期刊文献+

Prediction of alloy composition and microhardness by random forest in maraging stainless steels based on a cluster formula 被引量:1

Prediction of alloy composition and microhardness by random forest in maraging stainless steels based on a cluster formula
原文传递
导出
摘要 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-n MoxTiyNbzAlmVn)) (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. 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-n MoxTiyNbzAlmVn)) (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.
出处 《Journal of Iron and Steel Research(International)》 SCIE EI CAS CSCD 2018年第7期717-723,共7页 钢铁研究学报(英文版)
关键词 Maraging stainless steel Composition design MICROHARDNESS Valence electron concentration Random forest Maraging stainless steel Composition design Microhardness Valence electron concentration Random forest
  • 相关文献

同被引文献41

引证文献1

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部