The present study is intended to develop an intelligent model for the prediction of color strength of cotton knitted fabrics using fuzzy knowledge based expert system (FKBES). The factors chosen for developing the prediction model are dye concentration, dyeing time and process temperature. Besides, such factors are nonlinear and have mutual interactions among them; so it is not easy to create an exact correlation between the inputs variables and color strength using mathematical or statistical methods. In contrast, artificial neural network and neural-fuzzy models require massive amounts of experimental data for model parameters optimization which are challenging to collect from the dyeing industries. In this context, fuzzy knowledge based expert system is the most efficient modeling tool which performs exceptionally well in a non-linear complex domain with lowest amount of trial data like human experts. In this study, laboratory scale experiments were conducted for three types of cotton knitted fabrics to verify the developed fuzzy model. It was found that actual and predicted values of color strength of the knitted fabrics were in good agreement with each other with less than 5% absolute error.
HussainT., WardamanR H, and ShameyR, “A knowledge –based expert system for dyeing of cotton, part 1: Design and Development”, Coloration Technology, 2005, Vol. 121, pp. 53–58.
2.
ShameyM., and NobbsJ.H., “A review automation and computer control in Dyeing machinery”, Advance Colour Science and Technology, 1998, pp. 46–51.
3.
BahadirM. C., BahadirS. K., and KalaogluF., “An Artificial Neural Network Model for Prediction of Bursting Strength of Knitted Fabrics”, IMLCS’2012, PP. 167–170.
4.
JamshaidH., HussainT., and MalikZ. A., “Comparison of Regression and Adaptive neuro-fuzzy Models for Predicting the Bursting Strength of Plain Knitted Fabrics”, Fibers and Polymers, 2013, Vol. 14, No. 7, 1203–1207.
5.
ZavarehM., HamadaniA.Z., and TavanaiH., “Application of Central Composite design (CCD) to model color Yield of six diazo direct dyes on cotton fabric”. The Journal of Textile Institute, 2010, Vol. 101, No. 12, pp. 1068–1074.
6.
ZeydanM., and TogaG., “Reducing variation of Dyeing process in Textile Manufacturing Industry”, World Academy of Science, Engineering and Technology, 2011, Vol. 59, pp. 1626–1634.
7.
HuangC.C., and YuW.H., “Control of dye concentration, pH and temperature in dyeing process”, Textile Research Journal, 1999, Vol. 69, pp. 914–918.
8.
MajumdarA., and GhoshA., “Yarn Strenghth Modeling Using Fuzzy Expert System”, Journal of Engineered Fibers and Fabrics, 2008, Vol. 3, No. 4, pp. 61–68.
9.
AsimF., MahmoodM., and SiddiquiM.A., “Optimization of process parameters for simultaneous fixation of reactive printing and crease resistant finishing using desirability function”, Journal of Textile and Apparel, Technology and Management, 2012, Vol. 7, No. 3, pp. 1–12.
10.
FazeliF., TavanaiH., and HamadaniA. Z., “Application of Taguchi and Full Factorial Experimental Design to Model the color Yeild of cotton Fabtic Dyed with Six Selected Direct Dyes”, Journal of Engineered Fibers and Fabrics, 2012, Vol. 7, No. 3, pp. 34–42.
11.
KuoC.C., and PietrasS., “Applying regression analysis to improve dyeing process quality: a case study”, International Journal Advance Manufacturing Technologies, 2010, Vol. 49, pp. 357–368.
12.
KuoC.F.J., and FangC.C., “Optimization of the Processing Conditions and Prediction of the Quality for Dyeing Nylon and Lycr-Blended Fabrics”, Fibers and Ploymers, 2006, Vol. 7, No. 4, pp. 344–351.
13.
ElemenS., KumbasarE. P. A., and YaparS., “Modeling the adsorption of textile dye on organoclay using an artificial neural network”, Dyes and Pigments, 2012, Vol. 95, No. 1, pp. 102–111.
14.
Khataee, “Neural network modeling of biotreatment of triphenyl methane dye solution by a green macroalgae”, Chemical Engineering Research and Design, 2011, Vol. 89, No. 2, pp. 172–178.
15.
Shams-Nateri, “Estimation of fabric color by camera besed neuro-fuzzy technique”, Indian journal of Fibre & Textile Resarch, 2011, Vol. 36, pp. 74–80.
16.
HossainA., RahmanA., and MohiuddinA. K. M., Fuzzy expert system for controlling swamp terrain intelligent air-cushion tracked vehicle, International Journal of automotive Technology, 2011, Vol. 12, No. 5, pp. 745–753.
17.
CarmanK., “Prediction of soil compaction under pneumatic tires using a fuzzy logic approach”, Journal of Terramechanics, 2008, Vol. 45, pp. 103–108.
18.
JahmeerbacusM.I., KistamahbN., and RamgulamR.B., “Fuzzy control of dyebath pH in exhaust dyeing”, Coloration Technology, 2004, Vol. 120, No. 2, pp. 51–55.
19.
SmithB., and LuJ., “Improving Computer Control of Batch Dyeing Operations”, American Dyestuff Reporter, September, 1993, pp. 17–36.
20.
NasriM., and BerlikS., “Modeling of Polyester Dyeing Using an Evolutionary Fuzzy System”, IFSA-EUSFLAT, 2009, pp. 1246–1251.
21.
TavanaiH., TaheriS.M., and NasiriM., “Modelling of Colour Yield in Polyethylene Terephthalate Dyeing with Statistical and Fuzzy Regression”, Iranian Polymer Journal, 2005, Vol. 14, No. 11, pp. 954–967.
22.
Baumann, “Determination of Relative Colour Strength and Residual Colour Difference by Means of Reflectance Measurements”, Society of Dyers and Colourist, 1987, Vol. 103, pp. 100–105.
23.
ZadehL.A., “Fuzzy Sets”, Information and Control, 1965, Vol. 8, pp. 338–353.
24.
GopalM., “Digital control and state variable methods: conventional and intelligent control systems”, 3rd ed. Singapore: Tata McGraw-Hill Education Pvt. Ltd.; 2009.
25.
HossainA., RahmanA., and MohiuddinA.K.M., “Fuzzy evaluation for an intelligent air-cushion tracked vehicle performance investigation”, Journal of Terramechanics, 2012, Vol. 49, pp. 73–80.
26.
PassinoK.M., and YurkovichS., “Fuzzy control”, Menlo Park, (USA): Addison Wesley Longman, Inc.; 1998.
27.
RajasekaranS., and VijayalakshmiPai GA., “Neural networks, fuzzy logic and genetic algorithms: synthesis and applications”, Prentice-Hall of India Private Limit, New Delhi, 2007.
