Abstract
This chapter presents an intelligent self-healing design of fiber-reinforced polymer (FRP) composites for predictive self-healing using the Dynamic Data-Driven Application Systems (DDDAS) paradigm through damage prognosis and a non-autonomous self-healing protocol. The proposed intelligent self-healing structural concept is composed of three inter-connected modules: (1) a damage sensing module, (2) a damage-prognosis module, and (3) a self-healing module. The current study focuses on developing the self-healing module of the proposed intelligent self-healing structural system - repeatable self-healing of FRP using thermoplastic healing agents and shape memory polymers (SMP) in FRP composites structures. This self-healing mechanism is motivated by the bio-mimetic process of ‘close then heal’ mechanism where the SMP complements the cracks’ closing, and the thermoplastic healing agent performs the healing process. For this purpose, double-cantilever beam (DCB) tests were carried out to quantify the healing efficiency in terms of Mode-I interlaminar fracture toughness (G Ic) following the ASTM D5528-13 testing protocol, and the healing efficiencies of seven different healing cycles were assessed to test the repeatability of the healing mechanism. The tests showed promising healing efficiencies ranging from 58% to 73% in terms of regaining the fracture toughness of virgin specimens. Furthermore, fractography analysis using Scanning Electron Microscopy (SEM) and the optical microscope of the fractured FRP composite specimens were also carried out qualitatively to understand the mechanisms responsible for enhancing healing efficiency.
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Acknowledgments
Funding for this work has been provided by the AFOSR Dynamic Data-Driven Application System Directorate (FA9550-17-1-0033). Scanning electron microscopy was carried out in the Optical Analysis Facility, which is supported by the Department of Biological Sciences. The authors would like to thank Professor Mark E. Barkey and Dr. Paul G. Allison for allowing to use of their lab facilities for various testing. The support of the Department of Aerospace Engineering and Mechanics at The University of Alabama, Tuscaloosa, is also acknowledged.
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Thapa, M., Jony, B., Mulani, S.B., Roy, S. (2022). Development of Intelligent and Predictive Self-Healing Composite Structures Using Dynamic Data-Driven Applications Systems. In: Blasch, E.P., Darema, F., Ravela, S., Aved, A.J. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-74568-4_9
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