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Record W4214911895 · doi:10.5267/j.esm.2022.2.001

Modeling and simulation of friction stir welding process for AA6061-T6 aluminum alloy using finite element method

2022· article· en· W4214911895 on OpenAlexvenueno aff
Muleta Tiki Lemi, Endalkachew Mosisa Gutema, M. Gopal

Bibliographic record

VenueEngineering Solid Mechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFriction stir weldingMaterials scienceWeldingRotational speedFinite element methodFriction weldingPerpendicularDeformation (meteorology)Heat generationWeld lineAluminiumComposite materialAlloyMetallurgyMechanical engineeringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Friction Stir Welding (FSW) is a process of welding materials that generates heat through friction. Plastic deformation, nonlinear material movement, tool-to-structural evolution friction, and heat production from friction and plastic deformation all have an impact on FSW operation. In this paper, thermo-mechanical characteristics of aluminum alloy AA6061-T6 during the FSW process were simulated based on COMSOL® software using a finite element approach. A conceptual model was created to interpret the thermal and structural analyses. According to the obtained results, the temperature rises on the top and bottom surfaces as the axial force increases but decreases along the line perpendicular to the weld direction. The overall temperature decreases as the forward welding speed rises within the acceptable induced temperature range of the workpiece, while the axial force and rotational speeds stay stable.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.306
Teacher spread0.282 · 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 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

Citations22
Published2022
Admission routes1
Has abstractyes

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