The influence of micro coolers on the physical-mechanical characteristics of the steels used in making railway monoblock wheels
Original scientific paper The paper introduces the possibilities of improving mechanical characteristics of the steels used in making railway monoblock wheels, by using micro coolers on steel casting. The laboratory experiments are aimed at studying the control of round cross-section steel ingot solidification by adding crystallization germs in their central area, in order to influence the internal structure and the mechanical characteristics of the steel. The stimulation of heterogeneous germination on the surfaces generated by the particles introduced in the center of the ingot, while it was in a liquid state, leads to the formation of a second solidification front and heat absorption in this area. The data we obtained during the research have been processed in Excel, in order to obtain the correlation equations that express the variation of the steel mechanical characteristics as a function of the chemical structure both for the experimental samples and for the reference ones.
Contributions related to the control of steel ingot solidification
The paper introduces the influence of the control over steel ingot solidification upon the quality characteristics of carbon steels. The laboratory experiments focused on the solidification control of steel ingots with cylindrical cross-section, by addition of crystallizing germs (micro-coolers) in their central area, in order to influence their inner structure and quality characteristics. The use of graphical and analytical correlations allows the establishing of optimal technological domains of variation for the steel casting parameters, with use of micro-coolers, in order to obtain the desired values for their mechanical characteristics.
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.