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Record W4303954684 · doi:10.3390/met12101686

Wear-Resistant Fe6AlCoCrNi Medium-Entropy Alloy Coating Made by Laser Cladding

2022· article· en· W4303954684 on OpenAlexaff
Ke Chen, Hongbo Pan, Mingyu Wu, Xianfa Wang, Dongyang Li

Bibliographic record

VenueMetals · 2022
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversity of Alberta
FundersChina Postdoctoral Science Foundation
KeywordsCoatingMaterials scienceComposite materialAlloyWear resistanceAbrasion (mechanical)Metallurgy

Abstract

fetched live from OpenAlex

An Fe6AlCoCrNi medium-entropy (MEA) coating was coated on a steel substrate by laser cladding. The micro-structure, crystal structure, phases, and wear properties of the coating were investigated. The coating was mainly composed of a dendritic face-center cubic (FCC) phase, which showed preferred crystal orientation of <2 0 0>, normal to the coating surface, and a body-center cubic (BCC) phase. The MEA coating exhibited satisfactory rigidity with superior wear resistance at different loads and temperatures, much higher than that of the steel substrate. When the test temperature increased from 293 K to 573 K, the coefficient of friction (COF) of the coating markedly decreased from about 0.75 to 0.35; a large decrease in wear was also observed. The wear mechanism of the MEA coating was abrasion wear at room temperature, while the wear of the coating at high temperatures involved considerable oxidation, which enhanced the wear resistance of the coating.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

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