employee
Stariy Oskol, Belgorod, Russian Federation
employee
Stary Oskol, Belgorod, Russian Federation
UDC 621.9.048
Russian Library and Bibliographic Classification 346
The study objective is to find the possibility of using intelligence systems together with vibration turning technology to ensure the quality of the surface layer and improve the performance properties of products in industries such as automotive, aircraft, space technology, mining and metallurgical engineering, etc. The task to which the paper is devoted is to ensure the operational properties of machine parts by vibration turning. To a greater extent, the operational properties depend on the quality parameters of the surface layer, which determine the wear resistance, fatigue strength and other operational properties of machine parts. Research methods. Theoretical analysis of literature references on the formation of regular microrelief on the contacting surfaces of machine parts, which makes it possible to improve the operational properties of machine parts and equipment. The novelty of the work is in the fact that neural network models will be obtained that allow determining surface treatment modes based on a given shape of a regular microrelief, degree, and depth of the surface layer. The practical significance is in the development of a technology that will allow obtaining the required regular microrelief, degree, and depth on the surface of the parts, providing them with the necessary operational characteristics. Study results. As a result of the theoretical analysis, the prospects of using vibration turning together with intelligence systems to ensure the quality of the surface layer of machine parts during the formation of a regular microrelief are presented. Conclusions: one of the main directions of developing modern mechanical engineering is creating automated product lifecycle management systems based on artificial intelligence. Special attention is paid to the prospects of using vibration cutting to form a regular microrelief. The assumption is substantiated about the expediency of creating an intelligence quality assurance system for the surface layer of machine parts based on artificial neural networks, which makes it possible to provide a regular microrelief of a given shape on the surface of the part at the required degree and depth.
microrelief, roughness, surface, characteristics, vibrations, turning, learning, neural networks
1. Schneider YuG. Operational properties of parts with regular microrelief. Leningrad: Mashinostroenie; 1982.
2. Krasny VA, Maksarov VV. Tribotechnical characteristics of mining machine parts with regular surface microgeometry. Metalworking. 2016;1(91):29-35.
3. Caixu Yu, Haining G, Xianli L, Steven Y. Liang part functionality alterations induced by changes of surface integrity in metal milling process: review. Applied Science. 2018;8(2550):18. doihttps://doi.org/10.3390/app8122550.
4. Kabaldin YuG, Shatagin DA, Kolchin PV. Management of cyberphysical machining systems in digital production based on artificial intelligence and cloud technologies. Moscow: Innovatsionnoe Mashinostroenie; 2019.
5. Wang, Kusiak A. Computational intelligence in manufacturing: handbook. CRC Press, Boca Raton, FL; 2001.
6. Awasthi S, Carlos Travieso-Gonzalez M, Goutam S. Artificial intelligence for a sustainable industry 4.0. Springer; 2021.
7. Akinin MV, Nikiforov MB, Taganov AI. Neural network systems of artificial intelligence in image processing tasks. Moscow: Hotline Telecom; 2017.
8. Shelkovnikov EYu, Tyurikov AV, Gulyaev PV, Osipov NI. Classification of surface nanostructure images using a neuro-fuzzy network. Chemical Physics and Mesoscopics. 2017;19(2):326-332.
9. Sinha A, Bai J, Ramani K. Deep learning 3D shape surfaces using geometry images. In: Leibe B, Matas J, Sebe N, Welling M. Computer vision – European Conference on Computer Vision 2016. Lecture Notes in Computer Science, vol. 9910. Springer; Cham. doi.org/10.1007/978-3-319-46466-4_14
10. Medvedeva OI. Quality management of the treated surface during cutting based on artificial intelligence [dissertation]. [Komsomolsk-on-Amur (RF)]; 2002.
11. Baranov DS, Duyun TA. Use of artificial neural networks for predicting roughness during finishing and semi-finishing turning. Bulletin of BSTU named after VG. Shukhov. 2019;7:128-134. DOI:https://doi.org/10.34031/article_5d35d0b62dc823.22670125
12. Anuja B, Kirubakaran , Ranjit L. Surface roughness prediction using artificial neural network in hard turning of AISI H13 steel with minimal cutting fluid application. Procedia Engineering, 2014:205-211. doi.org/10.1016/j.proeng.2014.12.243.
13. Asiltürk I, Çunkaş M. Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Systems with Applications. 2011;32(5):5826-5832. doi.org/10.1016/j.eswa.2010.11.041.
14. Hashmi MSJ. An optical method and neural network for surface roughness measurement. Optics and Lasers in Engineering. 1998;29(1):1-15. doi.org/10.1016/S0143-8166(97)00088-2.
15. Quintana G, Garcia-Romeu ML, Ciurana J. Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. Journal of Intelligent Manufacturing. 2011;22:607-617. doi.org/10.1007/s10845-009-0323-5
16. Huaian Yi, Jian Liu, Peng Ao, Enhui Lu, Hang Zhang Visual method for measuring the roughness of a grinding piece based on color indice. Optics Express. 2016;24(15):17215-17233.
17. Samta G. Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network. International Journal Advencher Manufacturing Technology. 2014;73:353-364.
18. Altunin KA, Sokolov MV. Application of neural networks for modeling turning. Transactions of the TSTU. 2016;1:122-133.
19. Vrabe M, Mankova I, Beno J, Tuharsky J. Surface roughness prediction using artificial neural networks when drilling Udimet 720. Procedia Engineering. 2012;48:693-700.
20. Boukezzi F, Noureddine R, Benamar A, Noureddine F. Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network. International Journal of Industrial and Systems Engineering. 2017;26(4):567-583.
21. Vladimirov A, Afonin A, Makarov A, Titova A. Revisiting the tangential oscillations of the tool to form the microrelief of the workpiece surface. Vibroengineering PROCEDIA. 2020;32:1–5. doi.org/10.21595/vp.2020.21361
22. Vladimirov AA, Afonin AN, Makarov A, Nazarova MYu. Geometric model of roughness during turning with pendulum vibrations of a cutter. Fundamental and Applied Problems of Technics and Technology. 2019;4-1 (336):15-19.
23. Vladimirov AA, Afonin AN, Makarov AV. Features of the mechanism of forming microroughnesses of the surface during vibration turning. Scientific and Technical Volga region Bulletin. 2019;2:27-29.
