Predicting the breaking elongation of ring spun cotton yarns using mathematical, statistical, and artificial neural network models

This paper presents a comparative study of three modeling methodologies for predicting the breaking elongation of ring spun cotton yarns. Constituent cotton fiber properties and yarn count are used as inputs to these models. The predictive powers of the three different models-mathematical, statistical, and artificial neural network-are estimated and compared. The relative importance of various cotton fiber properties measured by a high volume instrument is also investigated using the artificial neural network model.