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Record W1980500560 · doi:10.1177/0731684405043556

Modeling of Thermoforming of Low-density Glass Mat Thermoplastic

2005· article· en· W1980500560 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2005
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsQueen's UniversityRoyal Military College of Canada
Fundersnot available
KeywordsThermoformingMaterials scienceComposite materialThermoplasticGlass fiberHyperelastic materialMoldDeformation (meteorology)PolypropyleneFinite element methodStructural engineering

Abstract

fetched live from OpenAlex

The glass mat thermoplastic (GMT) is made from random-chopped glass fibers and polypropylene in a sheet form. This low-density compressible material is used extensively in the automotive industry for making panels. The mechanical behavior of this material in large deformation and at thermoforming process temperature is far from being well understood. The objective of this research is to determine a constitutive law of this Azdel thermoplastic composite used for thermoforming process. A series of biaxial tests was performed to study the stress-strain behavior of the low-density thermoplastic sheet reinforced with 55% glass fiber. Different strain rates and temperatures were employed to study their effects on the mechanical behavior. Pressure-thickness model parameters were obtained using a laboratory press. A nonisothermal hyperelastic model was used for modeling this material. The results of the simulation are compared with data from a laboratory thermoforming machine and a small, simple mold.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.382

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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