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ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels

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Abstract

In the present study, an artificial neural networks-based model was developed to predict the ferrite fraction of microalloyed steels during continuous cooling. Fourteen parameters affecting the ferrite fraction were considered as inputs, including the cooling rate, initial austenite grain size, and different chemical compositions. The network was then trained to predict the ferrite fraction amounts as outputs. A multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the ferrite fraction.

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Authors and Affiliations

  1. Department of Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran

    Gholamreza Khalaj, Mahdi Khoeini & Meysam Khakian-Qomi

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  1. Gholamreza Khalaj
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  2. Mahdi Khoeini
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  3. Meysam Khakian-Qomi
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Correspondence to Gholamreza Khalaj.

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Khalaj, G., Khoeini, M. & Khakian-Qomi, M. ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels. Neural Comput & Applic 23, 769–777 (2013). https://doi.org/10.1007/s00521-012-0992-4

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