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Improving Friction Stir Welding between Copper and 304L Stainless Steel

2011· article· en· W1964407327 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWeldingMaterials scienceFriction stir weldingMetallurgyRotational speedIndentation hardnessElectric resistance weldingButt weldingCopperComposite materialHeat-affected zonePerpendicularFriction weldingMicrostructureMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

As a solid-state welding technology, friction stir welding (FSW) can join dissimilar materials with good mechanical properties. In this paper, friction stir welding between 304L stainless steel and commercially pure copper plates with thicknesses of 3 mm was performed. A number of FSW experiments were carried out to obtain the optimum mechanical properties by adjusting the rotational speed to 1000 rpm and welding speed in the range of 14-112 mm/min and with an adjustable offset of the pin location with respect to the butt line. Microstructural analyses have been done to check the weld quality. Cross-sectioning of the welds for metallographic analysis in planes perpendicular to the welding direction and parallel to the weld crown was also performed. The mechanical properties of the welds were determined using a combination of conventional microhardness and tensile testing. From this investigation it is found that the offset of the pin is an essential factor in producing defect free welds in friction stir welding of copper and steel.

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.

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)0.001
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 score0.028
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.063
GPT teacher head0.330
Teacher spread0.267 · 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