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Record W7071645890

Three-dimensional numerical modeling of segregation in powder injection molding

2009· article· en· W7071645890 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

VenueNPARC · 2009
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsMolding (decorative)SinteringParticle (ecology)DiffusionFlow (mathematics)Computer simulationPhase (matter)
DOInot available

Abstract

fetched live from OpenAlex

The ability to predict segregation of the solid phase in processes such as powder injection molding and injection molding of semi-solid materials is of special interest since such phenomenon affects the final properties and characteristics of the molded parts. In pwder injection molding, for example, defects appear very often in the debinding and sintering stages but are caused by filling problems and determined by a non-uniform distribution of the solid particles within the molded part. In this paper we present the results obtained by a 3D numerical solution algorithm for the simulation of particle migration in dense suspensions. The particle migration is modeled using the diffusion flux model and integrated into the NRC's 3D injection molding software. The solution algorithm is first validated by solving flow problems for which experimental and numerical data are available. Then, the approach is applied to injection molding problems and the segregation inside the molded parts is studied.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.276

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 head0.018
GPT teacher head0.223
Teacher spread0.205 · 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