Numerical simulation of martensitic transformation plasticity of 42CrMo steel based on spot continual induction hardening model

Zhong, Hanlie; Zhou, Wang; Gan, Jin; Wang, Xiaoli; Yang, Ying; He, Jiaxi; Wei, Tingting; Qin, Xunpeng

doi:10.1016/j.surfcoat.2020.125428

Cited by 17 publications

(9 citation statements)

References 29 publications

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“…For example, the maximum ferrite volume fraction in the temperature range of eutectoid temperature and the lower temperature boundary of the austenite region at low carbon contents for hypoeutectoid carbon steel, which is limited by the lever rule, or the equilibrium fraction of the ferrite in titanium alloys is limited by the solubility of intermetallic phases in Al-alloys, etc. The Avrami model in Equation ( 14) is improved in Equation (19) to consider the limitation.…”

Section: Diffusion Phase Transformation Modelmentioning

confidence: 99%

“…[18] Zhong et al showed an improved model by considering martensite transformation plasticity. [19] In their recent papers, Wen and Han presented interesting efforts in minimizing the edge effect to establish uniform temperature distribution in a large-sized internal gear using numerical simulation. [20,21] M. Parvinzadeh et al implemented an experiment with the statistical approach to minimize the edge effect in induction hardening.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mobile Induction Heat Treatment of Large‐Sized Spur Gear—The Effect of Scanning Speed and Air Gap on the Uniformity of Hardened Depth and Mechanical Properties

Saputro

Chen

Jheng³

et al. 2023

steel research int.

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Prematurely fractured teeth of SCM440 large‐sized spur gear are commonly found among gears of a mechanical press machine. The macroscopic inspection and microstructure examination of a fractured tooth are established to show that the previous induction hardening process produced two serious issues; insufficient hardening in the root areas and nonuniform hardened depth along the tooth circumference. The numerical simulation is executed to replicate the previous induction hardening process, which consists of simulations for tooth‐to‐tooth mobile induction heating, water spray quenching, and the tempering process. This study highlights the influence of most adjustable parameters, such as the scanning speed of the inductor coil and air gap, to collect beneficial insights for further improvements. Experimental and numerical results are compared to validate the numerical model used. The adjustment of scanning speed and air gap highly affects the magnitude and uniformity of hardened depth along the tooth profile. Promising improvements can be achieved by slower, smaller scanning speeds and thinner air gaps, where the distortion is still in the allowed allowable range. This research provides a method and insights for future explorations to deal with relevant issues in the tooth‐to‐tooth mobile induction hardening process.

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Section: Diffusion Phase Transformation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mobile Induction Heat Treatment of Large‐Sized Spur Gear—The Effect of Scanning Speed and Air Gap on the Uniformity of Hardened Depth and Mechanical Properties

Saputro

Chen

Jheng³

et al. 2023

steel research int.

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“…29 ANNs have overcome the limitations of traditional optimization algorithms, such as the structural constraints and poor fitting with possess characteristics of self-organization, adaptability, and self-learning. 30 In recent years, researchers have effectively employed this methodology to solve nonlinear problems and continuously optimized its algorithms. It has found widespread application in fields such as the intelligent robotics, automation, biology, and medicine.…”

Section: Optimization Of Parameters In Laser Quenching Process Based ...mentioning

confidence: 99%

Optimization of laser irradiation process parameters for 40Cr steel based on back propagation neural network and genetic algorithm

Chang

Deng

Sun

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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40Cr is a significant material in the manufacturing of gears. However, this process is plagued by uneven distribution of quenching layers and excessive residual stress, leading to a decline in the precision of the workpiece, deformation, and cracking. Relying solely on the trial-and-error method in experiments is inadequate for effectively revealing the evolution mechanism of the laser quenching process. This approach is detrimental to improving research and development efficiency. In this study, a multi-field coupled numerical model for the multi-track overlapping laser quenching of the 40Cr gear steel is established. This model quantitatively reveals the coupling evolution laws of the temperature field, stress field, and phase transformation field during the laser quenching. The focus is on calculating the size of the tempering zone under different overlapping rates and conducting correlation analysis between parameters. Based on the artificial neural networks and genetic algorithms, the width and depth of the tempering zone are selected as target values for parameter optimization. The aim is to find the nonlinear relationship between the laser power, spot diameter, scanning speed, laser overlapping rate, and the size of the tempering zone, which can accurately predict the size of the tempering zone. The material microstructure of 40Cr gear steel was characterized by the scanning electron microscope, microhardness tester and friction and wear tester. The results show that the training error and testing error of the output parameters in genetic algorithm optimized back propagation network can accurately predict the size of the tempering zone. It is found that the higher the lap rate, the more uniform the hardness of the phase transformation hardening layer surface, and the tempering softening zone becomes larger. The parts surface is easy to produce defects, which can provide a theoretical basis for optimizing the laser quenching process parameters.

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“…It is important to have a high density of triangles close to the toothed region. The material parameters have been taken from data of a specific steel with a given composition (Table 1) assuring the desirable mechanical properties after the heat treating [21]. In Table 2 we can find the actual values we have taken in our numerical experiments.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Industrial Steel Heat Treating: Numerical Simulation of Induction Heating and Aquaquenching Cooling with Mechanical Effects

Moreno

Vázquez

Montesinos³

et al. 2021

Mathematics

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This paper summarizes a mathematical model for the industrial heating and cooling processes of a steel workpiece corresponding to the steering rack of an automobile. The general purpose of the heat treatment process is to create the necessary hardness on critical parts of the workpiece. Hardening consists of heating the workpiece up to a threshold temperature followed by a rapid cooling such as aquaquenching. The high hardness is due to the steel phase transformation accompanying the rapid cooling resulting in non-equilibrium phases, one of which is the hard microconstituent of steel, namely martensite. The mathematical model describes both processes, heating and cooling. During the first one, heat is produced by Joule’s effect from a very high alternating current passing through the rack. This situation is governed by a set of coupled PDEs/ODEs involving the electric potential, the magnetic vector potential, the temperature, the austenite transformation, the stresses and the displacement field. Once the workpiece has reached the desired temperature, the current is switched off an the cooling stage starts by aquaquenching. In this case, the governing equations involve the temperature, the austenite and martensite phase fractions, the stresses and the displacement field. This mathematical model has been solved by the FEM and 2D numerical simulations are discussed along the paper.

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Numerical simulation of martensitic transformation plasticity of 42CrMo steel based on spot continual induction hardening model

Cited by 17 publications

References 29 publications

Mobile Induction Heat Treatment of Large‐Sized Spur Gear—The Effect of Scanning Speed and Air Gap on the Uniformity of Hardened Depth and Mechanical Properties

Mobile Induction Heat Treatment of Large‐Sized Spur Gear—The Effect of Scanning Speed and Air Gap on the Uniformity of Hardened Depth and Mechanical Properties

Optimization of laser irradiation process parameters for 40Cr steel based on back propagation neural network and genetic algorithm

Industrial Steel Heat Treating: Numerical Simulation of Induction Heating and Aquaquenching Cooling with Mechanical Effects

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