Calculation of the steel hardness after continuous cooling

• Autorzy: Trzaska J.
• Języki publikacji: EN

Abstrakty

EN

Purpose: The paper presents method in predicting hardness 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: Modelling make improvement of engineering materials properties possible, as well as prediction of their properties, even before the materials are fabricated, with the significant reduction of expenditures and time necessary for their investigation and application. Practical implications: The worked out relationships may be used in computer systems of steels’ designing for the heat-treated machine parts. Originality/value: The paper presents the method for calculating hardness of the structural steels, depending on their chemical composition, austenitizing temperature and cooling rate.

Słowa kluczowe

EN

computational materials science; steel; statistic method; CCT diagram

PL

komputerowa nauka o materiałach; stal; metoda statystyczna; diagram CCT

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2013
• Tom: Vol. 61, nr 2
• Strony: 87--92
• Opis fizyczny: Bibliogr. 17 poz.

Twórcy

autor

Trzaska J.

• Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, 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. Cížek, 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] 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 (2005) 1637-1643.
• [8] J. Trzaska, L.A. Dobrzański, Modelling of CCT diagrams for engineering and constructional steels, Journal of Materials Processing Technology 192 (2007) 504-510.
• [9] 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.
• [10] 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.
• [11] 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).
• [12] 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.
• [13] 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.
• [14] 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.
• [15] P. Maynier, B. Jungmann, J. Dollet, Creusot-Loire System for the prediction of the mechanical properties of low alloy steel products, Hardenability Concepts With Applications to Steel, The Metallurgical Society of AIME (1978) 518-545.
• [16] R. Blondeau, P. Maynier, J. Dollet, Forecasting the hardness and resistance of carbon and low-alloy steels according to their structure and composition, Memoires Scientifiques de la Revue de Metallurgie 12 (1973) 883-892 (in French).
• [17] R. Blondeau, P. Maynier, J. Dollet, B. Vieillard-Baron, Forecasting the hardness, resistance and breaking point of carbon and low-alloy steels according to their composition and heat treatment, Memoires Scientifiques de la Revue de Metallurgie 11 (1975) 759-769 (in French).