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izvor podataka: crosbi

Possibility of grain size prediction in AA5754 aluminium ingots using neural networks (CROSBI ID 145132)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Lela, Branimir ; Duplančić, Igor ; Prgin, Jere Possibility of grain size prediction in AA5754 aluminium ingots using neural networks // International journal of cast metals research, 21 (2008), 5; 357-363. doi: 10.1179/136404608X334053

Podaci o odgovornosti

Lela, Branimir ; Duplančić, Igor ; Prgin, Jere

engleski

Possibility of grain size prediction in AA5754 aluminium ingots using neural networks

The approach to the grain size prediction in AA5754 Al alloy ingots based on artificial neural networks (ANN) has been used in the present study. The ANN has been trained on data that was measured in the real industrial conditions during the process of direct chill Al ingots casting. A very complex relation between the numerous casting parameters and the microstructure of the ingots justifies the application of neural networks, which are known for mapping complex and nonlinear systems. A feed forward ANN model with the resilient back-propagation learning algorithm and weight decay regularisation has been developed to relate the grain size to casting rate, meniscus level, casting temperature, water flow for the metal mould cooling and speed of wire for master alloy addition. The results obtained from the ANN are found to be consistent with the theoretical researches and experience from the foundry.

Grain size prediction; Direct chill casting; Computer simulation; Neural networks

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Podaci o izdanju

21 (5)

2008.

357-363

objavljeno

1364-0461

10.1179/136404608X334053

Povezanost rada

Strojarstvo

Poveznice
Indeksiranost