articles

Currently, artificial intelligence methods are widely used in the practice of serial production enterprises. They are used to detect defects, classify and eliminate them, identify the causes of defects, predict the quality and properties of the resulting product, select optimal parameters of the production process, and identify and study its patterns. However, outside the field of research remains an important task from a practical point of view, the task of determining regulations for production parameters, i. e. the task of determining such ranges of values of these parameters, in which the probability of defects is minimal. In addition, there are no reports in the literature on the use of neural networks to eliminate or reduce the undesirable consequences of abnormal situations due to the prompt change of technological production parameters. The purpose of this article is to eliminate these gaps. This article shows the possibilities of solving these two important production problems using the example of serial production of casting ceramic rods intended for the manufacture of blades for gas turbine engines. This production is characterized by increased requirements for product quality, so the percentage of rejected products is especially high here and the problem of combating defects is especially relevant. The article describes the creation of neural network models of the production process, which are combined using the user interface into a single software package. Using this software package, a series of virtual computer experiments are performed using the scenario forecasting method, which made it possible to identify important patterns in the modeled process and, based on them, obtain and justify regulations for process parameters that ensure a minimum probability of defects. Further, using the same software package, emergency situations are modeled, for example, compressor failure, which is accompanied by a drop in the pressing pressure of the product and, as a consequence, an increase in the probability of defects. The article shows that using the developed software package, while the compressor is being repaired, without stopping the production process, it is possible to quickly reduce the increased percentage of defects caused by an emergency situation by changing only the process parameters. The methods for determining regulations and eliminating or reducing the undesirable consequences of emergency situations proposed in the article can be applied to other production processes.