Peter Korba

The propulsion system for an aircraft is one of its most crucial systems; therefore, its reliable work must be ensured during all operational conditions and regimes. Modern materials, techniques and methods are used to ensure this goal; however, there is still room for improvement of this complex system. The proposed manuscript describes a progressive approach for the mechanical properties prediction of the turbine section during jet engine operation using an artificial neural network, and it illustrates its application on a small experimental jet engine. The mechanical properties are predicted based on the measured temperature, pressure and rpm during the jet engine operation, and targets for the artificial neural network are finite element analyses results. The artificial neural network (ANN) is trained using training data from the experimental measurements (temperatures, pressure and rpm) and the results from finite element analyses of the small experimental engine turbine section proposed in the paper. The predicted mechanical stress by ANN achieved high accuracy in comparison to the finite element analyses results, with an error of 1.38% for predicted mechanical stress and correlation coefficients higher than 0.99. Mechanical stress and deformation prediction of the turbine section is a time-consuming process when the finite element method is employed; however, the method with artificial neural network application presented in this paper decreased the solving time significantly. Mechanical structural analyses performed in ANSYS software using finite element modeling take around 30–40 min for one load step. In contrast, the artificial neural network presented in this paper predicts the stress and deformation for one load step in less than 0.00000044 s.

The main goal of every production is an optimally set and stable production process with the lowest possible costs. Such settings can only be achieved through many years of experience or very specific research, which focuses on several critical factors. An example of such factors can be the size and use of available space or the location of the production line and the logistical location of individual production sites, which is individual for each production process. Specific research can be carried out, for example, by means of the TX Plant simulation application, which was used in the present article for the production process of making fiber from pellets. The output of this research is the effective use of the so-called “Digital factory” to make the process in the already created conditions more efficient. This was achieved by the TX Plant simulation application, resulting in a reduced production time and increasing overall productivity. An intuitive interaction with factory equipment is possible with this approach, which allows users to immerse themselves in the virtual factory environment. As a result, a layout’s efficiency of surface use, flow of martial, and ergonomics can be assessed in real time. This paper aims to demonstrate how virtual reality (VR) can be used to simulate a digital factory to aid in decision making and enhance factory efficiency.

English is used in all parts of aviation, especially in aircraft maintenance, where all documentation and various manuals are written in this language. However, there are no formal requirements for aircraft mechanics to have a certain level of English. Nevertheless, aircraft mechanics must master their English language skills, since they are needed for managing their work tasks. This study deals with the issue of insufficiency of standardized English courses, and determines the English language needs of aircraft maintenance in the Slovak Republic, using a triangular approach. The gathered data from 80 respondents for this paper were subjected to descriptive and inferential statistics, which were calculated by the means of Jamovi statistical software. The results revealed that aircraft mechanics engage most often in communication situations that require their knowledge of the reading skill. The importance of all language skills (reading, writing, listening, speaking) is proven by the determination of the most-common communication activities of aircraft mechanics, which should be included in specialized English courses. The results of this study are useful in the creation of language courses which increase the level of English in aircraft maintenance, thus, increasing safety in aviation.