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Record W2205490752 · doi:10.12783/jmc.v2i2.97

Investigating the Unrecovered Displacement of Glass Fibre Reinforced Polymers Due to Manufacturing Conditions

2014· article· en· W2205490752 on OpenAlexvenueno aff
Maziar Shah Mohammadi, L. Solnickova, Bryan Crawford, Mojtaba Komeili, Abbas Milani

Bibliographic record

VenueJournal of Medical Cases · 2014
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCreepComposite materialCuring (chemistry)Glass transitionMaterials scienceFibre-reinforced plasticDifferential scanning calorimetryGlass fiberPolymerStress relaxationDisplacement (psychology)

Abstract

fetched live from OpenAlex

In this industrial case study, the effect of processing conditions on the cure progression as well as the magnitude of creep strain due to storage/post-operations after de-moulding was investigated via a wet lay-up manufacturing of a glass fibre reinforced polymer (GFRP), commonly used in boat building. In addition, how the creep and the related permanent deformation in its recovery stage can be prevented or controlled was a focus of the study. Dynamic Mechanical Analysis (DMA) was used to monitor the creep rate of test samples while a constant stress was applied to mimic the sagging condition of GFRP parts during assembly stages. Differential Scanning Calorimetry (DSC) was used to determine the degree of cure as well as the glass transition temperature (Tg) at different curing temperatures. A direct relationship was found between the curing and the operating temperatures, and the unrecovered displacement seen in the final GFRP part. The unrecovered displacement was hypothesized to occur mainly due to a combination of cure progression and creep during the manufacturing process. Namely, cure progression results in the development of stiffness retaining the elastic deformation, while creep can create irreversible viscous flow. The results obtained may be particularly helpful to manufacturers of open moulded parts to prevent the costly consequence from excessive recurring of parts after de-moulding. doi:10.12783/issn. 2168-4286/2.2/Milani

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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