灰色关联及熵权法对碳纤维增强树脂复合材料防撞梁的耐撞性优化设计

Optimization design of carbon fiber reinforced polymer anti-collision beam crashworthiness by grey relational analysis with entropy method

  • 摘要: 为改善和提升汽车的轻量化和耐撞性,针对碳纤维增强树脂基复合材料特性提出一种基于灰色关联分析与熵权法结合的复合材料防撞梁结构的设计策略。建立了考虑整车实际工况的数值简化模型,并通过碳纤维增强树脂复合材料力学性能试验确定了T300碳纤维/5113环氧树脂复合材料的材料力学性能,为碰撞工况下碳纤维增强树脂保险杠防撞梁模型计算提供真实准确的材料参数。基于正面碰撞仿真模型,采用哈默斯雷试验设计方法确立60组样本点建立了设计变量与响应之间的关系,采用熵权法求出各响应指标的权重值,结合灰色关联分析对碳纤维增强树脂复合材料防撞梁的耐撞性和轻量化进行优化设计,获取防撞梁结构的最优尺寸参数组合,确定优化方案。研究结果显示,与初始防撞梁相比,优化方案吸能量峰值提高了11.4%,峰值力降低了48%,质量减少了56.5%。该方法在满足安全性指标的前提下实现了汽车轻量化优化设计。

     

    Abstract: To improve the vehicle lightweight and crashworthiness, a design strategy for the characteristics of carbon fiber reinforced polymer composite anti-collision beam structure was proposed based on grey relational analysis with the entropy weight method. A numerical simplified model considering the actual working condition of the whole vehicle was established. The mechanical properties of the material were determined by the mechanical properties test of carbon fiber reinforced plastics, which provides accurate material property for the carbon fiber bumper anti-collision beam model under collision conditions. The Hammersley experimental design method can generate 60 sample points to establish the relationship between design variables and responses based on the frontal collision simulation model. The entropy weight method was used to determine the weight of each response index, and the crashworthiness and lightweight of the composite anti-collision beam were optimized by the grey relational analysis method. The optimal size parameter combination of the anti-collision beam structure was obtained and the optimization scheme was determined. The results show that the optimal model peak energy absorption is increased by 11.4%, the peak force is reduced by 48%, and the mass is reduced by 56.5% compared with the initial model. The method achieves lightweight optimization design for the vehide under the premise that the safety index is satisfied.

     

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