Prediction of the vortex yarn tenacity from some process and nozzle parameters based on numerical simulation and artificial neural network

Abstract

In this study, two methods, the numerical simulation and the artificial neural network, are adopted to predict the vortex yarn tenacity from some process and nozzle parameters. The fiber-airflow interaction and the motional characteristics of the fiber inside the vortex spinning nozzle are analyzed through the numerical simulation method to predict the yarn tenacity from the parameters of the jet orifice diameter and the yarn delivery speed. The back-propagation multilayer perceptron (MLP) neural network is adopted to predict the tenacity of vortex yarn spun from modal fibers. The following parameters are used as inputs: nozzle pressure, jet orifice angle, distance between the guiding needle and the spindle, and spindle cone angle. The predicted and measured tenacities show low relative errors and a high correlation coefficient, indicating the artificial neural network method provides accurate prediction results. In addition, the effects of these parameters on the yarn tenacity are also analyzed.