Rail grinding continues to be one of the most effective techniques for extending rail life, improving wheel/rail contact behavior, and reducing the overall cost of track maintenance. While the ability to more effectively implement improved rail grinding programs continues to expand, the understanding of the grinding mechanism itself has not kept pace with the improved implementation. Thus, while railroad engineering and maintenance personnel have learned to better develop grinding patterns and profiles through empirical testing and field evaluation, the fundamental theoretical bases for the improved grinding performance have not kept pace.

One such fundamental area of understanding is the modeling of the rail grinding process itself, both individually, as a function of a single grinding motor on the head of the rail, and in the more complex configuration of multiple grinding motors in a range of patterns. This paper presents the results of research directly aimed at better understanding these mechanisms and then utilizing this better understanding to develop a detailed rail grinding model that allows for the accurate analysis of not only an individual grinding motor but also a full grinding train application, as a function of pattern and speed.

In the case of the single grinding motor on the head of the rail, this research looks at the fundamental mechanism associated with each cutting abrasive grinding grain in the grinding stone, and then expands that mechanism to a full 10 inch diameter grinding wheel as it cuts into the rail head at a defined angle and speed. Using actual rail profile data and grinding data, a theoretical grinding wheel model is developed and then calibrated with wheel test data and actual grinding (field) data.

This single motor model is then expanded into a full grinding train model, such as for a 96 stone grinding train with 48 motors per rail, where it is able to analyze the full sequence of 48 motors as each motor individually and sequentially removes metal from the rail head. The resulting analysis is sensitive to such key factors as grinding speed, and the key pattern parameters of motor angles, sequence and power. The model is then calibrated to and compared with actual full scale rail grinding metal removal data from a major Class 1 railroad.

Such an analysis tool allows railroads to analyze the performance of different grinding patterns in a real world operating setting, to improve their rail grinding practices and take further advantage of new technologies in rail grinding to better manage the grinding process and improve planning of grinding activities.

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