Design of high strength medium-Mn steel using machine learning

Jin Young Lee, Minjeong Kim, Young Kook Lee

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The objective of present study was to develop a medium-Mn steel with superior tensile properties using machine learning. For this purpose, 1075 datasets on tensile properties of medium-Mn steels were collected from the literature. Based on the datasets, boosted decision tree regression (BD) models were constructed to predict ultimate tensile strength (UTS) and total elongation (TE) of medium-Mn steels. The BD models showed low mean absolute errors of ∼65 MPa for UTS prediction and ∼4.9% for TE prediction. The trained BD models predicted that Fe-5.5Mn-0.2C-0.3Si (wt%) steel would have high UTS of 1957 MPa and TE of 10.7%, when austenitized at 780 °C for 4 min and air-cooled. The predicted UTS and TE matched well with experimentally measured values of UTS of 1952 MPa and TE of 9.9%, indicating the outstanding predictability of the BD models. In addition, the measured UTS (1952 MPa) was ∼100 MPa higher, without a great loss of TE, than the highest UTS (1863 MPa) of Fe-(3–12)Mn-(<0.3)C-(<0.5)Si-(<1)Al (wt%) steels reported to date.

Original languageEnglish
Article number143148
JournalMaterials Science and Engineering A
Publication statusPublished - 2022 May 23

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education ( 2019R1A2C2004452 ), a Korea Institute for Advancement of Technology grant, funded by the Korea Government (MOITE) (P0002019), as part of the Competency Development Program for Industry Specialists, and the Technology Innovation Program (Alchemist Project, 20012196, Al-based supercritical materials discovery) funded by the Ministry of Trade, Industry & Energy, Korea.

Publisher Copyright:
© 2022 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering


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