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Record W1993293837 · doi:10.1243/03093247jsa608

Predicting the forming limit diagram of AA 5182-O

2010· article· en· W1993293837 on OpenAlexaff
B Li, T. J. Nye, Pengfei Wu

Bibliographic record

VenueThe Journal of Strain Analysis for Engineering Design · 2010
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNeckingForming limit diagramFinite element methodLimit (mathematics)DiagramMaterials scienceAluminiumStructural engineeringAlloyMechanicsComposite materialMathematicsMathematical analysisPhysicsEngineeringStatistics

Abstract

fetched live from OpenAlex

The prediction of a forming limit diagram (FLD) for aluminium alloy sheet using finite element analysis without implementing pre-defined geometrical imperfections or material imperfections is studied. The limit strains of the FLD are determined by applying a new proposed localization criterion in the dome stretching test. The elements just outside the necking area, where their major and minor principal strains have no simultaneous change after localized necking happens, are chosen as the reference elements for measurement of limit strains. Simulations are carried out for various strain paths ranging from balanced biaxial stretching to uniaxial stretching. The predicted FLD of AA 5182-O is compared with an experimentally determined FLD and very good agreement is achieved. It is demonstrated that FLDs can be predicted by the finite element method without requiring any assumed geometric or material imperfections in the numerical model.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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

Citations16
Published2010
Admission routes1
Has abstractyes

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