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Prediction of alloy composition and microhardness by random forest in maraging stainless steels based on a cluster formula

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Abstract

Fe–Ni–Cr-based super-high-strength maraging stainless steels were generally realized by multiple-element alloying under a given heat treatment processing. A series of alloy compositions were designed with a uniform cluster formula of [Ni16Fe192](Cr32(Ni16–x–y–z–m–nMoxTiyNbzAlmVn)) (at.%) that was developed out of a unique alloy design tool, a cluster-plus-glue-atom model. Alloy rods with a diameter of 6 mm were prepared by copper-mold suction-cast processing under the argon atmosphere. These alloy samples were solid-solutioned at 1273 K for 1 h, followed by water-quenching, and then aged at 783 K for 3 h. The effect of the valence electron concentration, characterized with the number of valence electrons per unit cluster (VE/uc) formula of 16 atoms, on microhardness of these designed maraging stainless steels at both solid-solutioned and aged states was investigated. The relationship between alloy compositions and microhardness in maraging stainless steels was firstly established by the random forest (RF, a kind of machine learning methods) based on the experimental results. It was found that not only the microhardness of any given composition alloy within the frame of cluster formula, but also the alloy composition with a maximum microhardness for any given VE/uc, could be predicted in good agreement with the guidance of the relationship by RF. The contributions of minor-alloying elements to the microhardness of the aged alloys were also discussed.

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Acknowledgements

This work was financially supported by the International Science & Technology Cooperation Program of China (No. 2015DFR60370), the National Natural Science Foundation of China (No. U1610256), the National Magnetic Confinement Fusion Energy Research Project (2015GB121004), the Fundamental Research Funds for the Central Universities (No. DUT16ZD212), the Natural Science Foundation of Liaoning Province of China (No. 2015020202), and the Ministry-Province Jointly-Constructed Cultivation Base for State Key Laboratory of Processing for non-ferrous metal and featured materials (No. GXKFJ16-11).

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Authors and Affiliations

  1. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Zhen Li

  2. Key Laboratory of Materials Modification by Laser, Ion and Electron Beams (Ministry of Education), School of Materials Science and Engineering, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Dong-hui Wen, Yue Ma, Qing Wang & Guo-qing Chen

  3. Science and Technology on Reactor Fuel and Materials Laboratory, Nuclear Power Institute of China, Chengdu, 610213, Sichuan, China

    Rui-qian Zhang & Rui Tang

  4. Ministry-Province Jointly-Constructed Cultivation Base for State Key Laboratory of Processing for Non-Ferrous Metal and Featured Materials, Guangxi University, Nanning, 530004, Guangxi, China

    Huan He

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  1. Zhen Li
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  2. Dong-hui Wen
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  3. Yue Ma
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  4. Qing Wang
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Correspondence to Qing Wang.

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Li, Z., Wen, Dh., Ma, Y. et al. Prediction of alloy composition and microhardness by random forest in maraging stainless steels based on a cluster formula. J. Iron Steel Res. Int. 25, 717–723 (2018). https://doi.org/10.1007/s42243-018-0104-5

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  • DOI: https://doi.org/10.1007/s42243-018-0104-5

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