# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 1 Introduction
First, an artificial-intelligence-driven load recognition method is employed to identify the loads applied to physical entities, using both measurement and computational data, with two recognition methods proposed: one based on a single-fidelity agent model and the other on deep learning. Second, a multi-fidelity surrogate model (MFS) is applied to enhance the accuracy of the MCC-DT, with two implementation approaches presented, along with an analysis of their respective advantages and disadvantages. Third, an algorithm is developed to estimate cumulative damage to the physical entity in near real time, allowing for damage assessment. Finally, the data generated from the first three steps is integrated into a 3D scene using a web-based graphics library, providing an intuitive visualization of the MCC-DT. An airplane model is used to validate the applicability and effectiveness of this framework [11]. Data prediction methods based on learning algorithms analyze both the linear and nonlinear relationships among input data features, enabling the discovery of underlying correlations between individual features and the prediction of aircraft structural strength [12; 13]. Common learning algorithms include clustering, random forests, and neural networks. The primary objective is to understand the data structure and fit it into models that are interpretable and useful for researchers [14; 15]. These algorithms are trained on input data sources, employing statistical analysis techniques to generate output values within a specified range. Structural models are then developed from sample data, automating the decision-making process. [PERSON] et al. investigated a method for predicting the stress field of a two-dimensional linear elastic cantilever structure using volume data and neural networks. The study employed two distinct prediction architectures: a single-channel input network and a multi-channel input network. By comparing the predictive performance of these models, it was observed that the multi-channel input network exhibited a significantly lower prediction error compared to the single-channel input network. This study indicates that deep learning models could serve as an effective alternative to traditional prediction methods [16]. To provide effective data input for engine turbine disk life management and subsequent engineering applications, [PERSON] et al. developed a turbine disk stress prediction model for aero-engines, leveraging dimensionality reduction and random forest techniques grounded in statistical learning and machine learning methods. The model uses measurable parameters as initial features and performs dimensionality reduction through correlation analysis, principal component analysis, and cluster analysis to extract key control factors. A prediction model is then constructed using the random forest algorithm. The results demonstrate that this approach achieves higher prediction accuracy compared to a random forest model without dimensionality reduction, highlighting its effectiveness in turbine disk stress prediction and its significant technical value for aero-engine life management [17]. Ultrason nanocrystalline surface modification (UNSM) is a surface treatment technique that uses ultrasonic vibrational energy to enhance the mechanical properties of materials. The dynamic nature of UNSM induces deformation in the surface and subsurface layers at very high strain rates, making direct observation of residual stresses and refinement layers challenging. [PERSON] et al. proposed an alternative approach using artificial neural networks (ANNs) to predict residual stresses and hardness in various nickel-based alloys treated with UNSM. After training and validating the ANN with experimental data, the model demonstrated strong predictive capability. The study concludes that an ANN can serve as a more accurate alternative in the absence of a mathematical model, providing a useful tool for optimizing UNSM processing parameters [18].