application of bayesian ann and rjmcmc to predict the grain size of hot strip low carbon steels
Clicks: 319
ID: 188063
2012
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
319 views
81 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #18 of 151 articles by views in meditsinskaia radiologiia
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 151 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
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.
| Reference Key |
mohsen2012journalapplication
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Botlani-Esfahani Mohsen;Toroghinejad Reza Mohammad |
| Journal | meditsinskaia radiologiia |
| Year | 2012 |
| DOI |
10.2298/JSC111115011B
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.