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

Autori
Lela, Branimir ; Duplančić, Igor ; Prgin, Jere

Izvornik
International Journal of Cast Metals Research (1364-0461) 21 (2008), 5; 357-363

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

POVEZANOST RADA