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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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기계학습을 이용한 색상형 미세조직의 결정립 크기 측정
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정준호, 김희수
Korean J. Met. Mater.
2023;61(5):379-387. Published online 2023 Apr 20
DOI:
https://doi.org/10.3365/KJMM.2023.61.5.379
Abstract
We constructed a convolutional neural network to estimate average grain size from microstructure images. In the previous study from our research group, the network was trained using GB-type images in which the grain matrix and grain boundary were represented in white and black, respectively. The model well estimated the same.....
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기계학습과 데이터 전처리 기법을 활용한 주물사 특성 예측에 관한 연구
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이정민, 김문조, 최경환, 김동응
Korean J. Met. Mater.
2023;61(1):18-27. Published online 2022 Dec 28
DOI:
https://doi.org/10.3365/KJMM.2023.61.1.18
Abstract
The main components of molding sand used in sand casting are sand, clay and water. The composition of the molding sand has a great influence on the properties of the casting. In order to obtain high-quality castings, it is important to manage the components that affect the properties of the.....
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인공지능을 기반으로 한 대면적 CNT 기반 촉각 센서의 실시간 위치 탐색 연구
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조민영, 김성훈, 김지식
Korean J. Met. Mater.
2022;60(10):793-799. Published online 2022 Sep 29
DOI:
https://doi.org/10.3365/KJMM.2022.60.10.793
Abstract
For medical device and artificial skin applications, etc., large-area tactile sensors have attracted strong interest as a key technology. However, only complex and expensive manufacturing methods such as fine pattern alignment technology have been considered. To replace the existing smart sensor, which has to go through a complicated process, a.....
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Web of Science 1
Crossref 1
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기계학습 모델 복잡도에 따른 템퍼드 마르텐사이트 경도 예측 정확도 비교 연구
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전준협, 김동응, 홍준호, 김휘준, 이석재
Korean J. Met. Mater.
2022;60(9):713-721. Published online 2022 Aug 30
DOI:
https://doi.org/10.3365/KJMM.2022.60.9.713
Abstract
We investigated various numerical methods including a physical-based empirical equation, linear regression, shallow neural network, and deep learning approaches, to compare their accuracy for predicting the hardness of tempered martensite in low alloy steels. The physical-based empirical equation, which had been previously proposed with experimental data, was labelled and used.....
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Web of Science 3
Crossref 3
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머신러닝을 이용한 화합물 조성기반 초경질 소재 특성 예측
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남충희
Korean J. Met. Mater.
2022;60(8):619-627. Published online 2022 Jul 12
DOI:
https://doi.org/10.3365/KJMM.2022.60.8.619
Abstract
In this study, the mechanical properties of materials were predicted using machine learning to search for superhard materials. Based on an AFOW database consisting of DFT quantum calculation values, the mechanical properties of materials were predicted using various machine learning models. For supervised learning, the entire data was divided into.....
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Web of Science 3
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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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주철 미세조직 분석을 위한 합성곱 신경망에서의 중간층 시각화
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이현지, 황인규, 정상준, 조인성, 김희수
Korean J. Met. Mater.
2021;59(6):430-438. Published online 2021 May 26
DOI:
https://doi.org/10.3365/KJMM.2021.59.6.430
Abstract
We attempted to classify the microstructural images of spheroidal graphite cast iron and grey cast iron using a convolutional neural network (CNN) model. The CNN comprised four combinations of convolution and pooling layers followed by two fully-connected layers. Numerous microscopic images of each cast iron were prepared to train and.....
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Web of Science 5
Crossref 4
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기계 학습을 활용한 마그네슘 합금의 통전 비선형 온도 예측
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유진영, 이명재, 문영훈, 노유정, 이태경
Korean J. Met. Mater.
2020;58(6):413-422. Published online 2020 May 19
DOI:
https://doi.org/10.3365/KJMM.2020.58.6.413
Abstract
Electropulse-induced heating has attracted attention due to its high energy efficiency. However, the process gives rise to a nonlinear temperature variation, which is difficult to predict using a traditional physics model. As an alternative, this study employed machine-learning technology to predict such temperature variation for the first time. Mg alloy.....
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Web of Science 10
Crossref 8
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전기화학촉매의 제일원리 계산 및 하이스루풋 스크리닝 연구 동향: 리뷰
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이창수, 방기훈, 홍두선, 이혁모
Korean J. Met. Mater.
2019;57(1):1-9. Published online 2018 Dec 14
DOI:
https://doi.org/10.3365/KJMM.2019.57.1.1
Abstract
There are many ongoing efforts to develop sustainable, clean, efficient, and economical pathways to produce renewable energy sources to satisfy worldwide energy demands. Electrochemical conversion processes, such as water splitting, CO2 conversion and N2 electroreduction, have been considered as successful approaches to solve these energy issues. Over the past decade,.....
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Web of Science 5
Crossref 7
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