Design of comprehensive mechanical properties by machine learning and high-throughput optimization algorithm in RAFM steels

Chenchong Wang; Chunguang Shen; Xiaojie Huo; Chi Zhang; Wei Xu

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자료유형: 학술저널

저자정보: Chenchong Wang Chunguang Shen Xiaojie Huo Chi Zhang Wei Xu

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제52권 제5호

발행연도: 2020.1

수록면: 1,008 - 1,012 (5page)

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In order to make reasonable design for the improvement of comprehensive mechanical properties ofRAFM steels, the design system with both machine learning and high-throughput optimization algorithmwas established. As the basis of the design system, a dataset of RAFM steels was compiled from previousliteratures. Then, feature engineering guided random forests regressors were trained by the dataset andNSGA II algorithm were used for the selection of the optimal solutions from the large-scale solution setwith nine composition features and two treatment processing features. The selected optimal solutions bythis design system showed prospective mechanical properties, which was also consistent with thephysical metallurgy theory. This efficiency design mode could give the enlightenment for the design ofother metal structural materials with the requirement of multi-properties

참고문헌 (39)

참고문헌 신청

G. B. Olson / 2013 / Genomic materials design : the ferrous frontier / Acta Mater 61:771~781

C. Wang / 2015 / Multi-scale simulation of hydrogen influenced critical stress intensity in high CoeNi secondary hardening steel / Mater. Des 87:501~506

C. Wang / 2016 / Microstructure analysis and yield strength simulation in high CoeNi secondary hardening steel / Mater. Sci. Eng. A 669:312~317

Chenchong Wan / 2017 / Multiscale Simulation of Yield Strength in Reduced-Activation Ferritic/Martensitic Steel / Nuclear Engineering and Technology 49(3):569~575

J. S. Wang / 2013 / Prediction of the yield strength of a secondary-hardening steel / Acta Mater 61:4939~4952

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