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Korean J. Met. Mater.
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Korean Journal of Metals and Materials
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설명 가능한 인공지능으로 해석한 Al-Sc-X 합금의 경도 예측 기계학습 모델과 역설계
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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.....
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Korean J. Met. Mater.
2023;61(9):652-658. Published online 2023 Aug 30
DOI:
https://doi.org/10.3365/KJMM.2023.61.9.652
Abstract
A cold roll-bonding (CRB) process is applied to study the effects of stacking number on the microstructure and mechanical properties of roll-bonded and age-treated Al sheets. Commercial AA1050 and AA6061 sheets with a thickness of 2 mm were stacked alternately on each other to two and four layers, and roll-bonded.....
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기계학습에 의한 Al-Si 주조 합금 미세조직 이미지 생성
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황인규, 이현지, 정상준, 조인성, 김희수
Korean J. Met. Mater.
2021;59(11):838-847. Published online 2021 Oct 28
DOI:
https://doi.org/10.3365/KJMM.2021.59.11.838
Abstract
In this study, we constructed a deep convolutional generative adversarial network (DCGAN) to generate the microstructural images that imitate the real microstructures of binary Al-Si cast alloys. We prepared four combinations of alloys, Al-6wt%Si, Al-9wt%Si, Al-12wt%Si and Al-15wt%Si for machine learning. DCGAN is composed of a generator and a discriminator......
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Web of Science 3
Crossref 3
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