Tribological Behavior of Ultra-High Molecular Weight Polyethylene Polymer with Artificial Neural Network Modeling

Year 2021, Volume: 4 Issue: 2, 171 - 178, 30.11.2021

Kemal Ermiş Hüseyin Ünal

https://doi.org/10.34088/kojose.961118

Abstract

This study presents the tribological properties, wear and friction, of ultra-high molecular weight polyethylene under conditions of dry sliding and Hank’s balanced salt solution lubrication. A pin-on-stainless steel disc apparatus was used for the friction and wear tests. Applied load conditions were 38, 50, 88, 100, 138, and 150N. Sliding speed conditions were 0.4, 0.5, 0.8, 1.0, 1.2 and 1.5 m/s. The results show that the coefficient of friction and the wear rate values decrease with the increase of applied load. The coefficient of friction and the wear rate values were highest under the dry sliding condition for the ranges of the sliding speed values and the applied loads tested in the study. In addition, the applicability of artificial neural networks (ANN) for predicting both the coefficients of friction and 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 wear rates and coefficients of friction

Keywords

ANN modeling, Medical grade UHMW-PE, Tribology, Wear

References

• [1] Bartel D. L., Burstein A. H., Toda M. D., Edwards and D. L., 1985 The Effect of Conformity and Plastic Thickness on Contact Stresses in Metal-Backed Plastic Implants. Journal of Biomechanical Engineering, 107(3), pp. 193–199.
• [2] Brach del Prever E. M., Bistolfi A., Bracco P., Costa L., 2009. UHMWPE for arthroplasty: Past or future?. Journal of Orthopaedics and Traumatology, 10(1), pp. 1–8.
• [3] Chandrasekaran M., Loh N. L., 2001. Effect of counterface on the tribology of UHMWPE in the presence of proteins. Wear, 250(1–12), pp. 237–241.
• [4] Briscoe B. J., Sinha S. K., 2002. Wear of polymers, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology. 216(6), pp. 401–413. 2002.
• [5] Lin T. Y., Tseng C. H., 2000. Optimum design for artificial neural networks: an example in a bicycle derailleur system. Engineering Applications of Artificial Intelligence, 13(1), pp. 3–14.
• [6] Zamyad H., Naghavi N., Godaz R., Monsefi R., 2020. A recurrent neural network-based model for predicting bending behavior of ionic polymer-metal composite actuators. Original Article Journal of Intelligent Material Systems and Structures, 31(17), pp. 1973–1985.
• [7] Kurt H H. I. and Oduncuoglu M., 2015. Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites. International Journal of Polymer Science, 315710, pp. 1–11.
• [8] Velten K., Reinicke R., Friedrich K., 2000. Wear volume prediction with artificial neural networks. Tribology International, 33(10), pp. 731–736.
• [9] Abdelbary A., Abouelwafa M. el Fahham N. I. M., Hamdy A. H., 2012. Modeling the wear of Polyamide 66 using artificial neural network. Materials and Design, 41, pp. 460–469.
• [10] Rajesh D., Verma K., Kumar Kharwar P., Kumar Verma R., Mohan M., 2020. Artificial Neural Network-Based Modeling of Surface Roughness in Machining of Multiwall Carbon Nanotube Reinforced Polymer (Epoxy) Nanocomposites. FME Transactions, 48(3), pp. 693-700.
• [11] Sabouhi R., Ghayour H., Abdellahi M., and Bahmanpour M., 2016. Measuring the mechanical properties of polymer-carbon nanotube composites by artificial intelligence. International Journal of Damage Mechanics, 25(4), pp. 538-556.
• [12] Zhang Z., Friedrich K., Velten K., 2002. Prediction on tribological properties of short fibre composites using artificial neural networks. Wear, 252(7–8), pp. 668–675. [13] Khan S. M., Malik S. A., Gull N., Saleemi S., Islam A., Butt M. T. Z., 2019. Fabrication and modelling of the macro-mechanical properties of cross-ply laminated fibre-reinforced polymer composites using artificial neural network. Advanced Composite Materials, 28(4), pp. 409–423.
• [14] Gyurova L. A., Friedrich K., 2011. Artificial neural networks for predicting sliding friction and wear properties of polyphenylene sulfide composites. Tribology International, 44(5), pp. 603–609.
• [15] Kazi M. K., Eljack F., Mahdi E., 2020. Optimal filler content for cotton fiber/PP composite based on mechanical properties using artificial neural network. Composite Structures, 251, pp. 1-7.
• [16] Pajchrowski T., Siwek P., Wójcik A., 2020. Adaptive controller design for electric drive with variable parameters by Reinforcement Learning method. Bulletin of the Polish Academy of Sciences: Technical Sciences, 68(4), pp. 1019–1030.
• [17] Rajeev D., Dinakaran D., Singh S. C. E., 2017. Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool. Bulletin of the Polish Academy of Sciences: Technical Sciences, 65(4), pp. 553–559.
• [18] Ufnalski B. Grzesiak L. M., 2012. Particle swarm optimization of artificial-neural-network-based on-line trained speed controller for battery electric vehicle. Bulletin of the Polish Academy of Sciences: Technical Sciences, 60(3), pp. 661–667. [19] Ermis K., Sen Y., 2017. Investigating Performance on Intercooler in Turbocharger Diesel Engine with ANN. 5th International Symposium on Innovative Technologies in Engineering and Science, Baku – Azerbaijan, 29-30 September, pp. 1383–1392.
• [20] Şen Y., Ekmekçi İ., Çallı İ., 2003. Açık kanatlı düz çarklı sirkilasuon pompalarında sıcaklığa bağlı Değişimlerin Yapay sinir ağları ile analizi. International XII Symposium of Artifıcial İntelligence and Neural Networks. Çanakkale, Turkey, 02-04 July, pp. 1-7.
• [21] Sablani S. S., Kacimov A., Perret J., Mujumdar A. S., Campo A., 2005. Non-iterative estimation of heat transfer coefficients using artificial neural network models. International Journal of Heat and Mass Transfer, 48(3–4), pp. 665–679.