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Synthesis and optimization for shape memory behaviour of 4D printed GNPs reinforced shape memory photopolymer composite

N. Dhanunjayarao Borra (Department of Mechanical Engineering, National Institute of Technology Raipur, Raipur, India and Department of Mechanical Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam, India)
Venkata Swamy Naidu Neigapula (Department of Mechanical Engineering, National Institute of Technology Raipur, Raipur, India)

Rapid Prototyping Journal

ISSN: 1355-2546

Article publication date: 24 January 2023

Issue publication date: 2 June 2023

135

Abstract

Purpose

Shape memory materials are functional materials having a good number of applications due to their unique features of programmable material technology such as self-stretching, self-assembly and self-tightening. Advancements in today’s technology led to the easy fabrication of such novel materials using 3D printing techniques. When an external stimulus causes a 3D printed specimen to change shape on its own, this process is known as 4D printing. This study aims to investigate the effect of graphene nano platelet (GNPs) on the shape memory behaviour of shape memory photo polymer composites (SMPPCs) and to optimize the shape-changing response by using the Taguchi method.

Design/methodology/approach

SMPPCs are synthesized by blending different weight fractions (Wt.%) of flexible or soft photopolymer (FPP) resin with hard photopolymer (HPP) resin, then reinforced with GNPs at various Wt.% to the blended PP resin, and then fabricated using masked stereolithography (MSLA) apparatus. The shape memory test is conducted to assess the shape recovery time (T), shape fixity ratio (Rf), shape recovery ratio (Rr) and shape recovery rate (Vr) using Taguchi analysis by constructing an L9 orthogonal array with parameters such as Wt.% of a blend of FPP and HPP resin, Wt.% of GNPs and holding time.

Findings

SMPPCs with A3, B3 and C2 result in a faster T with 2 s, whereas SMPPCs with A1, B1 and C3 result in a longer T with 21 s. The factors A and B are ranked as the most significant in the Pareto charts that were obtained, whereas C is not significant. It can be seen from the heatmap plot that when factors A and B increase, T is decreasing and Vr is increasing. The optimum parameters for T and Vr are A3, B3 and C2 at the same time for Rf and Rr are A1, B3 and C1.

Research limitations/implications

Faster shape recovery results from a higher Wt.% of FPP resin in a blend than over a true HPP resin. This is because the flexible polymer links in FPP resin activate more quickly over time. However, a minimum amount of HPP resin also needs to be maintained because it plays a role in producing higher Rf and Vr. The use of GNPs as reinforcement accelerates the T because nanographene conducts heat more quickly, releasing the temporary shape of the specimen more quickly.

Originality/value

The use of FPP and HPP resin blends, fabricating the 4D-printed SMPPCs specimens with MSLA technology, investigating the effect of GNPs and optimizing the process parameters using Taguchi and the work was validated using confirmation tests and regression analysis, which increases the originality and novelty.

Keywords

Acknowledgements

Declaration: The authors declare that they have no relevant financial or non-financial interests to disclose and authors have no competing interests to declare that are relevant to the content of this article.

Citation

Borra, N.D. and Neigapula, V.S.N. (2023), "Synthesis and optimization for shape memory behaviour of 4D printed GNPs reinforced shape memory photopolymer composite", Rapid Prototyping Journal, Vol. 29 No. 6, pp. 1175-1194. https://doi.org/10.1108/RPJ-08-2022-0254

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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