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

• Autorzy: Trzaska J.

Identyfikatory

DOI

10.5604/01.3001.0009.7105

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

computational material science; steel; neural network; CCT diagram

PL

komputerowa nauka o materiałach; stal; sieć neuronowa; wykres CCT

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2016
• Tom: Vol. 82, nr 2
• Strony: 62--69
• Opis fizyczny: Bibliogr. 25 poz.

Twórcy

autor

Trzaska J.

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

Bibliografia

• [1] A. Śliwa, W. Kwaśny, W. Sitek, Computer simulation of the relationship between selected properties of PVD coatings, Archives of Metallurgy and Materials 61/2 (2016) 481-484.
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• [3] 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.
• [4] W. Sitek, L.A. Dobrzański, Application of genetic methods in materials' design, Journal of Materials Processing Technology 164 (2005) 1607-1611.
• [5] P. Papliński, W. Sitek, J. Trzaska, Modelling the structural steel hardness using genetic programming method, Advanced Materials Research 1036 (2014) 580-585.
• [6] 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:] Hashmi S. (ed.): Comprehensive Materials Processing, Volume 2. Materials Modelling and Characterization, Elsevier Science (2014) 161-198.
• [7] W. Sitek, J. Trzaska, L.A. Dobrzański, An Artificial Intelligence Approach in Designing New Materials, Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 277-280.
• [8] 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.
• [9] 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.
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• [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] J. Trzaska, Empirical formulae for the calculation of austenite supercooled transformation temperatures, Archives of Metallurgy and Materials 60/1 (2015) 181-185.
• [13] 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.
• [14] P. Payson, C.H. Savage, Reactions in Alloy Steels, Transactions ASM 33 (1944) 261-275.
• [15] L.A. Carapella, Computing A or Ms (Transformation temperature on quenching) from analysis, Metal Progress 46 (1944) 108-118.
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• [17] 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.
• [18] 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 (1999) 1038-1046.
• [19] 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.
• [20] B. Smoljan, Prediction of Mechanical Properties and Microstructure Distribution of Quenched and Tempered Steel Shaft, Journal of Materials Processing Technology 175 (2006) 393-397.
• [21] 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.
• [22] 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).
• [23] J. Trzaska, Empirical formulas for the calculations of the hardness of steels cooled from the austenitizing temperature, Archives of Metallurgy and Materials 61/3 (2016) 1297-1302.
• [24] 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.
• [25] 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).

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).