Shape Memory Gels

Arun, D I; Chakravarthy, P.; Arockiakumar, R; Santhosh, B

doi:10.1201/9781351119948-4

Cited by 1 publication

(2 citation statements)

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“…In the classical visco-elastic-plastic based rheological model framework, SMPs are conceptualized as viscoelastic-plastic materials, employing elements like linear elastic springs, viscous dashpots, and slip elements (figures 13(a)-(c)). This approach aligns with the descriptions provided by Yan and Li in their 2022 publication [26], among other scholarly works [31][32][33]. Within this framework, the polymer chains existing between crosslinking nodes are analogized to springs, while the substantial internal friction arising from the coiling of macromolecules is equated to the viscosity, represented by dashpots.…”

Section: Introductionsupporting

confidence: 61%

“…The resulting master curve provides values of the relaxation modulus for various time intervals, including the instantaneous relaxation modulus at t = 0, which are subsequently normalized using equation (31). These normalized relaxation modulus values for neat epoxy SMP and different epoxy/GnP SMNCs are presented in tables 4-7 considering that ABAQUS utilizes normalized modulus values.…”

Section: Stress Relaxation Analysismentioning

confidence: 99%

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

Gupta,

Mittal,

Kumar

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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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.

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Section: Introductionsupporting

confidence: 61%

Section: Stress Relaxation Analysismentioning

confidence: 99%