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Record W3028609609 · doi:10.1139/tcsme-2020-0035

Effect of tool pin profile on the hook geometry and mechanical properties of a friction stir spot welded AA6082-T6 aluminum alloy

2020· article· en· W3028609609 on OpenAlexvenueno aff
Hakan Aydın, Oğuz Tunçel, Mümin Tutar, Ali Bayram

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
FundersBursa Uludağ Üniversitesi
KeywordsMaterials scienceWeldingBrittlenessComposite materialConical surfaceAlloyDwell timeUltimate tensile strengthMetallurgy

Abstract

fetched live from OpenAlex

AA6082-T6 alloy was joined by friction stir spot welding using five different pin profiles, namely cylindrical, conical, triangular, hexagonal, and cylindrical with two grooves, at different dwell time. The joints welded by cylindrical pins had larger effective weld width. However, grooves on the cylindrical pin decreased the effective weld width. In the weld made with the hexagonal pin, the hook was bent downward from the interface of the sheets and had the smallest effective weld width. When the conical pin was used, the effective weld width increased with increasing dwell time. In the case of using tools with cylindrical and conical pins, HAZ hardness was relatively lower. With increasing dwell time, HAZ hardness of the joints made with the conical pin decreased. Effective weld width determined the weld strength under the tensile shear loading condition: larger effective weld width resulted in higher weld strength. Weld strength of the joints made with cylindrical pins was higher. The joints fabricated with hexagonal pins had the lowest weld strength. In general, higher dwell time led to higher weld strength. The welds with the higher strength experienced both brittle and ductile fractures, while the joints with the lower strength exhibited completely 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.528
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.011
GPT teacher head0.195
Teacher spread0.185 · 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 teacher head, 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

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAdvanced Welding Techniques AnalysisFrench-language works237,207