This article is cited in 7 scientific papers (total in 7 papers)
PHYSICS
Application of artificial neural networks and finite element method for determining the parameters of elliptic laser beam treatment of quartz sol-gel glasses
Abstract:
Modeling of the process of laser splitting of quartz glasses obtained by the sol-gel method using artificial neural networks and
the finite element method was carried out. To form a training data set and data for testing neural networks, calculations of temperature fields and fields of thermoelastic stresses were performed using the finite element method in the ANSYS program.
Calculations were completed for 875 variants of input parameters, 800 of which were used for training neural networks. The influence of the architecture of the neural network, the size of the training data array, and the training time on the accuracy of determining thermoelastic stresses and temperatures in the zone of laser processing of quartz sol-gel glasses were investigated.
Citation:
Yu. V. Nikitjuk, A. N. Serdyukov, V. A. Prohorenko, I. Y. Aushev, “Application of artificial neural networks and finite element method for determining the parameters of elliptic laser beam treatment of quartz sol-gel glasses”, PFMT, 2021, no. 3(48), 30–36
\Bibitem{NikSerPro21}
\by Yu.~V.~Nikitjuk, A.~N.~Serdyukov, V.~A.~Prohorenko, I.~Y.~Aushev
\paper Application of artificial neural networks and finite element method for determining the parameters of elliptic laser beam treatment of quartz sol-gel glasses
\jour PFMT
\yr 2021
\issue 3(48)
\pages 30--36
\mathnet{http://mi.mathnet.ru/pfmt792}
\crossref{https://doi.org/10.54341/20778708_2021_3_48_30}
Linking options:
https://www.mathnet.ru/eng/pfmt792
https://www.mathnet.ru/eng/pfmt/y2021/i3/p30
This publication is cited in the following 7 articles:
Yu. V. Nikityuk, V. A. Prokhorenko, O. M. Demidenko, V. S. Smorodin, A. V. Voruev, “Razrabotka programmnykh sredstv modelirovaniya i optimizatsii parametrov lazernoi rezki khrupkikh nemetallicheskikh materialov”, PFMT, 2024, no. 3(60), 18–22
Yu. V. Nikityuk, V. A. Prokhorenko, A. I. Kulyba, “Mnogokriterialnaya optimizatsiya parametrov lazernoi rezki kvartsevogo stekla s primeneniem neirosetevogo modelirovaniya i geneticheskogo algoritma”, PFMT, 2023, no. 3(56), 26–31
Yuri Nikitjuk, Vladislav Prokhorenko, Alina Semchenko, Dmitry Kovalenko, 2023 7th International Conference on Information, Control, and Communication Technologies (ICCT), 2023, 1
V. A. Emelyanov, E. B. Shershnev, Yu. V. Nikitjuk, S. I. Sokolov, I. Y. Aushev, “Estimating the Parameters of Laser Processing of Diamonds Using the Finite Element Method and Artificial Neural Networks”, Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki, 21:4 (2023), 40
Yu. V. Nikityuk, A. V. Semchenko, V. V. Sidskii, K. D. Danilchenko, V. A. Prokhorenko, “Prognozirovanie svoistv poluprovodnikovykh zol-gel sloev ZnxxMgyyO s pomoschyu iskusstvennykh neironnykh setei”, PFMT, 2022, no. 1(50), 28–32
Yu. V. Nikityuk, E. B. Shershnev, S. I. Sokolov, I. Yu. Aushev, “Opredelenie parametrov dvukhluchevoi lazernoi ochistki kvartsevogo syrya s primeneniem iskusstvennykh neironnykh setei i metoda konechnykh elementov”, PFMT, 2022, no. 3(52), 37–41
Yuri V. Nikitjuk, Anatoly N. Serdyukov, Igor Y. Aushev, “Determination of the parameters of two-beam laser splitting of silicate glasses using regression and neural network models”, Journal of the Belarusian State University. Physics, 2022, no. 1, 35
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