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Record W1554675986 · doi:10.1063/1.2740787

Three-Dimensional Modeling of Void Growth in Friction Stir Welding of Stainless Steel

2007· article· en· W1554675986 on OpenAlexfundno aff
Youliang He, Donald E. Boyce, Paul R. Dawson

Bibliographic record

VenueAIP conference proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceStreamlines, streaklines, and pathlinesPorosityVoid (composites)Friction stir weldingWeldingMechanicsFinite element methodViscoplasticityHydrostatic stressConstitutive equationMetallurgyComposite materialStructural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The growth of internal voids in the process of friction stir welding of stainless steel was simulated using a damage model that considers both strain hardening and porosity evolution. In the void growth equations, the mean stress (hydrostatic stress) was scaled by the state variable for plastic flow resistance, i. e. strength. The damage model was coupled with the viscoplastic deformation and thermal processes using a steady‐state Eulerian formulation in a finite element scheme. The porosity and strength were calculated by integration of the evolution equations along streamlines of the flow field. The distributions of microvoids as well as the strength within the material were obtained. These distributions were used to model the effects of operational parameters such as the tool rotational and translational speeds as well as the pin threads on the growth of porosity.

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)0.001
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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.251
Teacher spread0.226 · 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

Citations7
Published2007
Admission routes1
Has abstractyes

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