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A Model to Predict Recrystallization Kinetics in Hot Strip Rolling Using Combined Artificial Neural Network and Finite Elements

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Abstract

A thermo-mechanical model has been developed to establish a coupled heat conduction and plastic flow analysis in hot-rolling process. This model is capable of predicting temperature, strain, and strain rate distributions during hot rolling as well as the subsequent static recrystallization fraction and grain size changes after hot deformation. Finite element and neural network models are coupled to assess recrystallization kinetics after hot rolling. A new algorithm has been suggested to create differential data sets to train the neural network. The model is then used to predict histories of various deformation variables and recrystallization kinetics in hot rolling of AA5083. Comparison between the theoretical and the experimental data shows the validity of the model.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Sharif University of Technology, Azadi Ave., P.O. Box 11365-9466, Tehran, Iran

    M. Seyed Salehi & S. Serajzadeh

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  1. M. Seyed Salehi
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  2. S. Serajzadeh
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Correspondence to S. Serajzadeh.

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Seyed Salehi, M., Serajzadeh, S. A Model to Predict Recrystallization Kinetics in Hot Strip Rolling Using Combined Artificial Neural Network and Finite Elements. J. of Materi Eng and Perform 18, 1209–1217 (2009). https://doi.org/10.1007/s11665-009-9359-0

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  • DOI: https://doi.org/10.1007/s11665-009-9359-0

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