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Record W2285397641 · doi:10.3390/ma9020117

Tribo-Mechanical Properties of HVOF Deposited Fe3Al Coatings Reinforced with TiB2 Particles for Wear-Resistant Applications

2016· article· en· W2285397641 on OpenAlexafffund
M. Amiriyan, Carl Blais, Sylvio Savoie, Robert Schulz, Mario Gariépy, Houshang Alamdari

Bibliographic record

VenueMaterials · 2016
Typearticle
Languageen
FieldEngineering
TopicIntermetallics and Advanced Alloy Properties
Canadian institutionsHydro-QuébecUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceThermal sprayingTribologyBall millMetallurgyElastic modulusComposite materialCoatingRaw materialComposite numberIndentation hardnessMicrostructure

Abstract

fetched live from OpenAlex

This study reveals the effect of TiB2 particles on the mechanical and tribological properties of Fe3Al-TiB2 composite coatings against an alumina counterpart. The feedstock was produced by milling Fe3Al and TiB2 powders in a high energy ball mill. The high-velocity oxy-fuel (HVOF) technique was used to deposit the feedstock powder on a steel substrate. The effect of TiB2 addition on mechanical properties and dry sliding wear rates of the coatings at sliding speeds ranging from 0.04 to 0.8 m·s−1 and loads of 3, 5 and 7 N was studied. Coatings made from unreinforced Fe3Al exhibited a relatively high wear rate. The Vickers hardness, elastic modulus and wear resistance of the coatings increased with increasing TiB2 content in the Fe3Al matrix. The wear mechanisms strongly depended on the sliding speed and the presence of TiB2 particles but were less dependent on the applied load.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.201
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2016
Admission routes2
Has abstractyes

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