Development of Intelligent and Predictive Self-Healing Composite Structures Using Dynamic Data-Driven Applications Systems

Abstract

The chapter presents an intelligent self-healing composite structures design in fiber reinforced polymer (FRP) composites for predictive self-healing using the Dynamic Data-Driven Applications 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. This chapter focuses on the development of 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 closing of the cracks, 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 (GIc) following the ASTM D5528-13 testing protocol and the healing efficiencies were calculated for seven different healing cycles to assess the repeatability of the healing mechanism. The tests showed promising healing efficiencies ranging from 58% to 73% regaining of virgin fracture toughness during the DCB tests. Fractography analysis, using Scanning Electron Microscopy (SEM) and optical microscope, of the fractured FRP composite specimens qualitatively visualizes the results to understand the mechanisms responsible for the enhancement of healing efficiency.