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

压缩/注塑成型纤维增强聚合物复合材料在汽车和航空航天领域的应用具有减轻重量的潜力，同时保持理想的机械性能。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。尽管注射成型具有易于制造的优点，但这种方法，像所有其他方法一样，在材料内部产生缺陷。复杂的微观结构和缺陷如何影响不连续纤维增强复合材料的力学响应和完整性的不确定性，延长了这些材料在结构应用中的应用。纤维增强复合材料的使用需要经过大规模的测试程序，这是认证所必需的，这导致了制造过程中反复试验导致的过多时间和成本。计算模型能够减少认证所需的时间和成本，但首先必须在模型的预测中存在信心。在本研究中，在原位加载过程中，x射线断层扫描测量提供了复合材料中导致材料失效的初始缺陷、邻近微观结构和应变状态的知识。这些实验为材料强度模型的建立提供了基础信息和初始状态，以预测这些纤维增强复合材料的强度。此外，项目工作还包括多目标的推广方法，以教育和指导三年级到八年级的学生和本科生，并广泛传播项目期间创建的工具。在本研究中，复合材料的力学行为将通过原位加载期间的x射线断层扫描来研究，然后通过最先进的图像分析来重建、识别和跟踪复合材料内的微观结构特征。通过数字体积相关，在三维块体材料变形过程中，纤维旋转、拔出和断裂等单个事件的非均质变形将根据局部微观结构进行量化。该实验将通过量化微观结构特征的不可逆变形，为强度建模提供基础。基于现代概率论的统计建模，包括使用大偏差的罕见事件建模，将用于表征复合材料的整体强度相对于多元空间变化分布。正演建模公式将用于设计具有优越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原位加载、x射线断层扫描实验来制造和表征，以验证模型的预测性。

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.