# Mechanical Stress Prediction of an Aircraft Torque Tube Based on the Neural Network Application
## 2 Materials and Methods  
The subject of this study is the development of a new method for immediate stress prediction using multiple inputs during the taxiing, takeoff, and landing of an aircraft. The main goal of this study is to create a reliable methodology for predicting mechanical stress with application to ATT, on the basis of which it is possible to create a new method for calculating fatigue life. To achieve the stated goal, a methodology is developed, which is summarized in the following steps:* The specification and identification of torque-tube mechanical problems. The cracks in the torque tube were identified on the splines (see Figure 1). There are four ATTs with different cracks, which belong to a particular number of cycles. In Figure 2a-d, the cracks from 37 to 73 mm are shown and, for better visualization, a nondestructive method is used to reveal them (Figure 2e,f). Aircraft land under various conditions (outside temperature, braking temperature, landing speed, etc. ), and these different conditions produce different loads on ATT and affect a number of ATT life cycles and crack growth. According to the cracks, it is possible to determine critical areas, and the results from FEM can be validated in terms of area of interest. Material properties are essential for stress and fatigue analyses; therefore, the second step involves material identification and determination of its properties. Materials are used in aircraft engineering structures and fatigue failure is one of the most common failure cases. Materials are extremely lightweight and durable components and structures. They are characterized by excellent mechanical properties, elasticity, and plasticity [1]. Mechanical properties are dependent on materials that are used for manufacturing brake-torque tubes, since they reduce material failure. This alloy does not need post-welding surface treatment, due to its properties. Material properties are assessed experimentally using the tensile test. * The third step belongs to the creation of a 3D geometry model of the ATT. The 3D model is used to create a FEM model, which means 3D geometry serves as an input for the meshing process. The 3D geometry is modeled in ANSYS software. * In order to gain inputs for the neural network, mechanical stresses for different operational regimes of ATT have to be estimated. For this purpose, FEM analysis was chosen, and mechanical stress was calculated by FEA for multiple operational regimes. The ANSYS software was used for all FEAs. * Last step is building an artificial neural network (ANN) for predicting the mechanical stress oN ATT based on the landing parameters of an aircraft. Parameters are described in the last chapter of the paper. The ANN describes the interdependence of the landing parameters and the mechanical stress of ATT, and it is programmed in MATLAB software. This step is the main part of the study. In order to fulfill the aim of the article,  
Figure 1: The method used ANN for stress prediction applied to ATT with future application to life-cycle counting. a new method had to be created. This method is able to calculate the mechanical stress on ATT at its critical area using ANN during aircraft operation. By using this method, it is possible to create a life-cycle-counting algorithm. All the above steps are summarized in Figure 1, where the methodology can be seen. The first part of Figure 1 consists in the specification f the problem, a mechanical crack in ATT occurring during aircraft operation. This shows the area of interest, in which the FEM analysis has to be performed. The material properties (Figure 1) are essential for FEM modeling, so material samples were created from ATT material and the material properties were experimentally estimated using a tensile test. The FEM modeling was performed using constants calculated and experimentally obtained from the tensile test (third part of Figure 1).