Exploration of Machine Learning to Predict Hot Ductility of Cast Steel from Chemical Composition and Thermal Conditions
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- Title
- Exploration of Machine Learning to Predict Hot Ductility of Cast Steel from Chemical Composition and Thermal Conditions
- Authors
- Hong, Daegeun; Kwon, Sanghum; YIM, CHANG HEE
- Date Issued
- 2021-02
- Publisher
- KOREAN INST METALS MATERIALS
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/105598
- DOI
- 10.1007/s12540-020-00713-w
- ISSN
- 1598-9623
- Article Type
- Article
- Citation
- METALS AND MATERIALS INTERNATIONAL, vol. 27, no. 2, page. 298 - 305, 2021-02
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