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

• Autorzy: Trzaska J.
• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

computational material science; steels; statistic methods; CCT diagrams

PL

komputerowa nauka o materiałach; stal; metody statystyczne; wykres CCT

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2014
• Tom: Vol. 65, nr 1
• Strony: 38--44
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Trzaska J.

• Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland

Bibliografia

• [1] W. Sitek, J. Trzaska, L.A. Dobrzański, An artificial intelligence approach in designing new materials, Journal of Achievements in Materials and Manufacturing Engineering 17/1-2 (2006) 277-280.
• [2] W. Sitek, L.A. Dobrzański, Application of genetic methods in materials’ design, Journal of Materials Processing Technology 164 (2005) 1607-1611.
• [3] W. Sitek, Methodology of high-speed steels design using the artificial intelligence tools, Journal of Achievements in Materials and Manufacturing Engineering 39/2 (2010) 115-160.
• [4] W. Sitek, J. Trzaska, L.A. Dobrzański, Selection method of steel grade with required hardenability, Journal of Achievements in Materials and Manufacturing Engineering 17/1-2 (2006) 289-292.
• [5] L.A. Dobrzański, M. Drak, J. Trzaska, Corrosion resistance of the polymer matrix hard magnetic composite materials Nd-Fe-B, Journal of Materials Processing Technology 164-165 (2005) 795-804.
• [6] L.A. Dobrzański, T. Tański, J. Trzaska, L. Čížek, Modelling of hardness prediction of magnesium alloys using artificial neural networks applications, Journal of Achievements in Materials and Manufacturing Engineering 26/2 (2008) 187-190.
• [7] L.A. Dobrzański, T. Tański, J. Trzaska, Optimization of heat treatment conditions of magnesium cast alloys, Materials Science Forum 638 (2010) 1488-1493.
• [8] L.A. Dobrzański, J. Trzaska, A.D. Dobrzańska-Danikiewicz, Use of Neural Networks and Artificial Intelligence Tools for Modeling, Characterization, and Forecasting in Material Engineering, in: S. Hashmi (Ed.), Comprehensive Materials Processing, Vol. 2: Materials Modeling and Characterization, Elsevier, 2014, 161-198.
• [9] P. Papliński, W. Sitek, J. Trzaska, Modelling the Structural Steel Hardness Using Genetic Programming Method, Advanced Materials Research 1036 (2014) 580-585.
• [10] W. Sitek, J. Trzaska, Hybrid modelling methods in materials science - selected examples, Journal of Achievements in Materials and Manufacturing Engineering 54/1 (2012) 93-102.
• [11] J. Trzaska, A. Jagiełło, L.A. Dobrzański, The calculation of CCT diagrams for engineering steels, Archives of Materials Science and Engineering 39/1 (2009) 13-20.
• [12] B. Pawłowski, Critical points of hypoeutectoid steel - prediction of the pearlite dissolution finish temperature Ac1f , Journal of Achievements in Materials and Manufacturing Engineering 49/2 (2011) 331-337.
• [13] P. Maynier, J. Dollet, P. Bastien, Prediction of microstructure via empirical formulae based on CCT diagrams, Hardenability Concepts With Applications to Steel, The Metallurgical Society of AIME (1978) 163-178.
• [14] J. Trzaska, Calculation of the steel hardness after continuous cooling, Archives of Materials Science and Engineering 61/2 (2013) 87-92.
• [15] L.A. Dobrzański, J. Trzaska, Application of neural networks for prediction of hardness and volume fractions of structural components constructional steels cooled from the austenitising temperature, Materials Science Forum 437-438 (2003) 359-362.
• [16] J. Trzaska, Methodology of the computer modelling of the supercooled austenite transformations of the constructional steels, PhD thesis-unpublished, Main Library of the Silesian University of Technology, Gliwice, 2002 (in Polish).
• [17] L.A. Dobrzański, J. Trzaska, Application of neural network for the prediction of continuous cooling transformation diagrams, Computational Materials Science 30/3-4 (2004) 251-259.
• [18] J. Trzaska, L.A. Dobrzański, A. Jagiełło, Computer program for prediction steel parameters after heat treatment, Journal of Achievements in Materials and Manufacturing Engineering 24/2 (2007) 171-174.
• [19] J. Trzaska, L.A. Dobrzański, Modelling of CCT diagrams for engineering and constructional steels, Journal of Materials Processing Technology 192 (2007) 504-510.