Tytuł artykułu

Empirical Formulae for The Calculation of Austenite Supercooled Transformation Temperatures

Tytuł czasopisma

Archives of Metallurgy and Materials

Rocznik

2015

Numer

No 1 March

Autorzy

Trzaska, J.

Wydział PAN

Nauki Techniczne

Wydawca

Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences

Data

2015[2015.01.01 AD - 2015.12.31 AD]

Identyfikator

DOI: 10.1515/amm-2015-0029 ; e-ISSN 2300-1909

Źródło

Archives of Metallurgy and Materials; 2015; No 1 March

Referencje

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.