• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1270-1276
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• 2003

This paper describes an analytical method for wheel wear prediction. The outputs from vehicle dynamic software are used to calculation the wheel wear. Two calculation examples are shown for a high-speed line and a conventional line. Through the comparison of two cases, we can see the wheel wear characteristics on the conventional line and the high-speed line. The conventional line has many curved tracks that cause severe wheel flange wear. The influences of curve radius on wheel wear are also described considering the operational performance of the high speed trainset. A method of calculation using contact patch work model is presented for determination of the evolution by wear railway wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.172-177
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• 1998

An experimental study on the prediction of grinding wheel wear by dual detection methods was conducted by the laser displacement and acoustic emission(AE) system. The laser displacement sensor was located above the head of the grinding wheel and the AE sensor was set under the workpiece, where the wheel were condition can be detected. It was found that the dual detection methods by laser displacement system and AE system made it possible to predict the wheel wear. From the experiments, the root mean square(RMS) values both methods was found to be proportional to the grinding wheel wear.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.8
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• pp.150-156
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• 2001

This paper deals with the theoretical estimation and its experimental verification of grinding wheel wear in surface grinding process. A theoretical formulation is provided to predict the grinding wheel wear in surface grinding. To validate the theoretical prediction, the grinding wheel wear is measured by using a laser scanning micrometer. The associated surface roughness and grinding farce are also investigated both theoretically and experimentally. Through a series of simulations and experiments, it is shown that the predictions are in good agreement with the experimental results.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.3
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• pp.174-183
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• 2000

Non-Parallelism the axial direction occurs during grinding process of long slender shafts. The reason for the axial error is due to elastic deformation of the components, accumulation phenomenon of the grinding and wheel wear during the grinding process. The accumulation phenomenon, the size generation mechanism and the wheel wear process during traverse grinding result in complicated process at each step on the wheel surface. The grinding system stiffness obtained from the stiffness of the center on the tailstock and the workpiece varing according to the relative position of the wheel and the workpiece. Further more, the value of wheel wear increases as the grinding process advances. The above mentioned issues make the shape generation process during traverse grinding quite complicated. This research analyzes the shape generation process in the direction of the work spindle. First, the formulation of the grinding system stiffness was conducted and the simulation analysis method of the traverse grinding was established. Also, a measuring system for assessing the dimensinal accuracy of the workpiece has been developed.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.409-414
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• 2005

It is difficult to apply the Hertz theory to the wheel-rail contact problem which has the complicated geometric form and plastic deformation. Therefore, we perform the elastic-plastic FE analysis and compare the results with those of Hertz theory. Kalker's simplified theory of rolling contact is used to discretize the contact patches and calculate local traction and slip. The wear volumes are calculated using Archard wear equation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.977-978
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• 2012

• Journal of the Korean Society of Safety
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• v.38 no.4
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• pp.1-7
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• 2023

The steel wheels of urban railway vehicles gather a lot of data through regular measurements during maintenance. However, limited research has been carried out utilizing this data, resulting in difficulties predicting the maintenance period. This paper studied a machine learning model suitable for mileage prediction by studying the characteristics of mileage change according to diameter and flange thickness changes. The results of this study indicate that the larger the diameter, the longer the travel distance, and the longest flange thickness is at 30 mm, which gradually shortened at other times. As a result of research on the machine learning prediction model, it was confirmed that the random forest model is the optimal model with a high coefficient of determination and a low root mean square error.

• Proceedings of the KSR Conference
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• 2009.05b
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• pp.225-230
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• 2009

From the view point of railway vehicle dynamics, the interaction between wheel and rail have an huge effect on the behavior of the vehicle. This phenomenon is an unique motion, only for railway vehicles. Furthermore, close investigation of the backgrounds of the interaction is the key to estimate the dynamic behavior of the vehicle, successfully. To evaluate the model including flexible bodies such as car body and catenary system of the next generation express train, it is necessary to develop proper dynamic solver including a wheel rail contact module. In this study, wheel-rail contact module is developed using the general purpose dynamic solver. First of all, the procedure for calculation of the wheel-rail contact force has been established. Generally, yaw angle of the wheelset is ignored. Sets of information are summarized as tables and splined for further uses. With this information, normal force and creep coefficient can be extracted and used for FASTSIM algorithm, which has been shown good reliability over years. Normal force and longitudinal, lateral force at the contact surface are also calculated. Those data are verified by commercial railway simulation program 'VAMPIRE'. This procedure and program can offer a basic process for estimation of the dynamic behavior and wear of the wheel-rail system, even while running on the curved rail. Finally, multi-dimensional inspection tool will be developed including the prediction of the derailment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.836-842
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• 2001

The more sophisticated patterns of propagation model is presented in this paper, which includes three different source characteristics. The spherical, cosine and dipole radiation characteristics compared and sound event level and the maximum sound level are calculated by experiment and calculation. It is shown that patterns of propagation has dipole characteristics for low speed range(below about 150km/h) at electric multiple system. We know that push-pull high speed system(maximum speed: 300km/h) has cosine characteristics of noise propagation. For this purpose, We conduct the experiment of noise and know the empirical formula of noise level and radiation coefficient K. This model of simulation is conducted through point source array model at wheel/rail contact point by using program and experimental formula. We can guess prediction of profile, flat and wear of wheel by above modeling in near field.

• International Journal of Reliability and Applications
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• v.4 no.4
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• pp.183-190
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• 2003

Rail breaks and derailments can cause a huge loss to rail players due to loss of service, revenue, property or even life. Maintenance has huge impact on reliability and safety of railroads. It is important to identify factors behind rail degradation and their risks associated with rail breaks and derailments. Development of mathematical models is essential for prediction and prevention of risks due to rail and wheel set damages, rail breaks and derailments. This paper addresses identification of hazard modes, estimation of probability of those hazards under operating, curve and environmental condition, probability of detection of potential hazards before happening and severity of those hazards for informed strategic decisions. Emphasis is put on optimal maintenance and operational decisions. Real life data is used for illustration.