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Record W2050499935 · doi:10.1260/1750-9548.8.1.29

An integrated multiphysics model for friction stir welding of 6061 Aluminum alloy

2014· article· en· W2050499935 on OpenAlexafffund
Mohamadreza Nourani, Abbas S. Milani, S. Yannacopoulos, Cheng Yan

Bibliographic record

VenueThe International Journal of Multiphysics · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFriction stir weldingMultiphysicsMechanicsHeat transferWeldingFinite element methodMetallurgyStructural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This article presents a new, combined ‘integrated’- ‘multiphysics’ model of friction stir welding (FSW) where a set of governing equations from non-Newtonian incompressible fluid dynamics, conductive and convective heat transfer, and plain stress solid mechanics have been coupled for calculating the process variables and material behaviour both during and after welding. More specifically, regarding the multiphysics feature, the model is capable of simultaneously predicting the local distribution, location and magnitude of maximum temperature, strain, and strain rate fields around the tool pin during the process; while for the integrated (post-analysis) part, the above predictions have been used to study the microstructure and residual stress field of welded parts within the same developed code. A slip/stick condition between the tool and workpiece, friction and deformation heat source, convection and conduction heat transfer in the workpiece, a solid mechanics-based viscosity definition, and the Zener-Hollomon- based rigid-viscoplastic material properties with solidus cut-off temperature and empirical softening regime have been employed. In order to validate all the predicted variables collectively, the model has been compared to a series of published case studies on individual/limited set of variables, as well as in-house experiments on FSW of aluminum 6061.

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 score0.652
Threshold uncertainty score0.522

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 science0.0010.000
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 head0.016
GPT teacher head0.277
Teacher spread0.261 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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