有效预测。     

The robustness of objective fabric pilling evaluation method

Previously, we proposed a new method to identify fabric pilling and objectively measure fabric pilling intensity based on the two-dimensional dual-tree complex wavelet reconstruction and neural network classification. Here we further evaluate the robustness of the method. Our results indicate that the pilling identification method is robust to significant variation in the brightness and contrast of the image, rotation of the image, and 2 ⁱ (i is an integer) times dilation of the image. The pilling feature vector developed to characterize the pilling intensity is robust to brightness change but is sensitive to large rotations of the image. As long as all fabric images are adjusted to have the same contrast level and the sample is illuminated from the same direction, the pilling feature vectors are comparable and can be used to classify the pilling intensity.     

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 %).     

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.     

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.     

Automatic inspection of double-system-mélange yarn-dyed fabric density with color-gradient image

According to the color yarns in the fabric, the yarn-dyed fabrics are divided into two categories: single-systemmélange color fabrics and double-system-mélange color fabrics. The method for inspecting the density of double-systemmélange color fabrics is discussed in this study. By analyzing the pattern and color characters of double-system-mélange color fabrics, color-gradient image is proposed to detect the density. The gray-projection method and correlation coefficient method are selected to locate the wefts and warps. With the help of Fourier low-pass filter, the positions of yarns in double-system-mélange color fabric are found, and then the density can be obtained by counting the yarns in a unit length automatically. The experiment proved that the method proposed can detect double-system-mélange color fabric density successfully.     

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.     

Mechanical properties and crystallization behavior of polypropylene non-woven fabrics reinforced with POSS and SiO2 nanoparticles

PP/POSS and PP/SiO₂ composite non-woven fabrics filled with polyhedral oligomeric silsesquioxanes (POSS) and SiO₂ respectively using a convenient blending method were prepared through melt-blown process with corona charging. The morphology of the composite fibers and the distribution of POSS and SiO₂ nanoparticles in PP matrix were investigated by field-emission scanning electron microscope (FSEM) and transmission electron microscope (TEM), respectively. POSS and SiO₂ can act as nucleating agent and accelerate the crystallization process during nonisothermal cooling. The shear storage modulus G??, loss modulus G??, and complex viscosity ??* of non-woven fabric reduce when 1 wt % POSS was added and increase for PP5/POSS composite non-woven fabric compared with pure PP non-woven fabrics. However, all G??, G?? and ??* of PP/SiO₂ non-woven fabric decrease with increasing SiO₂ content owing to plasticization by SiO₂. Both stress and elongation at break of the PP/POSS melt-blown non-woven fabrics are improved compared with PP non-woven fabrics, however decrease when SiO₂ was added, as compared to the neat PP non-woven fabric. The onset temperature of decomposition for both the PP/POSS and PP/SiO₂ composite non-woven fabrics is higher (5?C10 °C) than pure PP and char content is increased with increasing POSS and SiO₂.     

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.     

Prediction of the fabric’s crease recovery property with Taguchi design of experiment

The creasing characteristic of fabrics is affected by many factors like yarn twist, fabric density, fabric constructions, fabric thickness apart from the fiber type. In the first part of this study, the effect of yarn fineness, yarn twist, fabric tightness and weave construction factors on crease recovery was studied. In the second part of the study, in order to improve the creasing recovery of the fabrics, shape memory alloy (SMA) wires were used and the effect of shape memory alloy (SMA) wire on the crease recovery of cotton fabrics produced with different types of weave constructions were determined. Due to the high cost of SMA wire and the weaving operation adversity the two experimental plans were designed according to Taguchi design of experiment (TDOE). From the analysis of the first part, it was found that the yarn linear density had the greatest effect on fabric crease recovery compare to others. Twist coefficient was the second, weft density was third and the weave construction had the least significant effect on the crease recovery. The fabrics produced with coarser and low twisted yarns with high tightness and longer floats in the weave construction have higher crease recovery property. In the second part of the study, the application of the SMA wire significantly increased the crease recovery angle of the fabrics. The thickness of the SMA wire is very important and the effect depends on the wire thickness. The increase of the SMA wire thickness increases the crease recovery significantly. However it must be appropriate with the yarn and fabric properties. The distance between the SMA wire distances was expected to increase the crease recovery however the effect was found not significant. The fabrics produced with coarser yarns with longer floats in the weave construction have higher crease recovery property. However, statistically the effects of these parameters were found not significant due to the dominant effect of the wire thickness.     

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.     

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.     

Ultra-porous flexible PET/Aerogel blanket for sound absorption and thermal insulation

Ultra porous and flexible PET/Aerogel blankets were prepared at ambient pressure, and their acoustic and thermal insulation properties were characterized. Two methods were selected for the preparation of PET/Aerogel blanket. Method I was a direct gelation of silica on PET. PET non-woven fabric was dipped and swelled in TEOS/ethanol mixture, and pH of reaction media was controlled to 2.5 using HCl to promote hydrolysis. After acid hydrolysis, pH was controlled to 7,8,9, and 10 with NH₄OH for the condensation. Method II was by the dipping of PET non-woven fabric in the dispersion of Silica hydrogel. The gelation process was same with Method I. However, PET fabric was not dipped in reaction media. After the hydrogel was dispersed and aged in EtOH for 24 hrs, then, PET non-woven fabric was dipped in the dispersion of hydrogel/EtOH for 24 hrs. The surface modification was carried out in TMCS/n-hexane solution, then the blanket was washed with nhexane and dried at room temperature to prevent the shrinkage. The silica areogels synthesized in optimum conditions exhibit porous network structure. Silica aerogel of highly homogeneous and smallest spherical particle clusters with pores was prepared by gelation process at pH 7. When direct gelation of silica was performed in PET nonwoven matrix (Method I), silica aerogel clusters were formed efficiently surrounding PET fibers forming network structure. The existence of a great amount of silica aerogel of more homogeneous and smaller size in the cell wall material has positive effect on the sound absorption and thermal insulation.     

Image analysis of the luster of fabrics with modified cross-section fibers

We have investigated the luster of modified cross-sectional fiber fabrics as one of the essential quality estimates for clothing development. We have confirmed an objective evaluation method, and have determined the experimental luster characteristics of modified cross-section fibers. The cross-section of the fibers in a fabric affects the appearance of a textile. We used the image analysis method to investigate the luster to determine the critical factors influencing the appearance of modified cross-section fiber fabrics. For similarly structured textiles in a component fabric, clear differences were observed in the fabric weave, density, percentage, and total area of blobs, which is image region. Color played a decisive role in the luster of the textiles, and luster was not significantly influenced by the modified cross-section fabric weave. In addition, the degree of luster did not increase in the order plain to twill to satin for modified cross-sectional fiber fabrics. All the split-type microfibers exhibited higher numerical luster values (percentage of pixels, and number and total area of blobs) than sea-island microfibers did. The degree of luster of the modified cross-sectional fiber fabrics was not high at specular reflection angles.