单向复合材料均质化弹性性能预测的集成学习模型:基于SHAP方法的可解释性分析

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中文摘要：本研究提出了一种可解释的集成学习(EML)数据驱动方法，用于快速预测单向纤维增强复合材料的均质化弹性性能。我们选择了三种机器学习模型——随机森林(RF)、极端梯度提升机(XGBoost)和轻量梯度提升机(LGBM)来构建EML模型。基于实验并建立微观纤维分布的概率统计模型，结合有限元仿真，建立了反映纤维随机分布特征的代表性体元(RVE)模型，并创建相关数据集。通过准确性、效率、可解释性和泛化性等指标对EML模型的性能进行了全面评估，研究结果表明:(1)EML模型有效提升了基机器学习模型的预测精度(R2=0。962，MSE=5。41);(2)我们使用基于合作博弈理论的SHAP可解释性方法对预测结果进行了分析，其中，全局解释发现纤维的体积含量是影响复合材料均质化弹性性能的决定性变量，而局部解释阐明了输入特征对预测结果的关键影响机制;(3)EML模型在实验数据上表现出良好的泛化能力，计算结果与实验数据吻合度高，实现了复合材料均质化弹性性能的高效精准预测。

外文标题：Ensemble machine learning for predicting the homogenized elastic properties of unidirectional composites:A SHAP-based interpretability analysis

外文摘要：This study aims to develop an interpretable ensemble machine learning(EML)method for predicting the homogenized elastic properties of unidirectional fiber-reinforced composite.Three machine learning models—Random Forest(RF),eXtreme Gradient Boosting Machine(XGBoost),and Light Gradient Boosting Machine(LGBM)are selected for constructing the EML model.The ground-truth dataset is created via knowledge-based finite element(FE)simulations with representative volume element(RVE)models of composites considering randomly dispersed fibers.We thoroughly evaluate the EML model's performance using the metrics of accuracy,efficiency,interpretability,and generalization.The obtained results indicate that:(1)the EML model outper-forms the base ML model with high precision(R2=0.962,MSE=5.41);(2)the SHapley Additive exPlanations(SHAP)based on cooperative game theory is used to interpret the predictions,with the global interpretations identifying fiber volume content as the most influential variable on the composite and the local interpretations examining the key influencing mechanisms of each feature;(3)the EML model demonstrates good generalization ability on experimental data,and it can accurately forecast the homogenized properties of the composite with genuine fibers.

外文关键词：

Composite materialsHomogenizationEnsemble learningInterpretationModel generalization

作者：

王文照、赵云妹、李岩

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作者单位：

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

关键词：

Composite materials Homogenization Ensemble learning Interpretation Model generalization

基金：

National Key Research and Development Program of ChinaLiaoning Open Competition Science and Technology Major ProjectNational Natural Science Foundation of ChinaShanghai Pujiang Talent Program

项目编号：

2022 YFB46020002022JH1/104000431230218422PJ1413800

出版年：

2024

DOI：

10.1007/s10409-023-23301-x

力学学报(英文版)

CSTPCD

影响因子：0.363

ISSN：0567-7718

年,卷(期)：2024.40(3)