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Record W2141981078 · doi:10.5539/mer.v2n1p64

Numerical Simulation and Experimental Verification of Upsetting Temperature Optimization of Aluminium Alloy

2012· article· en· W2141981078 on OpenAlexvenueno aff
Maroš Martinkovič, Mária Kapustová

Bibliographic record

VenueMechanical Engineering Research · 2012
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsnot available
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAV
KeywordsMaterials scienceAluminiumAlloyForming processesMetallographyMetallurgyAluminium alloyComputer simulationFinite element methodDeformation (meteorology)Metal formingDie (integrated circuit)PlasticityExtrusionComposite materialStructural engineeringMechanicsMicrostructureEngineering

Abstract

fetched live from OpenAlex

Warm forming of metal materials represents a profitable method of precise forming pieces production due to energy and time savings, obtaining of higher surface quality and dimension precision of forming pieces in comparison with hot forming. An optimal forming temperature was selected from experimental measurement of mechanical properties, plasticity and workability of an aluminium alloy. Upsetting forming process of aluminium alloy AlSiMg type was simulated using finite element method at warm forming temperatures. Numerical simulation of the alloy forming based on finite elements methods was verified, the results of the numerical simulation were compared with experimental result - degree of grain boundaries deformation obtained by stereological metallography. From the results an optimal temperature of warm forming was estimated.

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 score0.002
Threshold uncertainty score0.005

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 studies0.0000.000
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 head0.037
GPT teacher head0.340
Teacher spread0.303 · 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
Published2012
Admission routes1
Has abstractyes

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