Application of artificial neural network to predict the crystallite size and lattice strain of NiMnCrMoWx high entropy alloys prepared by powder metallurgy
Naveen Kumar Mindi, Jonna Naresh, Bibhuti Jaipuria, Syed Nasimul Alam, Krishna Dutta
Abstract. In this investigation, NiMnCrMoWx (x = 0.2, 0.4, 0.6, 0.8,1.0) High Entropy Alloys (HEAs) are synthesized through the powder metallurgy route. To identify the phases and morphology of the milled powders, X-ray diffraction and scanning electron microscope is used. The Crystallite Size (CS) and Lattice Strain (LS) is calculated for the BCC phase obtained in the milled powder alloys by using the Willamson-Hall method and predicted using the Artificial Neural Network (ANN) approach. The CS is reduced from 243 nm to less than 75 nm after 70 h of milling. Whereas the LS increased from 0.45 % to a maximum of 1.91 %. The results from the ANN modeling provided an excellent prediction of CS and LS with outstanding accuracies of 95.27 % and 92.02 %.
Keywords
High Entropy Alloys, Powder Metallurgy, Crystallite Size, Lattice Strain, Artificial Neural Network
Published online 5/10/2026, 10 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Naveen Kumar Mindi, Jonna Naresh, Bibhuti Jaipuria, Syed Nasimul Alam, Krishna Dutta, Application of artificial neural network to predict the crystallite size and lattice strain of NiMnCrMoWx high entropy alloys prepared by powder metallurgy, Materials Research Proceedings, Vol. 65, pp 64-73, 2026
DOI: https://doi.org/10.21741/9781644904138-10
The article was published as article 10 of the book Processing and Characterization of Materials
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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