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

压缩/注射成型的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降低重量同时保持期望的机械性能的潜力。 这些不连续纤维增强复合材料提供了金属的替代品，以提高效率。 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。 不连续纤维增强复合材料复杂的微观结构和缺陷如何影响其力学响应和完整性，这一认识使这些材料在结构应用中得到了长期的采用。 纤维增强复合材料的使用受到认证所必需的大规模测试程序的影响，这导致了制造过程中的试错方法所导致的过度时间和成本。 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，测量从X射线断层扫描在原位加载过程中提供的知识的初始缺陷，相邻的微观结构，和应变状态的复合材料内的材料失效。 这些实验提供了基础信息和初始状态，使材料强度模型，以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个多目标的方法来推广，以教育和指导三到八年级和本科生，并广泛传播在这个项目中创建的工具。在这项研究中，复合材料的力学行为将通过X射线断层扫描在原位加载过程中进行研究，其次是最先进的图像分析，以重建，识别，并跟踪复合材料内的微观结构特征。通过数字体积相关性，在3D块体材料变形过程中，将相对于局部微观结构量化单个事件（如纤维旋转、拔出和断裂）的非均匀变形。 实验将提供的基石强度建模量化的不可逆变形的微观结构特征。 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相对于多变量空间变化分布的总体强度。正演模拟公式将用于设计具有上级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，以验证模型的预测性质制造和表征。

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.