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Influence of tool thread on mechanical properties of dissimilar Al alloy friction stir spot welds

2012· article· en· W2022540593 on OpenAlexafffund
Akihiko Ikuta, Yuhuan Yin, T. H. North

Bibliographic record

VenueScience and Technology of Welding & Joining · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilKindai University
KeywordsThread (computing)Materials scienceRotational speedScrew threadAlloyGallingComposite materialMetallurgyStructural engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The influence of threaded and wear simulated (half thread) tools on the mechanical properties of dissimilar Al alloy friction stir spot welds is investigated. With lower tool rotational speed settings, the failure loads of Al 5754/Al 6111 lap joints made using a threaded tool were clearly higher than that of a half thread tool. However, the failure load of the joints made using a half thread tool increased when the tool rotational speed increased, and finally, as the rotational speed was further increased, the failure load became almost the same as the failure load of joints made using a threaded tool. In Al 5052/Al 6061 butt joints made using the threaded and half thread tools, the area of the stir zone on the bonded cross-section corresponded with the actual bonded region on the fracture surface. Therefore, the thread on the rotating pin has limited influence on the mechanical properties of the friction stir spot lap joints.

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)0.003
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.246
Teacher spread0.231 · 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

Citations14
Published2012
Admission routes2
Has abstractyes

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