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https://dspace.iiti.ac.in/handle/123456789/7475
Title: | Artificial neural network approach for microhardness prediction of eight component FeCoNiCrMnVAlNb eutectic high entropy alloys |
Authors: | Jain, Reliance Dewangan, Sheetal Kumar Kumar, Vinod Samal, Sumanta |
Keywords: | Entropy;Eutectics;High-entropy alloys;Microhardness;Solid solutions;Solidification;Artificial neural network approach;Eight component;Higher-order;Laves-phase;Measured values;Non-equilibrium solidification;Solid solution phase;Thermodynamic simulations;Neural networks |
Issue Date: | 2020 |
Publisher: | Elsevier Ltd |
Citation: | Jain, R., Dewangan, S. K., Kumar, V., & Samal, S. (2020). Artificial neural network approach for microhardness prediction of eight component FeCoNiCrMnVAlNb eutectic high entropy alloys. Materials Science and Engineering A, 797 doi:10.1016/j.msea.2020.140059 |
Abstract: | For the first time, we report here that higher-order eight component Fe32.5-xCo10Ni25Cr15Mn5V10Al2.5Nbx (x = 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. © 2020 Elsevier B.V. |
URI: | https://doi.org/10.1016/j.msea.2020.140059 https://dspace.iiti.ac.in/handle/123456789/7475 |
ISSN: | 0921-5093 |
Type of Material: | Journal Article |
Appears in Collections: | Department of Metallurgical Engineering and Materials Sciences |
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