Achieving high strength and ductility in equimolar FeMnNi medium entropy alloy by tuning microstructural entropy

Jha, Saumya R.; Biswas, Krishanu; Gurao, N.P.

doi:10.1016/j.msea.2021.141965

Cited by 7 publications

(2 citation statements)

References 47 publications

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“…Thus, the ∆E hcp-fcc values from DFT and CALPHAD are one of the vital features affecting SFE. MEAs are known to have higher configurational entropy [4]. For the consideration of enthalpy and entropy effect on SFE, specific heat, and heat of fusion of the alloys is present in the important feature space.…”

Section: Analysis Of Features Affecting the Sfementioning

confidence: 99%

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Accelerated prediction of stacking fault energy in FCC medium entropy alloys using multilayer perceptron neural networks: correlation and feature analysis

Mahato,

Gurao,

Biswas

2024

Modelling Simul. Mater. Sci. Eng.

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A multilayer perceptron neural networks (MLPNN) model is developed for robust and quick prediction of stacking fault energy (SFE) to overcome the challenges faced in the calculation of SFE via experimentation and atomistic calculations in FCC medium entropy alloys (MEA). The present investigation employs a three-step hybrid feature selection approach to obtain a comprehensive understanding of the prominent features that influence the SFE, as well as the interrelationships among these features. The feature space encompasses various features related to composition, lattice stability, and elemental properties, of medium entropy alloys (MEAs). The findings indicate that the estimation of SFE relies on five crucial factors: temperature, lattice stability, specific heat, ionization energy, and Allen electronegativities. Furthermore, a mathematical relationship for the estimation of the SFE is derived, considering the various influencing and prominent factors. Consequently, the MLPNN model for robust SFE prediction in MEAs is developed and the performance is evaluated using R2 scores, with values of 0.87 and 0.85 obtained for the training and testing datasets, respectively. This efficient strategy introduces a novel opportunity for the engineering of SFE in the extensive range of alloy chemistry of MEAs, enabling the quick prediction of SFE, and facilitating the systematic exploration of new alloys for the development of mechanisms that may accommodate deformation through octahedral/partial slip, twinning, and/or transformation.

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Section: Analysis Of Features Affecting the Sfementioning

confidence: 99%

“…MEAs exhibit superior properties in terms of strength, ductility, and fracture toughness from their superset HEAs. This has led the researchers to design MEAs to overcome the strength-ductility trade-off, making them promising systems for exploring untraversed property space [4].…”

Section: Introductionmentioning

confidence: 99%