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Record W2137533841 · doi:10.1002/aic.14275

Sag in commercial thermoforming

2013· article· en· W2137533841 on OpenAlexaff
H. M. Baek, A. Jeffrey Giacomin, M. J. Wurz

Bibliographic record

VenueAIChE Journal · 2013
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsThermoformingIsothermal processSuperposition principleMechanicsFinite element methodMaterials scienceCube rootIsotropyViscosityWork (physics)Composite materialThermodynamicsMathematicsMechanical engineeringEngineeringGeometryPhysicsMathematical analysisOptics

Abstract

fetched live from OpenAlex

Our previous analytical solution gives sag advancing implicitly as , or for , sag advances with the cube root of time for a thin wide rectangular Newtonian isothermal sheet. This previous analytical work applies to sheets that are pinned along just two edges, and not all the way around. Corresponding sagometer experimental results confirmed this cube root relation. This work compares the prediction with measured commercial thermoforming behavior on rectangular sheets that are, of course, pinned all the way around. Then sag parallel superposition is used to extend for a sheet pinned all the way around. We evaluate sag parallel superposition using a finite element method (FEM) employing ANSYS Polyflow. The equation assumes sagging sheet cylindricity, and from our FEM we find that this assumption is reliable when . We compare sag measured in commercial thermoforming, using high‐impact polystyrene (HIPS) sheets that are pinned all the way around, by extending with parallel superposition. It is found that the time evolution of the commercial sag follows nearly exactly the same shape as the isothermal prediction. We measure sag runaway, and although the isothermal analysis , predicts the sag runaway time accurately, our isothermal theory overpredicts the amount of sag in the nonisothermal commercial operation by as much as a factor of 14. It is also shown how to use sheet sag measurements from commercial thermoforming to deduce the Newtonian viscosity of a thermoforming resin at a temperature that is above its softening point. © 2013 American Institute of Chemical Engineers AIChE J , 60: 1529–1535, 2014

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.465

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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 designObservational
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

Citations6
Published2013
Admission routes1
Has abstractyes

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