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Record W2265686114

Effect of Tool Geometry on Joint Properties of Friction Stir Welded Al/Cu Bimetallic Lap Joints

2012· article· en· W2265686114 on OpenAlexaff
Manish Gupta, B. Balu Naik, K. G. K. Murti

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsAurora College
Fundersnot available
KeywordsFriction stir weldingBimetallic stripMaterials scienceWeldingLap jointAluminiumJoint (building)CopperBusbarMetallurgyComposite materialSTRIPSStructural engineeringEngineeringElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Friction Stir Welding (FSW) is an emerging solid state welding method which is finding increasingly widespread industrial acceptance for joining similar and dissimilar materials. Aluminium to copper bimetallic lap joints have got wide acceptance in the electrical and electronic industries. Commercially, pure Al/Cu bimetallic lap joints are fabricated by the FSW technology using tapered and straight fluted tools. This paper presents valuable information on electromechanical behavior of aluminium to copper bimetallic lap joints required for critical applications such as high current busbars, heavy duty earthing strips, etc. The experiments are conducted based on Design of Experiments (DoE) to reduce the number of trials. It is observed that better joint properties are obtained in the joints fabricated using straight fluted tool and the joint resistance is negligible, as in manufactured condition.

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)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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