Analysing the shape memory behaviour of GnP-enhanced nanocomposites: a comparative study between experimental and finite element analysis

Shape memory polymers (SMPs) are capable of enduring significant deformations and returning to their original form upon activation by certain external stimuli. However, their restricted mechanical and thermal capabilities have limited their broader application in engineering fields. To address this, the integration of graphene nanoplatelets (GnPs) with SMPs has proven effective in enhancing their mechanical and thermal properties while maintaining inherent shape memory functions. The study evaluated shape memory nanocomposites (SMNCs) using dynamic mechanical, thermogravimetric, and static tensile, flexural, and shape memory tests, along with scanning electron microscopy to analyse tensile fractures. The results indicate that the optimal content of GnP is 0.6 wt%, resulting in excellent shape memory, thermal, and mechanical properties. Specifically, this composition demonstrates a shape recovery ratio of 94.02%, a storage modulus of 4580.07 MPa, a tensile strength of 61.42 MPa, and a flexural strength of 116.37 MPa. Additionally, the incorporation of GnPs into epoxy reduces recovery times by up to 52% at the 0.6 wt% concentration. While there is a slight decrease in the shape fixity ratio from 98.77% to 93.02%, the shape recoverability remains consistently high across all samples. Current finite element (FE) models often necessitate complex, problem-specific user subroutines, which can impede the straightforward application of research findings in real-world settings. To address this, the current study introduces an innovative finite element simulation method using the widely used ABAQUS software to model the thermomechanical behaviour of SMNCs, importantly incorporating the time-dependent viscoelastic behaviour of the material. The effectiveness of this new approach was tested by comparing experimental results from bending test of SMNCs cantilever beam with outcomes derived from FE simulations. The strong agreement between the experimental data and simulation results confirmed the precision and reliability of this novel technique.

形状记忆聚合物（SMPs）能够承受显著的变形，并在受到某些外部刺激激活时恢复到其原始形状。然而，它们有限的力学和热学性能限制了它们在工程领域更广泛的应用。为了解决这个问题，将石墨烯纳米片（GnPs）与形状记忆聚合物结合已被证明在提高其力学和热学性能的同时能保持固有的形状记忆功能是有效的。该研究通过动态力学、热重分析以及静态拉伸、弯曲和形状记忆测试，并结合扫描电子显微镜分析拉伸断裂来评估形状记忆纳米复合材料（SMNCs）。结果表明，GnP的最佳含量为0.6 wt%，这使得材料具有优异的形状记忆、热学和力学性能。具体而言，这种成分的形状恢复率为94.02%，储能模量为4580.07 MPa，拉伸强度为61.42 MPa，弯曲强度为116.37 MPa。此外，在0.6 wt%的浓度下，将GnPs掺入环氧树脂可使恢复时间缩短多达52%。虽然形状固定率从98.77%略微下降到93.02%，但所有样品的形状恢复能力始终保持较高水平。当前的有限元（FE）模型通常需要复杂的、针对特定问题的用户子程序，这可能会阻碍研究成果在实际环境中的直接应用。为了解决这个问题，本研究引入了一种创新的有限元模拟方法，使用广泛应用的ABAQUS软件对形状记忆纳米复合材料的热机械行为进行建模，重要的是纳入了材料随时间变化的粘弹性行为。通过将形状记忆纳米复合材料悬臂梁弯曲试验的实验结果与有限元模拟得出的结果进行比较，测试了这种新方法的有效性。实验数据和模拟结果之间的高度一致性证实了这种新技术的精确性和可靠性。