Prediction of False Twist Textured Yarn Properties by Artificial Neural Network Methodology

Abstract

The false twist texturing method is one of the most common texturing techniques. This process depends on parameters such as tension, twist, and heater temperature. As false-twist texturing is a process which includes close interactions between machine working parameters and textured yarn properties, the effect of process parameters on yarn properties have been widely investigated. In this study the effect of first heater temperature, setting overfeed, and D/Y on the tenacity of set yarns and the effect of twist, texturing speed, and first heater temperature on crimp stability of stretch yarns are predicted using artificial neural network methodology.