The paper presents a methodology for training neural networks for vision tasks on synthe-sized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.

Currently, the determination of the emission rate of suspended solids from a unit of the surface area of a man- made mass at various parameters of the wind flow is not sufficiently described. The analysis of the world experience of researchers shows that existing laboratory installations have various design features that do not allow to correctly determine the mass of the dust being flapped and wind-blown. Based on the analysis results, the concept of an innovative laboratory installation for the study of dust-forming surfaces has been developed. It takes into account the influence of wind shadows, the deturbulization of an artificially created air flow, the possibility of regulating not only the flow velocity mode, but also the creation of a vacuum or disturbance in the area of sample placement, as well as the formation of a certain angle of wind flow attack relative to the surface. The concept provides for the possibility of determining the volume of dust emissions by the values of the lost dust masses in the sample and by the values of dust concentrations in the outgoing stream. The calculation of the main basic elements of the installation using the ANSYS FLUENT software package was carried out. The model and configuration of the wind tunnel have been developed and calculated, the main geometric parameters and functional elements for the possibility of use in scientific work have been determined. For practical use of the empirical roughness value of the underlying surface, its values are recommended in a wide range - from zero for the water surface to 0.44 for large cities with tall buildings and skyscrapers.

Qualification of employees who operate technological processes directly influences the safety of production. However, the employees’’ qualification cannot completely exclude human factor.
Today, there are many technologies that can minimize or eliminate human factor impact on production safety ensuring. The augmented reality technology is an example of this technology.
Nowadays, the augmented reality technologies and industrial technologies integration process moves to a new level of development. These technologies have huge experience, which has been accumulated in a long period of time. -This new level turns available by this experience combination and integration; it brings additional profit to the enterprise and can be a basis for completely new technologies. This paper shows an example of combination of augmented reality technology and oil pumps maintenance. For researching of efficiency of augmented reality system for oil pump maintenance, the laboratory unit with Grundfos vertical electric centrifugal pump (CR15-4 A-FGJ-AE-HQQE) was used. The laboratory unit is a physical model of one of the continuous oil processes. The oil pump of this laboratory unit is object of this research. The algorithm of servicing of oil pump was developed. The test of system and algorithms were carried out with four groups of people: the first one had only instructions to use on hand, the second one only used the internal recommendations of the system, the third one used only the help of an expert, and the fourth used internal recommendations and, if necessary, contacted the expert. The results show the efficiency and actuality of augmented reality technology for maintenance of industrial equipment, especially for the equipment operated in remote Arctic conditions.