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Record W2281406192

Finite Element Simulation of the Compaction and Springback of an Aluminum Powder Metallurgy Alloy

2012· dissertation· en· W2281406192 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2012
Typedissertation
Languageen
FieldEngineering
TopicPowder Metallurgy Techniques and Materials
Canadian institutionsnot available
Fundersnot available
KeywordsPowder metallurgyCompactionMetallurgyFinite element methodAlloyAluminiumMaterials scienceEngineeringStructural engineeringComposite materialSintering
DOInot available

Abstract

fetched live from OpenAlex

A new finite element model was developed to predict the density distribution in an Alumix 321 powder metallurgy compact. The model can predict the density distribution results of single-action compaction from 100 to 500 MPa compaction pressure. The model can also determine the amount of springback experienced by a compact upon ejection from the die at 100 and 300 MPa compaction pressure. An optical densitometry method, along with the creation of a compaction curve, was used to experimentally predict density distributions found within compacts, and found results that were consistent with both literature and finite element simulation. Further powder characterization included testing apparent density and flow rate of the powder. A literature review was also conducted and the results of which have been organized by three categories (powder type, material model, and finite element code) for easy reference by future powder researchers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.005
GPT teacher head0.169
Teacher spread0.164 · 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