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Record W4244456132 · doi:10.32920/ryerson.14662806

Methodology For Fabricating High Temperature Composite Panel And Evaluation

2021· preprint· en· W4244456132 on OpenAlexaff
Gopinath Thamilselvan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAerospaceAutoclaveMaterials scienceComposite numberComposite materialUltimate tensile strengthMolding (decorative)High pressureMechanical engineeringProcess engineeringManufacturing engineeringEngineeringEngineering physicsMetallurgy

Abstract

fetched live from OpenAlex

Ever increasing demand for composite materials in the aerospace industry has lead composite manufacturers to develop numerous innovations in the field of manufacturing. Composites play an important role in engine components and high-speed jet crafts where weight saving materials that can sustain high temperatures, with little reduction in performance are desired. A cost effective in-house novel manufacturing technique aimed at producing high glass transition temperature (Tg) composite panels on par with autoclave manufacturing technology has been designed and built. The designed compression molding system is integrated with an oven, to cater the need for high temperature and high pressure manufacturing system which can be a potential alternative for autoclaves in terms of fabricating panels for structural testing. Quality of the panel was demonstrated by conducting ultimate tensile test, fatigue test and microscopic examination results. High temperature mechanical testing was also carried out to study the behavior of high TgIM7/RP46 composites fabricated at the laboratory.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.111
GPT teacher head0.315
Teacher spread0.204 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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