Prediction of Hot Deformation Behavior in AlCoCrFeNi2.1 Eutectic High Entropy Alloy by Conventional and Artificial Neural Network Modeling

Reliance Jain1,2,3, Sandeep Jain1, Sheetal Kumar Dewangan4, L. Naveen1, Divik Patre1, Sumanta Samal1, Vinod Kumar1
1Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology Indore, Indore, India
2School of Materials Science and Engineering, Yeungnam University, Gyeongsan, Republic of Korea
3Department of Mechanical Engineering, Mandsaur University, Mandsaur, India
4Department of Materials Science and Engineering, Ajou University, Suwon, South Korea

Tóm tắt

In the present study, we report here the flow curve prediction of AlCoCrFeNi2.1 eutectic high entropy alloy (EHEA) at different temperatures and strain rates using different modeling techniques such as physics-based [modified Zerilli–Armstrong (ZA) model], phenomenological [modified Johnson–Cook (JC) model, Arrhenius model], and artificial neural network (ANN) modeling. Finally, the performance of all conventional (i.e., physics-based and phenomenological) and ANN modeling was evaluated by coefficient correlation (R) and average absolute relative error (AARE) parameters. It is found that the flow curve prediction by phenomenological modeling [i.e., modified JC model (R = 0.9646, AARE = 19.41%) and Arrhenius model (R = 0.9696, AARE = 14.62%)] is better as compared to physics-based modified ZA model (R = 0.9321, AARE = 21.42%). A comparative evaluation of obtained simulated results indicates that the prediction of hot deformation behavior of studied EHEA using ANN modeling (where R = 0.9985, and AARE = 4.57%) is matching excellently with experimental flow curve results as compared to conventional modeling approaches.

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