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Record W2152790554 · doi:10.1002/adv.21449

Controlling Process Parameters during Plastication in Plastic Injection Molding Using Model Predictive Control

2014· article· en· W2152790554 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

VenueAdvances in Polymer Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMaterials scienceInjection molding machineMultivariable calculusViscosityMolding (decorative)Model predictive controlController (irrigation)Rotational speedProcess (computing)Control theory (sociology)Mechanical engineeringControl engineeringComputer scienceComposite materialControl (management)EngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT This investigation presents a method for controlling two key parameters during plastication that can significantly influence melt viscosity on an injection molding machine (IMM). These parameters are screw rotational speed and hydraulic back pressure. Having the functionality to control these parameters at specific targets can potentially provide the mechanism to change the melt viscosity. Mathematical models were derived that described the interaction between these two parameters. These models were used to formulate a multivariable model predictive controller developed to control them. The controller was implemented on an industrial‐scale IMM with good closed‐loop performance.

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 score0.313
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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