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Protective coatings for mechanical aluminum–magnesium joints

2015· article· en· W1986382402 on OpenAlexafffund
D. Dzhurinskiy, V. Leshchinsky, Emil Strumban, E. Maeva, Roman Gr. Maev

Bibliographic record

VenueSurface Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCorrosionMetallurgyCoatingAlloyMicrostructureMagnesium alloyAluminiumConversion coatingGas dynamic cold spray5005 aluminium alloyAlonizing6111 aluminium alloyComposite material

Abstract

fetched live from OpenAlex

AZ31 alloy is used as a lightweight material for structural application in the automobile and aircraft production. However, alloy AZ31 is known to have poor corrosion resistant due to high electrochemical activity. In this study, the possibility of improving the corrosion resistance by applying protective coatings deposited by the low pressure cold spray process was investigated. The relative performance of each cold sprayed corrosion preventive coatings was assessed in accordance with American Society for Testing and Materials standards. The data for the bare AZ31 alloy were initially obtained and used as a reference point to compare the corrosion protective performance of different preventive coatings. Electrochemical behaviour of each coating composition was analyzed after a given time period of the accelerated corrosion test. Microstructure and mechanical properties of the deposited preventive coatings are also discussed. The cold sprayed preventive coatings provide sufficient protection to substantially reduce the corrosion rate of alloy AZ31.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.234
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 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

Citations11
Published2015
Admission routes2
Has abstractyes

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