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Record W2912150496 · doi:10.1177/096369350401300105

Internal Stresses and Warpage of Thin Composite Parts Manufactured by RTM

2004· article· en· W2912150496 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Composites Letters · 2004
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceThermosetting polymerComposite materialShrinkageComposite numberTransfer moldingRheologyFinite element methodYield (engineering)Sheet moulding compoundInjection mouldingStructural engineeringMold

Abstract

fetched live from OpenAlex

Resin transfer moulding (RTM) is a widely used manufacturing technique of composite parts. A proper selection of process parameters is the key to yield successful moulding results and obtain a good part. Among other things, when thermoset resins are processed, the shrinkage that occurs due to the polymerisation reaction further complicates the situation. In this paper, a finite difference analysis is proposed to simulate the effect of thermal and rheological changes during thin plates cooling after processing. Classical Laminate Theory is here implemented to compute composite internal stresses resulting from these thermo-rheological conditions. Laminate stresses are then computed and warpage obtained with the proposed numerical algorithm. Samples of thin plates were moulded combining two glass reinforcement materials. During cooling, after processing plates warpage was recorded and results compared to model predictions. This analysis presents the basis of a further numerical optimisation for thick composite parts.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.967

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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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