CAREER: Diffusion-Driven Degradation and Failure Mechanisms in Architected Composites

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This Faculty Early Career Development (CAREER) award will support fundamental research in elucidating how fiber architecture in reinforced polymer composite materials influences moisture ingression, and consequent degradation and failure when used in service. Owing to their high strength-to-weight ratios and significant in-service energy savings, composites have tremendous promise for lightweight structural applications in the aerospace, automotive, and marine industries, as well as civil infrastructure. However, these materials are inevitably exposed to and affected by moisture in the environment, even humidity in the air, which makes them vulnerable to degradation and damage, thus severely limiting their use in critical applications. This research will provide essential guidance for designing and fabricating composites for structural use. The research activities will be complemented with a series of integrated educational activities to train the next generation composites workforce, e.g., through a blended online course designed for working professionals and graduate students alike and research opportunities for a diverse group of undergraduate students. Additionally, the award will be used to create awareness in the public space through broad dissemination of research outcomes via various institutional programs, such as 4-H and Youth Conferences. The multi-fold objectives of this research are to: 1) determine how fiber architecture influences the tortuosity of moisture diffusion pathways within woven fiber-reinforced polymer composites (FRPCs); 2) establish the relationship between moisture diffusivity and effective mechanical properties; and 3) elucidate the impact of moisture diffusion pathways and concomitant degradation on mechanical failure modes. This research will use an integrated computational and experimental framework that resolves fiber architectures at multiple length scales common in woven FRPCs. The research approach relies on finite element modeling combined with convolutional neural network-based machine learning for exploring the correlations between tortuosity of fiber architecture, moisture diffusivity, and mechanical performance; fabrication of the selected specimens using textile weaving technology; and characterization of diffusion pathways through X-ray microtomography with digital volume correlation and mapped Fourier-transform infrared spectroscopy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.