# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 5 Conclusions
This difference may be attributed to the fact that the MSE and MAE metrics in the stress field model are averaged across all nodes, whereas the peak dataset is smaller, which may lead to incomplete capture of the mapping relationship between features and target values. The MLP neural-network-based stress prediction method proposed in this paper effectively maps node position and flight parameter information to stress. Finite element calculations for a single case take approximately 7 min, whereas the stress field prediction model completes data predictions for 30 flight cases in just 2 s. This results in a significantly faster computation speed compared to traditional finite element methods. The single-output structural characteristics of the network enhance its power and flexibility, offering a novel approach to structural strength assessment. However, there are still the following shortcomings in this work:  
1. The applicability of the prediction model for the airfoil rib structure requires further enhancement, specifically in enabling the MLP model to comprehensively account for the effects of variations in airfoil shape on stress distribution. To improve the model's applicability to the airfoil rib structure, it is essential to incorporate stress distribution data from various airfoil types. The MLP model proposed in this study currently addresses only a single airfoil type, indicating potential areas for further development. 2. Additionally, the application of the prediction model to other structural components necessitates expansion. The distribution of structural stress is intricately linked to the inherent characteristics of each structure; thus, evaluating the model's applicability involves assessing its performance across diverse structural types. The MLP model presented herein has solely been implemented for the wing rib structure, and its applicability to other structural locations warrants further investigation. Future research should aim to extend the application of the developed models systematically across a broader range of aircraft structures. This includes integrating additional structural parameters within the same framework to enhance the model's applicability to those structures. Furthermore, existing knowledge in deep learning and structural mechanics should be leveraged to refine the data learning process and construct a more explanatory network structure that elucidates the mechanisms underlying stress generation. W.J. : conceptualization, investigation, resources, supervision, project administration, writing--original draft, writing--review and editing; Q.C. : conceptualization, investigation, data curation, funding acquisition, writing--original draft, writing--review and editing. All authors have read and agreed to the published version of the manuscript. The authors are grateful for financial support from the Science Fund for Distinguished Young Scholars of Chongqing Municipality (CSTB2022 NSCQ JQX0024). Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no conflicts of interest.