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Record W2889052929 · doi:10.1139/tcsme-2018-0037

Influence of tool rotational speed on microstructural characteristics of dissimilar Mg alloys during friction stir welding

2018· article· en· W2889052929 on OpenAlexvenueno aff
P. Sevvel, C. Satheesh, V. Jaiganesh

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
FundersAll India Council for Technical Education
KeywordsFriction stir weldingRotational speedWeldingMaterials scienceExtrusionAlloyComposite materialFriction stir processingUltimate tensile strengthMetallurgyBase metalMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Dissimilar friction stir welding of the AZ80A Mg alloy as the advancing side and the AZ91C Mg alloy as the retreating side was carried out at a constant feed rate of 75 mm/min using a taper cylindrical pin profiled tool at different tool rotational speeds. Defect free welds were produced in the 700–900 rpm rotational speed range. During friction stir welding, extrusion of metal took place in the advancing side and this extruded material was dynamically recrystallized and redeposited on the retreating side. This experimental investigation revealed that friction stir welding leads to the formation of comparatively finer sized grains on the retreating side of the thermo-mechanically affected zone compared to grains in the thermo-mechanically affected zone on the advancing side. Moreover, the heat affected zone of AZ80A possessed fine sized grains compared to the heat affected zone of AZ91C. Additionally, increasing tool rotational speed influenced the tensile strength of the fabricated joints.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

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 head0.006
GPT teacher head0.208
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

Citations20
Published2018
Admission routes1
Has abstractyes

Explore more

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