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

Journal of the Serbian Chemical Society

关键词: artificial neural network;    grain size;    hot strip;    low carbon steel;   

DOI  :  10.2298/JSC111115011B

来源: DOAJ

【 摘 要 】

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 withexperimental data taken from Mobarakeh Steel Company (MSC). The developedmodel is capable of recognizing the role and importance of elements in grainrefinement. Furthermore, effects of these elements including manganese,silicon and vanadium are investigated in the present study, which are in goodagreement with the literature.

【 授权许可】

Unknown