https://orcid.org/http://orcid.org/0000-0002-4234-6216
https://orcid.org/http://orcid.org/0000-0002-5663-9482
https://orcid.org/http://orcid.org/0000-0003-4314-9799
https://orcid.org/http://orcid.org/0000-0002-4234-6216
https://orcid.org/http://orcid.org/0000-0002-5663-9482
https://orcid.org/http://orcid.org/0000-0003-4314-9799
Rail grinding efficiency greatly depends on the work organization, planning methods and work technology. As efficiency criterion, the probability of ingress of necessary time for grinding of any section of a way in the allotted “possession” has been accepted. Simulation modeling with AnyLogic was used to assess rail grinding efficiency. The creation of a digital duplicate of the technological process for grinding rails in the track allowed a number of simulation experiments to assess the effectiveness of grinding work with the existing approaches to the organization and planning of rail grinding work and with the existing data on the technological process parameters. As a result of the experiments, the required time was determined to perform the entire amount of grinding and measuring work required to achieve the desired quality of longitudinal and transverse geometry of the rail head and eliminate defects, under various combinations of conditions and parameters of the track section. The probability of getting the necessary time for grinding in the allotted “possession” was quantified. The probability was about 0.6, which indirectly indicates a high risk of obtaining poor quality results of rail grinding works. To improve the efficiency and quality of grinding works, it was proposed to retrofit the train with measuring carts to measure transverse and longitudinal geometric parameters of the rails and the related adjustment of the work technology. A repeated series of simulation experiments, taking into account the proposed changes, showed a 17% reduction in the risk of obtaining poor quality of work.
Simulation Modeling of the Technological Process for Grinding Rails in the Track by Rail Grinding Trains
Lect. Notes in Networks, Syst.
International School on Neural Networks, Initiated by IIASS and EMFCSC ; 2022 ; St.Petersburg, Russia February 08, 2022 - February 10, 2022
Networked Control Systems for Connected and Automated Vehicles ; Kapitel : 29 ; 301-311
16.11.2022
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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