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Record W4376881497 · doi:10.31399/asm.cp.itsc2023p0574

Cold Spray Deposition of Aluminium 6061 Decorated with Al2O3 Nanoparticles

2023· article· en· W4376881497 on OpenAlexaff
Bahareh Marzbanrad, Ehsan Marzbanrad, Hamid Jahed

Bibliographic record

VenueThermal spray · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceGas dynamic cold sprayComposite numberNanoparticleAluminiumScanning electron microscopeRecrystallization (geology)Composite materialCoatingSurface roughnessMetallurgyNanotechnology

Abstract

fetched live from OpenAlex

Abstract This research presents a novel approach for producing metal matrix composite powders using a nanoparticle decoration technique. A 1wt% stable suspension of 30nm Al2O3 particles was decorated onto primary AA6061using a redispersion method. The resulting AA6061-1wt% Al2O3 composite powder was mixed in a rotary mixer for one hour and subsequently dried at 45°C. Scanning electron microscopy of the composite powder confirmed the successful material composition. The composite powders were then deposited onto an AA6061 substrate using a low-pressure cold spray system, with the coating quality, deposition efficiency, surface roughness, and hardness of the deposited materials analyzed. After heat treatment at 430oC, the role of the nanoparticles in hindering recrystallization was studied, with Orowan strengthening shown to be the main mechanism for preventing recrystallization and grain growth. This technique provides a promising alternative method for producing metal matrix composites and offers potential for further exploration of their properties and applications.

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 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 score0.012
Threshold uncertainty score0.609

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)0.0000.000

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 head0.010
GPT teacher head0.190
Teacher spread0.179 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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