Using neural networks to predict parameters in the hot working of aluminum alloys

Using neural networks to predict parameters in the hot working of aluminum alloys

The ability of an artificial neural network model, using a back-propagation learning algorithm, to predict the flow stress, roll force and roll torque obtained during the hot compression and rolling of aluminum alloys, is studied. It is shown that well-trained neural network models provide fast, acc...

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Veröffentlicht in:Journal of materials processing technology 1999-02, Vol.86 (1-3), p.245-251
Hauptverfasser: Chun, M.S., Biglou, J., Lenard, J.G., Kim, J.G.
Format: Artikel
Sprache:eng
Schlagworte:
Aluminum
Back-propagation
Hot working
Neural network
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Zusammenfassung:The ability of an artificial neural network model, using a back-propagation learning algorithm, to predict the flow stress, roll force and roll torque obtained during the hot compression and rolling of aluminum alloys, is studied. It is shown that well-trained neural network models provide fast, accurate and consistent results, making them superior to other predictive techniques.
ISSN:0924-0136
DOI:10.1016/S0924-0136(98)00318-5