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.

摘要

螺旋拉胀纱是一种具有负泊松比的结构材料，它包含一根直径较大的柔软纱线作为芯成分，一根直径较小的硬纱线作为包裹成分，缠绕在芯成分上。在本研究中，基于理论模型和人工神经网络模型的改进，开发了一种半经验模型，用于预测螺旋拉胀纱的最大负泊松比。为了对模型进行验证，根据构件的初始螺旋角、构件的直径比和构件的模量比设计了实验。为了测量螺旋拉胀纱的泊松比，提出了一种基于图像处理技术的算法。与以往的研究结果不同，实验结果表明，增加部件的直径比，由于芯部件的更高的弯曲刚度，将降低螺旋拉胀纱的最大负泊松比。预测值与实验值的对比结果表明，人工神经网络模型的误差低于半经验模型。预计这项工作提供了工程工具，可以根据所需的精度预测螺旋拉胀纱线的拉胀行为。预测值与实验值的对比结果表明，人工神经网络模型的误差低于半经验模型。预计这项工作提供了工程工具，可以根据所需的精度预测螺旋拉胀纱线的拉胀行为。预测值与实验值的对比结果表明，人工神经网络模型的误差低于半经验模型。预计这项工作提供了工程工具，可以根据所需的精度预测螺旋拉胀纱线的拉胀行为。