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Research Article Volume 2 Issue 2
Friction and wear performance of medical grade UHMWPE polymer with numerical analysis
Kemal Ermis, Huseyin Unal
Faculty of Technology, Applied Science University, Turke
Received: October 22, 2018 | Published: November 16, 2018
Citation: Ermis K, Unal H. Friction and wear performance of medical grade UHMWPE polymer with numerical analysis. MOJ Poly Sci. 2018;2(2):60-66. DOI: 10.15406/mojps.2018.02.00049
Abstract
In this study, the tribological behavior of medical grade ultra-high molecular weight polyethylene (UHMWPE) polymer was used. A pin-on-disc wear test machine was used for the tribological tests. Medical grade UHMWPE as a polymer pin material and DIN X2 CrNiMo 17 13 2 stainless steel as a disc material were used. Friction and wear tests were done under distilled water, egg albumen and dry sliding conditions at 0.5, 1.0 and 2.0 m/s sliding speeds and under 50, 100 and 150 N applied loads. The results showed that the coefficient of friction (COF) for medical grade UHMWPE is more significantly influenced by the sliding speeds and applied loads under dry sliding condition rather than distilled water and egg albumen lubrication conditions. Furthermore, both the COF and wear rate values increased with the increment of applied load and sliding speed. For the range of applied load and sliding speed values of this study, the lower wear rate values were obtained using an egg albumen lubricant when compared to distilled water and dry sliding conditions. In this study, numerical analysis contains an artificial neural network (ANN) and linear regression respectively. In addition, both of numerical methods compared with experimental results for predicting both COF and wear rate values of medical grade UHMWPE polymer material in different sliding conditions. The developed ANN method is presented in the study. ANN results showed that the predicted data are perfectly suitable with experimental results than linear regression results.
Keywords: UHMWPE, polymer, friction, wear, ANN, regression, knee joints, quality of life, loosening, prosthesis, tribological performance, joint discomfort, grade polymer, carbon nanotube, neural network
©2018 Ermis, et al. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.