Integration of Manufacturing-Induced Variation in Multiscale Analysis of Composite Aerospace Structures

Composite structures are susceptible to localized flaws which drive failure initiation on the microscale (fiber-matrix) and are difficultto characterize with current modeling techniques. Multiscale models are beneficial because theycan capture the fiber-matrix interactions, but the current microscale models are not representative of actual as-manufacturedcomposites. In the proposed research, artificially generated volume elements will be rapidly generated which statistically correlateto actual fiber distributions. Scans will be obtained from different manufactured components which are widely used in space vehiclecomponents, recreated, and simulated with artificial models. Statistical descriptors which characterize random fiber distributionwill also be tied to strength using machine learning models. These models will provide insight on how part failure is initiated frommicroscale variation in manufacturing and will ultimately be integrated into NASA’s new multiscale tool NASMAT. Using modelswhich statistically represent actual composites will have better predictive capabilities and create more reliability in the design of newcomposite components. Design will also now take place at the microscale, saving cost and time for testing new components, and beused to develop new lightweight composite parts for space technology.

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