Modeling the Effect of Strain Rate on the Mechanical Properties of HDPE/Clay Nanocomposite Foams
Bibliographic record
Abstract
A constitutive model considering the effect of strain rate on the mechanical properties of semicrystalline polymer/clay nanocomposite foams was studied. Also, the influence of crystallinity on the effect of strain rate was incorporated in the model. High density polyethylene (HDPE)/clay nanocomposite foam was manufactured by a batch foaming process. Intercalated clay structures in the nanocomposite were investigated by means of transmission electron microscope (TEM), and the crystallinity of the material was measured using differential scanning calorimeter (DSC). Also, foam morphologies were studied by using scanning electron microscope (SEM). The favorable effect of nanoclay on the foaming was increased as crystallinity decreases. Also, the influence of crystallinity on the foaming decreased at high clay contents. The tensile strength of the foams increased linearly to the logarithmic scale of strain rate. The Young's modulus of the foams was reinforced by increasing the crystallinity. However, the rate of increase in the modulus was blunted as strain rate increases. Also, the Young's modulus increased gradually with increasing the strain rate, but the rate of increase diminished as crystallinity increases. This combining effect of strain rate and crystallinity on the Young's modulus was modeled and a viscoelastic stress-strain behavior of the foam was also proposed. The proposed constitutive model was validated by experiments.
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.
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 | 0.001 |
| 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".