Microvascular network optimization of self-healing materials using non-dominated sorting genetic algorithm II and experimental validation

Abstract

Self-healing is a new strategy for crack defect which is the main reason for the failure of composites. As an extrinsic self-healing system, the microvascular network system is capable of multiple healing cycles and rapid healing of large area damage. However, the embedment of micropipe network will affect the performance of matrix material. In this article, a microvascular network of self-healing material is optimized using non-dominated sorting genetic algorithm II. Two objective functions head loss and void volume fraction are considered. Finite element analysis and Hardy Cross iteration are performed to achieve the quantization of objective functions. One hundred sixty-five optimized solutions were obtained, and the void volume fraction was within the limits of [4.19%, 5.13%], whereas the head loss was within the limits of [9.63×10−7\u2009m, 6.51×10−6\u2009m]. According to the optimization results, the network was prepared and tested to validate the design and feasibility. The test result shows that the void volume fraction of the prepared network is 3.77%, lower than the designed value 4.43% which has a little effect on the matrix material. The network is interconnected and the healing agent can flow freely in it. The embedded network does not reduce the performance of epoxy resin. The optimization of microvascular network balances the mechanical properties and self-repairing properties of the matrix material.