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Record W2366560012

Heat Treatment of Cold Spray Aluminum Coating on Magnesium Substrates

2011· article· en· W2366560012 on OpenAlexaff
Lu Chen

Bibliographic record

VenueMetallic Functional Materials · 2011
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceIntermetallicCoatingMetallurgyAnnealing (glass)Gas dynamic cold sprayAluminiumMagnesiumPorosityMagnesium alloyCorrosionAlloyConversion coatingSubstrate (aquarium)Composite material
DOInot available

Abstract

fetched live from OpenAlex

Commercial pure aluminum powders were deposited onto as-cast AZ91D magnesium alloy substrate and formed a coating by using cold spray technology.The coating thickness was uniform and have a good bonding with the substrate,it have a higher density with a porosity content less than 1%.The Al coatings were annealed at 400 ℃ for 20 h and 40 h after milled to around 135 μm.The results show that as the holding time increased,the Al coatings all transformed to Mg17Al12 and Al3Mg2 intermetallics,which perform higher hardness and better corrosion resistance compared to Mg substrates.These intermetallics have a total thickness of 263 μm which is much more higher than Al coatings before annealing.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.047
GPT teacher head0.200
Teacher spread0.153 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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