Can I read Application of artificial neural network and full factorial method to predict the Poisson’s ratio of double core helical auxetic yarn on EtoBox?

Application of artificial neural network and full factorial method to predict the Poisson’s ratio of double core helical auxetic yarn by Milad Razbin; Mostafa Jamshidi Avanaki; Ali Asghar Asghariyan Jeddi is a Materials Science article available to read on EtoBox.

What is Application of artificial neural network and full factorial method to predict the Poisson’s ratio of double core helical auxetic yarn about?

In this work, an experiment according to the full factorial method (FFM) to investigate the effect of initial helical angle of wrap component, diameter ratio of components and modulus ratio of components on Poisson's ratio of double core helical auxetic yarn (DC-HAY) is designed. Then, after statistically confirming the similarity of maximum NPR in both main directions to reduce the calculations, a regression model based on the FFM equation and an artificial neural network (ANN)-based model to predict the maximum NPR of DC-HAY are developed. The results of the analysis of variance indicated that all terms of the regression model are statistically significant. Also, the Student-Newman-Keuls showed that all of the considered levels for parameters are in separated groups. Furthermore, the optimum architecture for the ANN model is selected based on a goodness function constructed by the coefficient of determination and mean squared error. The results of comparison between the models revealed that the ANN model has better performance than the regression model. In addition, the sensitivity analysis of ANN models introduced that the modulus ratio of components is the most sensitive parame

Who reads Application of artificial neural network and full factorial method to predict the Poisson’s ratio of double core helical auxetic yarn?

Author

Milad Razbin; Mostafa Jamshidi Avanaki; Ali Asghar Asghariyan Jeddi

Publisher

Informa UK Limited

Published

2022

Language

EN

Field

Materials Science (Physical Sciences)