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Record W3040622894 · doi:10.1177/0021998320938454

Injection molding of short fiber thermoplastic bio-composites: Prediction of the fiber orientation

2020· article· en· W3040622894 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 Composite Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceComposite materialMolding (decorative)FiberMoldThermoplasticRheologyOrientation (vector space)AnisotropyCompression moldingAspect ratio (aeronautics)Thermoplastic compositesFlow (mathematics)GeometryOpticsMathematics

Abstract

fetched live from OpenAlex

Injection molding of short fiber reinforced thermoplastic polymer results in a preferential fiber orientation in the part, which leads to an anisotropy in the material mechanical properties. To anticipate the molded part performances, it is necessary to predict the fiber orientation pattern. Our goal is to have a practical tool that accurately predicts fiber orientation patterns, and to use that information to estimate the final product properties. Consequently, an efficient way to determine the flow induced fiber orientation for different flow cases under real injection molding conditions is presented. The proposed approach allows the average orientation angle prediction in a section by considering the close interaction between the fibers and the flow rheology, the fibers aspect ratio and the mold geometry. Finally, to validate the model, experimental data were taken with different matrices, fibers and mold geometries, where good agreements (R 2 ≥ 0.8) were obtained for the fiber orientations measurements.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.291

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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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