Predicting thermal conductivity of steels using artificial neural networks

Žmak, Irena and Filetin, Tomislav (2010) Predicting thermal conductivity of steels using artificial neural networks. = Predicting thermal conductivity of steels using artificial neural networks. Transactions of FAMENA, 34 (3). pp. 11-20. ISSN 1333-1124. Vrsta rada: ["eprint_fieldopt_article_type_article" not defined]. Kvartili JCR: Q4 (2010). Točan broj autora: 2.

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The data on the physical properties of steels which depend on temperature are needed for the calculation and simulation of heating and cooling processes. A method for predicting thermal conductivity of steels at elevated temperatures (up to 700 °C) from the known steel chemical composition has been developed, and the results obtained by the simulation are given. A static multi-layer feed-forward artificial neural network with the back propagation training function and Levenberg-Marquardt optimization was used to predict the coefficient of thermal conductivity of steels. In order to prevent the over fitting the early stopping method was applied. The following groups of steel were included in the model: structural steels, hotwork tool steels, high-speed steels, stainless steels, heat resistant steels austenitic steels for elevated temperatures, and cobalt alloyed steels and alloys for elevated temperatures. The mean absolute error in predicting thermal conductivity and the standard deviation were found to be very acceptable.

Item Type: Article (["eprint_fieldopt_article_type_article" not defined])
Keywords (Croatian): Artificial neural networks; Property prediction; Steels; Thermal conductivity
Subjects: TECHNICAL SCIENCE > Mechanical Engineering
Divisions: 1000 Department of Materials > 1010 Chair of Materials and Tribology
Indexed in Web of Science: Yes
Indexed in Current Contents: No
Citations JCR: 0 (3.7.2015.)
Quartiles: Q4 (2010)
Citations SCOPUS: 0 (3.7.2015.)
Date Deposited: 17 Apr 2015 08:40
Last Modified: 27 Aug 2015 12:44

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