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The Use of ANN to Predict the Hot Deformation Behavior of AA7075 at Low Strain Rates

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Abstract

In this study, artificial neural network (ANN) was used to model the hot deformation behavior of 7075 aluminum alloy during compression test, in the strain rate range of 0.0003-1 s−1 and temperature range of 200-450 °C. The inputs of the model were temperature, strain rate, and strain, while the output of the model was the flow stress. The feed-forward back-propagation network with two hidden layers was built and successfully trained at different deformation domains by Levenberg-Marquardt training algorithm. Comparative analysis of the results obtained from the hyperbolic sine, the power law constitutive equations, and the ANN shows that the newly developed ANN model has a better performance in predicting the hot deformation behavior of 7075 aluminum alloy.

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Acknowledgments

The authors would like to thank the research board and the Department of Materials Science and Engineering of Sharif University of Technology, Tehran, Iran, for the provision of research facilities used in this study.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Sharif University of Technology, P.O. Box 11155-9466, Tehran, Iran

    A. Jenab & A. Karimi Taheri

  2. Educational Chair, Society of Reliability Engineering, Ottawa, Canada

    K. Jenab

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  1. A. Jenab
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  2. A. Karimi Taheri
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  3. K. Jenab
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Correspondence to A. Jenab.

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Jenab, A., Karimi Taheri, A. & Jenab, K. The Use of ANN to Predict the Hot Deformation Behavior of AA7075 at Low Strain Rates. J. of Materi Eng and Perform 22, 903–910 (2013). https://doi.org/10.1007/s11665-012-0332-y

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  • DOI: https://doi.org/10.1007/s11665-012-0332-y

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