Distributed sensing based real-time process monitoring of shape memory polymer components
Article
Article Title | Distributed sensing based real-time process monitoring of shape memory polymer components |
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ERA Journal ID | 1671 |
Article Category | Article |
Authors | Herath, Madhubhashitha (Author), Emmanuel, Chris (Author), Jeewantha, Janitha (Author), Epaarachchi, Jayantha (Author) and Leng, Jinsong (Author) |
Journal Title | Journal of Applied Polymer Science |
Journal Citation | 139 (22) |
Article Number | 52247 |
Number of Pages | 11 |
Year | 2022 |
Publisher | John Wiley & Sons |
Place of Publication | United States |
ISSN | 0021-8995 |
1097-4628 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/app.52247 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/full/10.1002/app.52247 |
Abstract | 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 real-time 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. |
Keywords | applications; characterization; composites; stimuli-sensitive polymers; viscoelasticity |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 401609. Polymers and plastics |
401602. Composite and hybrid materials | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Centre for Future Materials |
Harbin Institute of Technology, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q717x/distributed-sensing-based-real-time-process-monitoring-of-shape-memory-polymer-components
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