Effects of Quenching Temperature and Cooling Rate on the Microstructure and Mechanical Properties of U75V Rail Steel

In order to predict the high-temperature deformation behavior of hypereutectoid steel, the hot compression tests were conducted in the strain rate range of (0.001∼1) s −1 and the deformation temperature range of (950∼1100) • C. The experimental data were employed to develop the Arrhenius constitutive model and BP neural network model, and their predictability for high temperature flow stress of hypereutectoid steel was further evaluated. Comparatively, a higher correlation coefficient (R) can be obtained for the BP neural network model compared with the Arrhenius constitutive equations. And the relative error within ±1% was more than 68.75% for the BP neural network model, while only 18.75% for the constitutive equations. The BP neural network model is considered more efficient and accurate to predict the hot deformation behavior than the Arrhenius constitutive equations. Moreover, the well-trained BP neural network model is employed to predict the flow stress varying with the deformation temperature and strain rate. The flow stress decreases with the increasing deformation temperature and decreasing strain rate, which is in accordance with the experimental evaluation. The results indicate that BP neural network model is an efficient tool for modelling and predicting the flow behavior of hypereutectoid steels in high temperature applications.INDEX TERMS Hypereutectoid steel, hot deformation behavior, Arrhenius equations, BP neural network models.

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“…For all the stress level, the flow stress and strain rate meet the hyperbolic sine function and can be represented as the equation (3).…”

Section: B Arrhenius Constitutive Equationmentioning

confidence: 99%

“…Hypereutectoid steel is widely applied in many industrial fields for its great mechanical property, especially in the field of rail technology [1]- [3]. The understanding of the flow behaviors of steels has become more essential to researchers engaged in hot deformation condition.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel

et al. 2020

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“…The research object of this paper is U75V rail steel. The actual parameters of the rail are the following [ 13 , 30 ]: track length is 50 mm, track density 7894 kg/m 3 , induction coil section inner radius 110 mm, outer radius 120 mm, and induction coil width 50 mm. Five points of A1, A2, A3, A4, B on the rail are picked to measure the temperature of the equipment ( Figure 1 ).…”

Section: Three-dimensional Rail Welding Modelmentioning

confidence: 99%

“…When the rail temperature reaches the Curie point, the magnetic characteristics of the rail will disappear and induction heating will no longer be carried out. Therefore, the heating process has two stages: the first stage is induction heating caused by electromagnetic induction, and the second stage is conduction heating, that is, the temperature from high to low [ 13 , 38 ].…”

Section: Three-dimensional Meshing Mathematical Modelmentioning

confidence: 99%

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Modeling of Eddy Current Welding of Rail: Three-Dimensional Simulation

Sun

Liu

Song

et al. 2020

Entropy

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In this paper is given a three-dimensional numerical simulation of the eddy current welding of rails where the longitudinal two directions are not ignored. In fact, usually it is considered a model where, in the two-dimensional numerical simulation of rail heat treatment, the longitudinal directions are ignored for the magnetic induction strength and temperature, and only the axial calculation is performed. Therefore, we propose the electromagnetic-thermal coupled three-dimensional model of eddy current welding. The induced eddy current heat is obtained by adding the z-axis spatial angle to the two-dimensional electromagnetic-thermal, thus obtaining some new results by coupling the numerical simulation and computations of the electric field and magnetic induction intensity of the three-dimensional model. Moreover, we have considered the objective function into a weak formulation. The three-dimensional model is then meshed by the finite element method. The electromagnetic-thermal coupling has been numerically computed, and the parametric dependence to the eddy current heating process has been fully studied. Through the numerical simulation with different current densities, frequencies, and distances, the most suitable heat treatment process of U75V rail is obtained.

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