QRM: Microstructural Quantification and Virtual Reconstruction of Polymer Matrix Composites within the Integrated Computational Materials Engineering (ICME) Approach

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This grant will support research that will contribute new knowledge related to the manufacturing process of complex structures, promoting the progress of science, advancing national prosperity and securing national defense. Polymer composites are a lighter-weight alternative to metals that have the potential to transform energy efficient aerospace, automotive and infrastructure applications. Fiber-reinforced composites combine high-strength fibers embedded within a durable polymer to give them directional properties with very little weight. However, these materials suffer from less consistency in performance than metals, resulting in costly over-design. Some of this performance variability is attributed to dispersion of fibers in fiber-reinforced composites, and thus a fundamental understanding of the fiber behavior is critical to reliable manufacturing of fiber-reinforced polymer matrix composites. This award supports fundamental research to understand variability in composites by understanding the tie between fiber dispersion and polymer properties based on the manufacturing process. This research will enable the quantification of variability in composites after manufacturing, which will allow the design of more reliable structures with higher performance predictability, increasing safety and reducing cost. This research involves several disciplines, including materials science, manufacturing, structural engineering and mathematics. The multi-disciplinary approach will positively impact engineering education, and outreach activities are aimed at broadening participation in scientific research.This research aims to elucidate the manufacturing process-microstructure-property relationships of fiber-reinforced composite structures, and quantify uncertainty at the micro scale and its propagation at the composite level. The research team will perform material characterization, including high-resolution tomography and micro-Raman scattering studies to quantify fiber spatial distribution and matrix in-situ properties at the micro-scale; implement virtual structure reconstruction of fiber-reinforced composite microstructures, determine physics-based statistical descriptors, and virtually reproduce statistically equivalent curing models. Experiments and modeling are integrated to quantify the relationships among residual stress during curing, local microstructure, properties and performance.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.