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Record W3195956242 · doi:10.3390/jmmp5030093

A Comparative Study on Fatigue Response of Aluminum Alloy Friction Stir Welded Joints at Various Post-Processing and Treatments

2021· article· en· W3195956242 on OpenAlexafffund
Soran Hassanifard, A. Varvani‐Farahani

Bibliographic record

VenueJournal of Manufacturing and Materials Processing · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceWeldingFriction stir weldingMetallurgyBurnishing (metal)AlloyAluminiumMicrostructureComposite materialPolishing

Abstract

fetched live from OpenAlex

The present study examines the fatigue of friction stir welded (FSW) aluminum 6061, 7075, 1060 joints followed by (i) in situ and sequential rolling (SR) processes, (ii) plastic burnishing (iii) solution-treatment artificial aging (STA), (iv) local alloying through depositing thin copper foils, and (v) inserting alumina powder in the weld nugget zone (NZ). The microstructural features and fatigue life of post-processed joints were compared with those of as-welded joints. The in situ rolling technique offered simultaneous rolling and welding operations of aluminum joints, while through the sequential rolling process, the top surface of FSW joints was rolled after the welding process. The fatigue life of in situ rolled samples was increased as the ball diameter of welding tool increased. The fatigue life of friction stir welded joints after a low-plasticity burnishing process was noticeably promoted. The addition of 1 wt.% alumina in the NZ of joints resulted in a significant elevation on fatigue life of friction stir spot welded joints, while an increase in alumina powder to 2.5 wt.% adversely affected fatigue strength. Weld NZ was alloyed through the insertion of copper foils between the faying surfaces of joints. This localized alloy slightly improved the fatigue life of joints; however, its effects on fatigue life were not as influential as STA heat-treated or in situ rolled joints. The microstructure of weld joints was highly affected through post-processing and treatments, resulting in a substantial influence on the fatigue response of FSW aluminum 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.291
Teacher spread0.264 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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