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Record W3207271493 · doi:10.33774/miir-2021-80zsh

Modelling of the Friction Stir Welding Process for Aluminum

2021· preprint· en· W3207271493 on OpenAlexaff
Zilong Song, Huaxiong Huang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsYork University
Fundersnot available
KeywordsFriction stir weldingInertiaHeat generationCompressibilityMechanicsMechanical engineeringDeformation (meteorology)Inertial frame of referenceWeldingNewtonian fluidFriction weldingHeat transferMaterials scienceProcess (computing)Computer scienceEngineeringClassical mechanicsComposite materialPhysicsThermodynamics

Abstract

fetched live from OpenAlex

When the friction stir welding (FSW) process is used in practice, defects appear under uncertain operating conditions. It is crucial to understand the basic mechanisms of defect generation so that it could be avoided. During the workshop, simplified models were developed for heat generation and plastic deformation, and attempts were made to find analytical and numerical solutions and continued until shortly after the workshop. We found that the unrealistic solution obtained during the workshop was due to the choice of a balance between inertial forces and plastic stress. In contrast, when inertia is negligible, physically reasonable solutions can be obtained for plastic deformation irrespective of the material compressibility. Investigating the dominant physical processes that drive this FSW process, the solutions from the simplified one-dimensional heat model and a two-dimensional non-Newtonian fluid model are also presented. The temperature distribution matches the results using direct numerical simulation and the experiment.

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: Methods · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score0.633

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 head0.039
GPT teacher head0.275
Teacher spread0.236 · 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
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

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