复合材料科学与工程Issue(4)：78-88,11.DOI:10.19936/j.cnki.2096-8000.20260428.010
基于机器学习的复合材料工字形加筋板压缩屈曲行为预测研究
Research on the prediction of compression buckling behavior of composite I-shaped reinforced plates based on machine learning
杨馨怡 1聂小华 2张国凡 2常亮1
作者信息
- 1. 中国飞机强度研究所,西安 710065
- 2. 强度与结构完整性全国重点实验室,西安 710065
- 折叠
摘要
Abstract
The I-beam composite stiffened panel is a common engineering structure in aircraft load-bearing components.The axial compressive buckling performance of such panels is typically investigated using engineering methods and finite element analysis.However,these conventional approaches are characterized by low accuracy and high computational time,making it difficult to achieve efficient and precise research.In this study,an efficient ma-chine learning framework is established to address the prediction of the buckling load and buckling mode shape of I-beam composite stiffened panels under axial compression.By designing the sample space and constructing the dataset,the Extra Tree regression model and ANN(Artificial Neural Network)classification model are selected for prediction.The prediction accuracy for buckling load and mode shape reaches 98.34%and 93.75%,respectively.This signifi-cantly improves the prediction accuracy and efficiency,thereby overcoming the limitations of traditional methods.关键词
复合材料/加筋板/机器学习/压缩屈曲Key words
composite materials/stiffened panels/machine learning/compression buckling分类
通用工业技术引用本文复制引用
杨馨怡,聂小华,张国凡,常亮..基于机器学习的复合材料工字形加筋板压缩屈曲行为预测研究[J].复合材料科学与工程,2026,(4):78-88,11.
