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Computer Integrated Manufacturing of Polyurethane Foams Steered by Measured and Calculated Data in Comparison

2016· article· en· W2580842752 on OpenAlexvenueno aff
Heinrich Horacek

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsnot available
Fundersnot available
KeywordsIsocyanatePolyurethaneMaterials scienceViscosityActivation energyKinetic energyThermodynamicsComposite materialPolymer chemistryOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Polyurethane foams were manufactured under continuous measurements of isocyanate content, temperature, expansion and viscosity. Parallel model calculations were executed based on the driving force of 50kJ/mole heat of reaction of the energy rich isocyanate group and on thermodynamic balances of weight and heat of formation as well as on a kinetic model using a first order reaction for the isocyanate consumption and a relation for the viscosity dependent on conversion and on a viscosity constant. The agreement between continuous measured and calculated data was satisfying. The parameters isocyanate content, temperature, expansion and viscosity controlled and steered the variables of the production plant, like dosage of components, temperature and conveyer speed. The kinetic model was also valid for polyurethane foams with additives, which did not react with components of foam, like flame retardants or pigments. The thermodynamic balances of weight and heat predicted residues and consumed heats by degradation under fire, which correlated with those measured by TGA and DSC. The target was to establish computer integrated manufacturing without continuous measurements but by mere model calculation. This was possible with the knowledge of the first order velocity constant and the viscosity constant.

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)0.008
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 score0.022
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.063
GPT teacher head0.371
Teacher spread0.308 · 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
Published2016
Admission routes1
Has abstractyes

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