Work hardening in metastable high entropy alloys: a modified five-parameter model

Haridas, Ravi Sankar; Yadav, Surekha; Agrawal, P.; Gumaste, Anurag; Mishra, Rajiv S.

doi:10.1016/j.jmrt.2022.04.016

Cited by 16 publications

(1 citation statement)

References 43 publications

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“…In addition to that, a decrease in SFE further to around 6.5 mJ m 2 may display a bidirectional-TRIP effect in H/MEA [9]. With even further lowering in SFE, the basal slip in the HCP phase, and twinning along with the non-basal slip in HCP can also be activated [10]. In comparison to other alloy systems, H/MEAs have a relatively higher propensity to twinning and transformation due to severe lattice distortion [11].…”

Section: Introductionmentioning

confidence: 98%

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: Introductionmentioning

confidence: 98%

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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Surface Functionalization of Novel Work‐Hardening Multi‐Principal‐Element Alloys by Ultrasonic Assisted Milling

Preuß,

Lindner,

Hanisch

et al. 2024

Adv Eng Mater

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The development of multi‐principal‐element alloys (MPEAs) with unique characteristics such as high work hardening capacity similar to well‐known alloy systems like Hadfield steel X120Mn12 (ASTM A128) is a promising approach. Hence, by exploiting the core effects of MPEAs, the application range of conventional alloy systems can be extended. In the present study, work‐hardening MPEAs based on the equimolar composition CoFeNi are developed. Mn and C are alloyed in the same ratio as for X120Mn12. The production route consists of cast manufacturing by an electric arc furnace and surface functionalization via mechanical finishing using ultrasonic‐assisted milling (USAM) to initiate work hardening. The microstructure evolution, the hardness as well as the resulting oscillating wear resistance are detected. A pronounced lattice strain and grain refinement due to the plastic deformation during the USAM is recorded for the MPEA CoFeNi‐Mn12C1.2. Consequently, hardness increases by ≈380 HV0.025 in combination with a higher oscillating wear resistance compared to the X120Mn12. This shows the promising approach for developing work‐hardening alloys based on novel alloy concepts such as MPEAs.

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Explosive Welding and Friction Stir Welding/Processing of Multi-Principal Element Alloys

Ojo

2023

Met. Mater. Int.

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Work hardening in metastable high entropy alloys: a modified five-parameter model

Cited by 16 publications

References 43 publications

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

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

Surface Functionalization of Novel Work‐Hardening Multi‐Principal‐Element Alloys by Ultrasonic Assisted Milling

Explosive Welding and Friction Stir Welding/Processing of Multi-Principal Element Alloys

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