Experimental and numerical analysis of wheel–rail tribology under dry and contaminated conditions with oil, oil–water emulsion, and leaf debris
摘要
The wear of wheels and rails has a significant impact on the dynamic performance and safety of railway systems. Reliable prediction models are essential for effective maintenance planning. This study examines the effect of surface contamination on the friction and wear behaviour of commonly used wheel and rail steels. Twin-disc rolling–sliding experiments were performed to assess the effects of creep ratio, contact pressure, and tangential speed on friction coefficient and wear. Samples were cut from the rail head and locomotive wheel tyre, in the form of rollers and shoes. Various creep ratios were obtained from roller-on-roller tests on rollers with different diameters. To obtain pure sliding, the tests were conducted on a rotating roller with a stationary shoe. Under dry roller–roller conditions, the friction coefficient stabilizes within the range μ ≈ 0.168–0.236, with typical values of approximately 0.19–0.20 at 10% slip and 120 N. In contrast, water–leaf contamination results in a substantial reduction in adhesion, yielding μ ≈ 0.05 and significant fluctuations attributed to intermittent third-body entrainment. Oil lubrication further reduces the friction coefficient to μ ≈ 0.029, while oil–water emulsion conditions produce similarly low values of μ ≈ 0.03, indicating a transition to mixed or hydrodynamic lubrication. The lowest friction level is recorded under pure rolling, where µ ≈ 0.0187. For roller–shoe contact under pure sliding conditions, the friction coefficient remains relatively high (μ ≈ 0.19–0.20), accompanied by severe wear. Wear volumes were calculated using a modified Archard model with weighting factors and compared to results from finite element analysis (FEA). The FEA model provided reliable wear predictions and reduced reliance on costly experimental testing. The strong agreement between FEA predictions and experimental data supports the use of this approach for consistent wear prediction and improved railway maintenance strategies.