Machine Learning Model and Prediction Mechanisms of Bainite Start Temperature of Low Alloy Steels

Junhyub Jeon; Yoonje Sung; Namhyuk Seo; Jae Gil Jung; Seung Bae Son; Seok Jae Lee

doi:10.2320/matertrans.MT-MI2022007

Machine Learning Model and Prediction Mechanisms of Bainite Start Temperature of Low Alloy Steels

Junhyub Jeon
, Yoonje Sung
, Namhyuk Seo
, Jae Gil Jung
, Seung Bae Son
, Seok Jae Lee^*

^*Corresponding author for this work

Metallurgical System Engineering

Jeonbuk National University

Research output: Contribution to journal › Journal article › peer-review

3 Scopus citations

Abstract

The random forest regression (RFR) model was proposed to predict the bainite start temperature (B_s) 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.

Original language	English
Pages (from-to)	2214-2218
Number of pages	5
Journal	Materials Transactions
Volume	64
Issue number	9
DOIs	https://doi.org/10.2320/matertrans.MT-MI2022007
State	Published - 2023

Keywords

bainite start temperature
explainable artificial intelligence
low alloy steels
machine learning
prediction mechanism

Quacquarelli Symonds(QS) Subject Topics

Materials Science
Engineering - Mechanical
Physics & Astronomy

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10.2320/matertrans.MT-MI2022007

Cite this

@article{0fdef01c3dc445cf8125db9cbe96834c,

title = "Machine Learning Model and Prediction Mechanisms of Bainite Start Temperature of Low Alloy Steels",

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.",

keywords = "bainite start temperature, explainable artificial intelligence, low alloy steels, machine learning, prediction mechanism",

author = "Junhyub Jeon and Yoonje Sung and Namhyuk Seo and Jung, \{Jae Gil\} and Son, \{Seung Bae\} and Lee, \{Seok Jae\}",

note = "Publisher Copyright: {\textcopyright}2023 The Japan Institute of Metals and Materials.",

year = "2023",

doi = "10.2320/matertrans.MT-MI2022007",

language = "English",

volume = "64",

pages = "2214--2218",

journal = "Materials Transactions",

issn = "1345-9678",

number = "9",

}

TY - JOUR

T1 - Machine Learning Model and Prediction Mechanisms of Bainite Start Temperature of Low Alloy Steels

AU - Jeon, Junhyub

AU - Sung, Yoonje

AU - Seo, Namhyuk

AU - Jung, Jae Gil

AU - Son, Seung Bae

AU - Lee, Seok Jae

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - bainite start temperature

KW - explainable artificial intelligence

KW - low alloy steels

KW - machine learning

KW - prediction mechanism

UR - https://www.scopus.com/pages/publications/85172676468

U2 - 10.2320/matertrans.MT-MI2022007

DO - 10.2320/matertrans.MT-MI2022007

M3 - Journal article

AN - SCOPUS:85172676468

SN - 1345-9678

VL - 64

SP - 2214

EP - 2218

JO - Materials Transactions

JF - Materials Transactions

IS - 9

ER -

Machine Learning Model and Prediction Mechanisms of Bainite Start Temperature of Low Alloy Steels

Abstract

Keywords

Quacquarelli Symonds(QS) Subject Topics

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Findings from Jeonbuk National University Provide New Insights into Machine Learning (Machine Learning Model and Prediction Mechanisms of Bainite Start Temperature of Low Alloy Steels)

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Machine Learning Model and Prediction Mechanisms of Bainite Start Temperature of Low Alloy Steels

Abstract

Keywords

Quacquarelli Symonds(QS) Subject Topics

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Findings from Jeonbuk National University Provide New Insights into Machine Learning (Machine Learning Model and Prediction Mechanisms of Bainite Start Temperature of Low Alloy Steels)

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