MétaCan
Menu
Back to cohort
Record W1978138831 · doi:10.1002/pen.20258

Temperature control of injection molding. Part I: Modeling and identification

2004· article· en· W1978138831 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

VenuePolymer Engineering and Science · 2004
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMolding (decorative)Barrel (horology)Injection molding machineMaterials scienceSeries (stratigraphy)Temperature controlSystem identificationMechanical engineeringControl theory (sociology)Control (management)Computer scienceComposite materialEngineeringData modelingGeologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This paper develops a mathematical model for the dynamics of a plastic injection molding machine (IMM) that may be used for the design of a temperature‐control system. The research in this paper is novel in comparison to others since the derived models explicitly include the effects of zone interaction and backpressure, and do not lump these into an arbitrary disturbance signal. A series of experiments were conducted on a 150‐tonne IMM to identify the parameters of the proposed model using measurements of zone temperatures, percentage heater input, backpressure and screw speed. The identified model was validated using a series of blind tests that compared the model output with the measured barrel temperatures of the IMM. Polym. Eng. Sci. 44:2308–2317, 2004. © 2004 Society of Plastics Engineers.

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 score0.028
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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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