A systematic study on the predictability of different methods to predict the maximum Poisson’s ratio of helical auxetic yarn

Abstract

Helical auxetic yarn is a structural material with negative Poisson’s ratio which contains a soft yarn with a higher diameter as a core component and a stiff yarn with a lower diameter as wrap component that has been wrapped around the core component. In the present study, a semi-empirical model based on the improvement of a theoretical model and an artificial neural network model was developed for predicting the maximum negative Poisson’s ratio of helical auxetic yarn. In order to verify the models, an experiment according to the initial helical angle of wrap component, the diameter ratio of components, and the modulus ratio of components has been designed. To measure the Poisson’s ratio of helical auxetic yarn, an algorithm based on the image processing technique has been proposed. Unlike the results of previous studies, the experimental results showed that increasing the diameter ratio of components so much, will decrease the maximum negative Poisson’s ratio of helical auxetic yarn due to higher bending stiffness of core component. The results of the comparison between the predicted and experimental values indicated that the artificial neural network model has a lower error than the semi-empirical model. It is expected that this work provides engineering tools to predict the auxetic behavior of helical auxetic yarn based on the required precision.