Details

Autor(en) / Beteiligte

• Goldobin, Maxim A.
• Morozov, Andrey A.
• Okonechnikov, Dmitry V.
• Yudin, Anatoly S.
• Yasnitsky, Leonid N.

Titel

Intelligent Quality Management System for Casting Gas Turbine Engine Blades

Ist Teil von

• 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), 2020, p.696-700

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Quelle

IEEE Xplore

Beschreibungen/Notizen

• This article is devoted to the problem of reducing the number of defects and improving the quality of manufacturing gas turbine engine blades. The process of pressing a casting rod designed to form the inner cavity of the blades during their manufacture by pouring on the smelted models is considered. Information about 400 examples of pressing foundry rods has been collected. Each of the examples contained a set of parameters that characterize the process of obtaining the workpieces and the result of manufacturing. Based on the collected statistical information, the neural network was designed. Using virtual computer experiments, the process of pressing casting rods was studied. The parameters that have the greatest impact on the quality of the resulting products are identified. In the course of computer experiments, it was observed that changes in a number of pressing parameters which lead to a decrease in the probability of defects on one billet do not lead to a similar decrease in the probability of defects on another billet. The method of neural network modeling was able to identify parameters the same change in which leads to a decrease in the probability of any type of defect of all workpieces. For example, the probability of getting defects in all workpieces decreases with increasing the holding time of the rod in the mold without pressure. Thus, it is shown that the developed neural network model allows to control the quality of the obtained blades, select the optimal parameters of the technological process that provide the maximum reduction of defects.