Abstract

The article reviews some significant research trends in yarn texturising. Investigations have been carried out on the effect of yarn feed and process parameters used in the production of air-jet textured yarn on compression and recovery behavior of air-jet textured yarn fabric. Fabric low load compression-recovery behavior has been analyzed by defining compression parameter, recovery parameter and resiliency. The crimp features of false twist textured yarns have been determined by a new method by applying computer vision and image processing method. It enables to obtain accurate test results in a speedier manner compared with other existing methods. The properties of air jet textured yarn has been predicted by artificial neural network model and the performance of the model has been is compared with response surface model based on multiple non-linear regression analysis. Studies have been carried out on the influence of process parameters on properties of polyester/viscose blended air jet textured yarns.

Keywords: Air jet textured yarns; Compression properties; Algorithms; Image tracking; ANN; Regression model; Blended yarn; Physical bulk