Application of Artificial Neural Network to Predict the Crystallite Size and Lattice Strain of CoCrFeMnNi High Entropy Alloy Prepared by Powder Metallurgy

An equiatomic CoCrFeMnNi high entropy alloy (HEA) was prepared by the gas atomization process. In addition, high-energy milling was carried out to study the effects of milling parameters on the morphology and crystallographic properties of HEA powders. Phase identification and morphology of milled p...

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Vydané v:Metals and materials international Ročník 29; číslo 7; s. 1968 - 1975
Hlavní autori: Nagarjuna, Cheenepalli, Dewangan, Sheetal Kumar, Sharma, Ashutosh, Lee, Kwan, Hong, Soon-Jik, Ahn, Byungmin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Seoul The Korean Institute of Metals and Materials 01.07.2023
대한금속·재료학회
Predmet:
Characterization and Evaluation of Materials
Chemistry and Materials Science
Engineering Thermodynamics
Heat and Mass Transfer
Machines
Magnetic Materials
Magnetism
Manufacturing
Materials Science
Metallic Materials
Processes
Solid Mechanics
재료공학
High-entropy alloys
Crystallite size
Lattice strain
Artificial neural network
Powder metallurgy
ISSN:1598-9623, 2005-4149
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Popis
Shrnutí:An equiatomic CoCrFeMnNi high entropy alloy (HEA) was prepared by the gas atomization process. In addition, high-energy milling was carried out to study the effects of milling parameters on the morphology and crystallographic properties of HEA powders. Phase identification and morphology of milled powders were observed by X-ray diffraction and scanning electron microscopy, respectively. Both the atomized and milled powders exhibited a single-phase face-centered cubic solid solution. The resultant crystallite size (CS) and lattice strain (LS) of milled HEAs were estimated using the Williamson Hall method and predicted using an artificial neural network (ANN) approach. With increasing the milling time from 0 to 240 min, the CS decreased from 39.7 to 6.56 nm and the LS increased from 0.25%–1.48%, respectively. Furthermore, the developed ANN modeling provides an excellent method for the prediction of the CS and LS with excellent accuracies of 96.25% and 93.43%, respectively. Graphical Abstract
ISSN:1598-9623
2005-4149
DOI:10.1007/s12540-022-01355-w