CAREER: Pushing the Performance Limit of Composite Structures: Integrated Modeling of Manufacturing Processes and Materials

这项教师早期职业发展（Career）资助将侧重于了解纤维增强聚合物复合材料制造工艺的基本方面，开发高保真的、基于物理的模型来预测纤维复合材料的加工性能关系，并为美国复合材料制造业建立一个包容性的劳动力管道。采用轻质复合材料制造结构部件正在改变交通运输行业，该行业追求提高车辆性能、更好的燃油经济性和减少排放。然而，制造这些先进的复合材料涉及复杂的过程，不可避免地会导致零件变异性和意想不到的缺陷，如空隙、纤维褶皱、残余应力和几何扭曲。由于缺乏强大的建模工具，复合材料制造商严重依赖试错方法来最大限度地减少零件的可变性，导致制造成本高，并限制了新工艺和零件设计的创新。该研究项目将深入了解制造过程中产生的缺陷和变异性，并将阐明组成特性，加工条件和结构性能之间的相关性。由此产生的预测模型将大大节省新工艺和产品开发的成本，从而实现复合部件的一致和改进质量。该研究项目将与各种各样的教育和推广活动相结合，包括开发一个复合材料在线证书课程，为学生在先进制造业的工作做好准备，为大学和高中学生提供研究机会，并通过博物馆展示向公众介绍复合材料的社会影响和就业机会。研究目标是通过集成多物理场和多尺度建模框架，结合一种新的原位过程监测方法，预测加工引起的缺陷，并制定制造策略，以提高先进纤维增强聚合物基复合材料的性能。具体目标包括：(1)通过一种新颖的、基于织物结构的超热粘弹性模型来研究褶皱的形成；(2)通过流动-压实-固化耦合模型预测双尺度孔隙和尺度变异性；(3)将加工缺陷与原位过程监测传感器数据进行综合性能预测。我们对复合材料制造的了解将通过以下方式显著增加：(1)制定一种新的基于织物结构的力学模型，以捕获悬垂和固化过程中的纤维起皱；(2)结合独特的超热粘弹性模型来指示固化复合材料的本构响应；(3)实施树脂流动和固化耦合模型，研究孔隙的形成和迁移；(4)将加工缺陷与性能预测相结合；(5)用于生命周期评估的新型过程中和在役监测技术。该研究将为先进复合材料结构的虚拟设计、制造和分析提供一个集成的基于物理的过程和性能建模框架。这将加速采用新材料、工艺和零件设计，通过计算建模来增强结构性能，有效地打破制造商、工程师、材料科学家和研究人员之间的壁垒，这将改变复合材料制造和设计的传统方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.