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Record W2020243387 · doi:10.1021/ie049650s

Investigation of the Melting Mechanism in a Twin-Screw Extruder Using a Pulse Method and Online Measurement

2004· article· en· W2020243387 on OpenAlexafffund
Hongbing Chen, Uttandaraman Sundararaj, K. Nandakumar, Mark D. Wetzel

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2004
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePlastics extrusionResidence time distributionExtrusionPolypropylenePelletsComposite materialPolystyrenePolymerFlow (mathematics)Mechanics

Abstract

fetched live from OpenAlex

A perturbation (or pulse) method was used to investigate the melting of a polystyrene/polypropylene (PS/PP) blend in a 40-mm twin-screw extruder (TSE). A sliding-barrel technique was used to visualize the melting processes, map the temperature and pressure profiles along the channel, and obtain the residence time distribution (RTD) at different locations in the extruder. Pressure, pulse, and visualization results were used to determine where melting occurred. Three runs with ratios of the flow rate/screw speed ( Q / N ) varying from 5.0 to 11.3 g/revolution were studied. It was found that the melting of the PS/PP blend in the TSE had three distinct regions. Most of the melting occurred in a narrow transition region (∼50 mm) from the partially filled region to the fully filled region. The location of the transition region was found using four different techniques: visualization, pressure, temperature, and combined pulse/RTD methods. High-speed video of the extrusion processes shows that the solid polymer pellets melted through an “erosion” mechanism. Mechanical energy consumption for melting can be obtained through this perturbation method.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.280
GPT teacher head0.351
Teacher spread0.071 · 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

Citations26
Published2004
Admission routes2
Has abstractyes

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