A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS

Abstract

Rail grinding profile prediction in different grinding patterns is important to improve the grinding quality for the rail grinding operation site. However, because of high-dimensional and strong nonlinearity between grinding amount and grinding parameters, the prediction error and computational cost is relatively high. As a result, the accuracy and efficiency of conventional methods cannot be guaranteed. In this article, an accurate and efficient rail grinding profile prediction method is proposed, in which an interval segmentation approach is proposed to improve the prediction efficiency based on the geometric characteristic of a rail profile. Then, the accurate area integral approach with cubic NURBS is used as the grinding area calculation approach to improve the prediction accuracy. Finally, the normal length index is introduced to evaluate the prediction accuracy. The accuracy and stability of the proposed method are verified by comparing a conventional approach based on a practical experiment. The results demonstrate that the proposed method can predict the rail grinding profile in any grinding pattern with high accuracy and efficiency. Meanwhile, its prediction stability basically agrees with the conventional approach.