MétaCan
Menu
Back to cohort
Record W4393261356 · doi:10.18280/mmep.110316

Effect of Friction Stir Spot Welding with a Rotating Anvil on the Microstructure of Aluminum AA6061-T4 Alloy

2024· article· en· W4393261356 on OpenAlexvenueno aff
Salam O. Dahi, Ahmed Ali A. Al-Shawk, Hussein Al-Gburi

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMicrostructureSpot weldingAluminiumAlloyMaterials scienceMetallurgyWeldingFriction stir welding

Abstract

fetched live from OpenAlex

Friction Stir Spot Welding (FSSW) has emerged as a promising alternative to traditional riveting in the automotive industry.In this study, we explore the application of doublesided FSSW, a solid-state joining method, as an innovative approach to overcome the limitations of conventional resistance spot welding.Our focus is on welding 2 mm thick sheets of aluminum alloy AA6061-T4.To improve the strength and quality of welded joints, we employed the double-sided FSSW technique, which utilizes a revolving anvil and a pin-less tool.This approach not only enhances the tensile strength of the connections but also mitigates potential defects such as keyholes.Intriguingly, our numerical analysis reveals the complexity of material flow between the rotating anvil and the pin-less tool during the welding process.This complexity underscores the suitability of using a rotating anvil and a pin-less tool in FSSW operations, particularly when welding larger sheets.In conclusion, our study underscores the potential of double-sided FSSW with a rotating anvil and pin-less tool for achieving robust weld connections in the context of aluminum alloy AA6061-T4.These findings contribute to the ongoing efforts to advance solid-state joining methods in the automotive industry, offering a more efficient and reliable alternative to traditional riveting.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.562
Threshold uncertainty score0.591

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 designSimulation or modeling
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMathematical Modelling and Engineering ProblemsSame topicAdvanced Welding Techniques AnalysisFrench-language works237,207