title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð² Ð¼ÐµÑÐ°Ð»Ð»ÑÑÐ³Ð¸Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: The use of artificial neural networks in metallurgy
Creators: name: ÐÑÐ½ÑÐºÐ¸Ð½Ð°, ÐÐ»ÑÐ½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¾Ð±ÑÐ°ÑÐ½Ð¾Ð³Ð¾ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ñ
Ð¼ÐµÑÐ°Ð»Ð»ÑÑÐ³Ð¸Ñ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
neural networks
back propagation algorithm
metallurgy
forecasting

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description: The theme of the qualifying work: Â«The use of artificial neural networks in metallurgyÂ». Purpose of the work: optimization of the T-joint welding mode in automatic submerged arc welding of low-carbon steel using artificial neural networks. Work tasks: - analysis of periodical literature on the architecture of artificial neural networks, used activation functions, existing learning algorithms and their use in the metallurgical industry; - analysis of the capabilities of the MATLAB toolbox Neural Net Fitting package for building neural networks and processing experimental data; - development of a computer program for predicting the dimensions of a T-joint weld in automatic submerged-arc welding based on the results of training a neural network using experimental data; - optimization of the submerged-arc welding mode of the T-joint, provided that the weld form factor is in the range of 1.2 - 1.8. Currently, neural networks are actively used in the metallurgical industry to solve the following problems: 1. Quality control of products and diagnostics of technological processes. 2. Predicting results and solving optimization problems. Significant results have been achieved in predicting changes in the silicon content in cast iron during blast furnace smelting, hydrogen content in the metal during evacuation, checking the quality of welds, optimizing hot rolling and controlling deformation of the billet during rolling, planning to reduce rolling tolerances, modeling solidification of the continuously cast billet, control of the change in strip temperature during hot rolling and the thickness of the metal coating in the galvanization line, etc. As an example, the use of neural networks for predicting the geometric dimensions of a weld in automatic submerged arc welding of a T-joint is demonstrated. The selected optimal welding mode provides the required penetration depth, the leg of the fillet weld and such a coefficient of the weld shape, which avoids the appearance of undercuts and reduces the likelihood of hot cracks.