In-situ Performance Evaluation of Large Shape Memory Polymer Components via Distributed Optical Fibre Sensors

Herath, Madhubhashitha; Emmanuel, Chris; Jeewantha, L. H. Janitha; Epaarachchi, Jayantha

doi:10.1109/iciafs52090.2021.9605868

Cited by 3 publications

(4 citation statements)

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“…Smart hinges can be deformed into pre-defined shapes through a programming process but can subsequently return to their original shapes upon the application of external stimuli [1][2][3][4]. In comparison to SMAs, SMPs present many unique merits, including better shape recoverability [5], tunable mechanical properties [5], lightweight [6], affordability [7], and good manufacturability [8]. In addition, a wider range of external stimuli, such as electricity, light, magnetic fields, chemicals, and water content, can be utilized to induce shape change in smart hinges made from SMPs [5,9,10].…”

Section: Introductionmentioning

confidence: 99%

Hybrid 3D Printing of Functional Smart Hinges

Raymond,

Bandala,

Hua

et al. 2023

Machines

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Smart hinges fabricated using three-dimensional (3D) printing have been accepted in the aerospace, robotics, and biomedical fields since these devices possess a shape memory characteristic. Shape memory polymers (SMPs) are the preferred materials for creating smart hinges due to their ability to achieve programmable complex geometries. However, fabricating SMPs with embedded components remains a challenge due to the constraints of current 3D printing methods and material limitations. This study investigated the use of a hybrid 3D printing method, direct ink writing (DIW), and embedded 3D printing (e-3DP) to print smart hinges with an embedded circuit to act as a strain sensor. The main components of the SMP included tert-Butyl acrylate (tBA) and aliphatic urethane diacrylate (AUD), but this SMP ink had a low viscosity and could not be used for DIW or e-3DP. Fumed silica (FS) was added to the SMP to tune its rheology, and it was shown that the FS concentration significantly affected the rheological properties, dry-out process, filament geometries, and self-supporting capabilities. This study presents a hybrid 3D printing approach for creating smart hinges with internal strain sensors in one step, demonstrating the versatility of DIW/e-3DP. The findings from this work provide a foundational and reliable technical solution to efficiently fabricate functional, self-monitoring, smart devices from SMPs for diverse applications.

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Section: Introductionmentioning

confidence: 99%

Hybrid 3D Printing of Functional Smart Hinges

Raymond,

Bandala,

Hua

et al. 2023

Machines

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“…28 Interestingly, fiber optic sensors can be strategically embedded into fiber reinforced SMPC components during their manufacturing process. [30][31][32] Our previous studies revealed the ability of embedded optical fibers to send light energy into the SMPC materials for light activation, 30 analyze the curing kinetics of SMPCs, 33 and evaluate in-situ shape memory performances of SMPCs. 32 The state-of-the-art technique presented in this article can be used to monitor the real-time shape memory behavior of large-scale SMPC components such as deployable space habitats and solar panel arrays operating in their real working environment.…”

Section: Introductionmentioning

confidence: 99%

“…[30][31][32] Our previous studies revealed the ability of embedded optical fibers to send light energy into the SMPC materials for light activation, 30 analyze the curing kinetics of SMPCs, 33 and evaluate in-situ shape memory performances of SMPCs. 32 The state-of-the-art technique presented in this article can be used to monitor the real-time shape memory behavior of large-scale SMPC components such as deployable space habitats and solar panel arrays operating in their real working environment. Herein dynamic temperature and strain profiles during the full shape memory cycles of a CFR SMPC were measured by means of DOFSs.…”

Section: Introductionmentioning

confidence: 99%

“…At present, optical fiber sensors are widely used to monitor the structural health of reinforced composite structures 28 . Interestingly, fiber optic sensors can be strategically embedded into fiber reinforced SMPC components during their manufacturing process 30–32 . Our previous studies revealed the ability of embedded optical fibers to send light energy into the SMPC materials for light activation, 30 analyze the curing kinetics of SMPCs, 33 and evaluate in‐situ shape memory performances of SMPCs 32 .…”

Section: Introductionmentioning

confidence: 99%

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Distributed sensing based real‐time process monitoring of shape memory polymer components

Herath

Emmanuel

Jeewantha

et al. 2022

J of Applied Polymer Sci

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Shape memory polymer (SMP) materials have the capacity to undergo large deformations imposed by mechanical loading, hold a temporary shape, and then recover their original shape upon exposure to a particular external stimulus. The fiber reinforced shape memory polymer composites (SMPCs) with enhanced structural performances give a boost to breakthrough technologies for large-scale engineering applications. This article presents a novel technique for distributed optical fiber sensor (DOFS) embedded SMPCs intended for realtime process monitoring of large-scale engineering applications such as deployable space structures. Herein a carbon fiber reinforced SMPC was tested under a three-point flexural shape memory process and the DOFS data were acquired through optical backscatter reflectometry. Experiments were conducted in a temperature controlled thermal chamber coupled with a 10 kN electromechanical testing system. DOFSs offered unique advantages for spatially distributed dynamic temperature and strain measurements during the shape memory process. Compared to the standard test method dynamic mechanical analysis, larger samples can be tested effectively by using a single DOFS with large strain levels and shape complexity. The proposed technique demonstrated the ability of embedded DOFSs for in-situ shape memory characterization such as shape fixity ratio, shape recovery ratio and recovery rate. This technique will eliminate the challenges hindering the process monitoring and performance evaluation of large SMPC components operating in their real working environments.

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