Journal of the Korean Society for Heat Treatment (열처리공학회지)

The Korean Society for Heat Treatment (한국열처리공학회)
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Developing a Multiclass Classification and Intelligent Matching System for Cold Rolled Steel Wire using Machine Learning

머신러닝을 활용한 냉간압조용 선재의 다중 분류 및 지능형 매칭 시스템 개발

  • K.W. Lee (School of Advanced Materials Engineering, Kookmin University) ;
  • D.K. Lee (School of Advanced Materials Engineering, Kookmin University) ;
  • Y.J. Kwon (School of Advanced Materials Engineering, Kookmin University) ;
  • K.H, Cho (AD Technology Development Laboratory, NHN) ;
  • S.S. Park (Korea Steel Solution R&D Center, POSCO) ;
  • K.S. Cho (School of Advanced Materials Engineering, Kookmin University)
  • 이근원 (국민대학교 신소재공학부) ;
  • 이동건 (국민대학교 신소재공학부) ;
  • 권영준 (국민대학교 신소재공학부) ;
  • 조기훈 (NHN 광고기술개발랩) ;
  • 박성수 (포스코 철강솔루션 연구원 성능연구그룹) ;
  • 조기섭 (국민대학교 신소재공학부)
  • Received : 2023.01.11
  • Accepted : 2023.03.20
  • Published : 2023.03.30
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Abstract

In this study, we present a system for identifying equivalent grades of standardized wire rod steel based on alloy composition using machine learning techniques. The system comprises two models, one based on a supervised multi-class classification algorithm and the other based on unsupervised autoencoder algorithm. Our evaluation showed that the supervised model exhibited superior performance in terms of prediction stability and reliability of prediction results. This system provides a useful tool for non-experts seeking similar grades of steel based on alloy composition.

Keywords

Acknowledgement

이 논문은 2021년도 POSCO의 재원으로 수행된 연구임(2021Z051).

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