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A Study on the Prediction of Characteristics of Molding Sand Using Machine Learning and Data Preprocessing Techniques
기계학습과 데이터 전처리 기법을 활용한 주물사 특성 예측에 관한 연구
Jeong-Min Lee, Moon-Jo Kim, Kyeong-Hwan Choe, DongEung Kim
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..... More

                
A Comparative Study of the Accuracy of Machine Learning Models for Predicting Tempered Martensite Hardness According to Model Complexity
기계학습 모델 복잡도에 따른 템퍼드 마르텐사이트 경도 예측 정확도 비교 연구
Junhyub Jeon, DongEung Kim, Jun-Ho Hong, Hwi-Jun Kim, Seok-Jae Lee
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..... More

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