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Preprocessing of Inconsistent Creep Data Collected from a Literature Survey to Provide Reliable and Consistent Creep Life Prediction
Taejoo Lee, Chang-Seok Oh, Yoon Suk Choi
Korean J. Met. Mater. 2025;63(3):231-241.   Published online 2025 Mar 5
DOI: https://doi.org/10.3365/KJMM.2025.63.3.231

Abstract
Numerous studies on machine learning-based creep life prediction using the creep data collected from various existing experimental data have been reported. Since the prediction of creep life is heavily influenced by the quality and integrity of the collected creep data, data preprocessing is required to eliminate physically inconsistent creep datapoints,..... More

                
Predicting the Hardness of Al-Sc-X Alloys with Machine Learning Models, Explainable Artificial Intelligence Analysis and Inverse Design
설명 가능한 인공지능으로 해석한 Al-Sc-X 합금의 경도 예측 기계학습 모델과 역설계
Jiwon Park, Su-Hyeon Kim, Jisu Kim, Byung-joo Kim, Hyun-seok Cheon, Chang-Seok Oh
박지원, 김수현, 김지수, 김병주, 천현석, 오창석
Korean J. Met. Mater. 2023;61(11):874-882.   Published online 2023 Oct 29
DOI: https://doi.org/10.3365/KJMM.2023.61.11.874

Abstract
In this study, the Vickers hardness of precipitation-strengthened Al-Sc-X (X = Zr, Si, and Fe) alloys were predicted using machine learning models, depending on the alloys’ compositions, solid-solution treatment and aging conditions. The data used for machine learning were collected from the literature. Among the models, tree-based ensemble models such..... More

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