Color Strength Modeling of Viscose/Lycra Blended Fabrics Using a Fuzzy Logic Approach

Abstract

The aim of this study was to model the color strength of viscose/lycra (95:5) blended knitted fabrics using a fuzzy logic approach where color strength is a function of dye concentration, salt concentration, and alkali concentration. Dye concentration, salt concentration, and alkali concentration are the most important factors affecting color strength of viscose/lycra blended knitted fabrics. Moreover, these factors behave nonlinearly and interact: hence, it is very difficult to develop an exact functional relationship between the input variables and color strength using mathematical models, statistical models, or empirical models. Conversely, artificial neural network models are trained using large amounts of experimental data which is a time consuming process. One possible approach to deal with such a complex process is by using a fuzzy logic expert system (FLES), which perform remarkably well in non-linear and complex systems with minimum experimental data. In this study a laboratory scale experiment was conducted to validate the developed fuzzy model. The model was assessed by analyzing various numerical error criteria. The mean relative error was found to be 3.80%, the correlation coefficient was 0.992, and goodness of fit was 0.986 from the actual and predicted color strengths of the fabrics. The results show that the model developed performed well.

Study of the lightfastness properties of prints on blister foils by spectral reflectance

“…[20][21][22] A fuzzy logic expert model is demonstrated for the prediction of the colorfastness of viscose dyed fabric. 23 Johnston-Feller et al 24 have proposed the use of spectrophotometric reflectance measurements and modern color-matching computations based on the Kubelka-Munk theory, which has explained the decrease in the concentration of alizarin lake during fading and has also been applied to represent the fading of the dyed substrates. 25 The effects of temperature and humidity on the yellowing rates of digital prints in cultural heritage collections have been discussed by Nishimura et al 26 Aydemir and Yenido gan 27 have defined the light fastness properties of printed ink by a standard blue wool scale, where the pigment selection is considered an influencing factor in the lightfastness of printed material.…”

Predicting the Mechanical Properties of Viscose/Lycra Knitted Fabrics Using Fuzzy Technique

“…The demand of knitted fabric especially viscose knitted fabric is increasing rapidly due to their unique quality characteristics such as elasticity, drape, wrinkle resistance, comfort, softness, and easy-care properties over woven fabrics. Viscose knitted fabrics are very popular for apparel wears such as T-shirts, shirts, sweaters, blouses, underwear, casual wear, active wear, and sportswear because of their distinctive quality characteristics as compared to woven fabrics [1][2][3][4][5].…”

“…Moreover, all these factors affecting the bursting strength of knitted fabrics are nonlinear and interactive with other. Hence, it poses a great challenge for scientists and engineers to develop effectively an exact model based on mathematical and statistical techniques [2,9]. This is ascribed to the fact that both mathematical and statistical models often fail to capture the nonlinear relationship between inputs and outputs [10].…”

“…Zeydan studied ANN model for the prediction of woven fabric strength [11]. However, ANN and ANFIS models need large amounts of noisy input-output data for model parameters optimization, which are challenging, labor intensive, and time consuming processes to collect from the textile knitting industries [1,2,9]. In addition, ANN does not tell the core logic based on which decisions can be taken [10].…”

“…In this regard, fuzzy expert system (FES) is found to be the scientific and engineering solution for quality modeling as FES provides reliable prediction outcome with small experimental data in nonlinear and ill-defined textile domain [2,9]. Moreover, a fuzzy model is more reasonable, cheaper in design cost, and much easier to apply in comparison to other models.…”