Mathematical Modeling and Simulation of Hardness of Quenched and Tempered Steel

Smoljan, Božo; Iljkić, Dario; Totten, George E.

doi:10.1007/s11663-015-0451-6

Cited by 17 publications

(13 citation statements)

References 1 publication

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“…Results obtained during the simulation can be used to predict structural transformations and mechanical properties of parts after quenching [19][20].…”

Section: Resultsmentioning

confidence: 99%

Comparative evaluation of methods for the determination of heat transfer coefficients of liquid and gaseous quenching media

Shevchenko

Melnik

Смирнов

et al. 2017

Mechanics & Industry

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Abstract. Temperature dependences of heat transfer coefficients of liquid and gaseous quenching media were determined using a gradient probe and prismatic probe of more simple design. The probes of two different designs were tested in the same conditions. Analysis of heat transfer coefficients showed good agreement between the data obtained. The tests were carried out with liquid and gaseous quenching media: water, polymer quenchant, quenching oil and high-pressure nitrogen. Methods of mathematical modeling of steel samples quenching show the adequacy of the results.

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“…Results obtained during the simulation can be used to predict structural transformations and mechanical properties of parts after quenching [19][20].…”

Section: Resultsmentioning

confidence: 99%

Comparative evaluation of methods for the determination of heat transfer coefficients of liquid and gaseous quenching media

Shevchenko

Melnik

Смирнов

et al. 2017

Mechanics & Industry

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“…Mechanical properties of steel are directly related to its mechanical behavior during heat treatment. Therefore, successful prediction of the relevant mechanical properties is the first step in predicting the mechanical behavior of steel during and after heat treatment, such as resistance to fracture and distortions [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…One of the most common semi-empirical methods for predicting hardness during continuous cooling is based on the continuous cooling transformation (CCT) diagrams [11]. Additionally, distribution of hardness in quenched steel can be predicted using the Jominy test results by transferring the results from the Jominy curve to the hardness values for different points of the steel specimen [4,12].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance

Hanza

Marohnić

Iljkić

et al. 2021

Metals

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Successful prediction of the relevant mechanical properties of steels is of great importance to materials engineering. The aim of this research is to investigate the possibility of reducing the complexity of artificial neural networks-based prediction of total hardness of hypoeutectoid, low-alloy steels based on chemical composition, by introducing the specific Jominy distance as a new input variable. For prediction of total hardness after continuous cooling of steel (output variable), ANNs were developed for different combinations of inputs. Input variables for the first configuration of ANNs were the main alloying elements (C, Si, Mn, Cr, Mo, Ni), the austenitizing temperature, the austenitizing time, and the cooling time to 500 °C, while in the second configuration alloying elements were substituted by the specific Jominy distance. Comparing the results of total hardness prediction, it can be seen that the ANN using the specific Jominy distance as input variable (runseen = 0.873, RMSEunseen = 67, MAPE = 14.8%) is almost as successful as ANN using main alloying elements (runseen = 0.940, RMSEunseen = 46, MAPE = 10.7%). The research results indicate that the prediction of total hardness of steel can be successfully performed only based on four input variables: the austenitizing temperature, the austenitizing time, the cooling time to 500 °C, and the specific Jominy distance.

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“…On the quenching processes, mechanical properties of steel are responsible for its mechanical behaviour during quenching, such as resistance to fracture and distortions. Therefore, predicting the relevant mechanical properties is the first step in predicting the mechanical behaviour of a steel during quenching [2,11,12].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Microstructure Constituents’ Hardness after the Isothermal Decomposition of Austenite

Hanza

Smoljan

Štic

et al. 2021

Metals

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An increase in technical requirements related to the prediction of mechanical properties of steel engineering components requires a deep understanding of relations which exist between microstructure, chemical composition and mechanical properties. This paper is dedicated to the research of the relation between steel hardness with the microstructure, chemical composition and temperature of isothermal decomposition of austenite. When setting the equations for predicting the hardness of microstructure constituents, it was assumed that: (1) The pearlite hardness depends on the carbon content in a steel and on the undercooling below the critical temperature, (2) the martensite hardness depends primarily on its carbon content, (3) the hardness of bainite can be between that of untempered martensite and pearlite in the same steel. The equations for estimation of microstructure constituents’ hardness after the isothermal decomposition of austenite have been proposed. By the comparison of predicted hardness using a mathematical model with experimental results, it can be concluded that hardness of considered low-alloy steels could be successfully predicted by the proposed model.

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Mathematical Modeling and Simulation of Hardness of Quenched and Tempered Steel

Cited by 17 publications

References 1 publication

Comparative evaluation of methods for the determination of heat transfer coefficients of liquid and gaseous quenching media

Comparative evaluation of methods for the determination of heat transfer coefficients of liquid and gaseous quenching media

Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance

Prediction of Microstructure Constituents’ Hardness after the Isothermal Decomposition of Austenite

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