沈阳航空航天大学学报2025，Vol.42Issue(3)：1-9,9.DOI:10.3969/j.issn.2095-1248.2025.03.001
基于深度迁移学习的多参数耦合复合材料层合板力学性能
Mechanical properties of composite laminate with multi-parameter coupling based on deep transfer learning
摘要
Abstract
In order to address the challenges in structural strength analysis caused by limited training samples and extreme environmental conditions while improving analytical efficiency,deep transfer learning methods was applied to investigate the mechanical properties of composite laminates for the RX4E electric aircraft.Based on the analysis of experimental results obtained from composite laminates,multiple deep learning models were compared in terms of their ability to predict the experimental results.Finally,the convolutional long short-term memory(CLSTM)was selected as the optimal deep learning model.Furthermore,a transfer learning(TL)model was introduced to accurately predict the stress-strain relationships of composite laminates under varying temperatures,humidities and layup configurations.The results indicate that the proposed TL-CLSTM network model has exceptional capability in predicting the mechanical properties of composites,particularly in predicting the stress-strain relationship,with a mean squared error and a root-mean-square error of 10-5 and 10-3 respectively.The proposed model can effectively predict the mechanical properties of composite laminates for electric aircraft,overcoming the complexities and inefficiencies of traditional mechanical properties measurement methods,which providing a novel pathway for the future study of electric aircraft manufacturing.关键词
电动飞机/复合材料层合板/深度迁移学习/卷积-长短期记忆网络/力学性能Key words
electric aircraft/composite laminate/deep transfer learning/convolutional short-term memory/mechanical properties分类
航空航天引用本文复制引用
臧健,李宇腾..基于深度迁移学习的多参数耦合复合材料层合板力学性能[J].沈阳航空航天大学学报,2025,42(3):1-9,9.基金项目
国家自然科学基金(项目编号:11902203). （项目编号:11902203）
