RUDN University professors increased the “lifetime” of steel parts using a neural network
Repeated stress on metal parts causes “fatigue failure.” Each stress leads to microcracks that accumulate over time. Larger damage appears, and finally the part fails. Fatigue failure inevitably occurs in almost all mechanisms, this applies to all areas of industry, so technologists and scientists are looking for ways to strengthen the metal using various coatings and processing methods. RUDN University professors, together with colleagues from Italy, Canada and Turkey, built an artificial neural network that is able to predict the life of a part made of AISI 1045 steel, which makes up half of the mechanical engineering products, and select the optimal coating.
“Most machine components in the marine, oil and gas, and wind power industries are subject to repeated applied loads that cause fatigue failure. Since the phenomenon of fatigue failure is very sensitive to various parameters, including material, load, temperature, humidity, vibration, and so on, it is convenient to use neural networks for its analysis,” Reza Kashi Zadeh Kazem, professor of the Department of Transport of RUDN University.
Engineers have created a neural network that can estimate the “lifetime” of AISI 1045 carbon steel with different types of coatings under repeated loads. Nickel, hardened chrome and zinc were used as protective coatings in the model. RUDN University researchers have achieved 99% accuracy in neural network predictions. Moreover, the authors were able to select the optimal protective coating — a layer of nickel or zinc
First, RUDN scientists conducted a series of experiments with real steel parts. 23% of the obtained data was used to train the neural network, and the rest was used to test the resulting predictions. The scientists tested several neural networks, with different numbers of internal layers and neurons in each layer.
“We investigated the effect of various traditional industrial coatings, including nickel, chromium and zinc, which are commonly used to improve corrosion resistance, on the fatigue life of AISI 1045 carbon steel. The experimental results showed that nickel and hot-dip galvanized coatings with a thickness of 13 microns improved fatigue life. On the contrary, hardened chromium reduces the fatigue life of AISI 1045 steel,” Igor Danilov, Doctor of Technical Sciences, Director of the Department of Transport of the RUDN University.
The results were published in the journal JMSE.
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The National Demographic Report, 2023 Demographic Well-Being of Russian Regions (hereinafter - the National Demographic Report) was prepared by the scientific team of the Institute of Demographic Studies of the Federal Research Center of the Russian Academy of Sciences, the Vologda Scientific Center of the Russian Academy of Sciences, Peoples' Friendship University of Russia, the Center for Family and Demography of the Academy of Sciences of the Republic of Tatarstan, as well as with the participation of leading scientists from the Republic of Bashkortostan, Stavropol Krai, Volgograd, Ivanovo, Kaliningrad, Nizhny Novgorod, Sverdlovsk Oblasts and Khanty-Mansi Autonomous Okrug–Yugra.
RUDN summarized the results of the scientific competition "Project Start: work of the science club ". Students of the Faculty of Physics, Mathematics and Natural Sciences have created a project for a managed queuing system using a neural network to redistribute resources between 5G segments. How to increase flexibility, make the network fast and inexpensive and reach more users — tell Gebrial Ibram Esam Zekri ("Fundamental Computer Science and Information Technology", Master's degree, II course) and Ksenia Leontieva ("Applied Mathematics and Computer Science", Master's degree, I course).
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The National Demographic Report, 2023 Demographic Well-Being of Russian Regions (hereinafter - the National Demographic Report) was prepared by the scientific team of the Institute of Demographic Studies of the Federal Research Center of the Russian Academy of Sciences, the Vologda Scientific Center of the Russian Academy of Sciences, Peoples' Friendship University of Russia, the Center for Family and Demography of the Academy of Sciences of the Republic of Tatarstan, as well as with the participation of leading scientists from the Republic of Bashkortostan, Stavropol Krai, Volgograd, Ivanovo, Kaliningrad, Nizhny Novgorod, Sverdlovsk Oblasts and Khanty-Mansi Autonomous Okrug–Yugra.