Thermoforming-Stamping of Continuous Glass Fiber/Polypropylene Composites: Interlaminar and Tool–Laminate Shear Properties
Bibliographic record
Abstract
The results of interlaminar and tool–laminate shear tests performed on a twill 2 2 PP/glass fabric are described in this paper. The influence of the laminate temperature, pullout velocity and normal pressure on the interlaminar shear stress and friction coefficient are evaluated, as well as the effect of cooling the specimen from the melt to simulate real forming conditions. Opposite trends were observed for the variation of the shear stress and friction coefficient whether the tests were performed above the melt temperature of the matrix or above the crystallization temperature (135, 140, and 155 after cooling from the melt temperature. For the interlaminar shear tests, this was caused by the shift from an interlaminar to an intralaminar shear deformation mode occurring. For the tool–laminate shear tests, this was caused by the shift from matrix shear at the interface tool–laminate to direct Coulomb friction of the fibers with the tool with an increase of the normal pressure and/or an increase of the matrix viscosity with decreasing temperatures. Above the melt temperature of the matrix, the friction coefficient and shear stress were higher at the tool–laminate interface than in the interlaminar region while at temperatures close to the crystallization temperature they became lower at the tool–laminate interface. A summary of these observations is made and a discussion of their possible impact on the forming of parts is enlightened.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".