Parallel neural network feature extraction method for predicting buckling load of composite stiffened panels

• Published in: Thin-walled structures 2024-06, Vol.199, p.111797, Article 111797
• Main Authors: Zhang, Tao, Wang, Peiyan, Fu, Jianwei, Wang, Suian, Lian, Chenchen
• Format: Article
• Language: English
• Subjects: Composite stiffened panel; Compression buckling; Data-driven approach; Parallel neural network
• ISSN: 0263-8231; 1879-3223
• DOI: 10.1016/j.tws.2024.111797

•Parallels recurrent neural network and feedforward neural network, predicting buckling load of composite stiffened panels.•Proposes a novel recurrent neural network to improve the integrity of feature extraction of stacking sequences.•Solves the limitation of the previous machine learning models through the data-driven method. A novel Parallel Neural Network (PNN) feature extraction method is proposed in this paper to predict the buckling load of composite stiffened panels. The PNN effectively processes both stacking sequences and discrete variables by leveraging the parallel operation of the Recurrent Neural Network (RNN) and the Feedforward Neural Network (FNN). This approach addresses limitations of previous models, such as feature loss due to the Classical Laminate Theory (CLT) and struggled with variable length stacking sequences. A Self-attention-based Bidirectional Long Short-Term Memory network (T-Bi-LSTM) is introduced to handle variable length stacking sequences comprehensively. The T-Bi-LSTM, which incorporates self-attention and other mechanisms, improves the network's ability to capture crucial information. The dataset for training and testing is generated using a finite element model verified by the corresponding experiments, where the PNN with T-Bi-LSTM and other contrasting models are trained. The results suggest that the T-Bi-LSTM demonstrates better capability in extracting comprehensive stacking sequences than Bidirectional Long Short-Term Memory network (Bi-LSTM). Furthermore, the proposed PNN feature extraction method exhibits superior fitting ability and generalization performance than the feature extraction method based on CLT.