The paper presents formulas used to calculate critical temperatures of structural steels. Equations that allow calculating temperatures Ac₁, Ac₃, Ms and B_s 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.

Details var a2a_config = a2a_config || {}; a2a_config.onclick = 1; a2a_config.locale = "pl"; a2a_config.color_main = "D7E5ED"; a2a_config.color_border = "9D9D9D"; a2a_config.color_link_text = "ffffff"; a2a_config.color_link_text_hover = "ffffff"; a2a_config.color_bg = "d9d9d9"; a2a_config.icon_color = "#4e4e4e"; a2a_config.exclude_services = ["dihitt", "delicious", "whatsapp", "dzone", "sms", "stumpedia", "viber", "yahoo_messenger", "google_plus"]; Details PDF BIBTEX RIS Title Empirical Formulae for The Calculation of Austenite Supercooled Transformation Temperatures Journal title Archives of Metallurgy and Materials Yearbook 2015 Issue No 1 March Authors Trzaska, J. Divisions of PAS Nauki Techniczne Publisher Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences Date 2015[2015.01.01 AD - 2015.12.31 AD] Identifier DOI: 10.1515/amm-2015-0029 ; ISSN 1733-3490 Source Archives of Metallurgy and Materials; 2015; No 1 March References Dobrzański (2004), Application of neural networks for prediction of critical values of temperatures and time of the supercooled austenite transformations of Materials Processing, Journal Technology, 155. ; Sitek (2012), Hybrid modelling methods in materials science - selected examples of Achievements in Materials and Manufacturing, Journal Engineering, 54, 93. ; Trzaska (2009), The calculation of CCT diagrams for engineering steels of Materials Science and, Archives Engineering, 39, 13. ; Dobrzański (2005), Computer aided classification of flaws occurred during casting of alu - minum of Materials Processing, Journal Technology, 167. ; Zhao (1995), Continuous cooling transformation kinetics versus isothermal transformation kinetics of steels : a phenomenological rationalization of experimental observations and, Materials Science EngineeringR, 15, 135. ; Dobrzański (2010), Optimization of heat treatment conditions of magnesium cast alloys, Materials Science Forum, 638. ; Dobrzański (2004), Application of neural networks to forecasting the CCT diagram of Materials Processing, Journal Technology, 157. ; Trzaska (2005), Application of neural networks for designing the chemical composition of steel with the assumed hardness after cooling from the austenitising temperature of Materials Processing, Journal Technology, 164. ; Sitek (2008), Modified Tartagli method for calculation of Jominy hardenability curve, Materials Science Forum, 575. ; Trzaska (2013), Calculation of the steel hardness after continuous cooling of Materials Science and, Archives Engineering, 61, 87. ; Dobrzański (2003), 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. ; Trzaska (2007), Modelling of CCT diagrams for engineering and constructional steels of Materials Processing, Journal Technology, 192. ; Dobrzański (2004), Application of neural network for the prediction of continuous cooling transformation diagrams Materials, Computational Science, 30, 251. ; Dobrzański (2005), Corrosion resistance of the polymer matrix hard magnetic composite materials Nd of Materials Processing, Journal Technology, 164. ; Dobrzański (2008), Modelling of hardness prediction of magnesium alloys using artificial neural networks applications of Achievements in Materials and Manufacturing, Journal Engineering, 26, 187. ; Trzaska (2007), Computer program for prediction steel parameters after heat treatment of Achievements in Materials and Manufacturing, Journal Engineering, 24, 171. ; Sitek (2010), Methodology of high - speed steels design using the artificial intelligence tools of Achievements in Materials and Manufacturing, Journal Engineering, 39, 115. ; Trzaska (2006), Application of neural networks for selection of steel with the assumed hardness after cooling from the austenitising temperature of Achievements in Materials and Manufacturing, Journal Engineering, 16, 145.

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.