Prediction of Shrinkage Behavior of Stretch Fabrics Using Machine-Learning Based Artificial Neural Network

Abstract

Stretch fabric provides good formability and does not restrict the movement of the body for increased tension levels. The major expectations of a wearer in an apparel fabric are a high level of mechanical comfort and good aesthetics. The prediction of shrinkage in stretch fabric is a very complex and unexplored topic. There are no existing formulas that can effectively predict the shrinkage of stretch fabrics. The purpose of this paper is to develop a novel model based on an artificial neural network to predict the shrinkage of stretch fabrics. Different stretch fabrics (core-spun lycra yarn) with stretch in the weft direction were manufactured in the industry using a miniature weaving machine. A model was built using an artificial neural network method, including training of the data set, followed by testing of the model on the test data set. The correlation of factors, such as warp count, weft count, greige PPI, greige EPI, and greige width, was established with respect to boil-off width.