employee from 01.01.2014 until now
g. Zarechnyy, Penza, Russian Federation
UDK 621 Общее машиностроение. Ядерная техника. Электротехника. Технология машиностроения в целом
The paper provides formalization and construction of a model of the process of electrical discharge machining. When describing the process, a T-shaped equivalent circuit containing an RLC circuit was used. Determine the transfer function of the proposed substitution scheme. Also, a task is formulated and an algorithm for neural network parametric identification of a T-shaped equivalent circuit is proposed. The problem is posed and an algorithm is developed for neural network parametric identification of the equivalent circuit with a computational experiment, the formation of training samples on its basis, and the subsequent training of dynamic and static neural networks used in the identification problem. The process was simulated in Simulink, Matlab package. Acceptable coincidence of the calculated data with the experimental ones showed that the proposed model of electrical discharge machining reflects real electromagnetic processes occurring in the interelectrode gap.
EDM, modeling, interelectrode gap, material removal, productivity, interelectrode gap
1. Ioffe, V.F. Avtomatizirovannye elektroerozionnye stanki / V.F. Ioffe, M.V. Korenblyum, V.A. Shavyrin. – L. : Mashinostroenie, 1984. - 227 s.
2. Eliseev, Yu.S. Elektroerozionnaya obrabotka izdeliy aviacionno-kosmicheskoy tehniki / Yu.S. Eliseev, B. P. Saushkin. – M. : Izd-vo MGTU im. N. E. Baumana, 2010. – 437 s.
3. Avdeeva, O.V. Modelirovanie sistemy avtomaticheskogo regulirovaniya mezhelektrodnogo zazora pri elektroerozionnom profilirovanii almaznyh krugov / O.V. Avdeeva, A.D. Semenov, A.S. Nikitkin // Problemy avtomatizacii i upravleniya v tehnicheskih sistemah: tr. mezhdunar. nauch.-tehn. konf. – Penza : Izd-vo PGU, 2009. - S. 290-294.
4. Sarilov, M.Yu. Issledovanie processov elektroerozionnoy obrabotki / M.Yu. Sarilov, V.V. Myl'nikov // Zhurnal tehnicheskoy fiziki. – 2019. – T. 89, № 6. - S. 887-892. - DOI: 10.21883/JTF.2019.06.47636.66-18.
5. A hybrid process model for EDM based on finite-element method and Gaussian process regression / Ming, W., Zhang, G., Li, H. [et al.] // International Journal of Advanced Manufacturing Technology. ‒ 2014. - № 74. - P. 1197–1211. - DOI: 10.1007/s00170-014-5989-y.
6. Sahu, S.N. Multi-objective optimization of EDM process with performance appraisal of GA based algorithms in neural network environment / S.N. Sahu, S. Murmu, N. Nayak // Materials Today: Proceedings. - 2019. - № 18(4). – P. 3982-3997. – DOI: 10.1016/j.matpr.2019.07.340.
7. Yadav, R. Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique / R. Yadav, V. Yadava, G. Singh // Frontiers of Mechanical Engineering. ‒ 2013. - № 8(3). – DOI: 10.1007/s11465-013-0269-3.
8. Sidhu, H.S. Analysis and multi-objective optimisation of surface modification phenomenon by EDM process with copper-tungsten semi-sintered P/M composite electrodes / H.S. Sidhu, S.S. Banwait // American Journal of Mechanical Engineering. – 2014. - № 2(5) - P. 130-142. - DOI: 10.12691/ajme-2-5-2.
9. Avdeeva, O.V. Modelirovanie sistem upravleniya. Laboratornyy praktikum / O.V. Avdeeva, A.D. Semenov, D.V. Artamonov. ‒ Penza: izd-vo PGU, 2019 -80 s.
10. Tarasik, V.P. Matematicheskoe modelirovanie tehnicheskih sistem / V.P. Tarasik. - M.: Infra-M, 2017. - 160 c.
11. Feofanov, A.N. Razrabotka, modelirovanie i optimizaciya raboty mehatronnyh sistem / A.N. Feofanov. - M. : Akademiya, 2018. - 320 c.
12. Cibizova, T.Yu. Metody identifikacii nelineynyh sistem upravleniya / T.Yu. Cibizova // Sovremennye problemy nauki i obrazovaniya. – 2015. – № 2 (ch. 14). – S. 3070–3074.
13. Pupkov, K.A. Funkcional'nye ryady v teorii nelineynyh sistem / K.A. Pupkov, A.S. Kapalin, A.S. Yuschenko. – M.: Nauka, 1976. – 448 s.
14. Haykin, S. Neyronnye seti / S. Haykin. – M.: Izd-vo «Vil'yams», 2018. – 1104 s.
15. Osovskiy, S. Neyronnye seti dlya obrabotki informacii / S. Osovskiy. - M.: Finansy i statistika, 2002. - 344 s.
16. Anisimov, A.A. Identifikaciya elektromehanicheskih sistem s ispol'zovaniem iskusstvennoy neyronnoy seti / A.A. Anisimov, M.N. Goryachev // Vestnik Ivanovskogo gosudarstvennogo energeticheskogo universiteta. – 2008. – № 3. – S. 55–58.
17. Fedorov M.M. Ispol'zovanie neyrosetevyh metodov dlya resheniya zadach identifikacii ob'ektov [Elektronnyy resurs] / M.M. Fedorov // Sovremennye nauchnye issledovaniya i innovacii. – 2013. – № 9. – Rezhim dostupa: http://web.snauka.ru/issues/2013/09/26285 (data obrascheniya: 23.10.2020).
18. Shumihin, A.G. Primenenie neyrosetevyh dinamicheskih modeley v zadache parametricheskoy identifikacii tehnologicheskogo ob'ekta v sostave sistemy upravleniya / A.G. Shumihin, A.S. Boyarshinova // Vestnik Permskogo nacional'nogo issledovatel'skogo politehnicheskogo universiteta. Himicheskaya tehnologiya i biotehnologiya. – 2015. – № 3. – S. 21–38.
19. Shumihin, A.G. Parametricheskaya identifikaciya tehnologicheskogo ob'ekta v rezhime ego ekspluatacii s primeneniem tehnologii neyronnyh setey / A.G. Shumihin, A.S. Aleksandrova, A.I. Mustafin // Vestnik Permskogo nacional'nogo issledovatel'skogo politehnicheskogo universiteta. Elektrotehnika, informacionnye tehnologii, sistemy upravleniya. – 2018. – № 26. – S. 29–41.
20. Recent advances in physical reservoir computing: a review / G. Tanaka, T. Yamane, J.B. Héroux [et al.] // Neural Networks. ‒ 2019. ‒ V. 115. ‒ P. 100–123. ‒ DOI: 10.1016/j.neunet.2019.03.005.
