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Influences of welding parameters on mechanical properties of AZ31 friction stir spot welds

2012· article· en· W2093146989 on OpenAlexafffund
Ning Sun, T. H. North, D R Chen, Yue Yin

Bibliographic record

VenueScience and Technology of Welding & Joining · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Toronto
FundersChina Scholarship CouncilChina Postdoctoral Science FoundationNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMaterials scienceDwell timeWeldingSpot weldingRotational speedComposite materialUltimate tensile strengthFriction stir weldingShear (geology)BrittlenessOptical microscopeMicrostructureFracture (geology)Scanning electron microscopeMetallurgyMechanical engineering

Abstract

fetched live from OpenAlex

In this study, the influence of welding parameters, including tool rotational speed, plunge rate and dwell time, on the overlap tensile shear properties of AZ31 friction stir spot welds was investigated. The microstructures in stir zones and fracture surfaces were observed using optical microscope and scanning electron microscope respectively. The bonded width and h value (the distance from the tip of the partially bonded region to the top of the weld surface) were measured. The results indicated that larger bonded width and higher h value of the AZ31 weld result in better mechanical property. It is proposed that high tensile shear loads are produced when the tool rotational speed range of 1500–2250 rev min−1 and 1 s dwell time are applied during the friction stir spot welding of AZ31. The plunge rate range from 2·5 to 10 mm s−1 has insignificant influence on the tensile shear load of AZ31 joints under the present conditions. The failure mode changes from interfacial to pullout when the tool rotational speed is >2250 rev min−1. The fracture feature of AZ31 welds is brittle fracture.

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)0.002
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.023
GPT teacher head0.248
Teacher spread0.225 · 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".

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Citations6
Published2012
Admission routes2
Has abstractyes

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