International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The article focuses on the definition of industrial business risks that are associated with risk management. Following the description of the risks, the article then focuses on the elimination of the hazard or the reduction of each of the two criteria that determine the risk in question, such as the severity of the damage caused by the hazard and the likelihood that the damage will occur, separately or simultaneously, are two ways to achieve the purpose of risk reduction. The article is highly relevant to the implementation and development of technical systems and equipment.

Artificial intelligence become more widespread in all manufacturing subjects. In manufacturing artificial intelligence deals with such tasks as quality control, robot navigation, computer vision, processes controlling, etc. The area of maintenance in machining is a great prospect for implementing artificial intelligence tools for analysis, prediction of monitored parameters, optimization, and improvement of the quality of the maintenance process. In particular, the article refers to predictive maintenance as a modern trend in mechanical engineering. In this article, a quick review of using methods of artificial intelligence and predictive analytics in maintenance and one p ractical implementation case of NAR network for time-series prediction was provided.

This article describes the actual trends and applications in industry where artificial intelligence models are deployed. This paper provides a more detailed description of the principles and methods of deploying models in the field of quality evaluation in industry and also in the areas of predictive maintenance and data analytics in the manufacturing process. Computer vision is increasingly coming to the fore due to its wide range of applications – object detection, categorisation of objects, reading QR codes and others. The area of predictive maintenance is important in terms of reducing downtime and saving costs for machine components. Models designed for data analytics, in turn, help to optimize the parameters of the production process so that the desired parameter is maximized or its optimal value is achieved.