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Numerical investigation of smart auxetic three-dimensional meta-structures based on shape memory polymers via topology optimization
Journal of Intelligent Material Systems and Structures ( IF 2.7 ) Pub Date : 2020-07-02 , DOI: 10.1177/1045389x20935569
Ehsan Jebellat 1 , Majid Baniassadi 1 , Alireza Moshki 1 , Kui Wang 2 , Mostafa Baghani 1
Affiliation  

Today, the human being endeavors to manufacture devices and materials capable of doing something in an intelligent way. Shape memory polymers are a series of smart materials, capable of retrieving their original shape from a temporary form by applying external stimuli, for example, heat, electricity, magnetism, light, pH, and humidity. In this research, the behavior of temperature-sensitive shape memory polymer–based structures with positive and negative Poisson’s ratio has been analyzed. The purpose is the material design of smart structures with tunable Poisson’s ratio using topology optimization. In this study, a meta-structure is designed, which is made by a smart material. Not only does this structure have shape memory effects, but also it has negative Poisson’s ratio, which can be used in new sensors, actuators, and biomedical applications. After creation of the unit cell and the representative volume element and formation of final three-dimensional structure, finite element modeling is conducted based on a thermo-visco-hyperelastic constitutive model at large deformations. Examining the behavior of structures in tensile pre-strains of 20%, 10%, and 5%, it is observed that pre-strain has no considerable effect on Poisson’s ratio, but under compressive strain of 20%, it is concluded that the type of loading is effective on Poisson’s ratio and the results are different in tension and compression modes. Finally, the influence of temperature rate on the behavior of structures is inspected, and it is concluded that the more slowly the temperature changes, the more strain or shape recovery is accomplished at a specific temperature.

中文翻译:

基于形状记忆聚合物的智能拉胀三维元结构的拓扑优化数值研究

今天,人类努力制造能够以智能方式做某事的设备和材料。形状记忆聚合物是一系列智能材料,能够通过施加外部刺激(例如热、电、磁、光、pH 值和湿度)从临时形式恢复其原始形状。在这项研究中,分析了具有正负泊松比的温度敏感形状记忆聚合物基结构的行为。目的是使用拓扑优化设计具有可调泊松比的智能结构的材料。在这项研究中,设计了一种由智能材料制成的元结构。这种结构不仅具有形状记忆效应,而且具有负泊松比,可用于新型传感器、执行器和生物医学应用。在创建单位单元和代表性体积元素并形成最终的三维结构后,基于大变形下的热粘超弹性本构模型进行有限元建模。检查结构在 20%、10% 和 5% 的拉伸预应变下的行为,观察到预应变对泊松比没有显着影响,但在 20% 的压缩应变下,得出的结论是加载对泊松比有效,拉伸和压缩模式下的结果不同。最后,检查温度速率对结构行为的影响,得出结论:温度变化越慢,在特定温度下完成的应变或形状恢复越多。
更新日期:2020-07-02
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