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.     

A scanner based neural network technique for color matching of dyed cotton with reactive dye

Conventional theory for color matching is Kubelka-Munk, but it fails in some situations. New intelligent procedures such as neural networks could learn the behavior of a complex system and produce accurate prediction. This paper investigates the ability of MLP (multiple layer perceptron) neural network for color matching of cotton fabric. Three reactive dyes, namely Levafix Red CA, Levafix Yellow CA and Levafix Blue CA were used for experiments. The dyed samples were scanned and L * a * b * histogram were extracted. Different neural networks were trained and tested using L * a * b * histogram of fabric’s images and also L * a * b * values (D65, 10°) of fabrics. The results were encouraging. For neural networks including the L * a * b * histogram in input vector, colorants and their concentration were predicted with a mean square error (MSE) less than 10^?5 and an average value of color difference (CMC (1:2)) less than 1.5 for approximately 80 % of testing data.     

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.     

Digital ink-jet printing for chitosan-treated cotton fabric

In this paper, chitosan was suggested for using as a replacement for sodium alginate in the pretreatment print paste for digital ink-jet printing for cotton fabric. Pretreatment print pastes prepared from the mixture of chitosan and acetic acid with the appropriate viscosity gave satisfactory prints on the cotton fabric. Chitosan-treated cotton fabrics were digitally irk-jet printed with four different colors and the color fastness rating of the printed fabrics was satisfactory. Experimental results revealed the possibility of pre-treating the cotton with chitosan to replace the sodium alginate normally present in the pretreatment print paste recipe.     

Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models

Changes on the CIELab values of the dyed materials after the different chemical finishing treatments using artificial neural network (ANN) and linear regression (LR) models have been predicted. The whole structural properties of fabrics and some process data which were from fiber to the finishing parameters were accepted as inputs in these models. The networks having different structures were established, and it was also focus on the parameters which could affect the performance of the established networks. It was determined that we could successfully predict the color differences values occurring on the material after the finishing applications. In addition, we realized that some ANN parameters affected the prediction performance while establishing the models. After training ANN models, the prediction of the color difference values was also tried by linear regression models. Then, extra ANN models were established for all outputs using the parameters as inputs in the LR equations, and the prediction performances of both established models were compared. According to the results, the neural network model gives a more accurate prediction performance than the LR models.     

Evaluation of the Electromagnetic Shielding Effectiveness of Carbon-Based Screen Printed Polyester Fabrics

Electromagnetic shielding has a very emerging role in the textile applications. Screen-printing is a well-known, easy and cost effective process for textile printing. In this study, carbon black and graphite particles were used to impart electromagnetic shielding property to polyester fabric by screen printing technique. To this aim, printing pastes containing carbon materials were prepared with different binder concentrations. The electrical resistance, surface morphology, color coordinates and washing fastness properties of screen printed polyester fabrics were investigated. The washing durability of electromagnetic shielding effectiveness of carbon based printed fabrics as a function of binder concentration have also been studied. Electromagnetic shielding effectiveness was evaluated in the frequency ranges between 15-3000 MHz. The results showed that the electromagnetic shielding properties of fabric were affected by increased binder concentration. The most durable electromagnetic shielding effectiveness after washing process was obtained at highest binder concentration. The surface morphologies and color difference values of printed fabrics after washing process also provided a positive contribution.     

基于霍特林变换的稻米彩色图像粒形检测算法

利用稻米分割后轮廓灰度图与背景灰度图的灰度均值之差和灰度方差之差进行米粒图像分割效果定量评价,对7个彩色通道的稻米图像进行分割评判,选取I1(红色、绿色、蓝色通道的平均值)通道进行稻米图像分割。提取分割后标注的单粒米粒边界的二维坐标向量,对坐标向量进行霍特林变换,通过计算变换后米粒最小外接矩阵来表征稻米粒形,简化了现有的稻米粒形检测算法。检测稻米粒型时,算法在MATLAB7.5.0环境下运行。该算法所得米粒长宽比与人工检测结果的平均相对误差为1.65%,每幅图像平均耗时0.323s;而最小外接矩形算法的平均相对误差为2.24%,每幅图像平均耗时2·837s。     

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.     

