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Korean J. Met. Mater.
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Korean Journal of Metals and Materials
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기계 학습 및 실험적 검증을 활용한 La 함량 별 알루미늄 합금 설계
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김경훈, 박종구, 양해웅, 허우로, 강남현
Korean J. Met. Mater.
2024;62(7):524-532. Published online 2024 Jun 27
DOI:
https://doi.org/10.3365/KJMM.2024.62.7.524
Abstract
The development and design of metal materials have been carried out through experimental method and simulation based on theoretic. Recently, with the widespread application of artificial intelligence (AI) in various fields, many studies have been actively incorporating artificial intelligence into the field of metal material design. Especially, many studies have.....
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알루미늄 5052-H32 합금과 6014-T4 합금의 저항 점 용접 시 용접성 및 미세조직 비교
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전현욱, 김재훈, 김원호, 천주용, 배기만, 이은경, 박영도, 지창욱
Korean J. Met. Mater.
2024;62(1):1-11. Published online 2023 Dec 29
DOI:
https://doi.org/10.3365/KJMM.2024.62.1.1
Abstract
This study was performed to compare the resistance spot weldability of Al 5052-H32 alloy and Al 6014-T4 alloy, and the cause of the differences in the weldability was investigated. In general, the surface of the aluminum alloy has an oxide film several nm thick, and local heat input is generated.....
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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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Web of Science 1
Crossref 1
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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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Web of Science 5
Crossref 5
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고압비틀림 공정 처리된 Al7068 합금의 기계적 특성 및 미세조직 변화
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오주희, 손수정, 김형섭, 설재복, 성효경, 김정기
Korean J. Met. Mater.
2023;61(1):1-8. Published online 2022 Dec 28
DOI:
https://doi.org/10.3365/KJMM.2023.61.1.1
Abstract
The combination of severe plastic deformation, deformational heat, and frictional heat generated during the high-pressure torsion process has a great effect on microstructural evolutions, including grain refinement, dynamic recrystallization and recovery. In particular, the low melting temperature metallic alloys (e.g., aluminum and magnesium) also induce nano-precipitations in the matrix during.....
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Web of Science 4
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Al 5052-H32 합금의 저항 점 용접 시 가압력 제어 방식에 따른 미세조직 및 용접특성 비교
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김원호, 김재훈, 전현욱, 김재원, 이은경, 지창욱
Korean J. Met. Mater.
2022;60(5):350-359. Published online 2022 Apr 22
DOI:
https://doi.org/10.3365/KJMM.2022.60.5.350
Abstract
Recently, lightweight vehicle bodies are in increasing demand to satisfy exhaust gas and environmental regulations around the world. In particular, aluminum alloys are widely used to manufacture lightweight parts, because of their excellent properties including corrosion resistance and mechanical properties. After the forming process, the welding process is important for.....
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Web of Science 1
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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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알루미늄 합금의 표면처리에 있어서 2단계 양극산화의 젖음성 영향
[corrected-article]
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어재동, 김진규, 정용석, 이종항, 김욱배
Korean J. Met. Mater.
;59(2):73-80. Published online 2021 Jan 27
DOI:
https://doi.org/10.3365/KJMM.2021.59.2.73
Abstract
Industrial anodizing of aluminum alloys is widely employed for various products, to improve corrosion and contamination protection as well as aesthetic appearance. At the same time, nanostructure fabrication using highly ordered porous aluminum oxides has been increasingly investigated in academic research for diverse micro-/nano applications. This approach is based on.....
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Web of Science 6
Crossref 7
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Pore-Widening 시간에 따른 단계적 양극산화방법을 이용한 초친수 알루미늄 6061 합금 개발
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박영주, 지혜정, 정찬영
Korean J. Met. Mater.
2020;58(2):97-102. Published online 2020 Jan 8
DOI:
https://doi.org/10.3365/KJMM.2020.58.2.97
Abstract
This study created alumina structures with the highest hydrophilic properties on 6061 aluminum alloy. The anodization process was applied to make various aluminum oxide structures. To create uniform alumina structures on top of a 6061 aluminum alloy surface, after conducting the first anodization in 0.3 M oxalic acid at 40.....
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Web of Science 4
Crossref 1
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항공용 7xxx계 알루미늄 합금의 고온 노출 온도가 인장 및 샤르피 충격 특성에 미치는 영향
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안우진, 김도희, 김봉환, 김세광, 김상식, 성효경
Korean J. Met. Mater.
2019;57(4):214-226. Published online 2019 Apr 5
DOI:
https://doi.org/10.3365/KJMM.2019.57.4.214
Abstract
Aluminum alloys have been widely used for aerospace applications due to their high strength and lightweight. New demand is growing for aluminum alloys with high temperature mechanical properties that can sustain plastic deformation at high temperature. In this study, correlations among microstructure, tensile properties and Charpy impact properties were analyzed.....
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Web of Science 4
Crossref 4
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화상인식을 이용한 Al-Si 주조용 합금의 화학조성 예측
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정상준, 황인규, 조인성, 김희수
Korean J. Met. Mater.
2019;57(3):184-192. Published online 2019 Feb 14
DOI:
https://doi.org/10.3365/KJMM.2019.57.3.184
Abstract
In this study, we analyzed the chemical composition of Al-Si cast alloys from microstructural images, using image recognition and machine learning. Binary Al-Si alloys of Si = 1~10 wt% were cast and prepared as reference images in the dataset used for machine learning. The machine learning procedure was constructed with.....
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Web of Science 9
Crossref 8
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항공용 고강도 7xxx계 알루미늄 합금의 3.5% NaCl 수용액에서의 응력부식균열 저항성
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이유림, 최희수, 안수진, 이철주, 김상식
Korean J. Met. Mater.
2018;56(10):708-717. Published online 2018 Oct 5
DOI:
https://doi.org/10.3365/KJMM.2018.56.10.708
Abstract
For proper maintenance and inspection scheduling of aircraft, it is extremely important to measure the resistance to stress corrosion cracking (SCC) behavior of aluminum alloys used for aircraft structures. The SCC resistances of high-strength 7xxx-series aluminum alloys, including 7050-T7451, 7475-T7351 and 7140-T7451, were evaluated along longitudinal (L) and transverse (T).....
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