OBRABOTKAMETALLOV Vol. 24 No. 3 2022 51 TECHNOLOGY References 1. Sonawane A., Deshpande A., Chinchanikar S., Munde Y. Dry sliding wear characteristics of carbon fi lled polytetrafl uoroethylene (PTFE) composite against Aluminium 6061 alloy. Materials Today: Proceedings, 2021, vol. 44, pp. 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) composite against AISI 304 stainless steel counterface. Materials Science Forum, 2021, vol. 1034, 51–60. DOI: 10.4028/www.scientifi c.net/MSF.1034.51. 3. Unal H., Mimarolu A., Kadioglu U., Ekiz H. Sliding friction and wear behavior of PTFE and its composite under dry sliding conditions. Materials and Design, 2004, vol. 25, pp. 239–245. DOI: 10.1016/j.matdes.2003.10.009. Modeling of sliding wear characteristics of Polytetrafl uoroethylene (PTFE) composite reinforced with carbon fi ber against SS304 Satish Chinchanikar * Vishwakarma Institute of Information Technology, Survey No. 3/4, Kondhwa (Budruk), Pune - 411039, Maharashtra, India https://orcid.org/0000-0002-4175-3098, satish.chinchanikar@viit.ac.in Obrabotka metallov - Metal Working and Material Science Journal homepage: http://journals.nstu.ru/obrabotka_metallov Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science. 2022 vol. 24 no. 3 pp. 40–52 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2022-24.3-40-52 ART I CLE I NFO Article history: Received: 14 July 2022 Revised: 26 July 2022 Accepted: 27 July 2022 Available online: 15 September 2022 Keywords: PTFE Wear Artifi cial neural network Pin-on-disk SS304 ABSTRACT Introduction. Over the last decade, composite materials based on polytetrafl uoroethylene (PTFE) have been increasingly used as alternative materials for automotive applications. PTFE is characterized by a low coeffi cient of friction, hardness and corrosion resistance. However, this material has a high wear rate. A group of researchers attempted to improve the wear resistance of PTFE material by reinforcing it with different fi llers. The purpose of the work: This study experimentally investigates the dry sliding wear characteristics of a PTFE composite reinforced with carbon fi ber (35 wt.%) compared to SS304 stainless steel. In addition, experimental mathematical and ANN models are developed to predict the specifi c wear rate, taking into account the infl uence of pressure, sliding speed, and interface temperature. The methods of investigation. Dry sliding experiments were performed on a pin-on-disk wear testing machine with varying the normal load on the pin, disk rotation, and interface temperature. Experiments were planned systematically to investigate the effect of input parameters on specifi c wear rates with a wide range of design space. In total, fi fteen experiments were carried out at a 5-kilometer distance without repeating the central run experiment. Sliding velocities were obtained by selecting the track diameter on the disk and corresponding rotation of the disk. A feedforward back-propagation machine learning algorithm was used to the ANN model. Results and Discussion. This study fi nds better prediction accuracy with the ANN architecture having two hidden layers with 150 neurons on each layer. This study fi nds an increase in specifi c wear rates with normal load, sliding velocity, and interface temperature. However, the increase is more prominent at higher process parameters. The normal load followed by sliding velocity most signifi cantly affects the specifi c wear rate. The results predicted by the developed models for specifi c wear rates are in good agreement with the experimental values with an average error close to 10%. This shows that the model could be reliably used to obtain the wear rate of PTFE composite reinforced with carbon fi ber (35 wt.%) compared to SS304 stainless steel. This study fi nds scope for further studies considering the effect of varying ANN architectures, different amount of neurons, and hidden layers on the prediction accuracy of the wear rate. For citation: Chinchanikar S. Modeling of sliding wear characteristics of Polytetrafl uoroethylene (PTFE) composite reinforced with carbon fi ber against SS304. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2022, vol. 24, no. 3, pp. 40–52. DOI: 10.17212/1994-6309-2022-24.3-40-52. (In Russian). ______ * Corresponding author Chinchanikar Satish, Ph.D. (Engineering), Professor Vishwakarma Institute of Information Technology, Pune - 411039, Maharashtra, India Tel.: 91-2026950441, e-mail: satish.chinchanikar@viit.ac.in
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