Tensile creep of a long‐fiber glass mat thermoplastic composite. I. Short‐term tests
Bibliographic record
Abstract
Abstract This work is part of a larger experimental program aimed at developing a semi‐empirical constitutive model for predicting creep in random glass mat thermoplastic (GMT) composites. The tensile creep response of a long‐fiber GMT material has been characterized for 3‐ and 6‐mm thick material. Tensile tests showed that the variability within and between plaques are comparable with an overall variability of about 6% and 8% for the 3‐ and 6‐mm thick materials, respectively. The thicker material exhibited slightly higher variability and directional dependence due to greater flow during molding of the plaques. Short‐term creep tests consisting of 30 min creep and recovery, respectively, were performed over the stress range between 5 and 60 MPa. Three tests for determining the linear viscoelastic region were considered which showed that the 3‐ and 6‐mm thick GMT are linear viscoelastic up to 20 and 25 MPa respectively. The 6‐mm thick GMT consisting of a higher fiber weight fraction was linear over wider stress range. Furthermore, it was found that plastic strains were accumulated during creep, which suggests that a nonlinear viscoelastic–viscoplastic model would be more appropriate for long‐term creep at relatively high stresses, which will be presented in our companion paper. The magnitude of the plastic strains developed in the creep tests presented here was lower because a single specimen was loaded at multiple stress level over short durations. Hence, a nonlinear viscoelastic constitutive model has been developed for the two thickness materials. POLYM. COMPOS., 2009. © 2008 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".