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Analysis of tool geometry in dissimilar Al alloy friction stir welds using optical microscopy and serial sectioning

2013· article· en· W2110982048 on OpenAlexafffund
Hossein Izadi, J. Fallu, A. Abdel-Gwad, T. Liyanage, A.P. Gerlich

Bibliographic record

VenueScience and Technology of Welding & Joining · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of WaterlooHitachi (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Education Nationale, de l'Enseignement Superieur et de la RechercheMinistry of Higher Education and Scientific Research
KeywordsMaterials scienceMaterial flowFriction stir weldingOptical microscopeWeldingAluminiumAlloyFlow (mathematics)Aluminium alloyMetallurgyComposite materialButt weldingViscosityGeometryScanning electron microscope

Abstract

fetched live from OpenAlex

The influence of tool geometry on material flow during friction stir welding of dissimilar aluminium alloys is investigated. Sheets of Al 2024 and Al 6061 alloys are friction stir welded in lap and butt configurations using different welding conditions. Optical microscopy with serial sectioning is utilised to systematically study material flow when small variations are made to the tool pin. It is shown that three flat features on the pin impose vertical material flow which can promote intermixing. When a threaded tool is used, the material flow and formation of the intermixed region depends on the orientation of the base materials, since the differences in viscosity of material on the advancing versus retreating side of the tool will inhibit intermixing. Decreasing the travel speed will promote intermixing by increasing the residence time to compensate for the differences in material viscosity that otherwise limit intermixing.

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)0.0000.000

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 head0.261
Teacher spread0.253 · 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

Citations26
Published2013
Admission routes2
Has abstractyes

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