Investigating the Unrecovered Displacement of Glass Fibre Reinforced Polymers Due to Manufacturing Conditions
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".