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

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Abstract

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.

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https://github.com/HEAresearch/CS-and-LS-prediction-model

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), 2021R1A2C1005478 (BA) and 2021R1A4A1031357 (BA). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, 2022R1I1A1A01055105 (CN), 2022R1I1A1A01053047 (SKD), and 2021R1A6A1A10044950 (SKD).

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Author notes

  1. Cheenepalli Nagarjuna and Sheetal Kumar Dewangan have contributed equally to this work.

Authors and Affiliations

  1. Department of Materials Science and Engineering, Ajou University, Suwon, 16499, Republic of Korea

    Cheenepalli Nagarjuna, Sheetal Kumar Dewangan, Ashutosh Sharma & Byungmin Ahn

  2. Department of Advanced Materials Engineering, Kyungsung University, Busan, 48434, Republic of Korea

    Cheenepalli Nagarjuna & Kwan Lee

  3. Division of Advanced Materials Engineering and Center for Advanced Materials and Parts of powder (CAMP2), Kongju National University, Cheonan, 32588, Republic of Korea

    Cheenepalli Nagarjuna & Soon-Jik Hong

  4. Department of Energy Systems Research, Ajou University, Suwon, 16499, Republic of Korea

    Byungmin Ahn

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  1. Cheenepalli Nagarjuna
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  2. Sheetal Kumar Dewangan
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  3. Ashutosh Sharma
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Correspondence to Soon-Jik Hong or Byungmin Ahn.

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Nagarjuna, C., Dewangan, S.K., Sharma, A. et al. Application of Artificial Neural Network to Predict the Crystallite Size and Lattice Strain of CoCrFeMnNi High Entropy Alloy Prepared by Powder Metallurgy. Met. Mater. Int. 29, 1968–1975 (2023). https://doi.org/10.1007/s12540-022-01355-w

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