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Fluid Flow Investigation of Die Cast Tensile Test Bars

2006· article· en· W2129649722 on OpenAlex
Martin Forté, D. Bouchard, André B. Charette

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2006
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à ChicoutimiNational Research Council Canada
FundersNational Research Council CanadaUniversité du Québec à Chicoutimi
KeywordsMaterials scienceDie (integrated circuit)Die castingUltimate tensile strengthTensile testingCastingAluminiumAlloyFlow (mathematics)Finite element methodFluid dynamicsComposite materialMetallurgyStructural engineeringMechanicsEngineering

Abstract

fetched live from OpenAlex

The fluid flow of an A356 semi-solid aluminum alloy filling a die consisting of four tensile test bars was investigated. Numerical simulations were carried out by implementing a mathematical model in a finite element software. Additional simulations were also obtained with a physical model in which tomato paste was used as the analogue fluid for the semi-solid aluminum. The modeling results were complemented by a series of experiments where tensile test bars were produced from semi-solid aluminum with a high pressure die casting press. The correspondence observed with the two modeling approaches and the casting experiments is discussed along with the effect the die geometry had on the flow patterns.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.003
Research integrity0.0000.001
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 head0.022
GPT teacher head0.238
Teacher spread0.216 · 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