Predictive Modeling of Composite Molding Processes: Simulation of Compression-Resin Transfer Molding Process for Manufacturing Net Shape Structures

本研究的目的是建立压缩树脂传递模塑（CRTM）过程中树脂流动和压缩力的过程模型，开发数值公式并将其应用于我们目前的液体复合模塑模拟中，以交互式地探索该过程并无缝地为工业提供技术转移。本研究方法将对CRTM过程的流压耦合特性进行探索性实验。将制定和分析控制方程，以确定关键的材料和工艺参数，并制定实验和分析确定的方法。数值公式和有效的算法来解决这些方程，为CRTM过程中成功填充和纤维变形所需的流动模式、时间和力提供预测模型，将集成在我们的模拟中。最后，利用仿真方法对网形零件原型进行模具设计和工艺设计。用聚合物复合材料代替汽车上的金属结构，由于重量减轻，可以节省大量燃料。我们将以经济有效的方式制定适用于净形复合材料结构快速制造的新工艺的预测模型。这将加快大批量生产的原型开发周期，并解决全球对开发和利用科学基础来改进和发明复合材料制造工艺的需求。我们将与能源部和汽车复合材料协会（ACC）合作，解决他们感兴趣的部件的预测建模问题。这项研究由美国国家科学基金会和美国能源部的FreedomCAR项目共同资助。

The objective of this research is to formulate a process model for the resin flow and compressive forces during the Compression Resin Transfer Molding (CRTM) process, develop the numerical formulation and implement it in our current simulation of Liquid Composite Molding to explore the process interactively and seamlessly provide technology transfer to industry. This research approach will conduct exploratory experiments to characterize flow-compression coupling in CRTM process. The governing equations will be formulated and analyzed to identify key material and process parameters and to develop methods for their experimental and analytic determination. Numerical formulation and efficient algorithms to solve these equations, to provide predictive modeling for flow patterns, times and forces required for successful filling and fiber deformation in the CRTM process, will be integrated in our simulation. Finally, the simulation will be used to design the mold and the process for prototype net shaped components. Replacement of metallic structures in automobiles with polymeric composites would provide substantial fuel savings due to the weight reduction. We will formulate predictive models of novel processes applicable for rapid manufacturing of net-shape composite structures in a cost effective way. This will accelerate the prototype development cycle for high volume production and addresses the global need to develop and use the science base for improving and inventing composite manufacturing processes. We will collaborate with Department of Energy and Automotive Composites Consortium (ACC) to address predictive modeling of components of interest to them. This research is co-funded by NSF and DOE's FreedomCAR program.