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

QRM：集成计算材料工程 (ICME) 方法中聚合物基复合材料的微观结构量化和虚拟重建

• 批准号: 1826232
• 负责人: Marianna Maiaru
• 金额: $ 46.09万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826232&HistoricalAwards=false
• 关键词: QRM; Microstructural; Quantification; Virtual; Reconstruction

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.

该拨款将支持那些能够贡献与复杂结构制造过程相关的新知识、促进科学进步、促进国家繁荣和确保国防安全的研究。聚合物复合材料是一种重量更轻的金属替代品，有可能改变节能航空航天，汽车和基础设施应用。纤维增强复合材料联合收割机将高强度纤维嵌入耐用的聚合物中，以非常小的重量赋予它们定向性能。 然而，这些材料在性能上的一致性不如金属，导致昂贵的过度设计。 这种性能变化的一部分归因于纤维增强复合材料中纤维的分散，因此对纤维行为的基本理解对于纤维增强聚合物基复合材料的可靠制造至关重要。 该奖项支持基础研究，通过了解基于制造工艺的纤维分散和聚合物性能之间的联系来了解复合材料的可变性。这项研究将能够量化制造后复合材料的可变性，这将允许设计更可靠的结构，具有更高的性能可预测性，提高安全性并降低成本。这项研究涉及多个学科，包括材料科学，制造，结构工程和数学。多学科的方法将积极影响工程教育，推广活动旨在扩大科学研究的参与。本研究旨在阐明纤维增强复合材料结构的制造工艺-微观结构-性能之间的关系，并量化微观尺度上的不确定性及其在复合材料水平上的传播。 研究团队将进行材料表征，包括高分辨率断层扫描和显微拉曼散射研究，以量化微观尺度下的纤维空间分布和基质原位特性;实现纤维增强复合材料微观结构的虚拟结构重建，确定基于物理的统计描述符，并虚拟再现统计等效固化模型。实验和建模相结合，量化固化过程中残余应力、局部微观结构、性能和性能之间的关系。该奖项反映了NSF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。

项目成果

THE EFFECTS OF DEBULKING ON THE MICROSTRUCTURE OF CARBON FIBER REINFORCED COMPOSITES

压实对碳纤维增强复合材料微观结构的影响

• DOI: 10.12783/asc36/35951
• 发表时间: 2021
• 期刊: Inc.
• 作者: SCHEY, MATHEW;STAPLETON, SCOTT;BEKE, TIBOR
• 通讯作者: BEKE, TIBOR

A Novel Closed-form Solution for Transverse Tensile Strength of Polymer Composites based on Virtual Testing

• DOI: 10.2514/6.2022-1241
• 发表时间: 2022-01
• 期刊: AIAA SCITECH 2022 Forum
• 作者: Sagar P. Shah;M. Maiarù

Identification and Quantification of 3D Fiber Clusters in Fiber-Reinforced Composite Materials

纤维增强复合材料中 3D 纤维簇的识别和定量

• DOI: 10.1007/s11837-021-04703-0
• 发表时间: 2021
• 期刊: JOM
• 影响因子: 2.6
• 作者: Schey, Mathew J.;Beke, Tibor;Appel, Lars;Zabler, Simon;Shah, Sagar;Hu, Jie;Liu, Fuqiang;Maiaru, Marianna;Stapleton, Scott
• 通讯作者: Stapleton, Scott

Cure shrinkage characterization of a thermosetting resin with three-dimensional digital image correlation (3D-DIC)

利用三维数字图像相关 (3D-DIC) 表征热固性树脂的固化收缩率

• 发表时间: 2023
• 期刊: American Society of Composites
• 作者: Bukenya, K;Sabato, A;Maiarù, M
• 通讯作者: Maiarù, M

Quantification of Thermoset Composite Microstructures for Process Modeling

用于过程建模的热固性复合材料微观结构的量化

• DOI: 10.2514/6.2021-1774
• 发表时间: 2021
• 期刊: American Institute of Aeronautics and Astronautics
• 作者: Shah, Sagar;Plaka, Evgenia;Schey, Mathew;Hu, Jie;Liu, Fuqiang;Beke, Tibor;Stapleton, Scott E.;Maiaru, Marianna
• 通讯作者: Maiaru, Marianna