影响茶叶包装色彩设计的因素

远看色,近看花,自古以来人们就认识到色彩具有先声夺人的视觉效果。因此,色彩对于需要具有强大的货架冲击力的茶叶包装设计来说具有举足轻重的作用。本文在对市场上茶叶包装进行相关调查后,初步归纳了几种较为明显的影响茶叶包装设计的色彩因素,以作为今后茶叶包装色彩方案的一些参考之用。     

The effect of carbon black nanoparticles on some properties of air plasma printed cotton/polyamide 6 fabrics

In this study, cotton/nylon blended fabrics were treated with atmospheric air plasma at various times (30–60 s) and were subsequently printed with pastes containing carbon black nanoparticles. Properties of plasma treated fabrics such as visible-near infrared (Vis-NIR) reflectance, water contact angle, air permeability, and color fastness were measured. It was shown that increasing plasma treatment time decreases reflection level of treated fabrics in Vis-NIR region. Plasma treatment also enhanced the hydrophobicity of cotton/nylon fabrics observed by an increase in water contact angle. Plasma treated samples for 60 s demonstrated lower air permeability than those treated for 30 s. Furthermore, printed samples possessed acceptable levels of fastness against washing, light and crocking.     

Plasma-assisted surface modification of polyester fabric for developing halochromic properties

In the field of textiles, introducing pH-sensitive dyes onto fibrous materials is a promising approach for the development of flexible sensor. In this study, poly(ethylene terephthalate) (PET) textile surface with halochromic properties was fabricated by plasma-assisted sol-gel coating, followed by immobilization of two different azo pH-indicator dyes; namely Brilliant yellow and Congo red by conventional printing technique of fabrics. 3-aminopropyltriethoxysilane (APTES) was used as a coupling agent for attaching the pH-sensitive dyes through its terminal amines. The surface immobilization of APTES on PET fabric was conducted by the pad-dry-cure method. Moreover, the influence of oxygen plasma pre-treatment and the method of post-treatment either by oxygen plasma or by thermal treatment on the stability of sol-gel based matrix was investigated. The morphology and chemistry of 3-aminopropyltriethoxysilane coated PET surfaces were examined by using surface sensitive methods including electrokinetic and time-dependent contact angle measurements as well as X-ray photoelectron spectroscopy (XPS). In addition, fastness tests of the printed fabrics and color strength were carried out to assess the effectiveness of the fabric surface modification. Results indicate that sol-gel matrix exhibited a more stability by thermal post-treatment at 150 C for 5 min. Also, the results revealed that the printed fabrics with halochromic properties demonstrated sufficient stability against leaching by washing. The current work opens up a novel opportunity to develop flexible sensors based on fibrous materials, which have the potential to be employed in variable industrial applications.     

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.     

几种不同品系暹罗斗鱼体色遗传规律的初步研究

利用人工繁殖的方法，进行暹罗斗鱼蓝色系、紫色蝶翼系内的杂交试验，分析品系内杂交组合的体色分离比例。结果表明，土耳其绿色、皇室蓝色和铁锈蓝色在F2中个体的分离比例为1∶2∶1，断定F1和F2代中的皇室蓝色均为杂合体，符合一对等位基因的孟德尔分离规律，土耳其绿色与铁锈蓝色之间为不完全显性关系。紫色蝶翼系斗鱼皮肤的红色和黄色，无论正交还是反交，F1全部为红色，F2中红色与黄色的个体分离比例为3∶1，符合孟德尔遗传规律，红色由显性基因控制，黄色由隐性基因控制。     

Intergenerational differences of color sensation and preference for naturally dyed fabrics

