Fatigue resistance of continuous glass fiber/polypropylene composites: Temperature dependence
Bibliographic record
Abstract
Abstract The effect of testing temperature on the fatigue resistance of continuous glass fiber/polypropylene (CGF/PP) composites was studied. Fatigue resistance curves (or S‐N curves) were obtained at −40°C, 23°C and 50°C. Both on an absolute stress basis and on a normalized stress basis (with respect to the yield stress at the temperature considered), the S‐N curves showed that CGF/PP composites had excellent fatigue performance at 23°C and that their performance was actually improved at −40°C (below Tg of the PP matrix). The S‐N curves at 50°C showed that, although the composite flexural strength was reduced because of PP matrix softening, their fatigue performance remained relatively high, as it is controlled by the CGF reinforcement. Comparison with a CGF/thermoset isophthalic polyester composite of identical fiber architecture and similar flexural strength at 23°C indicated that the properties of the thermoplastic PP matrix provided improved fatigue resistance, both on an absolute and a normalized basis, especially below the glass transition temperature. It was concluded that the fact that the fatigue performance of the CGF/polyester composite is only weakly temperature‐dependent, while that of the CGF/PP composite is strongly temperature‐dependent, does not necessarily mean that it shows superior performance. Polym. Compos. 25:622–629, 2004. © 2004 Society of Plastics Engineers.
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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 | 0.001 |
| 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) | 0.001 | 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".