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

职业：突破复合结构的性能极限：制造工艺和材料的集成建模

• 批准号: 2105448
• 负责人: Dianyun Zhang
• 金额: $ 55万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105448&HistoricalAwards=false
• 关键词: CAREER; Pushing; Performance; Limit; Composite

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.

该学院的早期职业发展（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、基于物理的模型来预测纤维复合材料的加工性能关系，以及建立包容性的劳动力管道对于美国复合材料制造行业来说，结构部件采用轻质复合材料正在改变运输行业，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，制造这些先进复合材料涉及复杂的工艺，不可避免地会导致零件的变化和排放。意外缺陷，例如空隙、纤维皱纹、残余应力和几何变形 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变异性，从而导致制造成本高昂并限制了创新。新工艺和零件设计。该研究项目将深入了解制造工艺中产生的缺陷和变异性，并将阐明成分特性、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产生重大影响。新工艺和产品的成本节约该研究项目将与各种教育和推广活动相结合，包括开发复合材料在线证书项目，为学生在先进制造领域的工作做好准备，为大学和大学提供研究机会。高中生，并通过博物馆演示向公众宣传复合材料的社会影响和职业机会。研究目标是预测加工引起的缺陷并制定制造策略，以通过以下方式提高先进纤维增强聚合物基复合材料的性能集成的多物理场多尺度建模框架与新颖的原位过程监测方法相结合，具体目标包括：（1）通过新颖的基于织物结构的超热粘弹性模型研究皱纹形成；（2）预测双尺度空隙。通过耦合流动-压实-固化模型来测量尺寸变化；(3) 将加工引起的缺陷和来自现场过程监测传感器的数据与复合材料性能预测相结合，我们对复合材料制造的了解将通过以下方式显着增加：(1) ）新型织物的配方基于体系结构的力学模型，用于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确定固化复合材料的本构响应；(3) 实施耦合树脂流动和固化模型研究空洞的形成和迁移；（4）将加工引起的缺陷与性能预测相结合；以及（5）用于生命周期评估的新型过程中和使用中监测技术。过程和用于先进复合结构的虚拟设计、制造和分析的性能建模框架这将加速新材料、工艺和零件设计的采用，通过计算建模来增强结构性能，有效打破制造商、工程师和材料科学家之间的隔阂。和研究人员，这将改变复合材料制造和设计的传统方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Cure process modeling and characterization of composites using in-situ dielectric and fiber optic sensor monitoring

使用原位介电和光纤传感器监测对复合材料的固化过程进行建模和表征

• 发表时间: 2022-10
• 期刊: The Composites and Advanced Materials Expo
• 作者: Elenchezhian, M.;Enos, R.;Martin, N.;Sen, S.;Zhang, D.;Pantelelis, N.
• 通讯作者: Pantelelis, N.

Effects of manufacturing processes on progressive damage of composites

制造工艺对复合材料渐进损伤的影响

• 发表时间: 2023-06
• 期刊: and Materials Conference
• 作者: Meka, S. K.;Enos, R.;Zhang, D.
• 通讯作者: Zhang, D.

A textile architecture-based discrete modeling approach to simulating fabric draping processes

基于纺织结构的离散建模方法来模拟织物悬垂过程

• DOI: 10.1177/15280837231159678
• 发表时间: 2023-01-01
• 期刊: Journal of Industrial Textiles
• 影响因子: 3.2
• 作者: Qingxuan Wei;Dianyun Zhang
• 通讯作者: Dianyun Zhang

A Novel Anisotropic Hyper-viscoelastic Model for Predicting Fabric Draping Responses

用于预测织物悬垂响应的新型各向异性超粘弹性模型

• DOI: 10.2514/6.2023-0522
• 发表时间: 2023-01-19
• 期刊: AIAA SCITECH 2023 Forum
• 作者: Qingxuan Wei;Dianyun Zhang
• 通讯作者: Dianyun Zhang

A textile architecture-based discrete modeling approach for fabric draping simulations

基于纺织体系结构的织物悬垂模拟离散建模方法

• 发表时间: 2022-09
• 期刊: American Society for Composites 37th Technical Conference
• 作者: Wei, Q.;Zhang, D.
• 通讯作者: Zhang, D.