Artificial neural network modeling of flow stress in hot rolling
Data(s) |
01/01/2014
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Resumo |
In this study, an artificial neural network model is proposed to predict the flow stress variations during the hot rolling process. Optimization of the proposed neural network with respect to number of neurons within the hidden layer, different training methods and transfer functions of the neural network is performed. The results of the optimal network were compared with those of the conventional analytic method and it is shown that using an optimal neural network the mean calculated error is drastically reduced. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Iron and Steel Institute of Japan |
Relação |
http://dro.deakin.edu.au/eserv/DU:30071850/abdi-artificialneural-2014.pdf http://www.dx.doi.org/10.2355/isijinternational.54.872 |
Direitos |
2014, Iron and Steel Institute of Japan |
Palavras-Chave | #Artificial neural network #Flow stress #Hot rolling #Modeling #Optimization #Science & Technology #Technology #Metallurgy & Metallurgical Engineering #PREDICTION |
Tipo |
Journal Article |