Predicting the colour properties of viscose knitted fabrics using soft computing approaches

Abstract

The aim of this paper was to predict the colour strength of viscose knitted fabrics by using fuzzy logic (FL) model based on dye concentration, salt concentration and alkali concentration as input variables. Moreover, the performance of fuzzy logic (FL) model is compared with that of artificial neural network (ANN) model. In addition, same parameters and data have been used in ANN model. From the experimental study, it was found that dye concentration has the main and greatest effects on the colour strength of viscose knitted fabrics. The coefficient of determination (R²), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by FL model are found to be 0.977, 1.025 and 4.61%, respectively. Further, the coefficient of determination (R²), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by ANN model are found to be 0.992, 0.726 and 3.28%, respectively. It was found that both ANN and FL models have ability and accuracy to predict the fabric colour strength effectively in non-linear domain. However, ANN prediction model shows higher prediction accuracy than that of Fuzzy model.

Keywords:

• Colour strength
• prediction
• viscose knitted fabric
• fuzzy logic
• artificial neural network

Acknowledgements

The authors are grateful to the Faculty of Engineering, University of Malaya and University of Malaya Research Grant (UMRG) for their support under the grant [RP020-2012B]. The authors are also thankful to the Management of Textile research laboratory APS and APS apparels ltd, APS Group Bangladesh for providing the facilities for this research work.