穿孔泡填充CFRP矩形管的防撞性预测使用机器学习的撞击盒
Harri Junaedi1, Khaled Akkad2, Tabrej Khan1
1Department of Engineering Management, College of Engineering, Prince Sultan University, Riyadh 12435, Saudi Arabia.
Polymers
|November 13, 2025
概括
填充聚氨泡 (PUF) 的碳纤维增强聚合物 (CFRP) 管显著提高了防撞性,几乎将能量吸收量增加了三倍. 机器学习模型,特别是决策树回归器,准确预测性能,优化CFRP撞击盒设计.
科学领域:
- 材料科学与工程 材料科学与工程
- 机械工程 机械工程
- 计算力学 计算力学 计算力学
背景情况:
- 碳纤维增强聚合物 (CFRP) 管具有高特异性强度和能量吸收,使其适合用于汽车撞击箱.
- 优化防撞要求了解设计参数的影响,如孔径和内部填充.
- 传统的实验测试 crashworthiness 是耗时且昂贵的.
研究的目的:
- 为了研究不同孔配置和聚氨泡 (PUF) 填充的矩形CFRP管的轴向防撞性.
- 评估孔径,数量和位置以及PUF填充对碰撞性能的影响.
- 评估使用机器学习 (ML) 预测CFRP撞击盒性能以减少实验力度的可行性.
主要方法:
- 准静态轴向压缩测试是在设计的CFRP管道上进行的.
- 记录了防撞性指标,包括初始峰值力 (P_ip),平均压碎力 (P_m) 和能量吸收 (EA).
- 多个ML算法 (DTR,LR,RR,LAR,ENs,MLP) 用于使用实验数据预测防撞指标.
主要成果:
- 充满PUF的管道显示出显著增强的防撞性,与未充满的管道相比,PM和EA增加了近三倍.
- 在未填充的管道中,根据直径和位置,孔具有可变的影响;在PUF填充的管道中,孔降低了性能.
- 决策树回归器 (DTR) 模型显示出最高的预测准确度,RMSE为1251,MAPE为11.37%.
结论:
- 聚氨泡填充对于提高CFRP管的防撞性至关重要.
- 穿孔设计显著影响充满和未充满的CFRP管的碰撞性能.
- 机器学习模型,特别是DTR,为优化CFRP撞击箱设计提供了可行和高效的方法.
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