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Record W7132537811

Simulation and validation of 3D compression resin transfer moulding

2022· article· en· W7132537811 on OpenAlexfundvenueno aff
S. Sarojini Narayana, L. Barcenas, L. Khoun, P. Hubert

Bibliographic record

VenueNPARC · 2022
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermosetting polymerTransfer moldingProcess (computing)Work (physics)CompactionComposite numberCompression (physics)Flow (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

In recent years, resin manufacturers have been formulating thermoset resins with a decrease in cure time. This has helped to shorten the process cycle times and paved the way for the use of cost-effective compression resin transfer moulding (CRTM) process to produce high performance composite parts. However, highly reactive resins pose a major challenge in producing high quality parts. Currently, many researchers have been working on the process simulation of CRTM process. However, there is still a large gap to be addressed in terms of coupling between heat transfer, cure kinetics, resin flow and compaction (thermal-chemical-mechanical) during the CRTM process especially for 3D structures. The main objective of this work is to perform thermal-chemical-mechanical coupled CRTM simulation for a flat 3D part. The work involved development of resin and fibre material models. The material models developed were implemented to perform the simulations. These simulations were validated using interrupted resin flow CRTM experiments.

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: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.215

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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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