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Empirical formulas for calculating Continuous Cooling Transformation diagrams

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The paper presents empirical formulas for the calculation of Continuous Cooling Transformation (CCT) diagram basing on the chemical composition and austenitizing temperature. Design/methodology/approach: In the method of calculating CCT diagrams proposed in the paper, two types of tasks are solved. First task is classification and consists in determining the range of cooling rate for particular phase transformations. The second task is regression, which aims at calculating the transformations temperature, hardness and volume fraction of phases in steel. The model of CCT diagrams was developed using multiple regression and logistic regression methods. Research limitations/implications: CCT diagrams can be calculated according to the presented method, if the chemical composition of steel meets the criteria defined by the application range of the model. Practical implications: The formulas presented in the article can be used to determine the conditions for heat treatment of structural steels. Originality/value: The paper presents the method for calculating CCT diagrams of the structural steels and engineering steels, depending on their chemical composition as well as austenitizing temperature.
Rocznik
Strony
21--30
Opis fizyczny
Bibliogr. 40 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
Bibliografia
  • [1] E. Pereloma, D.V. Edmonds (Eds.), Phase transformations in steels, Woodhead Publishing, Cambridge, 2012.
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  • [3] R.C Sharma, Principles of Heat Treatment of Steels, New Age International, 2008.
  • [4] B. Smoljan, S. Smokvina Hanza, N. Tomašič, D. Iljkič, Computer simulation of microstructure transformation in heat treatment processes, Journal of Achievements in Materials and Manufacturing Engineering 24/1 (2007) 275-282.
  • [5] A. Grajcar, M. Morawiec, W. Zalecki, Austenite decomposition and precipitation behaviour of plastically deformed low-Si microalloyed steel, Metals 8/12 (2018) 1028-1039, DOI: https://doi.org/10.3390/met8121028.
  • [6] M. Opiela, B. Grzegorczyk, Thermo-mechanical treatment of forged products of Ti-V-B microalloyed steel, Proceedings of the Metal’2013 Conference, Brno, 2013.
  • [7] A.N. Kolmogorov, On statistical theory of metal crystallization, Izvestia Academy of Science USSR, ser. Math. 3 (1937) 355-360.
  • [8] M. Avrami, Kinetics of phase change I. General theory, Journal of Chemical Physics 7/12 (1939) 1103-1112, DOI: https://doi.org/10.1063/1.1750380.
  • [9] J. Johnson, R. Mehl, Reaction kinetics in processes of nucleation and growth, Transactions of the AIME 135 (1939) 416-458.
  • [10] E. Scheil, Anlaufzeit der austenitumwandlung, Archiv Eisenhuttenwes 8 (1935) 565-567.
  • [11] C. Zener, Kinetics of the decomposition of austenite, Transactions of the AIME 167 (1946) 550-583.
  • [12] M. Hillert, Phase equilibrium in steel, Jernkontorets Ann. 141 (1957) 757-761.
  • [13] J.S. Kirkaldy, B.A. Thomson, E.A. Baganis, Prediction of multicomponent equilibrium and transformation diagrams for low alloy steels, in: D.V. Doane, J.S. Kirkaldy (Eds.): Hardenability concepts with applications to steel, The Metallurgical Society of AIME, New York, 1978, 82-119.
  • [14] H.K.D.H. Bhadeshia, A thermodynamic analysis of isothermal transformation diagrams, Metal Science 16/3 (1982) 159-165, DOI: https://doi.org/10.1179/030634582790427217.
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  • [16] T.C. Tszeng, G. Shi, A global optimization technique to identify overall transformation kinetics using dilatometry data-applications to austenitization of steels, Materials Science and Engineering: A 380/1-2 (2004) 123-136, DOI: https://doi.org/10.1016/j.msea.2004.03.040.
  • [17] P. Payson, C.H. Savage, Martensite reactions in alloy steels, Transactions ASM 33 (1944) 261-275.
  • [18] L.A. Carapella, Computing A or Ms (Transformation temperature on quenching) from analysis, Metal Progress 46 (1944) 108-118.
  • [19] A.A. Gorni, Steel forming and heat treating handbook, Available at: www.gorni.eng.br/e/Gorni_SFHTHandbook.pdf, Access in: 28.11.2019.
