Enabling the development of safe lightweight next-generation vehicles using high-performance composite materials

该研究旨在研究通过快速高压树脂转移模塑（HP-RTM）工艺制造的高性能碳纤维增强塑料（CFRP）复合材料在大幅减轻汽车前端结构部件重量方面的潜力。这些材料以其高比强度和刚度特性、低密度（1.5 g/cm3）和碰撞条件下的高能量吸收能力而闻名，并已被确定为大众市场车辆大幅减重的关键材料。尽管由于高成本和制造时间的限制，高性能CFRP复合材料尚未集成到汽车正面碰撞结构中，但新开发的加工技术（如HP-RTM）可以提供所需的快速生产速度，从而使CFRP进入大众市场车辆。该研究将解决基本材料表征和零件制造工艺挑战，以开发准确的冲击模拟平台，这将需要将高性能轻质CFRP复合材料集成到下一代汽车的吸能结构中。将评估固有的加工缺陷，并将其与CFRP复合材料高度复杂的变形响应相结合，以预测CFRP零件的冲击性能。作为验证，将在各种负载条件下测试具有不同几何形状代表正面车辆结构的CFRP零件，并根据实验评估模型预测。

The proposed research aims to investigate the potential for substantial weight reduction of front end automotive structural components using high performance carbon fiber reinforced plastic (CFRP) composite materials fabricated by a rapid high pressure resin transfer moulding (HP-RTM) process. These materials are known for their high specific strength and stiffness characteristics, low densities (1.5 g/cm3) and high energy absorption capabilities under crash conditions, and have been identified as key materials for enabling substantial weight reductions in mass market vehicles. Although high performance CFRP composites have not been integrated into frontal crash structures of automobiles to date due to high cost and manufacturing time constraints, newly developed processing techniques such as HP-RTM can provide the required rapid production rates that will allow the realization of CFRPs into mass-market vehicles. The research will address fundamental material characterization and part fabrication processing challenges in order to develop an accurate impact simulation platform, which will be required to integrate high performance lightweight CFRP composites into energy absorbing structures of next-generation automobiles. The inherent processing defects will be assessed, and coupled to the highly complex deformational response of the CFRP composite in order to predict impact performance of CFRP parts. As a validation, fabricated CFRP parts with differing geometries representative of frontal vehicle structures will be tested under various loading conditions and the model predictions will be assessed against the experiments.