在低速冲击下使用机器学习和FEA对自然纤维复合材料的能量吸收和损伤预测
N Rekha1, Aswatha Munipapanna1, N Santhosh2
1Department of Mechanical Engineering, Bangalore Institute of Technology, Bangalore, Karnataka, India.
Scientific reports
|November 18, 2025
概括
香纤维复合材料在结构应用中显示出出色的能量吸收能力. 机器学习和FEA准确地预测了它们的冲击行为,突出了它们在汽车和航空航天行业的潜力.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 复合材料 复合材料 复合材料
背景情况:
- 香纤维复合材料提供了一个可持续的,高强度的替代合成材料.
- 它们的能量吸收和抗冲击能力对于结构应用至关重要.
- 了解它们在低速度冲击下的行为是材料开发的关键.
研究的目的:
- 为了研究香纤维复合板材在低速度冲击下的能量吸收和损伤预测.
- 评估实验测试,有限元分析 (FEA) 和机器学习 (ML) 在模拟复合行为的有效性.
- 评估香纤维复合材料在轻量化,高强度应用中的潜力.
主要方法:
- 使用手工布置技术制造香纤维复合板材.
- 实验性低速撞击测试,以测量能量吸收和损伤.
- 有限元分析 (FEA) 模拟用于模拟冲击反应.
- 开发机器学习 (ML) 模型 (逻辑回归,天真贝叶斯) 用于行为预测.
主要成果:
- 香纤维复合材料显示出显著的能量吸收 (14.36 kJ在1.8米的落下高度).
- FEA和ML模型准确地预测了实验能量吸收值.
- ML模型在预测能量吸收和损伤启动方面取得了1.0的准确性.
结论:
- 香纤维复合材料表现出对抗冲击的有希望的能量吸收能力.
- 集成的FEA和ML方法提供了对复合材料行为的强大而准确的预测.
- 这些发现支持在汽车和航空航天领域使用香纤维复合材料用于轻质结构部件.
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