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.
The main purpose of this study was to find the optimal dyeing conditions as well as predict the colour strength (CS) of viscose/lycra blended knitted fabrics using Taguchi method. The controllable factors such as dye concentration, temperature, time, alkali concentration, salt concentration and liquor ratio have been used as input variables and CS of the fabric as response variable for the construction of Taguchi model. An L 25 orthogonal array design has been chosen and conducted 25 experiments with three runs for each experiment. The optimal parameters in the dyeing process have been identified as dye concentration 9%, time 60 min, temperature 75°C, salt concentration 50 g/l, alkali concentration 14 g/l and liquor ratio 1:8. Taguchi mathematical model built in this study has been confirmed by confirmation experiment as well as unseen experimental data. The mean absolute error and coefficient of determination (R 2 ) were found to be 3.48% and 0.88, respectively, from the actual and predicted CS. It is concluded that Taguchi method is efficient on the optimisation and prediction of fabric CS in non-linear complex dyeing.
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.
The main objective of this research is to predict the mechanical properties of viscose/lycra plain knitted fabrics by using fuzzy expert system. In this study, a fuzzy prediction model has been built based on knitting stitch length, yarn count, and yarn tenacity as input variables and fabric mechanical properties specially bursting strength as an output variable. The factors affecting the bursting strength of viscose knitted fabrics are very nonlinear. Hence, it is very challenging for scientists and engineers to create an exact model efficiently by mathematical or statistical model. Alternatively, developing a prediction model via ANN and ANFIS techniques is also difficult and time consuming process due to a large volume of trial data. In this context, fuzzy expert system (FES) is the promising modeling tool in a quality modeling as FES can map effectively in nonlinear domain with minimum experimental data. The model derived in the present study has been validated by experimental data. The mean absolute error and coefficient of determination between the actual bursting strength and that predicted by the fuzzy model were found to be 2.60% and 0.961, respectively. The results showed that the developed fuzzy model can be applied effectively for the prediction of fabric mechanical properties.
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