Simulation of Injection Molding of Thermoplastics Reinforced with Fibers and Nano-Particles

本研究的目的是利用允许流动和颗粒取向耦合的本构关系(即应力张量和取向生成表达式)来模拟短纤维和长纤维增强的热塑性塑料(TP)熔体的充模过程，并考虑前沿流动的运动学。预测的颗粒取向分布将与微观机械模型一起用于预测整个注塑零件的刚度变化。一个关键因素是流变学方法的发展，它将为数值模拟程序包提供基本的材料流动特性。将对与注塑成型相关的基本流的预测颗粒取向分布进行实验评估，以评估模拟的性能。这项工作有许多社会效益。通过引入流动和结构以及粘弹性耦合的模型，预计增强TP的模具设计和注塑条件的选择将得到显著改进和加快。这将有助于提高美国汽车制造商在全球汽车市场上的竞争能力，因为设计更改可以更快、更便宜地进行。一旦为注塑成型开发了模拟包，就可以很容易地将其扩展到其他工艺，如压缩成型和注塑-压缩成型。从教育学的角度来看，研究生将了解到复合材料零件的性能高度依赖于加工条件，并且存在预测这种关系的可能性。此外，学生还将学习合作解决复杂问题的重要性，因为工程专业的学生将学习使用有限元方法进行数值模拟，而数学专业的学生将学习传输过程和结构流体的流变学。该项目是弗吉尼亚理工大学和能源部实验室(ORNL和PNWL)之间的合作努力，由NSF和能源部的FreedomCAR计划共同资助。

The goal of this research is to simulate the mold filling process for thermoplastic(TP) melts reinforced with short and long fibers and eventually nano-particles of high aspect ratio using constitutive relations (i.e. stress tensors coupled with an orientation generation expression) which allow coupling between the flow and particle orientation and to include kinematics of frontal flow. The predicted particle orientation distribution will be used along with micro-mechanical models to predict the stiffness variation throughout an injection molded part. A key element is the development of the rheological methodology which will provide basic material flow properties to the numerical simulation package. An experimental evaluation of the predicted particle orientation distribution for basic flows associated with injection molding will be carried out to assess the performance of the simulation. There are numerous societal benefits to this work. By incorporating models in which the flow and structure are coupled along with viscoelasticity, it is expected that the design of molds and selection of injection molding conditions will be significantly improved and accelerated for reinforced TP's. This will help improve the ability of US automakers to compete in the world-wide automotive market as design changes can be made more rapidly and cheaply. Once a simulation package is developed for injection molding, it can easily be extended to other processes such as compression molding and injection-compression molding. From an educational view graduate students will learn that the properties of composite parts are highly dependent on processing conditions and the potential to predict this relation exists. Furthermore, the students will learn the importance of a cooperative effort in solving complex problems as the engineering student will learn about numerical simulation using the finite element method while the mathematics student will learn about transport processes and the rheology of structured fluids. This project is a cooperative effort between Virginia Tech and DOE laboratories (ORNL and PNWL) and is co-funded by NSF and DOE's FreedomCAR program.