抽象的

Prediction of the wear behavior of UHMWPE using artificial neural networks

D.Adss, T.S.Mahmoud*, H.M.Zakaria, T.A.Khalifa

In the present investigation, the tribological behavior of ultra-high molecular weight polyethylene (UHMWPE) was investigated under dry, distilled water and physiological saline lubricated conditions against a 316L stainless steel disc. The effect of the applied load, sliding velocity as well as the lubrication type on the coefficient of friction and the wear rate of UHMWPE were investigated. The results revealed that the highest and lowest wear rates of UHMWPE have been taken place under dry sliding and distilled water lubrication, respectively. The steady-state friction coefficient in dry sliding is about two times the value in saline, and about 3-4 times that in distilled water.An artificial neural network (ANN) model for predicting the effect of the applied load, the sliding speed and type of lubricant on wear rate and the coefficient of friction of the UHMWPE was developed. It has been observed that the experimental results coincided with ANNs results