Microstructure Evolution and an ANN Approach for Microhardness Prediction of Suction Cast FeCoNiCrMnVNb Eutectic High-Entropy Alloys

Jain, Reliance; Dewangan, Sheetal Kumar; Umre, Priyanka; Kumar, Vinod; Samal, Sumanta

doi:10.1007/s12666-021-02335-1

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…Most research studies in the field of HEAs modeling primarily focus on predicting phases or physical properties 4 – 12 , 14 , 16 . However, an inherent issue with these prediction models is that they are constructed based on the assumption that the input alloy will be a HEA.…”

Section: Resultsmentioning

confidence: 99%

“…By taking these descriptors into account, the model was able to accurately predict the phase behavior of HEAs, which is important for developing new materials with desirable properties. Jain et al 12 developed artificial neural network (ANN) models to predict the microhardness of cast FeCoNiCrMnVNb x eutectic HEAs. The developed algorithms were found to be generic and applicable for predicting other mechanical properties as well but limited to a particular system of alloy.…”

Section: Introductionmentioning

confidence: 99%

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Designing of high entropy alloys with high hardness: a metaheuristic approach

Poonia,

Kishor,

Ayyagari

2024

Sci Rep

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The near-infinite compositional space of high-entropy-alloys (HEAs) is a huge resource-intensive task for developing exceptional materials. In the present study, an algorithmic framework has been developed to optimize the composition of an alloy with chosen set of elements, aiming to maximize the hardness of the former. The influence of phase on hardness prediction of HEAs was thoroughly examined. This study aims to establish generalized prediction models that aren’t confined by any specific set of elements. We trained the HEA identification model to classify HEAs from non-HEAs, the multi-labeled phase classification model to predict phases of HEAs also considering the processing route involved in the synthesis of the alloy, and the hardness prediction model for predicting hardness and optimizing the composition of the given alloy. The purposed algorithmic framework uses twenty-nine alloy descriptors to compute the composition that demonstrates maximum hardness for the given set of elements along with its phase(s) and a label stating whether it is classified as HEA or not.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Designing of high entropy alloys with high hardness: a metaheuristic approach

Poonia,

Kishor,

Ayyagari

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Most research studies in the eld of HEAs modeling primarily focus on predicting phases or physical properties [4][5][6][7][8][9][10][11][12][13]15 . However, an inherent issue with these prediction models is that they are constructed based on the assumption that the input alloy will be a HEA.…”

Section: Heas Identi Cationmentioning

confidence: 99%

“…By taking these descriptors into account, the model was able to accurately predict the phase behavior of HEAs, which is important for developing new materials with desirable properties. Jain et al 12 developed arti cial neural network (ANN) models to predict the microhardness of cast FeCoNiCrMnVNb x eutectic HEAs. The developed algorithms were found to be generic and applicable for predicting other mechanical properties as well but limited to a particular system of alloy.…”

Section: Introductionmentioning

confidence: 99%

Designing of High Entropy Alloys with High Hardness – A Metaheuristic approach

Poonia,

Modalavalasa,

Ayyagari

2023

Preprint

View full text Add to dashboard Cite

The near-infinite compositional space of High-Entropy-Alloys (HEAs) is a huge resource-intensive task for developing exceptional materials. In the present study, an algorithmic framework has been developed to optimize the composition of an alloy with chosen set of elements, aiming to maximize the hardness of the former. The influence of phase on hardness prediction of HEAs was thoroughly examined. This study aims to establish generalized prediction models that aren’t confined by any specific set of elements. We trained the HEA identification model to classify HEAs from non-HEAs, the multi-labeled phase classification model to predict phases of HEAs also considering the processing route involved in the synthesis of the alloy, and the hardness prediction model for predicting hardness and optimizing the composition of the given alloy. The purposed algorithmic framework uses twenty-nine thermodynamic descriptors to compute the composition that demonstrates maximum hardness for the given set of elements along with its phase(s) and a label stating whether it is classified as HEA or not.

show abstract