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Exploration of Machine Learning to Predict Hot Ductility of Cast Steel from Chemical Composition and Thermal Conditions

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Abstract

This study explores the use of machine learning (ML) as a data-driven approach to estimate hot ductility of cast steel from literature data. Four ML algorithms were used to predict hot ductility by considering elemental composition and thermal conditions. Experimentally-measured reduction of area (RA) values were converted to a low-temperature limit, center-temperature, and high-temperature limit, which were represented as Gaussian curves. The prediction accuracy of the four ML models was evaluated using RMSE for these three output variables. In a case study of three steels that had different contents of alloying elements, only the Neural-net model predicted the RA trough more accurately in all cases. These results demonstrate the utility of ML models to predict hot ductility of cast steels.

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Authors and Affiliations

  1. Graduate Institute of Ferrous Technology, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea

    Daegeun Hong & Changhee Yim

  2. Steel Making Research Group, POSCO Research Institute, Pohang, 37859, Republic of Korea

    Sanghum Kwon

Authors
  1. Daegeun Hong
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  2. Sanghum Kwon
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  3. Changhee Yim
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Correspondence to Changhee Yim.

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Hong, D., Kwon, S. & Yim, C. Exploration of Machine Learning to Predict Hot Ductility of Cast Steel from Chemical Composition and Thermal Conditions. Met. Mater. Int. 27, 298–305 (2021). https://doi.org/10.1007/s12540-020-00713-w

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