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Numerical Simulation of Warm Deep Drawing of AZ31 Magnesium Alloy Sheet with Variable Blank Holder Force

2007· article· en· W1969796846 on OpenAlexaff
Ying Peng, Qun Feng Chang, Dayong Li, Jie Hu, Xiaoqin Zeng

Bibliographic record

VenueKey engineering materials · 2007
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsCanadian Association of Emergency Physicians
FundersNatureBritish Heart Foundation
KeywordsBlankDeep drawingFormabilityMagnesium alloySheet metalMaterials scienceMetallurgyDie (integrated circuit)Computer simulationAlloyComposite materialMechanicsPhysics

Abstract

fetched live from OpenAlex

Blank holder force (BHF) plays an important role in sheet metal forming. Previous studies demonstrated that variable blank holder forces can improve the cold formability of steel blank, but the research on the application of variable blank holder force in warm forming of magnesium sheet forming has not been well investigated. In this study, the mechanical property of AZ31 magnesium alloy sheet is measured through some uniaxial tensile tests. In order to obtain the variational rule of the BHF, a mathematical model of BHF is deduced based on the energy theory. The variational rule of the BHF over the punch stroke is analyzed. Finally, three profiles of the BHF curve are designed, and the numerical simulation of warm deep drawing process of magnesium alloy sheet is also performed. A suitable variable blank holder force scheme is obtained through comparison among three results of simulation. The simulation indicates that the limiting drawing ratio of AZ31 magnesium alloy sheet can be improved from 3.0 to 3.5 with the suitable blank holder force varied by an inverted V curve.

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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.215
Teacher spread0.209 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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