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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 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score1.000

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 head0.025
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