Abstract
In the high-precision electrochemical shaping of new high-strength and hard materials, diagnostics may be based on neural-network identification algorithms incorporated in the automated control system of the equipment.
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References
Zhitnikov, V.P. and Zaitsev, A.N., Impul’snaya elektrokhimicheskaya razmernaya obrabotka (Pulsed Electrochemical Dimensional Processing), Moscow: Mashinostroenie, 2008.
Idrisov, T.R., Mannapov, A.R., and Smirnov, M.S., Tekhnologiya elektrokhimicheskogo formirovaniya macro-i mikrorel’efov na poverkhnostyakh detalei: uchebnoe posobie (The Technology of Electrochemical Formation of Macro-and Microreliefs on the Surfaces of Parts: Manual), Yfa: Ufimsk. Gos. Aviats. Tekh. Univ., 2011.
Frank, M.P., Ding, S.X. and Köppen-Seliger, B., Current developments in the theory of FDI, Proc. 4th IFAC Symp. on Fault Detection Supervision and Safety for Technical Processes, June 14–16, 2000, Budapest: Int. Fed. Autom. Control, 2000, vol. 1, pp. 16–27.
Haykin, S., Neural Networks: A Comprehensive Foundation, Upper Saddle River, NJ: Prentice-Hall, 2008.
Borovikov, V.P., Populyarnoe vvedenie v sovremennyi ananliz dannykh v sisteme STATISTICA (The Popular Introduction to the Modern Data Analysis using STATISTICA Software), Moscow: Goryachaya Liniya–Telekom, 2013.
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Original Russian Text © K.A. Masalimov, R.A. Munasypov, 2017, published in STIN, 2017, No. 4, pp. 16–20.
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Masalimov, K.A., Munasypov, R.A. Neural-network diagnostics of electrochemical machining. Russ. Engin. Res. 37, 817–820 (2017). https://doi.org/10.3103/S1068798X17090179
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DOI: https://doi.org/10.3103/S1068798X17090179