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Record W2237277596 · doi:10.4271/2008-01-0146

Selection of Welding Parameter during Friction Stir Spot Welding

2008· article· en· W2237277596 on OpenAlexafffund
A.P. Gerlich, M. Yamamoto, Toshiya Shibayanagi, T. H. North

Bibliographic record

VenueSAE International Journal of Materials and Manufacturing · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWeldingSpot weldingFriction stir weldingSelection (genetic algorithm)Materials scienceMetallurgyMechanical engineeringEngineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">The selection of parameters during friction stir spot welding of Al-alloys and Mg-alloys is discussed. The role of tool rotation speed, plunge rate, and dwell time is examined in relation to the tool heating rate,temperature, force, and torque that occur during spot welding. In order to reduce the cycle time and tool force during Al- alloy spot welding, it is necessary to increase the tool rotation speed >1500 RPM. The measured peak temperature in the stir zone is determined by the rotation speed and dwell time, and is ultimately limited by the solidus of the alloy. When tool rotation speeds >1500 RPM are employed during AZ91 Mg-alloy spot welding, the tendency for melted film formation and cracking are greatly increased.</div>

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: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.395

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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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

Citations11
Published2008
Admission routes2
Has abstractyes

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