航空学报2025，Vol.46Issue(24)：100-114,15.DOI:10.7527/S1000-6893.2025.31877
基于深度学习的复合材料结构性能参数反演
Inversion of structural performance parameters of composite materials based on deep learning
摘要
Abstract
This paper proposes a deep-learning-based inversion method for determining the structural performance parameters of composite materials.Traditional testing methods for composite material performance typically require substantial experimental resources.In contrast,this study integrates finite element simulation with neural network models to achieve efficient and accurate identification of composite laminate parameters.This study evaluates the per-formance of deep learning models in parameter inversion and analyzes the effects of model hyperparameters,feature types,and quantities on inversion accuracy.The results demonstrate that the proposed method can accurately identify the elastic modulus,Poisson's ratio,strength,and other parameters of composite materials,with errors of less than 10%,except for Poisson's ratio,thus validating the effectiveness of the deep-learning-based composite material pa-rameter inversion method.Additionally,this study examines the impact of dataset composition and layer information on the accuracy of parameter inversion,providing a theoretical foundation for further optimization of composite material structure design.关键词
复合材料/参数反演/深度学习/特征融合/有限元方法Key words
composite material/parameter inversion/deep learning/feature fusion/finite element method分类
航空航天引用本文复制引用
XIANG Zijian,MA Zhenyu,YANG Xixiang..基于深度学习的复合材料结构性能参数反演[J].航空学报,2025,46(24):100-114,15.基金项目
国家自然科学基金(52372438,51605484) National Natural Science Foundation of China(52372438,51605484) （52372438,51605484）
