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- Title
Machine Learning Model and Prediction Mechanisms of Bainite Start Temperature of Low Alloy Steels.
- Authors
Junhyub Jeon; Yoonje Sung; Namhyuk Seo; Jae-Gil Jung; Seung Bae Son; Seok-Jae Lee
- Abstract
The random forest regression (RFR) model was proposed to predict the bainite start temperature (Bs) using alloying elements, such as C, Mn, Si, Ni, Cr, and Mo, as well as the prior austenite average grain size (AGS). RFR demonstrated a performance improvement of approximately 1.2% over the empirical equation. Cr, C, Mo, Mn, Si, AGS, and Ni were assigned importance, in that order, in the RFR using Shapley additive explanation (SHAP) analysis. The detailed prediction mechanisms of the RFR are discussed using the SHAP dependence plot.
- Subjects
MACHINE learning; LOW alloy steel; LOW temperatures; BAINITE; PREDICTION models
- Publication
Materials Transactions, 2023, Vol 64, Issue 9, p2214
- ISSN
1345-9678
- Publication type
Article
- DOI
10.2320/matertrans.MT-MI2022007