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Record W3175151370 · doi:10.4050/jahs.60.011005

Study of Processing Conditions on the Forming of Ribbed Features Using Randomly Oriented Strands Thermoplastic Composites

2015· article· en· W3175151370 on OpenAlexfundno aff
Dominic LeBlanc, Benoit Landry, Arthur Lévy, Pascal Hubert, Steven Roy, Ali Yousefpour, Erin Quinlan

Bibliographic record

VenueJournal of the American Helicopter Society · 2015
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
FundersPratt and Whitney CanadaMcGill University
KeywordsMaterials scienceComposite materialThermoplastic compositesMoldPorosityVoid (composites)ThermoplasticUltimate tensile strengthCompression moldingConsolidation (business)Molding (decorative)Adhesive

Abstract

fetched live from OpenAlex

Compression molding of randomly oriented strands (ROS) composites offers the possibility to manufacture complex parts with a fast processing cycle. In this paper, effects of pressure, strand size, temperature, and material placement in the mold cavity on the quality of a T-shape part were studied experimentally with carbon/polyether ether ketone ROS composites. Minimum filling pressure was obtained for the consolidation of a 25-mm deep cavity. Parts processed at filling pressure showed a void content no greater than 1.2%. Increasing pressure to 70 bar resulted in decreased void content as low as 0.03%. Mechanical testing of the T-shape showed similar strengths for parts processed at filling pressure and higher (70 bar). At the component level, initial strand placement greatly affected mechanical performance as the presence of a knit line was responsible for a reduction of 60% in tensile strength. The main findings show that processing a complex feature at filling pressure P fill was sufficient to reach nominal mechanical properties. This suggested that moderate porosity was not detrimental to the mechanical performance for the given tests where fatigue performance was not evaluated.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.273

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 head0.029
GPT teacher head0.271
Teacher spread0.242 · 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

Citations22
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Helicopter SocietySame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207