MétaCan
Menu
Back to cohort
Record W4379141428 · doi:10.1080/00325899.2023.2218678

Mold filling behaviour of LPIM feedstocks using numerical simulations and real-scale injections

2023· article· en· W4379141428 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePowder Metallurgy · 2023
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMoldMaterials scienceBar (unit)Flow (mathematics)Scale (ratio)Raw materialComposite materialMechanicsComputer simulationDie (integrated circuit)Nanotechnology

Abstract

fetched live from OpenAlex

This study aims to compare the flow patterns and in-cavity pressures obtained experimentally and numerically for different conditions. Four feedstocks based on 17-4PH stainless steel powder were fully characterised and implemented as material laws in an Autodesk Moldflow package before to obtain numerical simulations that were then validated using real-scale injections. The flow patterns obtained numerically for the different flat bar mold geometries were in good agreement with the experimental flow patterns, showing an almost perfect fit, whereas for the flow patterns of the complex mold geometry, there were some minor discrepancies. The simulated pressure profiles obtained for different mold geometries, feedstock temperatures, mold temperatures and solid loadings were in good agreement with the experimental pressure profiles in terms of trend and pressure values, with maximum relative differences varying from 30 to 64% depending on particular feedstocks and process parameters.

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 score0.014
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.263
Teacher spread0.231 · 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