Abstract
A digital model of a cutting tool produced by neural-network modeling is considered. By means of this model, the composition and structure of a wear-resistant coating may be optimized. The machining conditions that ensure maximum wear resistance of the tool may also be identified.
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REFERENCES
Frankel, A. and Larsson, J., A better way: finding efficiencies: Part 1 and Part 2, CAD/CAM/CAE Observer, 2016, no. 3, pp. 36–40.
Buriko, A.V., Digital evolution or why RUSAL is digitalizing, Tsifrovoe Proizvod., 2017, no. 2, pp. 18–23.
Kabaldin, Yu.G., Bilenko, S.V., and Seryi, S.V., Upravlenie dinamicheskim kachestvom metallorezhushchikh stankov na osnove iskusstvennogo intellekta (Dynamic Quality Control of Metal-Cutting Machines by Means of Artificial Intelligence), Komsomolsk-on-Amur: Komsomol’sk-na-Amure Gos. Tekh. Univ., 2009.
Korotkii, S., Neural networks: key points. http://www.orc.ru/~stasson.
Kabaldin, Yu.G., et al., Iskusstvennyi intellect i kiber-fizicheskie mekhanoobrabatyvayushchie sistemy v tsifrovom proizvodstve: monografiya (Artificial Intelligent and Cyber-Physical Machining Systems in Digital Production: Monograph), Kabaldin, Yu.G., Ed., Nizhny Novgorod: Nizhegorod. Gos. Tekh. Univ., 2018.
Bobrov, V.F., Osnovy teorii rezaniya metallov (Fundamental Theory of Metal Cutting), Moscow: Mashinostroenie, 1975.
Kabaldin, Yu.G., Laptev, I.L., Shatagin, D.A., and Seryi, S.V., Diagnostics of output parameters of real time cutting based on fractal and wavelet analyses using National Instruments and nVidia CUDA software and hardware, Vestn. Mashinostr., 2014, no. 8, pp. 80–82.
Kabaldin, Yu.G., Laptev, I.L., Shatagin, D.A., et al., Intelligent systems for diagnostics of equipment condition and tool wear, Mashinostroenie, 2014, no. 2, pp. 47–50.
Kabaldin, Yu.G., Laptev, I.L., Shatagin, D.A., et al., Real-time assessment of cutting tool condition based on nonlinear dynamics approaches using nVidia CUDA in the LABVIEW software, Tr. Nizhegorod. Gos. Univ. im. A.A. Alekseeva, 2013, no. 5 (102), pp. 114–121.
Kabaldin, Yu.G., Shatagin, D.A., Laptev, I.L., and Sidorenkov, D.A., The development of the machine dynamic passport based on neural network modeling of its working space using nVidia CUDA technology and deep learning approaches, Izv. Vyssh. Uchebn. Zaved., Mashinostr., 2016, no. 10 (679), pp. 49–56.
Vereshchaka, A.S., Rabotosposobnost’ rezhushchego instrumenta s pokrytiem (Performance of Cutting Coated Tool), Moscow: Mashinostroenie, 1993.
Kabaldin, Yu.G., Kretinin, O.V., Shatagin, D.A., and Vlasov, E.E., Vybor sostava i struktury iznosostoikikh nanostrukturnykh pokrytii dlya tverdosplavnogo rezhushchego instrumenta na osnove kvantovo-mekhanicheskogo modelirovaniya (Choice of Composition and Structure of Wear Resistant Nanocoatings for Carbide Cutting Tool Based on Quantum-Mechanical Modeling), Moscow: Innovatsionnoe Mashinostroenie, 2017.
Kabaldin, Yu.G., Shatagin, D.A., Laptev, I.L., and Zotov, V.O., RF Patent 159948, 2015.
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Kabaldin, Y.G., Shatagin, D.A. & Kuz’mishina, A.M. Selection of a Cutting Tool by Means of a Digital Model. Russ. Engin. Res. 39, 761–765 (2019). https://doi.org/10.3103/S1068798X19090089
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DOI: https://doi.org/10.3103/S1068798X19090089