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Record W4294874181 · doi:10.1504/ijaac.2022.125287

Closed-loop thickness control and sensor placement in extrusion blow moulding

2022· article· en· W4294874181 on OpenAlexaff
Mostafa Darabi, Raffi Toukhtarian, Hossein Vahid Alizadeh, Benoît Boulet

Bibliographic record

VenueInternational Journal of Automation and Control · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsExtrusionDie swellPlastics extrusionInjection mouldingController (irrigation)Die (integrated circuit)NeckingMaterials scienceBlow moldingMechanical engineeringControl theory (sociology)EngineeringComposite materialComputer scienceControl (management)

Abstract

fetched live from OpenAlex

Extrusion blow moulding (EBM) is a polymer forming technique used to manufacture hollow plastic parts, such as fuel tanks. In this work, the feasibility of using closed-loop control in EBM is explored to compensate for machine drift and disturbances. A control system is proposed to regulate the extrusion process in EBM. The extrusion controller aims to increase process consistency by minimizing part thickness deviations. The thickness of the extrudate is measured during the extrusion cycle and any deviation from the desired thickness profile is compensated by changing the die gap in real time. The controller, which offers flexibility in thickness sensor placement, features a Smith predictor configuration embedding an H∞ controller. It compensates for the input-dependent polymer transport delay, the nonlinear steady-state swell, and nonminimum phase necking effects affecting the extrudate. This extrusion control technique may help reduce the production rate of off-specification parts and improve product quality.

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.

How this classification was reachedexpand

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.301

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 head0.005
GPT teacher head0.233
Teacher spread0.228 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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