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Improvement of the Working Body of the Electric Ballasting Machine Based on Parametric Optimization to Increase the Efficiency of the Track Repair
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DOI：10.3390/eng7040159.png)
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Abstract
En 中文
The efficiency of railway track maintenance and repair is largely determined by the technological productivity and reliability of track machines operating under conditions of increasing loads and limited time intervals for operational performance. In this regard, the improvement of the working bodies of ballasting machines is an important direction for increasing the efficiency of the repair and track processes. The paper deals with the improvement of the working body of the electric ballasting machine based on parametric optimization methods aimed at increasing the efficiency of the track repair. The study has analyzed the geometric and process parameters of the working body, which have the greatest effect on the quality of the ballast redistribution, energy consumption, and the stress-strain state when interacting with the ballast prism. A parametric model of the working body has been developed, which makes it possible to perform numerical modeling and identify the most sensitive design parameters, including the blade geometry, the angles of their installation, the penetration depth, and the modes of operation. Based on the results of the optimization, the paper suggests a design solution that provides a more uniform load distribution, reduces peak stresses, and improves the quality of the ballast prism profiling. The obtained results demonstrate an increase in the operational productivity of the electric ballasting machine. The proposed approach is linked to the methodology of optimizing the track machine fleet, as the increase in the efficiency of individual machines contributes to downtime reduction, more accurate planning of operations, and increased efficiency of the track maintenance system based on the predicted condition of the railway tracks.
Keywords:
electric ballasting machine
working body
parametric optimization
track repair
fleet optimization
track machines
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E
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2.4
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302
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