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Stress prediction of fuselage tube section based on PSO⁃BiLSTM neural network
Received date: 2022-01-24
Revised date: 2022-02-18
Accepted date: 2022-04-01
Online published: 2022-07-08
Supported by
National Level Project
Before aircraft fuselage components are assembled and docked， shape control tools are usually used to adjust the shape of the fuselage tube section to meet the docking standard. In order to avoid damage to the fuselage due to excessive local stress during the shape control process， it is necessary to monitor changes in stress. This research proposes a stress prediction method， which combines Particle Swarm Optimization and Bidirectional Long Short-Term Memory （PSO-BiLSTM） neural network. In this paper， the model input data is converted into sliding window sequential data with extremely high data correlation， and the stress data set is trained and tested. The experimental results show that the PSO-BiLSTM neural network has obvious advantages in processing sequential data. This is because the PSO-BiLSTM network has long memory cells and high model capacity. Stress prediction loss converges within 0.3% Root Mean Square Error （RMSE） error range. Compared with the competitive model RNN network， the standard Long Short-Term Memory （LSTM） network and the Bidirectional Long Short-Term Memory （BiLSTM） neural network， the PSO-BiLSTM model not only predicts more accurately， but also significantly improves training efficiency.
Chao YANG , Kaifu ZHANG . Stress prediction of fuselage tube section based on PSO⁃BiLSTM neural network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(7) : 426991 -426991 . DOI: 10.7527/S1000-6893.2022.26991
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