Failure analysis of cyclic damage to a railway rail – A case study

Fedorko, Gabriel; Molnár, Vieroslav; Blaho, Peter; Gašparík, Jozef; Zitrický, Vladislav

doi:10.1016/j.engfailanal.2020.104732

Cited by 17 publications

(7 citation statements)

References 17 publications

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“…The transport train passes through a railroad connection, where the technique used to connect the railroad tracks is using thermite welding. Thermite welding has a weakness that can be a factor in the occurrence of railroad faults [3], [4]. These weaknesses include low ductility and toughness, rough surface grains, dendritic structure, inclusions, and the formation of porosity.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Stress Intensity Factor and J-Integral on UIC 54 Railway Failure Using A Fracture Mechanics Approach

Kurniawan,

Darto,

Iswantoko

et al. 2024

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Repeated loading on the railroad tracks will result in fatigue failure. Fatigue failure combined with a defect in the form of a crack in the railroad track will result in a decrease in strength. Defects in the form of cracks are formed due to improper manufacturing and treatment processes. One treatment that can cause the formation of defects in the form of cracks is thermite welding. Improper and non-standard thermite welding techniques can trigger the formation of defects in the form of cracks in the railroad joints. Based on these problems, this simulation aims to obtain information about the value of maximum stress, SIF, J-Intergal, number of cycles, and crack extension from variations in crack size to repeated loading. The method consists of preprocessing, processing and postprocessing. Preprocessing begins with the design of the UIC 54 railroad crack which consists of 3 variations of crack length, namely 10 mm, 15 mm, and 20 mm. The design was tested through static structural simulation using the ANSYS 2021 R2 application. Meshing is configured using an element size of 5 mm and uses curvature capture. The results of the simulation obtained maximum stress values, SIF, J-Integral, number of cycles, and crack extension. Based on the simulation of SIF 1 and J-Integral values on the specimen design with a crack length of 10 mm it shows 362.03 Mpa.mm1/2 of 0.5708 mJ/mm2, for an initial crack length of 15 mm that is equal to 482.81 Mpa.mm1/2 and 0.91738 mJ/mm2, and for an initial crack length of 20 mm, it is 600.54 Mpa.mm1/2 and 1.4465 mJ/mm2. The results show that the increase in SIF 1 and J-Integral will be proportional to the increase in the initial crack length value. .

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Section: Introductionmentioning

confidence: 99%

Simulation of Stress Intensity Factor and J-Integral on UIC 54 Railway Failure Using A Fracture Mechanics Approach

Kurniawan,

Darto,

Iswantoko

et al. 2024

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“…Cracks are the most challenging to detect among the different types of defects because they are usually small and shallow [10]. The micro-cracks and sub-surface oblique cracks, Coatings 2022, 12, 1790 2 of 22 which occur at the early stage, are highly concealable [11]. However, from the view of fracture mechanics, rail defects are usually generated by rail fatigue cracks, expansion to internal injury, and eventually fracture, especially in welding parts [12].…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Testing Technologies for Rail Inspection: A Review

et al. 2022

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Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the past. However, these methods have several challenges in terms of detection speed and accuracy. Combined inspection methods have emerged as a promising approach to overcome these limitations. Nondestructive testing (NDT) techniques in conjunction with artificial intelligence approaches have tremendous potential and viability because it is highly possible to improve the detection accuracy which has been proven in various conventional nondestructive testing techniques. With the development of information technology, communication technology, and sensor technology, rail health monitoring systems have been evolving, and have become equally significant and challenging because they can achieve real-time detection and give a risk warning forecast. This paper provides an in-depth review of traditional nondestructive techniques for rail inspection as well as the development of using machine learning approaches, combined nondestructive techniques, and rail health monitoring systems.

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“…Scientific research and development in the field of science and technology also brings innovative solutions in the field of diagnostics of the technical condition of railway infrastructure. The current development of information and communication technologies creates opportunities to improve and streamline the diagnostics of rail superstructure components, which includes the track length [4,5].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Track Length Defects

Kanis¹,

Zitrický

2022

LOGI – Scientific Journal on Transport and Logistics

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Ensuring the safety of railway transport operation requires constant monitoring of the technical condition of individual elements of railway infrastructure. The necessary activities that contribute to maintaining good operational condition of the railway transport line also include the diagnostics of track length. Diagnostics of railway tracks is most often performed by means of regular visual inspection (in the conditions of the infrastructure manager – ŽSR). The objective of the article is to provide information on the application of a new approach to diagnostics of the technical condition of railway infrastructure. The new approach to defect identification on railway infrastructure uses non-invasive diagnostic methods based on the latest knowledge in the field of information and communication technologies. These facts resulted in investigating the possibilities of automatic detection of the technical condition of the track length using neural networks. The article is part of the following scientific research task: ‘Research into new knowledge and observational experience of a new generation of diagnostic systems in industrial production and transport industry – research into the physical nature of an automated track length video inspection system’, supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic.

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Failure analysis of cyclic damage to a railway rail – A case study

Cited by 17 publications

References 17 publications

Simulation of Stress Intensity Factor and J-Integral on UIC 54 Railway Failure Using A Fracture Mechanics Approach

Simulation of Stress Intensity Factor and J-Integral on UIC 54 Railway Failure Using A Fracture Mechanics Approach

Nondestructive Testing Technologies for Rail Inspection: A Review

Automatic Detection of Track Length Defects

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