Obrabotka Metallov 2022 Vol. 24 No. 3

ОБРАБОТКА МЕТАЛЛОВ Том 24 № 3 2022 50 ТЕХНОЛОГИЯ к 10 %. Однако результаты, предсказанные моделью ИНС, в большей степени совпадают (средняя погрешность 3,57 %) с экспериментальными результатами, чем результаты, полученные с использованием статистической модели (средняя погрешность 10,16 %). Список литературы 1. Dry sliding wear characteristics of carbon fi lled polytetrafl uoroethylene (PTFE) composite against Aluminium 6061 alloy / A. Sonawane, A. Deshpande, S. Chinchanikar, Y. Munde // Materials Today: Proceedings. – 2021. – Vol. 44. – P. 3888–3893. – DOI: 10.1016/j. matpr.2020.12.929. 2. Chinchanikar S., Barade A., Deshpande A. Sliding wear characteristics of carbon fi lled polytetrafl uoroethylene (PTFE) сomposite against AISI 304 stainless steel counterface // Materials Science Forum. – 2021. – Vol. 1034. – P. 51–60. – DOI: 10.4028/www.scientifi c. net/MSF.1034.51. 3. Sliding friction and wear behavior of PTFE and its composite under dry sliding conditions / H. Unal, A. Mimarolu, U. Kadioglu, H. Ekiz // Materials and Design. – 2004. – Vol. 25. – P. 239–245. – DOI: 10.1016/j. matdes.2003.10.009. 4. Sahin Y. Analysis of abrasive wear behavior of PTFE composite using Taughi’s technique // Cogent Engineering. – 2015. – Vol. 2, N 1. – P. 1–15. – DOI: 10.1080/23311916.2014.1000510. 5. Venkateswarlu G., Sharada R., Rao M.B. Effect of fi llers on mechanical properties of PTFE based composites // Archives of Applied Science Research. – 2015. – Vol. 7, N 7. – P. 48–58. 6. Wang Q., Zhang X., Pei X. Study on the synergistic effect of carbon fi ber and graphite and nanoparticle on the friction and wear behavior of polyimide composites // Materials and Design. – 2010. – Vol. 31, N 8. – P. 3761–3768. – DOI: 10.1016/j.matdes.2010.03.017. 7. Song F., Wang Q., Wang T. Effect of glass fi ber and MoS2 on tribological behaviour and PV limit of chopped carbon fi ber reinforced PTFE composite // Tribology International. – 2016. – Vol. 104. – P. 392–401. – DOI: 10.1016/j.triboint.2016.01.015. 8. Gujrathi S.M., Dhamande L.S., Patare P.M. Wear studies on polytetrafl uroethylene (PTFE) composites: Taguchi approach // Bonfring International Journal of Industrial Engineering and Management Science. – 2013. – Vol. 3, N 2. – P. 47–51. – DOI: 10.9756/BIJIEMS.4406. 9. Wear and friction performance of PTFE fi lled epoxy composites with a high concentration of SiO2 particles / J.T. Shen, M. Top, Y.T. Pei, M. Hosson // Wear. – 2015. – Vol. 322–323, N 15. – P. 171–180. – DOI: 10.1016/j.wear.2014.11.015. 10. Abrasive wear behavior of PTFE for seal applications under abrasive-atmosphere sliding condition / M. Shen, B. Li, Z. Zhang, L. Zhao // Friction. – 2020. – Vol. 8. – P. 755–767. – DOI: 10.1007/s40544-019-0301-7. 11. A study on the friction and wear behavior of PTFE fi lled with alumina nanoparticles / W.G. Sawyer, K.D. Freudenberg, P. Bhimaraj, L.S. Schadler // Wear. – 2003. – Vol. 254. – P. 573–580. – DOI: 10.1016/S00431648(03)00252-7. 12. Kim D.W., Kim K.W. Effects of sliding velocity and normal load on friction and wear characteristics of multi-layered diamond-like carbon (DLC) coating prepared by reactive sputtering // Wear. – 20013. – Vol. 297, N 1–2. – P. 722–730. –DOI: 10.1016/j.wear.2012.10.009. 13. Wang M., Zhang C., Wang X. The wear behavior of textured steel sliding against polymers // Materials. – 2017. – Vol. 10, N 330. – P. 1–14. – DOI: 10.3390/ ma10040330. 14. Desale D.D., Pawar H.B. Performance analysis of Polytetrafl uoroethylene as journal bearing material // Procedia Manufacturing. – 2018. – Vol. 20. – P. 414– 419. – DOI: 10.1016/j.promfg.2018.02.060. 15. Specifi c wear rate modeling of polytetrafl ouroethylene composites via artifi cial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) tools / M.A. Ibrahim, Y. Şahin, A. Ibrahim, A.Y. Gidado, M.N. Yahya // Virtual Assistant. – IntechOpen, 2021. – DOI: 10.5772/intechopen.95242. 16. Paturi U.M., Cheruku S., Reddy N.S. The role of artifi cial neural networks in prediction of mechanical and tribological properties of composites – A comprehensive review // Archives of Computational Methods in Engineering. – 2022. – Vol. 29. – P. 1–41. – DOI: 10.1007/s11831-021-09691-7. 17. Artifi cial neural network algorithms for 3D printing / M.A. Mahmood, A.I. Visan, C. Ristoscu, I.N. Mihailescu // Materials. – 2020. – Vol. 14, N . – P. 163. – DOI: 10.3390/ma14010163. 18. Naderpour H., Kheyroddin A., Amiri G.G. Prediction of FRP-confi ned compressive strength of concrete using artifi cial neural networks // Composite Structures. – 2010. – Vol. 92, N 12. – P. 2817–2829. – DOI: 10.1016/j.compstruct.2010.04.008. Конфликт интересов Автор заявляет об отсутствии конфликта интересов.  2022 Автор. Издательство Новосибирского государственного технического университета. Эта статья доступна по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная (https://creativecommons.org/licenses/by/4.0/)

RkJQdWJsaXNoZXIy MTk0ODM1