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Record W4382988699 · doi:10.1080/17436753.2023.2227446

Localised surface modification of high-strength aluminium–alumina metal matrix composite coatings using cold spraying and friction stir processing

2023· article· en· W4382988699 on OpenAlexafffund
Wania Jibran, Priti Wanjara, Javad Gholipour Baradari, Maria Ophelia Jarligo, André McDonald

Bibliographic record

VenueAdvances in Applied Ceramics Structural Functional and Bioceramics · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsNational Research Council CanadaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFriction stir processingMaterials scienceCoatingGas dynamic cold sprayUltimate tensile strengthComposite numberAluminiumComposite materialDuctility (Earth science)AlloyMetal matrix compositeMetallurgyBase metalParticle (ecology)Aluminium alloyCreep

Abstract

fetched live from OpenAlex

This study presents a novel approach for surface repairs of high-strength, cold-worked aluminium (Al) alloys, without negatively affecting the base material strength. Low-pressure cold spraying was used to fabricate Al–Al 2 O 3 metal matrix composite overlays, with varying concentrations of Al 2 O 3 deposited on an Al alloy. Friction stir processing (FSP) was employed to disperse and consolidate the overlay coating on the base material. The FSP was found to improve the Al 2 O 3 particle distribution in the coating and reduce the mean free path between Al 2 O 3 particles. The coating hardness increased after FSP. The post-FSPed overlays also exhibited lower wear rates compared to the as-sprayed condition. Remarkable improvement was observed in the tensile properties of the coatings, which were attributed to the improved dispersion of Al 2 O 3 particles in the matrix, while the enhanced ductility was attributed to the possible grain refinement that occurred due to the recrystallisation of Al during FSP.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
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
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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 designSimulation or modeling
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

Citations6
Published2023
Admission routes2
Has abstractyes

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