# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 5 Conclusions  
In this paper, in order to realize the prediction of the stress field of aircraft structure, a fast assessment method of the stress field based on an MLP neural network is proposed, and the stress prediction of the wing rib structure is carried out by this method. In this paper, the wing aeroelasticity calculation results are analyzed to summarize the wing stress distribution law and establish the wing rib stress field dataset. The MLP model is trained so that the model can accurately realize the prediction of the equivalent stress field, and the accuracy level of the MLP model is verified by analyzing the prediction data of the validation set and test set in combination with the evaluation index and the equivalent stress prediction cloud diagram. By comparing with the RF model, the powerful fitting ability and adaptability of the MLP model are demonstrated. The following conclusions were obtained by summarizing the work:  
1. In this paper, we propose a method that involves finite element meshing of the wing ribs and using node information along with the angle of attack and inflow velocity as the input tensor for the model. This approach enables the model to learn the stress distribution of the wing ribs and establishes a separate prediction model for the peak stress region. The results demonstrate that this method effectively establishes a robust  
Figure 11: Prediction Results for Nodes 18,403, 35,182, and 35,207.  
mapping relationship between node information and stress distribution, maintains high flexibility in network inputs without requiring a fixed data size, and achieves high prediction accuracy. 2. Compared with traditional machine learning algorithms like the RF model, the MLP demonstrates higher accuracy and computational efficiency in stress field prediction. The MLP particularly exhibits stronger adaptability when handling high-dimensional input data. In this study, the prediction performance of the MLP model is contrasted with that of the RF algorithm. Both models show commendable prediction performance on the validation set; however, the MLP model significantly outperforms the RF model on the test set, which includes extreme-condition data. The prediction error of the RF model on the test set increases with load, notably rising after the stress value exceeds 20 MPa. In contrast, the prediction error of the MLP model remains relatively stable until the stress value reaches 100 MPa. This difference can be attributed to the nonlinear characteristics of the load variations on the wing ribs as they approach ultimate operating conditions. The MLP model is more adept at capturing these non-linear relationships than the RF model, resulting in a higher threshold for significant increases in prediction error. Overall, the results underscore the MLP's superiority in adapting to complex, high-dimensional data and handling nonlinear relationships in stress predictions, particularly under extreme loading conditions. 3. Analysis of the stress field prediction results reveals that the MLP models achieve the following metrics on the validation set: MSE within 0.09 MPa, MAE within 0.16 MPa, MAPE within 5%, and R\({}^{2}\) above 0.999. On the test set, the models achieve MSE within 0.998 MPa, MAE within 0.5 MPa, MAPE within 5%, and R\({}^{2}\) above 0.99. 4. A peak stress prediction model is established separately for nodes with peak stress and stress concentration, enhancing the accuracy of peak equivalent stress predictions. In the validation set, the model achieves MSE within 0.035 MPa, MAE within 0.13 MPa, MAPE within 0.504%, and R\({}^{2}\) above 0.999. In the test set, the model achieves MSE within 1.8 MPa, MAE within 0.998 MPa, MAPE within 1.25%, and R\({}^{2}\) above 0.99. Compared to the stress field prediction model, the peak stress prediction model shows higher MSE and MAE but lower MAPE.