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Record W3006860714

The influence of tool geometry on mechanical properties of friction stir welded AA-2024 and AA-2198 joints

2016· preprint· en· W3006860714 on OpenAlexaff
Mahdi Masoumi Khalilabad, Yasser Zedan, Damien Texier, Mohammad Jahazi, Philippe Bocher

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2016
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceWeldingFriction stir weldingUltimate tensile strengthComposite materialScanning electron microscopeTaperingFillet (mechanics)
DOInot available

Abstract

fetched live from OpenAlex

The tool geometry in friction stir welding (FSW) is a critical parameter to produce reliable and consistent joints, especially when it comes to joining dissimilar alloys with different mechanical and thermal properties. The aim of the present work is to investigate the effect of FSW tool design on the mechanical characteristic of as-welded AA2198(T3)-AA2024(T3) joints. Three shoulder profiles (flat, spiral, and fan) and five different pin profiles (tapered cylindrical, straight cylindrical, threaded cylindrical, cone and square) were tested. A visual inspection and metallographic characterization were conducted to evaluate the sound state of the joint. Tensile tests and scanning electron microscopy combined with energy-dispersive X-ray spectroscopy were used to assess the mechanical properties of the different joints in regard to fractographic analyses and local chemical composition. Significant flash was produced for flat shoulder configurations. FSW tools with fan or with spiral shoulders prevent the formation of the flash defect. Lack of penetration (LOP) and kissing bond defects were avoided by tapering the tip of the pin with a diameter greater than or equal to the plate thickness. The tapered cylindrical pin with a fan shoulder was the optimal tool design configuration for mechanical properties.

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)0.003
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0010.001
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 head0.214
Teacher spread0.202 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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