Geodesic
Determination of the parameters of two-beam laser cleaning of quartz raw materials using artificial neural networks and the finite element method
Problemy fiziki, matematiki i tehniki, no. 3 (2022), pp. 37-41.

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With the help of artificial neural networks, the process of two-beam laser cleaning of quartz raw materials has been modeled. For the formation of training data sets and data sets for testing neural networks, the ANSYS finite element analysis program was used. The calculations were performed for 500 variants of input parameters, 40 of which were used to test neural networks. The influence of the parameters of neural network models on the accuracy of determining the maximum temperatures in quartz particles formed as a result of two-beam exposure were studied. The parameters of neural networks were determined that provided acceptable results when predicting temperatures in the laser treatment zone. The results obtained can be used in determining the technological parameters of the processes of two-beam laser cleaning of quartz raw materials.
Keywords: neural network, laser cleaning, quartz raw materials, ANSYS. 
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Yu. V. Nikitjuk; E. B. Shershnev; S. I. Sokolov; I. Y. Aushev. Determination of the parameters of two-beam laser cleaning of quartz raw materials using artificial neural networks and the finite element method. Problemy fiziki, matematiki i tehniki, no. 3 (2022), pp. 37-41. http://geodesic.mathdoc.fr/item/PFMT_2022_3_a5/

[1] V.I. Arbuzov, Osnovy radiatsionnogo opticheskogo materialovedeniya, SPb GU ITMO, SPb, 2008, 284 pp.

[2] V. Bokarev, E. Gornev, “Kontaktnaya litografiya v nanotekhnologii”, Nanoindustriya, 2010, no. 5, 22–25

[3] S. Avakov i dr., “Optiko-mekhanicheskie kompleksy dlya bezdefektnogo izgotovleniya fotoshablonov 0,35 mkm i 90 nm”, Fotonika, 2007, no. 6, 35–39

[4] G.A. Ivanov, V.P. Pervadchuk, Tekhnologiya proizvodstva i svoistva kvartsevykh opticheskikh volokon, Izd-vo Permskogo nats. issled. politekhnicheskogo universiteta, Perm, 2011

[5] E.B. Shershnev, Yu.V. Nikityuk, S.I. Sokolov, Sposob obogascheniya kvartsevoi krupki, pat. 21228 Resp. Belarus, MPK(2006) S 03V 1/00, B 07B 13/04, H 05B 6/00, zayavitel Gomel. gos. un-t. im. F. Skoriny No a20140188, zayavl. 21.03.14, opubl. 30.08.17

[6] E.B. Shershnev, Yu.V. Nikityuk, S.I. Sokolov, Ustanovka dlya obogascheniya zhilnogo kvartsa, pat. 9675 Resp. Belarus, MPK(2006) S 03V 1/00, zayavitel Gomel. gos. un-t. im. F. Skoriny No u20130334, zayavl. 15.04.13, opubl. 30.10.13

[7] Shershnev E. B. i dr., “Modelirovanie dvuluchevoi lazernoi separatsii kvartsevogo syrya”, Izvestiya Gomelskogo gosudarstvennogo universiteta imeni F. Skoriny, 2013, no. 6 (81), 216–220

[8] A.P. Dostanko i dr., Innovatsionnye tekhnologii i oborudovanie submikronnoi elektroniki, ed. A. P. Dostanko, Belaruskaya navuka, Minsk, 2020, 260 pp.

[9] V.A. Emelyanov, E.B. Shershnev, S.I. Sokolov, “Dvuluchevaya lazernaya ochistka kvartsevogo syrya”, Doklady BGUIR, 2021, no. 19 (3), 40–48

[10] V.P. Bessmeltsev, E.D. Bulushev, “Optimizatsiya rezhimov lazernoi mikroobrabotki”, Avtometriya, 50:6 (2014), 3–21 | Zbl

[11] P. Parandoush, A. Hossain, “A review of modeling and simulation of laser beam machining”, International Journal of Machine Tools and Manufacture, 85 (2014), 135–145 | DOI

[12] A.N. Bakhtiyari et al., “A review on applications of artificial intelligence in modeling and optimization of laser beam machining”, Optics Laser Technology, 135 (2021), 1–18

[13] B.F. Yousef et al., “Neural network modeling and analysis of the material removal process during laser machining”, International Journal of Advanced Manufacturing Technology, 22:1–2 (2003), 41–53 | DOI

[14] R. Kant, S.N. Joshi, U.S. Dixit, “An integrated FEM-ANN model for laser bending process with inverse estimation of absorptivity”, Mech. Adv. Mater Mod. Process., 2015, no. 1, 6 pp. | DOI

[15] M.B. Kadri et al., “Comparison of ANN and finite element model for the prediction of thermal stresses in diode laser cutting of float glass”, Optik - Int. J. Light Electron Optics, 126:19 (2015), 1959–1964 | DOI

[16] Yu.V. Nikityuk, A.N. Serdyukov, V.A. Prokhorenko, I.Yu. Aushev, “Primenenie iskusstvennykh neironnykh setei i metoda konechnykh elementov dlya opredeleniya parametrov obrabotki kvartsevykh zolgel stekol ellipticheskimi lazernymi puchkami”, Problemy fiziki, matematiki i tekhniki, 2021, no. 3 (48), 30–36

[17] Y.V. Nikitjuk, A.N. Serdyukov, I.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, 35–43 | DOI

[18] Yu.V. Nikityuk i dr., “Primenenie metoda konechnykh elementov i iskusstvennykh neironnykh setei dlya opredeleniya parametrov lazernoi obrabotki stali 12Kh18N9T”, Vestnik GGTU im. P.O. Sukhogo, 2022, no. 1, 48–55

[19] Yu.V. Koritskii, V.V. Pasynkov, B.M. Tareev, Spravochnik po elektrotekhnicheskim materialam, v. 3, Energoatomizdat, L., 1988, 728 pp.

[20] Yu.N. Knipovich, Yu.V. Morachevskii, Analiz mineralnogo syrya, GKhI, L., 1959, 1055 pp.

[21] V.A. Golovko, V.V. Krasnoproshin, Neirosetevye tekhnologii obrabotki dannykh, ucheb. posobie, BGU, Minsk, 2017, 263 pp.

[22] F. Sholle, Glubokoe obuchenie na Python, Piter, SPb., 2018, 400 pp.