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Record W4376881506 · doi:10.31399/asm.cp.itsc2023p0567

Development of a Synthesis Route for High-Entropy Alloy Feedstock Particles for Cold Spray Following Conventional and Sequential Mechanical Milling Regimes

2023· article· en· W4376881506 on OpenAlexaff
Aisa Grace D. Custodio, Gobinda C. Saha, Clodualdo Aranas

Bibliographic record

VenueThermal spray · 2023
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMaterials scienceAlloyIntermetallicRaw materialUltimate tensile strengthMetallurgyHigh entropy alloysNanoparticleComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Abstract Material’s tensile strength can be improved by the presence of a body-centered cubic (BCC) phase, which is essential in highstrength applications and highly corrosive environments. Thus, synthesizing a BCC single-phase, equiatomic AlCoCrFeNi high-entropy alloy (HEA) feedstock particle using a highenergy mechanical alloying (HE-MA) method was investigated. The transient alloy particles were developed using a planetary mill at a constant rotational speed of 580 rpm employing milling times in the range of 4 to 24 hours. During the process, stearic acid of 3 wt.% of the precursor composition was used as a process-controlling agent (PCA). Two HE-MA manufacturing regimes were utilized: i) conventional (milling constituent elements simultaneously), and ii) sequential (progressive milling while adding elements in a certain order). In addition to the conventional method, a sequential regime was employed to develop FeNiCoCrAl, wherein individual elements were added every 4 hours to the starting/milled Fe + Ni mixture. Based on the results, the HE-MA FeNiCoCrAl showed a BCC single-phase formation after 24 hours, with no intermetallic or contamination traceability. Finally, a nanoindentation hardness measurement was carried out to support the observed phase transformation before and after the HE-MA process.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.253
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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