Self-organizing Map Network for Automatically Recognizing Color Texture Fabric Nature

Kuo, Chung-Feng Jeffrey,Kao, Chih-Yuan The Korean Fiber Society 2007 Fibers and polymers Vol.8 No.2

The method of recognizing color texture brought forth in the present study is to employ unsupervised learning network to automatically recognize the fabric type and the main texture types. Firstly, the color scanner is adopted to extract fabric image which is afterwards saved as the digital image. Secondly, CIE-Lab color model is taken to obtain the feature value and wavelet transform is utilized to display the texture of the fabric image. Thirdly, co-occurrence matrix is employed to figure out the feature values of the texture structure such as angular second moment, entropy, homogeneity, contrast. Finally, self-organizing map (SOM) network is used as the classifier. The experiment result shows that the study can automatically and accurately classify the fabric types (including shuttle-woven fabric, jersey fabric and non-woven fabric) and main texture type of the fabric (such as plain weave, twill weave, satin weave, single jersey, double jersey and non-woven fabric).

Optimization of Multiple Quality Characteristics for Polyether Ether Ketone Injection Molding Process

Kuo Chung-Feng Jeffrey,Su Te-Li The Korean Fiber Society 2006 Fibers and polymers Vol.7 No.4

This study examines multiple quality optimization of the injection molding for Polyether Ether Ketone (PEEK). It also looks into the dimensional deviation and strength of screws that are reduced and improved for the molding quality, respectively. This study applies the Taguchi method to cut down on the number of experiments and combines grey relational analysis to determine the optimal processing parameters for multiple quality characteristics. The quality characteristics of this experiment are the screws' outer diameter, tensile strength and twisting strength. First, one should determine the processing parameters that may affect the injection molding with the $L_{18}(2^1{\times}3^7)$ orthogonal, including mold temperature, pre-plasticity amount, injection pressure, injection speed, screw speed, packing pressure, packing time and cooling time. Then, the grey relational analysis, whose response table and response graph indicate the optimum processing parameters for multiple quality characteristics, is applied to resolve this drawback. The Taguchi method only takes a single quality characteristic into consideration. Finally, a processing parameter prediction system is established by using the back-propagation neural network. The percentage errors all fall within 2%, between the predicted values and the target values. This reveals that the prediction system established in this study produces excellent results.

Optimization of the Processing Conditions and Prediction of the Quality for Dyeing Nylon and Lycra Blended Fabrics

Kuo Chung-Feng Jeffrey,Fang Chien-Chou The Korean Fiber Society 2006 Fibers and polymers Vol.7 No.4

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.

Analysis and Construction of a Quality Prediction System for Needle-Punched Non-woven Fabrics

Kuo Chung-Feng Jeffrey,Su Te-Li,Chiu Chin-Hsun,Tsai Cheng-Ping The Korean Fiber Society 2007 Fibers and polymers Vol.8 No.1

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 $L_{18}(2^1{\times}3^7)$ 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.

Dynamic Modeling and Control of a Padder Roller System

Kuo, Chung-Feng Jeffrey,Chen, Jia-Siang The Korean Fiber Society 2007 Fibers and polymers Vol.8 No.5

This is the first time in the literature dealing with the dynamic modeling and control of a rotating padder roller system. It is intended to design a control system with effective scheme and robustness to stabilize all vibration modes of a rotating padder roller system by using one set of sensor and actuator. The controller design depends on the specific pole-zero patterns. In practice, the pole-zero patterns remain the same, no matter how the physical system parameters are different. By properly placing the actuator and sensor, a realizable controller and sensor is designed to stabilize all the vibration modes and make the closed loop system absolutely stable. This will suppress the vibration without suffering from spillover and can eliminate an infinite number of vibration modes. The performance of this controller has been successfully implemented by computer simulation.

Computerized Color Separation System for Printed Fabrics by Using Backward-Propagation Neural Network

Kuo, Chung-Feng Jeffrey,Su, Te-Li,Huang, Yi-Jen The Korean Fiber Society 2007 Fibers and polymers Vol.8 No.5

Textile production must be coupled with hi-tech assistant system to save cost of labor, material, time. Therefore color quality control is one very important step in any textiles, however excellent the fabric material itself is, if it lacks good color, then it may still result in dull sale. Therefore, this paper proposes a printed fabrics computerized color separation system based on backward-propagation neural network, whose primary function is to separate rich color of printed fabrics pattern so as to reduce time-consuming manual color separation color matching of current players. What it adopted was RGB color space, expressed in red, green, and blue. Analyze color features of printed fabrics, use gene algorithm to find sub-image with same color distribution as original image of printed fabrics yet smaller area, for later color separation algorithm use. In terms of color separation algorithm, this paper relied on supervised backward-propagation neural network to conduct color separation of printed fabrics RGB sub-image, and utilized $PANTONE^{(R)}$ standard color ticket to do color matching, so as to realize accurate color separation.

Optimization of the Needle Punching Process for the Nonwoven Fabrics with Multiple Quality Characteristics by Grey-Based Taguchi Method

Kuo, Chung-Feng Jeffrey,Su, Te-Li,Tsai, Cheng-Ping The Korean Fiber Society 2007 Fibers and polymers Vol.8 No.6

This study is intended for finding out the optimal processing parameters for needle punching nonwoven fabrics in order to work out its maximal strength. Taguchi method together with grey relational analysis is employed to resolve the problem as regards multiple-quality optimization, and further discover the optimal combination of processing parameters for needle punching nonwoven fabrics. Firstly, orthogonal array $L_{18}(2^1{\times}3^7)$ is used to deal with the processing parameters that may exert influence over the manufacturing of needle punching nonwoven fabrics. Then grey relational analysis is applied to resolve the deficiency of Taguchi method that focus on single quality characteristic. Next, the response table of grey relational analysis is used to obtain the optimal combination of processing parameters for multiple quality characteristics. In the current experiment quality characteristic refers to the tensile strength and tear strength of the nonwoven fabrics. Additionally, signal-to-noise ratio (SN ratio) calculation and analysis of variance (ANOVA) can be adopted to explore the experimental results. Through ANOVA, the significant factors that exert comparatively significant influence over the quality characteristic of the needle punching nonwoven fabrics, that is, the control factors are determined so that the quality characteristic of the needle punching nonwoven fabrics can be effectively controlled. Finally, confirmation experiment is conducted within 95 % confidence interval to verify the experimental reliability and reproducibility.