scholarly journals Application of Bayesian Neural Networks to Predict Strength and Grain Size of Hot Strip Low Carbon Steels

Author(s):  
Mohammad Reza ◽  
Mohsen Botlani
2012 ◽  
Vol 77 (7) ◽  
pp. 937-944 ◽  
Author(s):  
Mohsen Botlani-Esfahani ◽  
Reza Toroghinejad

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.


1974 ◽  
Vol 10 (4) ◽  
pp. 458-459
Author(s):  
Yu. A. Shul'te ◽  
G. G. Maksimovich ◽  
F. P. Yanchishin ◽  
V. N. Fedirko

2019 ◽  
Vol 50 (6) ◽  
pp. 2574-2585 ◽  
Author(s):  
Minghao Shi ◽  
Rangasayee Kannan ◽  
Jian Zhang ◽  
Xiaoguang Yuan ◽  
Leijun Li

1999 ◽  
Vol 32 (2) ◽  
pp. 85-89 ◽  
Author(s):  
Bongyoung Ahn ◽  
Seung Seok Lee ◽  
Soon Taik Hong ◽  
Ho Chul Kim ◽  
Suk-Joong L. Kang

2012 ◽  
Vol 715-716 ◽  
pp. 617-622 ◽  
Author(s):  
Wei Shu ◽  
Xue Min Wang ◽  
Cheng Jia Shang ◽  
Xin Lai He

The low carbon steels were smelted with special oxide introduction technique and the HAZ properties has been studied with thermal simulation. The optical microscope, SEM and TEM were used to analyze the composition, size and distribution of the inclusions, and the mechanical properties after thermal simulation were also investigated. The influence of oxide inclusions on the austenite grain size was also studied. The results show that after the smelting the inclusion is complex, in the core is Ti oxides about 1-3 micron and around it is MnS. When the reheat temperature is below 1000, the size of austenite grain is the same for experimental steel and base steel. However, when the reheat temperature is over than 1100, the size of austenite grains in experimental steel is one third of that in base steels. After thermal simulation, with thet8/5increasing the toughness of HAZ decreased. The austnite grain size also increased. The microstructure is composed of intergranular ferrite and intragranular acicular ferrite. Therefore by introducing the fine oxide inclusion to the steel the austenite grain was refined and during the phase transformation the acicular ferrite formed at inclusions at first. These two factors are the main causes to improve the toughness of heat affected zone for steels produced by oxide metallurgy technique.


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