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Record W4403670099 · doi:10.1016/j.jalmes.2024.100120

Recent advances in additive manufacturing of refractory high entropy alloys (RHEAs): A critical review

2024· review· en· W4403670099 on OpenAlexaff
Akshay Yarlapati, Yudha Aditya, Deepak Kumar, R.J. Vikram, Mayank Yadav, Konda Gokuldoss Prashanth

Bibliographic record

VenueJournal of Alloys and Metallurgical Systems · 2024
Typereview
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRefractory (planetary science)High entropy alloysMaterials scienceComputer scienceMetallurgyAlloy

Abstract

fetched live from OpenAlex

Refractory High Entropy alloys (RHEAs) have evolved as a superior multicomponent material having a unique combination of microstructures and mechanical properties. RHEAs are a particular grade of high entropy alloys (HEAs) known for their outstanding high-temperature properties achieved thorough their constituent refractory elements. To date, majority of the RHEAs have been manufactured through several conventional methods, but are limited by certain limitations. Additive manufacturing (AM) has the potential to revolutionize the manufacturing industry by providing excellent opportunities to fabricate RHEAs while providing opportunities for enhancing the design freedom and complex geometrical shapes. As the field is rapidly evolving, a systematic examination of our comprehension is beneficial, and this article attempts to address this issue. To find the right place in industrial markets, additively manufactured RHEAs require further advancements. The present review provides a comprehensive overview of the additively manufactured RHEAs in terms of microstructural characterization, mechanical behaviour and other environmental properties published so far. This review article is presented lucidly with an aim of better understanding for the readers in this domain. To this end, future works and current challenges are also included and discussed briefly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.307
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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