Modelling of CIELAB values in vinyl sulphone dye application using feed-forward neural networks

Artificial neural network (ANN) technology has developed from the experimental stage into real industrial applications. To achieve this significant transition, careful planning and adjustment are required. This article is concerned with the CIELAB values' prediction based on a neural network developed for cotton fabric dyed with vinyl sulphone reactive dye. The neural network developed is a multilayer feed-forward network. In textile dyeing industry, achieving the required depth of colour is the important task. In this paper, to achieve the required depth of colour, the CIELAB values of the fabric to be dyed were predicted using trained feed-forward neural network. The results obtained from the network gives an average error of around 2.0% for vinyl sulphone dyes used for training the network in predicting the LAB values. The trained network brings out the same error for other dyes as well as for input and output parameters selected beyond the range used for training the network.