This study was performed in order to evaluate subjective color sensation and preference for yellowish and reddish natural dye fabrics and to provide meaningful objective colorimetric properties which can quantify the sensation and preference focused on intergenerational differences. Among lots of natural dye silk fabrics, four differently dyed fabrics for each hue were used as specimens by cluster analysis. College students as younger generation and high school teachers as middle-aged evaluated them subjectively in terms of eight aspects color sensation such as clearness, lightness, depth, warmth, strength, brightness, hardness, and fragrance. Intergenerational differences appeared more frequently as for the lightest and the most saturated yellowish fabrics dyed with amur cork than any other fabrics. All of sensation and preference were significantly explained by some colorimetric properties for each generation. Color lightness, L* was the only positive predictor for color preference of younger generation’s preference whereas color saturation, C* illustrated negatively for that of middle-aged.     

Optimization of the processing conditions and prediction of the quality for dyeing nylon and lycra blended fabrics

This paper is intended to determine the optimal processing parameters applied to the dyeing procedure so that the desired color strength of a raw fabric can be achieved. Moreover, the processing parameters are also used for constructing a system to predict the fabric quality. The fabric selected is the nylon and Lycra blend. The dyestuff used for dyeing is acid dyestuff and the dyeing method is one-bath-two-section. The Taguchi quality method is applied for parameter design. The analysis of variance (ANOVA) is applied to arrange the optimal condition, significant factors and the percentage contributions. In the experiment, according to the target value, a confirmation experiment is conducted to evaluate the reliability. Furthermore, the genetic algorithm (GA) is combined with the back propagation neural network (BPNN) in order to establish the forecasting system for searching the best connecting weights of BPNN. It can be shown that this combination not only enhances the efficiency of the learning algorithm, but also decreases the dependency of the initial condition during the network training. Most of all, the robustness of the learning algorithm will be increased and the quality characteristic of fabric will be precisely predicted.     

Colorimetric properties and color sensibility of naturally colored organic cotton fabrics

This study was done to propose color conditions and fabrics that satisfy the particular sensibilities of consumers and producers through an analysis of color sensibility factors for an environmentally-friendly material, i.e., naturally colored organic cotton (NaCOC) fabrics. Toward that end, the colorimetric properties of eight NaCOC fabrics were measured, and the fabrics?? subjective color sensibilities were evaluated. In addition, based on the relationship between the colorimetric properties and subjective color sensibilities, the prediction models for color sensibilities of NaCOC fabrics were developed. According to the established models, hard-soft, cool-warm, deep-pale, vague-distinct, plain-showy, and subdued-vivid sensibilities can be predicted by some variables of colorimetric properties such as L*, a*, C*, and h. As another ultimate goal of this study, suitable NaCOC fabrics to evoke certain sensibilities were proposed by multidimensional scaling method. The proposed fabrics and color sensibility factors are believed to offer an important guideline for those who design clothing products made of NaCOC.     

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 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.     

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.     

Reversible and washing resistant textile-based optical pH sensors by dyeing fabrics with curcuma

Curcuma powder was used to dye cotton and polyamide 6,6 fabrics in order to produce textile-based optical pH sensors. Both fabrics showed a bright yellow color after dyeing and demonstrated color changes (towards red) when contacted with basic solutions. Color change and sensitivity differ for cotton and for polyamide. Curcuma-dyed cotton shows color changes in particular in the range of pH between 6.5 and 8.5, whilst curcuma-dyed polyamide shows a wider pH range: from 8.5 to 13.0. The stability of pH sensing to washing was evaluated. Three different kinds of washing agents were used in order to simulate the real life conditions of a garment or a cloth. Standard test methods were used when available for washing tests. The pH sensing of the curcuma-dyed fabrics demonstrated an excellent fastness to all kinds of washing. Ionic strength of the solution does not affect the color changes. Moreover, color reversibility of the fabrics was proven, too. Color change and reversibility of the fabrics was assessed by an UV-visible spectrophotometer. Spectral changes were observed at 540 nm for curcuma-dyed cotton, and at 487 and 574 nm for polyamide.