Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/12496
Title: Integrated experimental and modeling approach for hot deformation behavior of Co–Cr–Fe–Ni–V high entropy alloy
Authors: Samal, Sumanta
Keywords: ANN;Dynamic materials modeling;EBSD;Finite element method;Hot deformation
Issue Date: 2023
Publisher: Elsevier Editora Ltda
Citation: Jain, R., Rahul, M. R., Chakraborty, P., Sabat, R. K., Samal, S., Park, N., Phanikumar, G., & Tewari, R. (2023). Integrated experimental and modeling approach for hot deformation behavior of Co–Cr–Fe–Ni–V high entropy alloy. Journal of Materials Research and Technology, 25, 840–854. https://doi.org/10.1016/j.jmrt.2023.05.257
Abstract: The study aims to investigate the hot deformation behavior of Co–Cr–Fe–Ni–V high entropy alloy (HEA) at temperatures ranging from 1073 K to 1373 K and strain rates of 0.001, 0.01, 1, and 10 s−1, and to generate processing maps using dynamic materials modeling (DMM) to identify the optimum processing domain for industrial applications. The material's hardening and softening characteristics are also explored under various hot working conditions. Deformation twinning is observed in materials deformed at 0.1 s−1 at 1273 K and 1373 K, contributing to their observed hardening. The mean free path of dislocation defines the material's strength, and the transition point from dynamic recovery to dislocation-dislocation or dislocation-solute interaction occurs when the mean free path of dislocation reaches its lowest value. The inhomogeneity in the deformed sample is correlated with the strain field distribution using an integrated approach using finite element method (FEM) modeling and electron backscattered diffraction (EBSD) results. EBSD characterization reveals the presence of deformation bands and annealing twins at low and high temperatures, respectively. Additionally, an artificial neural network (ANN) model is proposed to predict the hot deformation behavior of Co–Cr–Fe–Ni–V HEA, with promising results, as evidenced by a correlation coefficient (R) of 0.9983 and an average absolute relative error (AARE) of 2.71% on the test dataset. © 2023 The Authors
URI: https://doi.org/10.1016/j.jmrt.2023.05.257
https://dspace.iiti.ac.in/handle/123456789/12496
ISSN: 2238-7854
Type of Material: Journal Article
Appears in Collections:Department of Mechanical Engineering

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