Artificial neural network approach for microhardness prediction of eight component FeCoNiCrMnVAlNb eutectic high entropy alloys

被引：32

作者：

Jain, Reliance ^{[1
]}

Dewangan, Sheetal Kumar ^{[1
]}

Kumar, Vinod ^{[2
]}

Samal, Sumanta ^{[1
]}

机构：

[1] Indian Inst Technol, Discipline Met Engn & Mat Sci, Indore, Madhya Pradesh, India

[2] Indian Inst Technol Indore, Ctr Adv Elect CAE, Indore, Madhya Pradesh, India

来源：

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING | 2020年 / 797卷

关键词：

Eutectic high entropy alloys (EHEAs); Thermodynamic simulation; Microhardness; ANN; Solidification; MECHANICAL-PROPERTIES; MICROSTRUCTURE; TEXTURE; TEMPERATURE; COMPOSITES; STABILITY; EVOLUTION; HARDNESS; GROWTH;

D O I：

10.1016/j.msea.2020.140059

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

For the first time, we report here that higher-order eight component Fe32.5-xCo10Ni25Cr15Mn5V10Al2.5Nbx = 5, 7.5, 10, and 12.5 at. %) eutectic high entropy alloys (EHEAs) are designed and developed by integrating thermodynamic simulation approach and non-equilibrium solidification processing technique. The developed EHEAs consist of FeCoNiCr-rich FCC solid solution phase and eutectics mixture between FCC solid solution phase and the Co2Nb-type Laves phase. The predicted microhardness of EHEAs for x = 7.5% and x = 10% by using artificial neural networks (ANNs) modeling is 501 H V, and 618 H V, respectively, which is in good agreement with experimentally measured values having less than 5% error.

引用

页数：8

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