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Record W1969198690 · doi:10.2478/bpasts-2014-0086

Comparative investigation of friction stir welding and fusion welding of 6061 T6 – 5083 O aluminum alloy based on mechanical properties and microstructure

2014· article· en· W1969198690 on OpenAlexfundno aff
Sabitha Jannet, P. Koshy Mathews, R. Raja

Bibliographic record

VenueBulletin of the Polish Academy of Sciences Technical Sciences · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
FundersEgg Farmers of Canada
KeywordsWeldingFriction stir weldingMaterials scienceFusion weldingMetallurgyUltimate tensile strengthMicrostructureAlloyTensile testingHeat-affected zoneComposite materialElectric resistance welding

Abstract

fetched live from OpenAlex

Abstract This paper compares, the mechanical properties of welded joints 6061 T6 and 5083 O aluminium alloys obtained using friction stir welding (FSW) at four rotation speeds namely 450,560,710 and 900 rpm and that by conventional fusion welding. FSW welds were carried out on a milling machine. The performance of FSW and Fusion welded joints were identified using tensile test, hardness test and microstructure. The properties of FSW and fusion welded processes were also compared with each other to understand the advantages and disadvantages of these processes for welding applications for Al alloys. It was seen that the tensile strength obtained with FSW was higher as compared to conventional fusion welding process. The width of the heat affected zone of FSW was narrower than Fusion welded joints. The results showed that FSW improved the mechanical properties of welded 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 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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.032
GPT teacher head0.260
Teacher spread0.229 · 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

Citations52
Published2014
Admission routes1
Has abstractyes

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