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Record W2512402424 · doi:10.1177/096739110501300303

Cure Simulation of Hemp Fiber Acrylic Based Composites during Sheet Molding Process

2005· article· en· W2512402424 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

VenuePolymers and Polymer Composites · 2005
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThermosetting polymerComposite materialMaterials scienceSheet moulding compoundDifferential scanning calorimetryCuring (chemistry)Molding (decorative)Composite numberFiberGlass fiber

Abstract

fetched live from OpenAlex

In the present study, a new environmentally friendly thermoset resin was used to manufacture hemp fiber acrylic composites by sheet molding process for automotive applications. A finite difference method was applied to predict the cure behavior and temperature variation of hemp fiber acrylic based composites during the process. Dynamic Differential Scanning Calorimetry (DSC) was employed to determine the kinetic parameters for the curing reaction at different heating rates. It was found the experimental and predicted values are in good agreement at the lower heating rate. The thermophysical properties of the resin, fiber and composite were obtained to use in the model. The temperature profile and the degree of cure of the composite with 40% resin and 60% fiber were simulated and a comparison of numerical results with known experimental data confirms the approximate validity of the model.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.044
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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