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Record W2743017599

Impact of HP-RTM Process Parameters on Mechanical Properties with Epoxy and Polyurethane Systems

2017· article· en· W2743017599 on OpenAlexaboutno aff
Ian Swentek, Björn Beck, Vanja Ugresic, T Potyra, Frank Henning

Bibliographic record

VenuePublikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · 2017
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsEpoxyPolyurethaneComposite materialMaterials scienceProcess (computing)Computer science
DOInot available

Abstract

fetched live from OpenAlex

High pressure resin transfer molding is a method for processing continuous fiber reinforced composites at industrial production rates. This paper examines the more common HP-IRTM variant, where the 'I' stands for injection. To achieve a composite with the best mechanical properties, a combination of the fiber, resin and processing parameters must be understood. Two different matrix materials, epoxy and polyurethane, and two different fibers, glass and carbon, are processed on a KraussMaffei HP-RTM system at the Fraunhofer Project Center in London, Ontario, Canada. Several processing parameters are investigated during the manufacturing of these polymer matrix composites including the press force during injection, the press force during cure, and the injection rate. Subsequently, the manufactured parts are characterized and their mechanical properties are evaluated. The results of this study shed light on the critical properties and process settings in HP-RTM production.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.030
GPT teacher head0.259
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes1
Has abstractyes

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Same venuePublikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)Same topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207