Hardness of Ductile Cast Iron Estimated by Artificial Neural Networks (CROSBI ID 558264)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Žmak, Irena ; Filetin, Tomislav ; Hren, Smiljan
engleski
Hardness of Ductile Cast Iron Estimated by Artificial Neural Networks
The paper gives the results of application of artificial neural networks in determining hardness of ductile cast iron. Data for 147 melts were collected in a Croatian foundry. The error back-propagation algorithm was used to train the multilayer feed-forward network. The optimal size of the hidden neuron layer was selected by analysing error parameters in the testing data set. There were 8 input parameters: liquidus temperature, lowest eutectic temperature, the recalescence, solidus, graphite factor 1, graphite factor 2, cooling rate at solidus, and eutectoid temperature. A statistical analysis of errors in estimating hardness of ductile cast iron was made.
ductile cast iron; hardness; artificial neural networks; modelling
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nije evidentirano
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Podaci o prilogu
1401-1402.
2009.
objavljeno
Podaci o matičnoj publikaciji
Annals of DAAAM for 2009 & Proceedings of the 20th International DAAAM Symposium
Katalinić, Branko
Beč: DAAAM International Vienna
978-3-901509-70-4
1726-9679
Podaci o skupu
20th International DAAAM Symposium "Intelligent Manufacturing and Automation: Focus on Theory, Practice and Education"
poster
25.11.2009-28.11.2009
Beč, Austrija