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Investigation of Heterogeneous Deformation for Discontinuous Fiber Composites Through Combined Experiments and Modeling

通过实验和建模相结合研究不连续纤维复合材料的非均匀变形

基本信息

批准号：
1662554
负责人：
Michael Sangid
金额：
$ 44.62万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1662554&HistoricalAwards=false
关键词：
Investigation Heterogeneous Deformation Discontinuous Fiber

项目摘要

The application of compression / injection molded fiber-reinforced polymer composites in automotive and aerospace applications has the potential to decrease weight while maintaining desirable mechanical performance. These discontinuous fiber reinforced composites offer an alternative to metals for improved efficiency. Even though injection molding has the ease of manufacturing benefit, this method, like all others, produces defects within the material. Uncertainties about how the complex microstructure and defects influence the mechanical response and integrity of discontinuous fiber-reinforced composites have prolonged adoption of these materials for structural applications. Uses of fiber-reinforced composites are subjected to large-scale testing programs, necessary for certification, which lead to excessive time and cost resulting from a trial-and-error approach to manufacturing. Computational models have the ability to cut the time and cost necessary for certification, but first confidence must exist in the model's predictions. In this research, measurements from x-ray tomography during in situ loading provide knowledge of the initial defects, neighboring microstructure, and strain states within the composite materials contributing to material failure. These experiments provide the foundational information and initial states to enable material strength models, in order to predict the strength of these fiber-reinforced composites. Additionally, the project efforts include a multi-objective approach to outreach, in order to educate and mentor third through eighth grade and undergraduate students and broadly disseminate the tools created during this project.In this research, the mechanical behavior of composites will be studied through x-ray tomography scans during in situ loading, followed by state-of-the-art image analysis to reconstruct, identify, and track microstructural features within the composite. Through digital volume correlation, the heterogeneous deformation of individual events such as fiber rotation, pull-out, and breakage will be quantified with respect to the local microstructure during deformation in 3D bulk material. The experiments will provide the cornerstone for strength modeling by quantifying the irreversible deformation with respect to microstructural features. Statistical modeling based on modern probability theory, including rare event modeling using large deviations, will be used to characterize the composites overall strength with respect to the multivariate spatially varying distributions. A forward modeling formulation will be used to design fiber-reinforced thermoplastics with superior strength properties. These composites will be fabricated and characterized via a similar in situ loading, x-ray tomography experiment to validate the predictive nature of the model.

压缩/注射成型纤维增强聚合物复合材料在汽车和航空航天应用中的应用具有在保持所需机械性能的同时减轻重量的潜力。这些不连续的纤维增强复合材料为提高效率提供了金属的替代品。尽管注塑成型具有易于制造的优点，但这种方法与所有其他方法一样，会在材料中产生缺陷。对于复杂的微结构和缺陷如何影响非连续纤维增强复合材料的力学响应和完整性的不确定性，这些材料在结构应用中得到了广泛的应用。纤维增强复合材料的使用需要经过大规模的测试计划，这是认证所必需的，这会导致制造过程中的试错方法导致过长的时间和成本。计算模型有能力减少认证所需的时间和成本，但首先必须对模型的预测有信心。在这项研究中，原位加载过程中的X射线层析成像测量提供了导致材料失效的复合材料中的初始缺陷、邻近微观结构和应变状态的知识。这些实验为建立材料强度模型提供了基础信息和初始状态，以便预测这些纤维增强复合材料的强度。此外，该项目的努力包括一种多目标的外展方法，以教育和指导三年级到八年级和本科生，并广泛传播在该项目中创建的工具。在这项研究中，将通过原位加载过程中的X射线断层扫描来研究复合材料的力学行为，随后进行最先进的图像分析，以重建、识别和跟踪复合材料中的微观结构特征。通过数字体积关联，可以将纤维旋转、拔出和断裂等单个事件的非均匀变形与三维块体材料变形过程中的局部微观结构进行量化。这些实验将通过量化不可逆变形的微观结构特征，为强度建模提供基础。基于现代概率理论的统计建模，包括使用大偏差的罕见事件建模，将被用来表征相对于多变量空间变化分布的复合材料的总体强度。正向模拟公式将用于设计具有优异强度性能的纤维增强热塑性塑料。这些复合材料将通过类似的现场加载、X射线层析成像实验来制造和表征，以验证模型的预测性质。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Comparing non-destructive 3D X-ray computed tomography with destructive optical microscopy for microstructural characterization of fiber reinforced composites

DOI：
10.1016/j.compscitech.2019.107843
发表时间：
2019-11-10
期刊：
COMPOSITES SCIENCE AND TECHNOLOGY
影响因子：
9.1
作者：
Hanhan, Imad;Agyei, Ronald;Sangid, Michael D.
通讯作者：
Sangid, Michael D.

A Marked Point Process Model Incorporating Active Contours Boundary Energy

DOI：
10.2352/issn.2470-1173.2018.15.coimg-230
发表时间：
2018-01
期刊：
electronic imaging
影响因子：
0
作者：
Camilo Aguilar;M. Comer
通讯作者：
Camilo Aguilar;M. Comer

3D Fiber Segmentation with Deep Center Regression and Geometric Clustering

具有深中心回归和几何聚类的 3D 光纤分割

DOI：
发表时间：
2021
期刊：
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
影响因子：
0
作者：
Aguilar, Camilo;Comer, Mary;Hanhan, Imad;Agyei, Ronald;Sangid, Michael
通讯作者：
Sangid, Michael

Void detection and fiber extraction for statistical characterization of fiber-reinforced polymers