Empirical Formulae for The Calculation of Austenite Supercooled Transformation Temperatures

• Autorzy: Trzaska J.

Identyfikatory

DOI

10.1515/amm-2015-0029

Warianty tytułu

PL

Zależności empiryczne do obliczania temperatury przemian austenitu przechłodzonego

• Języki publikacji: EN

Abstrakty

EN

The paper presents empirical formulae for the calculation of austenite supercooled transformation temperatures, basing on the chemical composition, austenitising temperature and cooling rate. The multiple regression method was used. Four equations were established allowing to calculate temperature of the start area of ferrite, perlite, bainite and martensite at the given cooling rate. The calculation results obtained do not allow to determine the cooling rate range of ferritic, pearlitic, bainitic and martensite transformations. Classifiers based on logistic regression or neural network were established to solve this problem.

PL

W pracy przedstawiono zależności empiryczne do obliczania temperatury przemian austenitu przechłodzonego na podstawie składu chemicznego, temperatury austenityzowania i szybkości chłodzenia. Zastosowano metodę regresji wielorakiej. Opracowano cztery równania, które umożliwiają obliczenie temperatury początku przemiany ferrytycznej, perlitycznej, baini-tycznej i martenzytycznej. Wyniki obliczeń nie pozwalają na wyznaczenie zakresu szybkości chłodzenia, dla których występują przemiany ferrytyczna, perlityczna, bainityczna i martenzytyczna. Do rozwiązania problemu opracowano klasyfikatory stosując regresję logistyczną lub sztuczne sieci neuronowe.

Słowa kluczowe

EN

CCT diagram; modeling; heat treatment; steel

PL

diagram CCT; modelowanie; obróbka cieplna; stal

• Wydawca: Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences
• Czasopismo: Archives of Metallurgy and Materials
• Rocznik: 2015
• Tom: Vol. 60, iss. 1
• Strony: 181--185
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Trzaska J.

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

Bibliografia

• [1] W. Sitek, Methodology of high-speed steels design using the artificial intelligence tools, Journal of Achievements in Materials and Manufacturing Engineering 39(2), 115-160 (2010).
• [2] 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, 795-804 (2005).
• [3] L. A. Dobrzański, M. Krupiński, J. H. Sokolowski, Computer aided classification of flaws occurred during casting of alu-minum, Journal of Materials Processing Technology 167 (2-3), 456-462 (2005).
• [4] L. A. Dobrzański, T. Tański, J. Trzaska, L. Čıžek, Modelling of hardness prediction of magnesium alloys using artificial neural networks applications, Journal of Achievements in Materials and Manufacturing Engineering 26(2), 187-190 (2008).
• [5] L. A. Dobrzański, T. Tański, J. Trzaska, Optimization of heat treatment conditions of magnesium cast alloys, Materials Science Forum 638, 1488-1493 (2010).
• [6] W. Sitek, J. Trzaska, L. A. Dobrzański, Modified Tartagli method for calculation of Jominy hardenability curve, Materials Science Forum 575-578, 892-897 (2008).
• [7] W. Sitek, J. Trzaska, Hybrid modelling methods in materials science - selected examples, Journal of Achievements in Materials and Manufacturing Engineering 54(1), 93-102 (2012).
• [8] J. Trzaska, L. A. Dobrzański, Application of neural networks for selection of steel with the assumed hardness after cooling from the austenitising temperature, Journal of Achievements in Materials and Manufacturing Engineering 16, 145-150 (2006).
• [9] J. Trzaska, Calculation of the steel hardness after continuous cooling, Archives of Materials Science and Engineering 61(2), 87-92 (2013).
• [10] J. Trzaska, L. A. Dobrzański, Application of neural networks for designing the chemical composition of steel with the assumed hardness after cooling from the austenitising temperature, Journal of Materials Processing Technology 164-165, 1637-1643 (2005).
• [11] J. C. Zhao, M. R. Notis, Continuous cooling transformation kinetics versus isothermal transformation kinetics of steels: a phenomenological rationalization of experimental observations, Materials Science and Engineering R15, 135-207 (1995).
• [12] J. Trzaska, A. Jagiełło, L. A. Dobrzański, The calculation of CCT diagrams for engineering steels, Archives of Materials Science and Engineering 39(1), 13-20 (2009).
• [13] L. A. Dobrzański, J. Trzaska, Application of neural network for the prediction of continuous cooling transformation diagrams, Computational Materials Science 30(3-4), 251-259 (2004).
• [14] L. A. Dobrzański, J. Trzaska, Application of neural networks for prediction of critical values of temperatures and time of the supercooled austenite transformations, Journal of Materials Processing Technology 155-156, 1950-1955 (2004).
• [15] L. A. Dobrzański, J. Trzaska, Application of neural networks to forecasting the CCT diagram, Journal of Materials Processing Technology 157-158, 107-113 (2004).
• [16] 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, 359-362 (2003).
• [17] 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), 171-174 (2007).
• [18] J. Trzaska, L. A. Dobrzański, Modelling of CCT diagrams for engineering and constructional steels, Journal of Materials Processing Technology 192, 504-510 (2007).