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Numerical Simulation of the Phase Transformations in Steels: A Case Study on Newly-Developed Multi-Phase Steels for Drop Forgings

Wojtacha Anna, Opiela Marek

Advances in Science and Technology. Research Journal

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2021

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Vol. 15, no 1

126--133

EN

The aim of the work was to determine the diagrams of phase evolution under equilibrium conditions and numerical simulation of austenite phase transformations under non-equilibrium conditions, as well as to determine CCT (Continuous Cooling Transformation) and TTT (Temperature Time Transformation) diagrams with the use of JMatPro software. The subject of the analysis were two newly elaborated multiphase steels assigned for production of forgings: steel A, containing of 0.165% C, 2% Mn, 1.11% Si and steel B, containing 0.175% C, 1,87% Mn, 1% Si, 0.22% Mo and Ti and V microadditions at a concentration of 0.031% and 0.022%, respectively. The performed simulation revealed that the investigated steels have similar critical temperatures under equilibrium conditions: Ac1 ~ 680°C, Ac3 ~ 830°C. The chemical composition of steel B and the interaction of Mo, Ti and V in particular, determine that diffusion transformations, i.e. ferritic and pearlitic, in the elaborated CCT and TTT diagrams are significantly shifted to longer times in relation to the position of these transformations in the diagrams for steel A. A distinct delay also concerns the bainitic transformation. Moreover, it was found that the MS temperature of steel B is slightly lower. The determined CCT and TTT diagrams are essentially helpful in the development of heat and thermo-mechanical treatment conditions for new steel grades.

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Neural networks model for prediction of the hardness of steels cooled from the austenitizing temperature

Trzaska J.

Archives of Materials Science and Engineering

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2016

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Vol. 82, nr 2

62--69

EN

Purpose: The paper presents the new neural networks model making it possible to estimate the hardness of continuously-cooled steel from the austenitizing temperature. Design/methodology/approach: The method proposed in the paper employs two applications of the neural networks of: classification and regression. Classification and consists in determining the value of dichotomous variables describing the occurrence of: ferrite, pearlite, bainite and martensite in the microstructure of a steel. The values of dichotomous variables have been used for calculating steel hardness. The other task is regression, which aims at calculating the steel hardness. Findings: The presented neural networks model can be used only in the range of concentrations of alloying elements shown in this paper. Practical implications: The model worked out makes it possible to calculate hardness for the steel with a known chemical composition. This model deliver important information for the rational selection of steel for those parts of the machines that are subjected to the heat treatment. The presented model make it possible the analysis of the interaction of the chemical composition on the hardness curves of the steel cooled from the austenitizing temperature. Originality/value: The paper presents the method for calculating hardness of the structural and engineering steels, depending on their chemical composition, austenitizing temperature and cooling rate.

3

Calculation of volume fractions of microstructural components in steels cooled from the austenitizing temperature

Trzaska J.

Journal of Achievements in Materials and Manufacturing Engineering

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2014

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Vol. 65, nr 1

38--44

EN

Purpose: The paper presents method in predicting the volume fractions of ferrite, pearlite, bainite and martensite of steel cooled continuously from the austenitizing temperature, basing on the chemical composition, austenitizing temperature and cooling rate. Design/methodology/approach: In the paper it has been applied a hybrid approach that combined application of various mathematical tools including logistic regression and multiple regression to solve selected tasks from the area of materials science. Findings: Computational methods are an alternative to experimental measurement in providing the material data required for heat treatment process simulation.Research limitations/implications: All equations are limited by range of mass concentrations of elements which is presented in Table 2. Practical implications: The worked out formulae may be used in computer systems of steels’ designing for the heat-treated machine parts. Originality/value: The paper presents the method for calculating the volume fractions of ferrite, pearlite, bainite and martensite of the structural steels, depending on their chemical composition, austenitizing temperature and cooling rate.

4

Project of neural network for steel grade selection with the assumed CCT diagram

Dobrzański L., Malara S., Trzaska J.

Journal of Achievements in Materials and Manufacturing Engineering

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2008

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Vol. 27, nr 2

155-158

EN

Purpose: The aim of this paper was developing a project of neural network for selection of steel grade with the specified CCT diagram - structure and of harness after heat treatment. Design/methodology/approach: The goal has been achieved in the following stages: at the first stage characteristic points of CCT diagram have been determined. At the second stage neural network has been developed and optimized. Findings: The neural network was developed in this paper, that allowed selection of steel grade with the assumed CCT diagram. Research limitations/implications: Created method for designing chemical compositions is limited by the established ranges of mass concentrations of elements. The methodology demonstrated in the paper makes it possible to add new steel grades to the system. Practical implications: The method worked out may be used in computer steel selection systems for the machine parts put to heat treatment. Originality/value: Presented computer aided method makes use of neural networks, and may be used for selecting the steel with the required structure after heat treatment.