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

纤维和纳米粒子增强热塑性塑料的注射成型模拟

• 批准号: 0521918
• 负责人: Donald Baird
• 金额: $ 36万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0521918&HistoricalAwards=false
• 关键词: Simulation; Injection; Molding; Thermoplastics; Reinforced

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.

本研究的目标是利用本构关系（即与取向生成表达式耦合的应力张量）模拟由短纤维和长纤维增强的热塑性塑料（TP）熔体和最终具有高纵横比的纳米颗粒的充模过程，该关系允许流动和颗粒取向之间的耦合，并包括锋面流动的运动学。预测的颗粒取向分布将与微观力学模型一起用于预测整个注塑件的刚度变化。一个关键因素是流变学方法的发展，这将为数值模拟包提供基本的材料流动特性。将对与注射成型相关的基本流动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与粘弹性耦合的模型，可以大大改进和加快增强TP的模具设计和注塑条件的选择。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，因为设计变更可以更迅速、更廉价地进行。一旦为注塑成型开发了仿真包，它可以很容易地扩展到其他工艺，如压缩成型和注射-压缩成型。从教育的角度来看，研究生将学习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潜力。此外，学生将学习合作解决复杂问题的重要性，因为工程学学生将学习使用有限元方法进行数值模拟，而数学学生将学习结构流体的输运过程和流变学。该项目是弗吉尼亚理工大学和美国能源部实验室（ORNL和PNWL）的合作成果，由美国国家科学基金会和美国能源部的FreedomCAR计划共同资助。