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一个高度可通用的机器学习框架,用于分析短纤维增强聚合物复合材料的均性质

Yunmei Zhao¹, Zhenyue Chen¹, Xiaobin Jian²

¹School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China.

|October 14, 2023

概括

本研究介绍了一种集体机器学习 (EML) 模型,用于预测复合材料的机械性质. 该EML模型实现了高精度和通用性,为复杂的模拟提供了计算效率高的替代方案.

科学领域:

材料科学材料科学材料科学
计算力学计算力学计算力学
机器学习机器学习

背景情况:

预测短纤维增强聚合物复合材料的机械性能对于材料设计至关重要.
需要准确和高效的建模方法来克服传统实验和模拟方法的局限性.

研究的目的:

开发一种高度可通用的机器学习框架,用于预测短纤维增强聚合物复合材料的均质化机械性能.
评估拟议的整体机器学习 (EML) 模型的准确性,效率,可解释性和通用性.

主要方法:

一个集体机器学习 (EML) 模型使用一个堆叠算法与额外树 (ET),极端梯度增强 (XGBoost) 和光梯度增强 (LGBM) 基本模型.
整合两步同质化微机械模型与有限元模拟,以生成高质量的基准真实数据集.
使用R平方值的性能评估,SHapley添加式扩展 (SHAP) 以特征的重要性,并与实验数据和高保真模型进行比较.

主要成果:

该EML模型实现了高预测准确度,R2值为0.988 (列车) 和0.952 (测试).
SHAP分析确定了矩阵/纤维的模量和纤维含量作为关键因素,纤维取向主导着异构.
该EML模型在实验数据上表现出强大的概括性,并与高保真模型相比显著降低了计算成本,同时保持了准确性.

结论:

开发的EML模型为预测短纤维增强聚合物复合材料的机械性能提供了准确,高效和可通用的方法.
该框架为材料设计和优化提供了有价值的工具,在计算效率方面表现优于传统方法.
通过SHAP分析了解关键影响因素可以提高机器学习预测的可解释性和可靠性.

关键词:

解释SHAP的解释整体机器学习整体机器学习均的属性均的属性模型的概括性模型的概括性.短纤维增强聚合物复合材料的复合材料.

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