Predicting torque of worsted singles yarn using an efficient radial basis function network-based method

Abstract

The torque in single-spun yarns is an inherent property of the twisting and bending of staple fibres during the formation of yarn combined with the effect of applied tension on the yarn. The consequences of yarn torque are well known and are widely observed as yarn instability, e.g., yarn rotation under tension; local snarling and entanglement at low loads, and as distortion in fabric, i.e., edge-curl and skewing in knitted fabric. In this paper, a method for predicting the yarn torque based on the radial basis function networks is presented and evaluated. This method uses a 'universal approximator' based on neural network methodology to minimize noise during training of the network and to approximate the yarn torque as a function of the geometrical and physical parameters of yarns (twist, linear density) and the applied load. The current method is an integral radial basis function network-based approach suitable for textile engineering and gives very good prediction of yarn torque across a range of yarn structural parameters and test conditions.