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이근원,원윤정,송영범,조기섭,Lee, K.W.,Won, Y.J.,Song, Y.B.,Cho, K.S. 한국열처리공학회 2021 熱處理工學會誌 Vol.34 No.5
To predict mechanical properties of secondary hardening martensitic steels, a machine learning ensemble model was established. Based on ANN(Artificial Neural Network) architecture, some kinds of methods was considered to optimize the model. In particular, interaction features, which can reflect interactions between chemical compositions and processing conditions of real alloy system, was considered by means of feature engineering, and then K-Fold cross validation coupled with bagging ensemble were investigated to reduce R2_score and a factor indicating average learning errors owing to biased experimental database.
극초고강도 이차경화형 마르텐사이트강의 기계적성질에 미치는 오스포밍 공정의 영향
김수빈 ( S. B. Kim ),원윤정 ( Y. J. Won ),송영범 ( Y. B. Song ),조기섭 ( K. S. Cho ) 한국열처리공학회 2021 熱處理工學會誌 Vol.34 No.4
Two types of secondary hardening martensitic steels, 10Co-14Ni and 6Co-5Ni, were produced by vacuum induction melting to investigate the effect of ausforming process on mechanical properties. According to the results of present study, the alloy samples ausformed at low temperature indicated a rather low hardness level in overall aging time despite the refinement of martensite lath width. As the result can closely be related with the presence of primary carbides precipitated within the initial austenite matrix, we confirmed that, in ultrahigh strength secondary hardening martensitic alloy steels, the ausforming process can rather limit the degree of secondary hardening during the subsequent aging treatment. (Received July 8, 2021; Revised July 15, 2021; Accepted July 21, 2021)