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Record W7011195511

In-line Extrusion Monitoring and Product Quality

2010· dissertation· en· W7011195511 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2010
Typedissertation
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsnot available
Fundersnot available
KeywordsField (mathematics)Quality (philosophy)Noise (video)PopulationDeformation (meteorology)
DOInot available

Abstract

fetched live from OpenAlex

Defects in polyethylene film are often caused by contaminant particles in the polymer melt. In this research, particle properties obtainable from in-line melt monitoring, combined with processing information, are used to predict film defect properties.\n\n\t “Model” particles (solid and hollow glass microspheres, aluminum powder, ceramic microspheres, glass fibers, wood particles, and cross-linked polyethylene) were injected into low-density polyethylene extruder feed. Defects resulted when the polyethylene containing particles was extruded through a film die and stretched by a take-up roller as it cooled to form films 57 to 241mm in thickness. \n\t\n\tTwo off-line analysis methods were further developed and applied to the defects: polarized light imaging and interferometric imaging. Polarized light showed residual stresses in the film caused by the particle as well as properties of the embedded particle. Interferometry enabled measures of the film distortion, notably defect volume. From the images, only three attributes were required for mathematical modeling: particle area, defect area, and defect volume. These attributes yielded two ”primary defect properties”: average defect height and magnification (of particle area). For all spherical particles, empirical correlations of these properties were obtained for each of the two major types of defects that emerged: high average height and low average height defects. Analysis of data for non-spherical particles was limited to showing how, in some cases, their data differed from the spherical particle correlations.\n\n\tTo help explain empirical correlations of the primary defect properties with film thickness, a simple model was proposed and found to be supported by the high average height defect data: the “constant defect volume per unit particle area” model. It assumes that the product of average defect height and magnification is a constant for all film thicknesses. \n\n\tA numerical example illustrates how the methodology developed in this work can be used as a starting point for predicting film defect properties in industrial systems. A limitation is that each prediction yields two pairs of primary defect property values, one pair for each defect type. If it is necessary to identify the dominant type, then measurement of a length dimension of sufficient defects in the film is required.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.999

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 head0.197
Teacher spread0.189 · 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 designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

Explore more

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