Friction and Wear Performance of the Ultra-High Molecular Weight Polyethylene Polymer with ANN Analysis
Abstract
This study presents the friction and specific wear performance of the ultra-high molecular weight polyethylene (UHMW-PE) under conditions of dry sliding, egg albumen, and Hank’s balanced salt solution with hyaluronic acid lubrication (HASS+HA). The friction and wear tests were conducted using the equipment with pins on stainless steel discs. The coefficients of friction were obtained in dry, egg albumen, and HASS+HA sliding conditions at sliding speeds of 0.5, 1.0, and 1.5 m/s under applied loads of 50, 100, and 150 N. Specific wear rates were obtained in dry, egg albumen, and HASS+HA sliding conditions at 0.4, 0.8, and 1.2 m/s sliding speeds rate under 38, 88 and 138 N applied loads. The results showed that the coefficient of friction for UHMW-PE is more significantly influenced by the sliding speeds and applied loads under dry rather than egg albumen and HASS+HA lubrication sliding conditions. Furthermore, both the coefficient of friction and specific wear rate values increased with the increment of applied load and sliding speed. For this study's applied load and sliding speed values, the lower specific wear rate was obtained using the HASS+HA lubricant, compared with the egg albumen and the dry sliding conditions. In addition, the applicability of artificial neural networks (ANN) analysis for predicting both the coefficients of friction and specific wear rate values of the material in different sliding conditions was studied. The neural network results were in agreement with the experimental results for the specific wear rate and coefficient of friction.
References
[2] Bartel DL, Burstein AH, Toda MD, Edwards DL. The Effect of Conformity and Plastic Thickness on Contact Stresses in Metal-Backed Plastic Implants. J Biomech Eng 1985;107:193–9. DOI:10.1115/1.3138543.
[3] Briscoe BJ, Sinha SK. Wear of polymers. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 2002;216:401–13.
DOI:10.1243/135065002762355325
[4] Chandrasekaran M, Loh NL. Effect of counterface on the tribology of UHMWPE in the presence of proteins. Wear 2001;250:237–41. DOI:10.1016/S0043-1648(01)00646-9.
[5] Lin TY, Tseng CH. Optimum design for artificial neural networks: an example in a bicycle derailleur system. Eng Appl Artif Intell 2000;13:3–14. DOI:10.1016/S0952-1976(99)00045-7.
[6] Sabouhi R, Ghayour H, Abdellahi M, Bahmanpour M. Measuring the mechanical properties of polymer–carbon nanotube composites by artificial intelligence. International Journal of Damage Mechanics 2016;25:538–56. DOI:10.1177/1056789515604375.
[7] Zamyad H, Naghavi N, Godaz R, Monsefi R. A recurrent neural network–based model for predicting bending behavior of ionic polymer–metal composite actuators. J Intell Mater Syst Struct 2020;31:1973–85. DOI:10.1177/1045389X20942318
[8] Velten K, Reinicke R, Friedrich K. Wear volume prediction with artificial neural networks. Tribol Int 2000;33:731–6. DOI:10.1016/S0301-679X(00)00115-8.
[9] Kurt HI, Oduncuoglu M. Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites. Int J Polym Sci 2015;2015.
DOI:10.1155/2015/315710.
[10] Abdelbary A, Abouelwafa MN, El Fahham IM, Hamdy AH. Modeling the wear of Polyamide 66 using artificial neural network. Mater Des 2012;41:460–9. DOI:10.1016/J.MATDES.2012.05.013.
[11] Gyurova LA, Friedrich K. Artificial neural networks for predicting sliding friction and wear properties of polyphenylene sulfide composites. Tribol Int 2011;44:603–9. DOI:10.1016/j.triboint.2010.12.011.
[12] Kazi MK, Eljack F, Mahdi E. Optimal filler content for cotton fiber/PP composite based on mechanical properties using artificial neural network. Compos Struct 2020;251:112654. DOI:10.1016/J.COMPSTRUCT.2020.112654.
[13] Hui PCL. Artificial Neural Networks. Rijeka: IntechOpen; 2023. DOI: 10.5772/intechopen.100662.
[14] Alanis AY, Arana-Daniel N, López-Franco C. Artificial Neural Networks for Engineering Applications. Elsevier; 2019. DOI:10.1016/C2018-0-01649-7.
[15] Mirjalili S. Evolutionary Algorithms and Neural Networks. vol. 780. Springer International Publishing; 2019.
[16] Ermis K, Midilli A, Dincer I, Rosen MA. Artificial neural network analysis of world green energy use. Energy Policy 2007;35. DOI:10.1016/j.enpol.2006.04.015.
[17] Ermis K, Erek A, Dincer I. Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network. Int J Heat Mass Transf 2007;50. DOI:10.1016/j.ijheatmasstransfer.2006.12.017.
[18] Ermis K. ANN modeling of compact heat exchangers. Int J Energy Res 2008;32. DOI:10.1002/er.1380.
[19] Sablani SS, Kacimov A, Perret J, Mujumdar AS, Campo A. Non-iterative estimation of heat transfer coefficients using artificial neural network models. Int J Heat Mass Transf 2005;48:665–79.
DOI:10.1016/J.IJHEATMASSTRANSFER.2004.09.005.
