Assessment of the effectiveness of lubrication of Ti-6Al-4V titanium alloy sheets using radial basis function neural networks

Abstract

The aim of the research presented in this article was to determine the value of the friction coefficient using a simple tribological test and to build an empirical model of friction with the use of radial basis function artifi-cial neural networks. The friction tests were carried out on a specially designed friction simulator that allows a sheet metal strip to be drawn between two fixed dies. The test materials were sheets of Ti-6Al-4V titanium alloy with a thickness of 0.5 mm. The friction tests were carried out with variable contact forces of counter-samples with rounded surfaces and in various lubrication conditions. Mineral oils and bio-degradable oils with the addition of boric acid (5 wt %) were tested. Based on the results of friction investigations, neural models of friction were built using RBF artificial neural networks. The good properties of the RBF network 2:2-35-1:1 were confirmed by a high value of the determination coefficient R2 = 0.9984 and a low value of the S.D. ratio equal to 0.0557. It was found that the COF value was the highest for the average values of both the nominal pressure and kinematic viscosity. Over the entire range of nominal pressures applied, SAE10W-40 engine oil ensured the most effective reduction of the COF. The COF value was the highest for the average values of both the nominal pressure and kinematic viscosity.