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Record W1545457196 · doi:10.4271/2005-01-1255

Friction Stir Spot Welding of Aluminum and Magnesium Alloy Sheets

2005· article· en· W1545457196 on OpenAlexaff
P. Su, A.P. Gerlich, T. H. North

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpot weldingAluminiumMaterials scienceMagnesiumMetallurgyWeldingFriction stir weldingMagnesium alloyAlloy5052 aluminium alloy

Abstract

fetched live from OpenAlex

This paper demonstrates the application of a displacement controlled friction stir spot welding machine in joining 1.5 mm sheets of Al 5754 aluminum and AM60 base materials. Higher fracture loads during overlap shear testing were produced when the projected bonded area immediately adjacent to the keyhole periphery and the energy input during FSW spot welding increased. Partial pull-out of the stir zone occurred in high energy input FSW spot welds with fracture initiating from unbonded regions located on either side of the welded joint. FSW welding tools contaminated with magnesium had a markedly detrimental effect on the mechanical properties of Al 5754 spot welds. Mg-contamination decreased the energy input and the dimensions of the projected bonded area, and produced regions immediately adjacent to the keyhole periphery in Al 5754 spot welds, which contained brittle intermingled lamellae.

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 score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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

Citations53
Published2005
Admission routes1
Has abstractyes

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