Prediction of whiteness index of cotton using bleaching process variables by fuzzy inference system
Source abstract
Abstract
Abstract A fuzzy prediction model has been built based on hydrogen peroxide concentration, temperature and time of bleaching as the input variables and knitted fabric whiteness index as the output variable. The process parameters affecting the whiteness index of cotton knitted fabrics are very non-linear. Fuzzy inference system is a prospective modeling tool as it can map effectively in nonlinear domain with minimum investigational data. Triangular-shaped membership functions were considered for the variables and total 48 rules were created in this study. It was found that the sole effect of the concentration of hydrogen peroxide on whiteness is pretty low, but is affected by temperature noticeably even in a fixed concentration of hydrogen peroxide. The model proposed in the present study has been verified by additional experimental data set. The root mean square, mean absolute error percentage and coefficient of determination (R 2 ) between the predicted and experimental values were found to be 0.536, 0.798 and 0.959 respectively. The results validate that the model can be applied suitably for the prediction of fabric whiteness index in textile industries.
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Research context
OpenAlex topics: Textile materials and evaluations · Dyeing and Modifying Textile Fibers · Color Science and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (31)
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1. Abdel-Halim, E. S. (2012). Simple and economic bleaching process for cotton fabric. Carbohydrate Polymers, 88, 1233–1238.10.1016/j.carbpol.2012.01.082 ↗
2. Abdul, S. B., & Narendra, G. (2013). Accelerated bleaching of cotton material with hydrogen peroxide. Journal of Textile Science & Engineering, 3(4), 1000140.
3. Akgun, M. (2015). Effect of fabric layers on whiteness and yellowness indices of some polyester fabrics woven with different constructional parameters. Autex Research Journal, 15, 116–122.10.2478/aut-2014-0043 ↗
4. Choudhury, K. R. (2000). Modern concepts of color and appearance. Enfield: Science Publishers Inc.
5. Choudhury, K. R. (2006). Textile preparation and dyeing. New York: Science Publishers.
6. Ganz, E. (1979). Whiteness formulas: A selection. Applied Optics, 18, 1073–1078.10.1364/AO.18.001073 ↗
7. Gopal, M. (2010). Digital control and state variable methods: Conventional and intelligent control systems. Singapore: Tata McGraw-Hill Education.
8. Haghighat, E., Najar, S. S., & Etrati, S. M. (2014). The prediction of needle penetration force in woven denim fabrics using soft computing models. Journal of Engineered Fibers and Fabrics, 9, 45–55.
9. Haque, A. N. M. A., & Islam, M. A. (2015). Optimization of bleaching parameters by whiteness index and bursting strength of knitted cotton fabric. IJSTR, 4(4), 40–43.
10. Hatua, P., Majumdar, A., & Das, A. (2014). Modelling ultraviolet protection factor of polyester-cotton blended woven fabrics using soft computing approaches. Journal of Engineered Fibers and Fabrics, 9, 99–106.10.1177/155892501400900311 ↗
11. Hossain, I., Choudhury, I. A., Mamat, A. B., & Hossain, A. (2017). Journal of the Textile Institute. https://doi.org/10.1080/00405000.2017.1279004.10.1080/00405000.2017.1279004 ↗
12. Hossain, I., Choudhury, I. A., Mamat, A. B., Shahid, A., Khan, A. N., & Hossain, A. (2016). Predicting the mechanical properties of viscose/lycra knitted fabrics using fuzzy technique. Advances in Fuzzy Systems, 2016, 1–9.10.1155/2016/3632895 ↗
13. Hossain, I., Hossain, A., & Choudhury, I. A. (2015). Color strength modeling of viscose/lycra blended fabrics using a fuzzy logic approach. Journal of Engineered Fibers and Fabrics, 10, 158–168.10.1177/155892501501000117 ↗
14. Hossain, I., Hossain, A., Choudhury, I. A., Bakar, A. & Uddin, H. (2014). Prediction of fabric properties of viscose blended knitted fabrics by fuzzy logic methodology. In International conference on mechanical and civil and architectural engineering, 19–20 February 2014, Kuala Lumpur, Malaysia, pp.100–106.
15. Jafari, R., & Amirshahi, S. H. (2007). A comparison of the CIE and UCHIDA whiteness formulae as predictor of average visual whiteness evaluation of textiles. Textile Research Journal, 77, 756–763.10.1177/0040517507080688 ↗
16. Jafari, R., & Amirshahi, S. H. (2008). Variation in the decisions of observers regarding the ordering of white samples. Coloration Technology, 124, 124–131.10.1111/j.1478-4408.2008.00132.x ↗
17. Jamshaid, H., Hussain, T., & Malik, Z. A. (2013). Comparison of regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics. Fibers and Polymers, 14, 1203–1207.10.1007/s12221-013-1203-3 ↗
18. Jeguirim, S. E. G., Dhouib, A. B., Sahnoun, M., Cheikhrouhou, M., Schacher, L., & Adolphe, D. (2011). The use of fuzzy logic and neural networks models for sensory properties prediction from process and structure parameters of knitted fabrics. Journal of Intelligent Manufacturing, 22, 873–884.10.1007/s10845-009-0362-y ↗
19. Kabir, S. M. F., Iqbal, M. I., Sikdar, P. P., Rahman, M. M., & Akhter, S. (2014). Optimization of parameters of cotton fabric whiteness. ESJ, 10(36), 200–210.
20. Majumdar, A., & Ghosh, A. (2008). Yarn strength modeling using fuzzy expert system. Journal of Engineered Fibers and Fabrics, 3, 61–68.10.1177/155892500800300408 ↗
21. Marakoğlu, T., & Çarman, K. (2010). Fuzzy knowledge-based model for prediction of soil loosening and draft efficiency in tillage. Journal of Terramechanics, 47, 173–178.10.1016/j.jterra.2009.10.001 ↗
22. Ngai, E. W. T., Peng, S., Alexander, P., & Moon, K. K. (2014). Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles. Expert Systems with Applications, 41, 81–91.10.1016/j.eswa.2013.07.013 ↗
23. Shahid, A., & Hossain, I. (2015). Modeling the spirality of cotton knit fabric using fuzzy expert system. Turkish Journal of Fuzzy Systems, 6, 56–67.
24. Steen, D., & Dupont, D. (2003). Control of structured white textiles. Coloration Technology, 119, 205–211.10.1111/j.1478-4408.2003.tb00173.x ↗
25. Trotman, E. R. (1975). Dyeing and chemical technology of textile fibres. Bucks: Charles Griffin & CO. Ltd.
26. Uchida, H. (1998). A new whiteness formula, color research and application. Color Research and Application, 23, 202–209.10.1002/(SICI)1520-6378(199808)23:4<202::AID-COL4>3.0.CO;2-S ↗
27. Vadood, M. (2014). Predicting the color index of acrylic fiber using fuzzy genetic approach. The Journal of The Textile Institute, 105, 779–788.10.1080/00405000.2013.849844 ↗
28. Vigneswaran, A., Ananthasubramanian, M., & Anbumani, N. (2012). Prediction and process optimization of bioscouring of organic cotton fabrics through specific mixed enzymatic system using artificial neural network (ANN). Journal of Natural Fibers, 9, 1–22.10.1080/15440478.2012.651828 ↗
29. Wyszecki, G., & Stiles, W. S. (1982). Color science: Concepts and methods, quantitative data and formulae. New York: John Wiley.
30. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.10.1016/S0019-9958(65)90241-X ↗
31. Zollinger, H. (2003). Color chemistry, syntheses, properties, and applications of organic dyes and pigments. New York: Wiley-VCH.