Predictive modeling of the rail grinding process using a distributed cutting grain approach

Abstract

High-density railway lines experience a high rate of deterioration on the running surface of the rails; it can be addressed by rail grinding in order to reduce the frequency of rail replacement. Rail grinding includes additional complex features beyond what is usually considered in conventional grinding. Although extensive empirical experience exists to describe rail grinding, it can still be considered to be an emerging field that is in need of predictive theoretical guidance. This paper presents a newly developed modeling approach that is intended to provide a theoretical understanding of the rail grinding process and allow the prediction of rail grinding behavior and performance. The modeling is a bottom-up approach that starts from individual cutting grains and builds up to the rail grinding train level. First, grain distribution modeling is used to build a uniform template for the grinding simulation, based on the assumption of spherical grains with normally distributed sizes. Second, one representative slice is extracted as a grinding surface with stable grains. Protrusion heights and spacing distances of the cutting grains are analyzed to obtain the features of the grinding surface. Then the spherical grains are transformed into decahedrons with arbitrary poses, so as to closely approximate the actual surface of a grinding wheel. Third, the interactions of the cutting grains are combined into a model of a single grinding wheel and compared to test results from a single-wheel test. This allows for the connection between the utilized grinding parameters and the grinding results to be isolated, which is validated with supporting experiments performed on a single wheel. The individual wheel relationships can be combined into a full multi-wheel grinding pattern for estimating the simulation results of a multi-wheel grinding train. Eventually, the comparison between the simulations and grinding tests is used to show the effectiveness of the predictive rail grinding modeling at the level of a rail grinding train.