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Material Flow and Grain Deformation during Extrusion

2014· article· en· W1990373997 on OpenAlexafffund
Yahya Mahmoodkhani, Mary A. Wells, Lina M. Grajales, Warren J. Poole, Nick Parson

Bibliographic record

VenueMaterials science forum · 2014
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsUniversity of British ColumbiaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDie swellMaterials scienceExtrusionElectron backscatter diffractionFinite element methodDeformation (meteorology)Composite materialMaterial flowGrain sizeMetallurgyFlow stressDiffractionFlow (mathematics)AlloyMicrostructureOpticsMechanicsStructural engineering

Abstract

fetched live from OpenAlex

A combination of numerical simulation using the finite element method (FEM) and experimental characterization was used to study material flow and grain deformation during the hot extrusion process for an AA3003 aluminum alloy. The grain structure of the extrudate was experimentally studied using optical microscopy and Electron Back-Scattered Diffraction (EBSD) methods. Using the FEM model predictions of material flow, a simple procedure was used to predict the spatial variation of grain thickness both through the extrusion and along its length. Experimental measurements using EBSD of the grain thickness at the center of the extrudate show that the model predictions are in good agreement with the measurements. The model was then used to calculate the grain thickness changes along the extrudate.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 integrity0.0000.000
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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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 designObservational
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
Published2014
Admission routes2
Has abstractyes

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