MétaCan
Menu
Back to cohort

RAM POSITION CONTROL IN PLASTIC INJECTION MOLDING MACHINES WITH HIGHER-ORDER ITERATIVE LEARNING

2006· article· en· W2024010356 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueControl and Intelligent Systems · 2006
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsIterative learning controlControl theory (sociology)Position (finance)Controller (irrigation)Injection molding machineConvergence (economics)Molding (decorative)Feed forwardComputer sciencePID controllerControl engineeringNonlinear systemControl (management)EngineeringArtificial intelligenceTemperature controlMaterials scienceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

In plastic injection molding, the ram position plays an important role in production quality. This paper introduces a new method, which is a combination of the current cycle feedback control (a PI controller) and a feed-forward higher-order iterative learning control (ILC), to control the ram position in injection molding. The PI controller is used to stabilize the system, and the feed-forward higher-order ILC control is used to compensate for nonlinear/unknown dynamics and disturbances, thereby gaining the precision tracking to ram position. The simulation results indicate that the new method outperforms the conventional PI controller. In addition, it outperforms the conventional ILC in the convergence 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 categoriesMeta-epidemiology (narrow)
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 score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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