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预测CFRP复合材料的机械性能,使用数据驱动模型进行比较分析

Ammar Alsheghri^1,2, Amna Alhammadi³, Vassilis Drakonakis⁴

¹Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.

|April 7, 2025

概括

机器学习准确地预测了碳纤维增强聚合物 (CFRP) 复合材料的机械性能. 这种数据驱动的方法提高了材料设计,减少了实验测试需求.

科学领域:

材料科学材料科学材料科学
聚合物科学聚合物科学
机器学习应用机器学习应用

背景情况:

碳纤维增强聚合物 (CFRP) 复合材料由于其高强度与重量比在工程中至关重要.
预测CFRP的机械性能对于优化其应用和设计至关重要.
当前的预测方法可能耗时且资源密集.

研究的目的:

开发和评估用于预测CFRP机械性能的机器学习模型.
确定影响这些特性的关键因素,包括碳纳米管 (CNT) 含量和制造参数.
评估回归,随机森林和支持矢量回归模型的有效性.

主要方法:

设计和制造了62个不同的CFRP样本.
进行了实验测试,以获得机械性能数据.
训练和比较了回归,随机森林和支向量回归模型.

主要成果:

对屈曲强度 (R2 = 0.966),屈曲模量 (R2 = 0.871) 和模式II能量释放率 (R2 = 0.903) 实现了高预测精度.
机器学习模型有效地将输入参数与机械性能相关联.
这些模型在不同类型的CFRP中展示了强大的预测能力.

结论:

机器学习提供了一种强大的数据驱动方法,用于预测CFRP的机械性能.
这种方法可以显著减少对广泛的实验表征的依赖.
这些发现有助于更高效的材料设计和先进复合材料的开发.

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