Optimizing Railway Tribology: A Systematic Review and Predictive Modeling of Twin-Disc Testing Parameters

Twin-disc testing is crucial for understanding wheel–rail interactions in railway systems, but the vast array of testing parameters and conditions makes data interpretation challenging. This review presents a comprehensive analysis of the twin-disc literature experimental data, focusing on how various parameters influence friction and wear characteristics under stationary contaminant conditions. We systematically collected and analyzed data from numerous studies, considering factors such as contact pressure, speed, material hardness, sliding speeds, adhesion, and a range of contaminants. This research showed inconsistent data reporting across different studies and statistical analyses revealed significant correlations between testing parameters and wear rates. For sand-contaminated tests, a correlation between particle size and flow rate was also highlighted. Based on these findings, we developed a simple predictive model for forecasting wear rates under varying conditions. This model achieved an adjusted R2 of 0.650, demonstrating its potential for optimizing railway component design and maintenance strategies. Our study provides a valuable resource for researchers and practitioners in railway engineering, offering insights into the complex tribological interactions in wheel–rail systems and a tool for predicting wear behavior.