Wear of railway tyre steels modelling using artificial neural networks

In the paper the results of sliding wear tests were used to model the dependence of steel volume loss on railway wheel tyres on selected material parameters and sliding conditions. The material properties included in this modelling were the hardness and chemical composition of the tyre material (specimens) and the hardness of the mating material (counter-specimens). The conditions for sliding were the initial maximum Hertzian pressure and the sliding distance. The tests were carried out in the ring-block system. Artificial neural networks were used for modelling. It was found that the constructed model made it possible to quantify the volume loss from the above-mentioned factors. A clear influence of the pressure, friction distance, and hardness of both cooperating materials on the studied wear was found. The influence of the chemical composition is less noticeable due to the rather narrow range of its allowable changes. The microscopic tests allowed us to identify the main wear mechanisms in the sliding friction of the tested tyre and rail steels.