Journal of the Korean Society for Heat Treatment (열처리공학회지)

The Korean Society for Heat Treatment (한국열처리공학회)
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Comparative Study of Aus-Tempering Hardness Prediction by Process Using Machine Learning

기계학습을 활용한 공정 변수별 오스템퍼링 경도 예측 비교 연구

  • K. Kim (DX Technology Team, Pohang Institute for Materials Industry Advancement) ;
  • J-. G. Park (DX Technology Team, Pohang Institute for Materials Industry Advancement) ;
  • U. R. Heo (DX Technology Team, Pohang Institute for Materials Industry Advancement) ;
  • H. W. Yang (DX Technology Team, Pohang Institute for Materials Industry Advancement)
  • 김경훈 ((재)포항금속산업진흥원 DX기술팀) ;
  • 박종구 ((재)포항금속산업진흥원 DX기술팀) ;
  • 허우로 ((재)포항금속산업진흥원 DX기술팀) ;
  • 양해웅 ((재)포항금속산업진흥원 DX기술팀)
  • Received : 2023.11.06
  • Accepted : 2023.11.23
  • Published : 2023.11.30
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Abstract

Aus-tempering heat treatment is suitable for thin and small-sized in precision parts. However, the heat treatment process relies on the experience and skill of the operator, making it challenging to produce precision parts due to the cold forging process. The aims of this study is to explore suitable machine learning models using data from the aus-tempering heat treatment process and analyze the factors that significantly impact the mechanic properties (e.g. hardness). As a result, the study analyzed, from a machine learning perspective, how hardness prediction varies based on the quenching temperature, carbon (C), and copper (Cu) contents.

Keywords

Acknowledgement

본 연구는 경상북도의 재원으로 경북동해안철강벨트경쟁력강화사업의 지원을 받아 수행된 연구임.

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