Publication | Open Access
Integrated experimental and modeling approach for hot deformation behavior of Co–Cr–Fe–Ni–V high entropy alloy
15
Citations
29
References
2023
Year
EngineeringSevere Plastic DeformationMechanical EngineeringWork HardeningStructural MaterialsModeling ApproachMicrostructure-strength RelationshipThermodynamicsSolidificationMaterials ScienceMaterials EngineeringCrystalline DefectsHot WorkingEbsd CharacterizationDeformation TwinningThermomechanical ProcessingMicrostructureHot Deformation BehaviorHigh Temperature MaterialsAlloy PhaseMultiprincipal Element AlloyArtificial Neural NetworkMechanics Of MaterialsHigh-entropy Alloys
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.
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