Prediction of Rail Rolling Contact Fatigue Crack Initiation Life via Three-Dimensional Finite Element Analysis

Martua, Landong; Ng, Andrew Keong; Sun, George

doi:10.1109/icirt.2018.8641633

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Factors such as rail curve radius, axle load, stiffness of suspension system, rail-wheel profiles, traction and braking forces, and wheel/rail material properties affect RCF (Evans and Iwnicki, 2002;Thakkar et al, 2006). Examples of catastrophic damage observed related to rail failure due to RCF are the Seemanchal Express derailment that occurred on 3 February 2019 in India, the Hatfield derailment in the UK that occurred in 2000, the Columbus and Eliot city derailments in Ohio and Maryland, respectively, in the US in 2012 (Magel, 2011;Magel et al, 2016;Martua et al, 2018), the Bates derailment in South Australia in 2007, the Gainford derailment in Alberta, Canada, in 2013, and the Storsund-Koler derailment in Sweden in 2013 (Magel et al, 2016).…”

Section: Rolling Contact Fatigue Implications and Maintenance Strategiesmentioning

confidence: 99%

“…Rail failure due to cracks can potentially result in the derailment of railway vehicles. Such derailments have significant impacts on both human life and the environment (Magel, 2011;Martua et al, 2018). This study explores how crack orientations affect wheel/rail contact stress and, consequently, fatigue life.…”

Section: Rolling Contact Fatigue Implications and Maintenance Strategiesmentioning

confidence: 99%

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Crack influence and fatigue life assessment in rail profiles: a numerical study

Urassa,

Habte,

Mohammedseid

2023

Front. Built Environ.

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Rail transportation is a pivotal mode of land transport for its efficiency in transporting passengers and freight across short or long distances; hence, the reliability and safety of rail systems are of key importance. Rolling contact fatigue (RCF), characterized by the cyclic loading of wheel-rail contacts, presents a significant challenge in the rail industry. This study presents a comprehensive numerical investigation on the influence of different crack orientations on the contact stress of the rail profile and subsequently the fatigue life. Using finite element analysis (FEA) with Abaqus and FE-safe software, the study examined different crack orientations’ impact on stress distribution and fatigue life of rail profiles. Employing the extended finite element method (XFEM), this study modeled cracks in rail profiles with different orientations: parallel, perpendicular, and oblique to the rail axis. finite element analysis was used to obtain stress distribution results, highlighting the impact of crack presence, and orientation on maximum contact stresses. Subsequently, fatigue analysis was performed using FE-safe software, wherein the FEA results were imported to estimate fatigue life and damage evolution. The study revealed that the presence of a crack significantly influences contact stress, fatigue life, and damage accumulation. The results further demonstrated that crack orientation has an impact on the severity of those effects. Oblique cracks exhibited higher impact compared to lateral and longitudinal cracks. The study provides valuable insights into rolling contact fatigue-related failures, aiding in better understanding and mitigation of such issues, thereby contributing to improved rail maintenance practices and system safety.

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Section: Rolling Contact Fatigue Implications and Maintenance Strategiesmentioning

confidence: 99%

Section: Rolling Contact Fatigue Implications and Maintenance Strategiesmentioning

confidence: 99%

Crack influence and fatigue life assessment in rail profiles: a numerical study

Urassa,

Habte,

Mohammedseid

2023

Front. Built Environ.

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“…The methodology is based on the evaluation of stress of defects at the transient rolling contact, which takes into account the frictional rolling contact model. A prediction of RCF crack initiation life, with identification of crack plane orientation, is presented in [19]. The prediction is based on a dynamic three-dimensional FEM model and a simulation of axle box accelerations.…”

Section: Figure 1 Representation Of a Rail Head Defect [8]mentioning

confidence: 99%

Extending service life of rails in the case of a rail head defect

Ovchinnikov¹,

Bondarenko²,

Kou³

et al. 2021

JCE

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Rails are subjected to the processes of wear, corrosion and contact and bending fatigue during their lifecycle. As a result of these processes, various types of damage and defects are formed in rails. The residual life of rails depends on the size, position, and orientation of defects. Maximum permissible crack-size values are calculated in this paper using the finite element method. The crack plane orientation relative to the contact surface plane is analysed. The dependence of the stress intensity factor on the crack area is established. This allows continued use of defective rails and safe operation on low-activity railways.

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“…Therefore, several methods and approaches have been proposed that can forecast defective or sub-optimal states either from previous measurements or through user-defined models. In this category fall traditional techniques, such as Finite Element Analysis [1], [2], [3], particle-based methods [4] and time-series [5] that have certainly improved the respective processes in terms of robustness and productivity. Simultaneously, the transition to Industry 4.0 and the installation of Internet of Things (IoT) sensory networks on industrial shop-floors [6], [7] has enabled the continuous monitoring of production through analysis of productions data.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

Simulation Modelling Practice and Theory

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The prediction of upcoming events in industrial processes has been a longstanding research goal since it enables optimization of manufacturing parameters, planning of equipment maintenance and more importantly prediction and eventually prevention of defects. While existing approaches have accomplished substantial progress, they are mostly limited to processing of one dimensional signals or require parameter tuning to model environmental parameters. In this paper, we propose an alternative approach based on deep neural networks that simulates changes in the 3D structure of a monitored object in a batch based on previous 3D measurements. In particular, we propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters and predict upcoming events related to sub-optimal performance. We validate our framework on a microelectronics use-case using the recently published "PCB scans" dataset where we simulate changes on the shape and volume of glue deposited on an Liquid Crystal Polymer (LCP) substrate before the attachment of integrated circuits (IC). Experimental evaluation examines the impact of different choices in the cost function during training and shows that the proposed method can be efficiently used for defect prediction.

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Prediction of Rail Rolling Contact Fatigue Crack Initiation Life via Three-Dimensional Finite Element Analysis

Cited by 6 publications

References 12 publications

Crack influence and fatigue life assessment in rail profiles: a numerical study

Crack influence and fatigue life assessment in rail profiles: a numerical study

Extending service life of rails in the case of a rail head defect

A Deep Learning framework for simulation and defect prediction applied in microelectronics

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