# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 1 Introduction
Additionally, the flexible architecture and numerous adjustable parameters of MLP allow for effective optimization across different application scenarios, making it suitable for adapting to the complex environmental factors and flight conditions encountered by aircraft during operation. However, MLP is highly dependent on data quality and quantity; the presence of noise or incomplete data can reduce prediction accuracy, and insufficient data can lead to overfitting. The ideal application scenario for MLP involves predicting aircraft structural stresses with reliable, high-quality, and sufficient data to ensure model accuracy and reliability. With advancements in big data and health monitoring technologies, the large datasets required for MLP training are increasingly available, supporting its broader application. To enable rapid prediction of stresses in aircraft structures, this paper establishes a stress prediction model using an MLP neural network, capable of quickly forecasting the stress field. By performing finite element meshing of the wing ribs, the angle of attack, incoming flow velocity, and node coordinates are used as input tensors to the model, allowing it to learn the stress distribution of the wing ribs. The successful application of this model also opens the door for its potential use in predicting stresses in other structural components of the aircraft, such as rotor blades, tail blades, and other wing structures, thereby enhancing the structural safety and reliability of the aircraft. In this study, a finite element model is developed based on the wing design requirements of an air-to-air aircraft. Through the analysis of aerodynamic elasticity results, the equivalent stress distribution patterns of the wing ribs are summarized. A static aerodynamic elasticity calculation is employed to generate a wing rib stress dataset, comprising 173,700 data samples across 300 flight conditions. The MLP neural network is then utilized to construct a prediction model for the wing rib stress field, as well as a model for predicting the peak stress of the wing ribs. The dataset is used for training and prediction. The primary workflow of this paper is illustrated in Figure 1, and the key steps are as follows:  
1. Perform finite element modeling and static aerodynamic elasticity calculations of the wing based on the design requirements, followed by an analysis of the stress distribution. 2. Establish the wing stress field dataset from the numerical simulation results and divide the dataset into training, validation, and test sets according to standard dataset partitioning practices. 3. Identify the input variables for the model and build the MLP model, setting the hyperparameters based on the structure of the input variables. Different hyperparameters are tested to observe the model loss and performance indicators, and the model with the best overall performance is selected to predict the wing's stress. 4. Use common deep learning loss functions and evaluation metrics to assess and fine-tune the model. 5. Train the model and compare the predictions of the trained model with those of the RF model for further analysis.