MétaCan
Menu
Back to cohort
Record W2057808611 · doi:10.1002/pen.10988

Metal injection molding: 3D modeling of nonisothermal filling

2002· article· en· W2057808611 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolymer Engineering and Science · 2002
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceMechanicsRotational symmetryInertiaFlow (mathematics)Finite element methodTransfer moldingDiaphragm (acoustics)Free surfaceMoldComposite materialMolding (decorative)ThermodynamicsClassical mechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract A three‐dimensional transient finite element flow analysis code that includes inertia and free surface modeling is used to predict uniform (axisymmetric) and nonuniform (nonaxisymmetric) filling patterns in a thick‐walled tool with a diaphragm gate. The simulation for a metal injection molding compound, which is strongly influenced by thermal effects, predicted several observed flow patterns: initial annular free surface flow (jetting), bypass and folding flow to form surface defects, and the transition from uniform (axisymmetric) flow to nonuniform (nonaxisymmetric flow) with increasing fill time and lower temperatures. Simulations of filling through a thicker diaphragm gate showed jetting and the associated problems were eliminated. Simulations also demonstrate the effects of filling time, mold temperature, inertia, yield stress, and wall slip on filling patterns, particularly the processing conditions that separate regions of uniform and nonuniform flow.

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 score0.038
Threshold uncertainty score0.383

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 head0.191
Teacher spread0.173 · 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