# Mechanical Stress Prediction of an Aircraft Torque Tube Based on the Neural Network Application
## 1 Introduction  
The trend in the aerospace industry is to produce lightweight mechanical parts from light metals, composites, etc. ; however, some aerospace mechanical parts are still made of metal. Metallic materials are extensively used in engineering structures. At the same time, fatigue failure is one of the most common failure modes of metal structures. The fatigue phenomenon occurs when a material is subjected to fluctuating stresses and strains, which lead to failure due to damage accumulation [1]. Aviation maintenance faces the problem of early reveal fatigue damage. As written in [2], unscheduled maintenance can contribute significantly to an airline's cost outlay. Currently, there is no system of prediction based on numerical calculation during aircraft operation according to the real state of a given part. One of the most crucial parameters for estimating fatigue life is stress during the operation regimes of particular parts. At present, manuals that prescribe the procedures indicated by manufacturers are used for maintenance [3]. Specialized personnel, usually based on their skills, decide whether a component needs repair or replacement. All maintenance activities are based on operating cycles, or the hours during which the component is active (preventive planned maintenance) [4]. The intervals of inspection are prescribed for cycles, or flight hours. The cycles are calculated by the maintenance system, into which line mechanics enter information according to the aircraft computer. Other possibilities are predictive maintenance actions (predictive maintenance) based on the states of components [5]. Some studies estimate fatigue life and cracks using finite element methods; the essential parameter, as already mentioned, is the stress during particular regimes. Stresses are used for various methods (such as the rain-flow method), in addition to other inputs, to estimate life cycles. One of these methods is presented in [6], where the fatigue-crack initiation was calculated using the 3D finite element method (FEM). The authors compared the numerical method with their experimentally obtained results, which proved the good agreement between the two methods. Another mechanical problem of fatigue loading is presented in [7], where, similarly to [6], a riveted and bolted steel construction is investigated. The same combination of the FEM and the experimental approach was used for the study. The study also presented crack propagation as a result of fatigue [7; 8; 9]. In [10], low cycle fatigue (LCF) was performed on a cantilever beam with a tip mass, and numerical and experimental approaches were used. In [11], the authors emphasized the mean stress effect on high cycle fatigue. A large number of scientific articles on material fatigue and crack failure can be found across the scientific community, but stresses during loading cycles are not fundamental elements in any fatigue studies. Nevertheless, they are essential parts of every fatigue-life calculation. As described above, a combination of numerical and experimental approaches is one way to estimate the mechanical stresses of fatigue life. There is another approach that can combine two numerical methods. The main goal of the proposed article is to use numerical methods in order to develop a method that can estimate the mechanical loads of a particular part during its operation. One of the methods is already briefly mentioned (FEM), and the second is described below. The second numerical tool chosen for the research is an artificial neural network (ANN). The ANN is a numerical method used in various fields to predict or simulate very complex systems and difficult problems. These ANNs can also be used as tools for predicting mechanical stress, crack propagation, fatigue life, and other mechanical problems. For instance, an ANN was used to predict the fatigue life of carbon and low-alloy steel [12]. Crack growth and its prediction based on neural networks are presented in [13], demonstrating the solution to another mechanical problem by ANN.