Guidelines for the rail grade selection
M. Tomicic-Torlakovic, University of Belgrade, Faculty of Civil Engineering, Belgrade, Serbia Over the years the rail steel manufacturing process and quality of material produced have improved significantly through alloy design and/or head treatment procedure. The objective of this paper is to give the recommendations for the application of the available rail steel grades in accordance with the accessible investigations. The main source are the results of the Innotrack project in correlation with Euronorm prEN 13674-1. The conclusion for the Serbian Railway is made.
MATERIAL PROPERTY CHARACTERIZATION AND SPECIFICATIONS FOR IMPROVED RAIL STEELS
This article reports on efforts to isolate and measure the elements of steel rails that are most important to long, trouble-free service under specific conditions and to also measure the characteristics of steel that best meet those demands. Railroads already apportion different hardnesses of rail to different types of track, with the highest quality going into the track with the greatest curvature. Key considerations are resistance to wear and rolling-contact fatigue, which researchers can show at the microscopic level. Shear yield strength seems important, but only at the cold-worked and work- hardened surface, not as-manufactured. When research is completed, railroad operators will be able to set their specifications for rail so that manufacturers can produce steel that fits their needs more closely. Another goal is to identify stresses that weaken rails.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.