This Faculty Early Career Development (CAREER) grant will focus on understanding fundamental aspects of fiber-reinforced polymer composite manufacturing processes, developing high-fidelity, physics-based models to predict the processing–performance relation of fibrous composites, and building an inclusive workforce pipeline for the U.S. composites manufacturing industry. Adoption of lightweight composites for structural components is transforming the transportation industry, which pursues improved vehicle performance, better fuel economy, and reduced emissions. However, manufacturing these advanced composites involves complex processes that inevitably cause part variability and unintended defects, such as voids, fiber wrinkles, residual stresses, and geometric distortions. The lack of robust modeling tools makes the composite manufacturers heavily reliant on trial-and-error approaches to minimize part variability, resulting in high manufacturing costs and limiting innovations for new process and part designs. This research project will develop an in-depth understanding of defects and variability arising from manufacturing processes, and will elucidate the correlation between the constituent properties, processing conditions, and structural performance. The resulting predictive models will lead to significant cost savings in new process and product development which achieves consistent and improved quality of composite components. The research program will be integrated with a diverse range of education and outreach activities, including developing an online certificate program in composites to prepare students for jobs in advanced manufacturing, providing research opportunities to college and high school students, and informing the general public about the societal impact of composites and career opportunities through museum demonstrations.
The research goal is to predict the processing-induced defects and develop manufacturing strategies to improve the performance of advanced fiber-reinforced polymer matrix composites through an integrated multi-physics and multiscale modeling framework in conjunction with a novel in-situ process monitoring method. Specific aims include: (1) investigation of wrinkle formation through a novel, fabric architecture-based hyper-thermo-viscoelastic model; (2) prediction of dual-scale voids and dimensional variability through a coupled flow-compaction-cure model; and (3) integration of processing-induced defects and data from in-situ process monitoring sensors with composite performance prediction. Our knowledge of composites manufacturing will be significantly increased through: (1) formulation of a novel fabric architecture-based mechanics model to capture fiber wrinkling during the draping and curing processes; (2) incorporation of a unique hyper-thermo-viscoelastic model to dictate the constitutive response of a curing composite; (3) implementation of coupled resin flow and curing models to investigate void formation and migration; (4) integration of processing-induced defects with performance predictions; and (5) novel in-process and in-service monitoring techniques for life-cycle assessment. The research will result in an integrated physics-based process and performance modeling framework for virtual design, manufacturing, and analysis of advanced composite structures. This will accelerate the adoption of new materials, processes, and part designs for enhanced structural performance through computational modeling, effectively breaking down the walls between manufacturers, engineers, material scientists, and researchers, which will transform the traditional methodology for composites manufacturing and design.
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.
