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Record W2122693417 · doi:10.1002/pen.20196

A temperature‐dependent adaptive controller. Part II: Design and implementation

2004· article· en· W2122693417 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
FundersNational Research Council CanadaNational Science Council
KeywordsSetpointTemperature controlController (irrigation)MoldMaterials scienceControl theory (sociology)Tracking (education)Process (computing)PID controllerOpen-loop controllerAtmospheric temperature rangeMechanical engineeringComputer scienceControl engineeringControl (management)EngineeringClosed loopComposite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract A generic online temperature‐dependent adaptive control procedure to redefine the controller parameters was developed from dynamic models to achieve good tracking of injection‐velocity setpoint profiles during filling of a mold cavity. This adaptive control procedure (ACP) incorporates algorithms for process open‐loop testing and modeling, control simulations, and online controller updating. These functionalities are embedded into a main program that provides overall control of mold‐filling velocity. The controller's dynamic matrix is updated as changes in the setpoint melt temperature occur. It was shown that the ACP can provide an effective and systematic approach for controlling injection mold‐filling velocity for any plastic material over its operating melt temperature range. Polym. Eng. Sci. 44:1934–1940, 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.355

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 head0.011
GPT teacher head0.215
Teacher spread0.203 · 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