  • [20] J. Trzaska, Prediction methodology for the anisothermal phase transformation curves of the structural and engineering steels, Silesian University of Technology Press, Gliwice, 2017 (in Polish).
  • [21] 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 (1996) 433-437.
  • [22] W. Vermeulen, S. Van der Zwaag, P. Morris, T. De Weijer, Prediction of the Continuous Cooling Transformation diagram of some selected steels using artificial neural networks, Steel Research 68/2 (1997) 72-79, DOI: https://doi.org/10.1002/srin.199700545.
  • [23] J. Wang, P.J. Van Der Wolk, S. Van Der Zwaag, Effects of carbon concentration and cooling rate on continuous cooling transformations predicted by artificial neural network, ISIJ International 39/10 (1999) 1038-1046, DOI: https://doi.org/10.2355/isijinternational.39.1038.
  • [24] P.J. Van der Wolk, Modelling CCT-diagrams of engineering steels using neural networks, Delft University Press, Delft, 2001.
  • [25] J. Trzaska, L.A. Dobrzaski, Modelling of CCT diagrams for engineering and constructional steels, Journal of Materials Processing Technology 192-193 (2007) 504-510, DOI: https://doi.org/10.1016/j.jmatprotec.2007.04.099.
  • [26] J. Trzaska, Calculation of critical temperatures by empirical formulae, Archives of Metallurgy and Materials 61/2B (2016) 981-986, DOI: https://doi.org/10.1515/amm-2016-0167.
  • [27] J. Trzaska, Calculation of the steel hardness after continuous cooling, Archives of Materials Science and Engineering 61/2 (2013) 87-92.
  • [28] 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 (2014) 38-44.
  • [29] J. Trzaska, Empirical formulae for the calculation of austenite supercooled transformation temperatures, Archives of Metallurgy and Materials 60/1 (2015) 181-185, DOI: https://doi.org/10.1515/amm-20150029.
  • [30] Steel characteristics. Institute for Ferrous Metallurgy, Silesian Publishing House, Katowice, 1975, (in Polish).
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  • [32] 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 Modelling and Characterization, Elsevier Science, 2014, 161-198, DOI: https://doi.org/10.1016/B978-0-08-096532-1.00215-6.
  • [33] S. Chakraborty, P.P. Chattopadhyay, S.K. Ghosh, S. Datta, Incorporation of prior knowledge in neural network model for continuous cooling of steel using genetic algorithm, Applied Soft Computing 58 (2017) 297-306, DOI: https://doi.org/10.1016/j.asoc.2017.05.001.
  • [34] N.S. Reddy, J. Krishnaiah, H.B. Young, J.S. Lee, Design of medium carbon steels by computational intelligence techniques, Computational Materials Science 101 (2015) 120-126, DOI: https://doi.org/10.1016/j.commatsci.2015.01.031.
  • [35] L.A. Dobrzański, T. Tański, J. Trzaska, Optimization of heat treatment conditions of magnesium cast alloys, Materials Science Forum 638-642 (2010) 1488-1493, DOI: https://doi.org/10.4028/www.scientific.net/MSF.638-642.1488.
  • [36] P. Papliński, W. Sitek, J. Trzaska, Modelling the structural steel hardness using genetic programming method, Advanced Materials Research 1036 (2014) 580-585, DOI: https://doi.org/10.4028/www.scientific.net/AMR.1036.580.
  • [37] 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.
  • [38] W. Sitek., A. Irla, The use of fuzzy systems for forecasting the hardenability of steel, Archives of Metallurgy and Materials 61/2 (2016) 797-802, DOI: https://doi.org/10.1515/amm-2016-0134.
  • [39] J. Trzaska, Neural networks model for prediction of the hardness of steels cooled from the austenitizing temperature, Archives of Materials Science and Engineering 82/2 (2016) 62-69, DOI: https://doi.org/10.5604/01.3001.0009.7105.
  • [40] J. Trzaska, A new neural networks model for calculating the continuous cooling transformation diagrams, Archives of Metallurgy and Materials 63/4 (2018) 2009-2015, DOI: https://doi.org/10.24425/amm.2018.125137.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f65e1fe2-6be2-4431-a373-edc1efe5ecf1
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