机器学习辅助预测碳纤维增强形状记忆的恢复角度 环氧碳复合材料
Mehrab Hasan1, Iman Ghamarian1, Yingtao Liu1
1School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, Oklahoma, USA.
Macromolecular rapid communications
|January 20, 2026
概括
机器学习可以预测碳纤维复合材料的形状恢复. 支持向量回归 (SVR) 为智能材料执行器提供了最佳的精度,优于其他模型.
科学领域:
- 材料科学 材料科学 材料科学
- 智能材料是一种智能材料.
- 聚合物复合材料 聚合物复合材料
背景情况:
- 形状记忆聚合物和复合材料是执行器的关键智能材料.
- 碳纤维增强的环氧复合材料通过朱尔加热提供电活性形状恢复.
- 由于数据有限,难以预测时间依赖的恢复行为.
研究的目的:
- 开发一个超拟合意识的机器学习框架.
- 预测Joule加热激活的环氧复合材料的形状恢复角度.
- 分析各种回归算法的性能.
主要方法:
- 实现的随机森林 (RF),支持向量回归 (SVR),规则化的多项式和高斯过程回归 (GPR).
- 在贝叶斯超参数优化中集成过拟合约束.
- 使用分层的五倍交叉验证来对数据进行分区.
主要成果:
- 基于内核的方法 (SVR) 超过了树集 (RF).
- 在受控概括的情况下,SVR获得了最高的精度 (R2 = 0.944).
- 射频显示有效性有限 (R2 = 0.842),表明不适合小,光滑的数据集.
结论:
- SVR为工程预测提供了准确性和概括性之间的最佳平衡.
- 开发的框架能够可靠地预测形状恢复行为.
- 机器学习,特别是SVR,对于建模智能材料执行是有效的.
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