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Friction stir welding of co-cast aluminium clad sheet

2013· article· en· W1974172839 on OpenAlexafffund
J. Fallu, Hossein Izadi, A.P. Gerlich

Bibliographic record

VenueScience and Technology of Welding & Joining · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of WaterlooUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceWeldingFriction stir weldingHeat-affected zoneMetallurgyElectric resistance weldingComposite materialAlloyAluminiumConsolidation (business)Aluminium alloy

Abstract

fetched live from OpenAlex

The effects of welding parameters on material consolidation are examined during friction stir butt welding of 2 mm Al 5083 alloy aluminium sheet with a surface cladding of Al 3025 alloy, which was co-cast from the melt. The influence of welding parameters on joint consolidation is investigated when tool revolutions per minute, travel speed and penetration depth were varied. It was found that modifying the pin of the welding tool to have a two-flat profile improves material consolidation and avoids defect formation during welding, and optimum welding parameters involve a combination of high tool rotation speed and travel speed. Optical and electron microscopy revealed that the integrity of the surface cladding layer could be maintained during friction stir welding while avoiding defect formation within the stir zone of the weld. The tensile strength of the joint was ∼58% of the base material due to softening within the stir zone.

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 score0.002
Threshold uncertainty score0.003

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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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

Citations10
Published2013
Admission routes2
Has abstractyes

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