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Tool design and stir zone grain size in AZ31 friction stir spot welds

2009· article· en· W1986800832 on OpenAlexafffund
Ning Sun, Yue Yin, A.P. Gerlich, T. H. North

Bibliographic record

VenueScience and Technology of Welding & Joining · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFriction stir processingSpot weldingGrain sizeMetallurgyFriction stir weldingMicrostructureComposite materialWelding

Abstract

fetched live from OpenAlex

The influence of tool design and tool rotational speed variations on the torque, energy output, stir zone temperature and average grain size in the stir zones of AZ31 friction stir spot welds was investigated. The average stir zone grain size decreased by ∼1 μm in AZ31 friction stir spot welds made using a three-flat/threaded tool design and tool rotational speeds of 2250 and 3000 rev min−1. However, there was no statistically significant influence of tool design on the average grain sizes in friction stir spot welds made using tool rotational speeds of 1500 and 1000 rev min−1. There was no evidence of grain growth in the stir zones of AZ31 friction stir spot welds. Similar torque, calculated energy output and stir zone temperature values were found in AZ31 friction stir spot welds made using threaded and three-flat threaded tool designs and tool rotational speeds from 1000 to 3000 rev min−1.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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

Citations39
Published2009
Admission routes2
Has abstractyes

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