Application of Bayesian ANN and RJMCMC to predict the grain size of hot strip low carbon steels

Artificial Neural Network (ANN) and Reversible Jump Markov Chain Monte Carlo (RJMCMC) are used to predict the grain size of hot strip low carbon steels, as a function of steel composition. Results show a good agreement with experimental data taken from Mobarakeh Steel Company (MSC). The developed mo...

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Published in:Journal of the Serbian Chemical Society 2012, Vol.77 (7), p.937-944
Main Authors: Botlani-Esfahani, Mohsen, Toroghinejad, Reza
Format: Article
Language:English
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artificial neural network
grain size
hot strip
low carbon steel
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Toroghinejad, Reza
description Artificial Neural Network (ANN) and Reversible Jump Markov Chain Monte Carlo (RJMCMC) are used to predict the grain size of hot strip low carbon steels, as a function of steel composition. Results show a good agreement with experimental data taken from Mobarakeh Steel Company (MSC). The developed model is capable of recognizing the role and importance of elements in grain refinement. Furthermore, effects of these elements including manganese, silicon and vanadium are investigated in the present study, which are in good agreement with the literature. nema
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subjects artificial neural network
grain size
hot strip
low carbon steel
title Application of Bayesian ANN and RJMCMC to predict the grain size of hot strip low carbon steels
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