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Calculation of Critical Temperatures by Empirical Formulae

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Języki publikacji
EN
Abstrakty
EN
The paper presents formulas used to calculate critical temperatures of structural steels. Equations that allow calculating temperatures Ac1, Ac3, Ms and Bs were elaborated based on the chemical composition of steel. To elaborate the equations the multiple regression method was used. Particular attention was paid to the collection of experimental data which was required to calculate regression coefficients, including preparation of data for calculation. The empirical data set included more than 500 chemical compositions of structural steel and has been prepared based on information available in literature on the subject.
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  • Silesian University of Technology, Institute of Engineering Materials and Biomaterials, 18a Konarskiego Str., 44-100 Gliwice, Poland
Bibliografia
  • [1] P. Payson, C. H. Savage, Martensite Reactions in Alloy Steels, Transactions ASM 33, 261-275 (1944).
  • [2] A. B. Greninger, The Martensite Thermal Arrest in Iron-Carbon Alloys and Plain Carbon Steels,Transactions ASM 30, 1-26 (1942).
  • [3] A. B. Greninger, A. R. Troiano, Kinetics of the Austenite to Martensite Transformation in Steel, Transactions ASM 28, 537-574 (1940).
  • [4] Transactions ASM 37, 27-47 (1946).
  • [5] A. E. Nehrenberg, The Temperature Range of Martensite Formation, Transactions AIME 167, 494-498 (1946).
  • [6] R. A. Grange, H. M. Stewart, The Temperature Range of Martensite Formation, Transactions AIME 167, 467-490 (1946).
  • [7] W. Steven, A. G. Haynes, The temperature of formation of martensite and bainite in low-alloy steels. Some effects of chemical composition, Journal of the Iron and Steel Institute 183, 349-359 (1956).
  • [8] K. W. Andrews, Empirical formulae for the calculation of some transformation temperatures, Journal of the Iron and Steel Institute 203, 721-727 (1965).
  • [9] B. Pawłowski, Temperatury krytyczne w stalach, Wydawnictwa AGH, Kraków (2012).
  • [10] 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), 331-337 (2011).
  • [11 ] A. A. Gorni, Steel Forming And Heat Treating Handbook, 2015, http://www.gorni.eng.br/e/Gorni_SFHTHandbook.pdf
  • [12] T. Sourmail, C. Garcia-Mateo, A model for predicting the Ms temperatures of steels, Computational Materials Science 34, 213-218 (2005).
  • [13] W. Vermeulen, P. F. Morris, A. P. De Weijer, S. Van der Zwaag, Prediction of martensite start temperature using artificial neural networks, Ironmaking and Steelmaking 23(5), 433-437 (1996).
  • [14] C. Garcia-Mateo, C. Capdevila, F. Garcia Caballero, C. Garcıa de Andres, Artificial neural network modeling for the prediction of critical transformation temperatures in steels, Journal of Materials Science 42(14), 5391-5397 (2007).
  • [15] 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).
  • [16] 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).
  • [17] J. Trzaska, L. A. Dobrzański, Modelling of CCT diagrams for engineering and constructional steels, Journal of Materials Processing Technology 192-193, 504-510 (2007).
  • [18] D. T. Larose, Data mining methods and models, John Wiley & Sons (2006).
  • [19] J. Trzaska, Calculation of the steel hardness after continuous cooling, Archives of Materials Science and Engineering 61(2), 87-92 (2013).
  • [20] J. Trzaska, Calculation of volume fractions of microstructural components in steels cooled from the austenitizing temperature, Journal of Achievements in Materials and Manufacturing Engineering 65(1), 38-44 (2014).
Uwagi
EN
The publication was partially financed by a statutory grant of the Faculty of Mechanical Engineering at the Silesian University of Technology in 2015.
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8125aca9-1c10-400e-8195-5340bdb2b13d
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