scholarly journals Substantiation of the choice of thermokinetic parameters of cooling of steel k76f to increase the hardness over the section of the rail head

2020 ◽  
Vol 0 (4) ◽  
pp. 30-37
Author(s):  
O. I. Babachenko ◽  
H. A. Kononenko ◽  
R. V. Podolskyi
Keyword(s):  
Thermokinetic Parameters ◽  
Rail Head

Оценка реальной чувствительности приемочного ультразвукового контроля рельсов

2020 ◽  
pp. 17-27
Author(s):  
А.А. Шелухин
Keyword(s):  
Ultrasonic Pulse ◽  
Piezoelectric Transducers ◽  
Acoustic Path ◽  
Metallic Inclusion ◽  
Inclusion Type ◽  
The Real ◽  
Rail Head ◽  
Pulse Echo

In this article, the analysis of the acoustic path during the ultrasonic pulse echo testing of the rail head in production is carried out. The influence of the parameters of the applied piezoelectric transducers on the distribution of sensitivity for the sounding scheme used in the existing installations is estimated and the real sensitivity of detecting defects of the «non-metallic inclusion» type is estimated.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

The Effect of Deformation on Mechanical Properties and Shakedown on Railroad Wheels

2014 ◽  
Author(s):  
Steven L. Dedmon ◽  
Takashi Fujimura ◽  
Daniel Stone
Keyword(s):  
Mechanical Properties ◽  
Rolling Contact ◽  
Effect Of Temperature ◽  
Plastic Deformations ◽  
Rail Head ◽  
Class D ◽  
Shakedown Theory ◽  
Freight Car ◽  
Class C ◽  
Railroad Wheels

Plastic deformations alter the mechanical properties of many metals and alloys. Class C and Class D wheel steels such as are used in North American freight car service are particularly affected by plastic deformations occurring during rolling contact between the wheel tread and rail head. This investigation determines the effect plastic deformations have on the mechanical properties of Class C and D wheel steels and how those changes could relate to shakedown theory. The effect of temperature is also discussed.

Thermal decomposition and evaluation thermokinetic parameters for explosive type

2021 ◽  
Author(s):  
Chen-Rui Cao ◽  
Wei-Chun Chen ◽  
Wun-Cheng Jhang ◽  
Yi-Hong Chung ◽  
Wei-Cheng Lin
Keyword(s):  
Thermal Decomposition ◽  
Thermokinetic Parameters

scholarly journals Application of the wavelet and neural technologies for processing of signals obtained during railway tracks diagnostics by the magnetic flux leakage method

2017 ◽  
Vol 66 (4) ◽  
pp. 209-216
Author(s):  
Vitalij Nichoga ◽  
Liubomyr Vashchyshyn
Keyword(s):  
Magnetic Flux ◽  
Transverse Crack ◽  
Magnetic Flux Leakage ◽  
Rail Head ◽  
Railway Tracks ◽  
Further Development ◽  
Flux Leakage

In this article, the approach for detecting a transverse crack in the rail head via ANN with CWT and application created on its basis are presented. The ways of further development of the ANN for improving its work accuracy and the possibility of identification of other types of defects are also presented. Keywords: defect, transverse crack, CWT, ANN

Research on Quenching Equipment of Rail Head

2010 ◽  
Vol 145 ◽  
pp. 313-316 ◽  
Author(s):  
Hao Kang ◽  
Yong Hong Wang ◽  
Di Wu ◽  
Xian Ming Zhao ◽  
Yong Ming Wang
Keyword(s):  
Cooling Rate ◽  
Cross Section ◽  
Current Problem ◽  
Natural Aging ◽  
Al Alloy ◽  
Rail Head ◽  
Heating Device

As to current problem of straightness defects and uneven quenching thickness of rail head, heating device with several induction loops are used, and through a three step strategy to realize simultaneously heating to head and bottom of rail. In air jetting phase, spray nozzles are replaced by Al alloy plates(20mm thick) with holes, adjusting to cooling strengthen can be realized by changing cross-section of holes, cooling rate is 2~4°C/sec. After the air jetting phase, there is a waterfog jetting phase, which makes 200°C temperature gap between head and bottom of rail, to ensure quenched rail has good straightness after 48h natural aging.

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