MétaCan
Menu
Back to cohort
Record W2536424383 · doi:10.1002/maco.201609182

Effect of welding flash on the corrosion of friction stir spot welded AZ31B

2016· article· en· W2536424383 on OpenAlexafffund
Yuri Savguira, W. H. Liu, T. H. North, Steven J. Thorpe

Bibliographic record

VenueMaterials and Corrosion · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsMaterials scienceCorrosionMetallurgyAnodeWeldingFlash (photography)MicrostructureSpot weldingGalvanic corrosionTungsten carbideComposite materialElectrodeOpticsChemistry

Abstract

fetched live from OpenAlex

The influence of welding flash on the corrosion resistance of friction stir spot welded (FSSW) AZ31B was examined by mass loss testing complimented with the scanning reference electrode and microcapillary polarization techniques. The microstructure of the flash was characterized by optical microscopy and the use of tungsten carbide tracer and correlated with the corrosion morphology of the joints. It was observed that the flash increased the corrosion rate of the welds, and its removal can reduce the corrosion rate by 20%. The increase in susceptibility for corrosion was explained by examining the electrochemical characteristics of the flash, and in particular to show the presence of a second stir zone (SZ) region in the flash. The electrochemical properties of the flash were correlated to second phase particle dissolution using a detailed microscopy analysis. The coupling of the two regions resulted in the formation of a local galvanic cell in the flash, leading to accelerated corrosion. Increases in dwell time and/or rotational speed of the tool during FSSW resulted in the formation of a larger SZ region in the flash, and produced a greater cathode to anode ratio.

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 score0.033
Threshold uncertainty score0.223

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 head0.006
GPT teacher head0.218
Teacher spread0.211 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueMaterials and CorrosionSame topicAdvanced Welding Techniques AnalysisFrench-language works237,207