MétaCan
Menu
Back to cohort
Record W2624897602 · doi:10.4050/f-0070-2014-9562

Compression Molding of Composite Tailboom Frames

2014· article· en· W2624897602 on OpenAlexaff
Ali Yousefpour, Pierre Beaulieu, Steven Roy, Felix Bednar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsBell Helicopter Textron (Canada)National Research Council Canada
Fundersnot available
KeywordsCompression moldingComposite numberCompression (physics)Materials scienceMolding (decorative)Composite materialComputer science

Abstract

fetched live from OpenAlex

Composite helicopter tailboom frames were manufactured by compression molding using a carbon fiber thermoset bulk molding compound. A mold was designed for compression molding and installed in a hydraulic press. The mold features two shear edges, guide pins and an integrated part ejection system. A material preforming method was developed to improve consistency in material distribution, which improved process robustness. A number of parts were produced and inspected for void content and dimensional stability. No significant porosity or voids were found in the samples examined. Part thickness uniformity was studied and improved, in order to meet the required tolerances. Dimensional inspection before and after a free standing post cure showed no significant part distortion. This work showed that compression molded tailboom frames are a viable alternative to current tailboom frames using continuous pre-impregnated fabric materials and cured by autoclave. This alternate processing method has the potential to reduce touch time and manufacturing costs.

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)0.000
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: none
Teacher disagreement score0.568
Threshold uncertainty score0.166

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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 designSimulation or modeling
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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicManufacturing Process and OptimizationFrench-language works237,207