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

Kuo Chung-Feng Jeffrey (Department of Polymer Engineering, National Taiwan University of Science and Technology)
Fang Chien-Chou (Department of Polymer Engineering, National Taiwan University of Science and Technology)
Publication Information
Fibers and Polymers / v.7, no.4, 2006 , pp. 344-351 More about this Journal
Abstract
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.
Keywords
Nylon and lycra blended fabrics; Optimizing dyeing; Taguchi method; Neural network; Genetic algorithm (GA);
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1 D. Cristea and G. Vilarem, Dyes Pigment., 70(3), 238 (2006)   DOI   ScienceOn
2 B. David and C. G. Victor, J. Soc. of Dyers Colour., 196, 237 (1980)
3 K. D. Kim, D. N. Han, and H. T. Kim, Chem. Eng. J., 104(1-3), 55 (2004)   DOI   ScienceOn
4 I. Tasadduq, S. Rehman, and K. Bubshait, Renew. Energy, 25(4), 545 (2002)   DOI   ScienceOn
5 D. Sarkar and J. M. Modak, Chem. Eng. Sci., 58(11), 2283 (2003)   DOI   ScienceOn
6 J. S. Gruca and B. R. Klemz, Eur. J. Oper. Res., 146(3), 621 (2003)   DOI   ScienceOn
7 J. F. C. Khaw, B. S. Lim, and L. E. N. Lennie, Neurocomputing, 7(3), 225 (1995)   DOI   ScienceOn
8 X. Zhang, S. Zhang, and X. He, J. Cryst. Growth, 264(13), 409 (2004)   DOI
9 J. M. Liu, P. Y. Lu, and W. K. Weng, Mater. Sci. Eng. B-Solid State Mater. Adv. Technol., 85(2-3), 209 (2001)   DOI
10 N. A. Ibrahim, M. A. Youssef, M. H. Helal, and M. F. Shaaban, J. Appl. Polym. Sci., 89(13), 3563 (2003)   DOI   ScienceOn
11 K. W. Hench and A. Al-Ghanim, Proceedings of the Artificial Neural Networks in Engineering U.S.A., 5, 873 (1995)
12 X. Wang and M. Bide, Textile Chemist and Colorist, 30(4), 45 (1998)
13 S. S. Madaeni and S. Koocheki, Chem. Eng. J., 119(1), 37 (2006)   DOI   ScienceOn
14 A. A. Brice and W. R. Johns, Comput. Chem. Eng., 22(12), 47 (1998)   DOI   ScienceOn
15 E. Tsatsaroni and M. Liakopoulou-Kyriakides, Dyes Pigment., 29(3), 203 (1995)   DOI   ScienceOn
16 A. J. Greaves, Dyes Pigment., 46(2), 101 (2000)   DOI   ScienceOn
17 M. J. Jahmeerbacus, N. Kistamah, and R. B. Ramgulam, Color. Technol., 120(2), 51 (2004)   DOI   ScienceOn