Influence of Thermal Ratcheting on the Creep and Mechanical Properties of High Density Polyethylene (HDPE)
Bibliographic record
Abstract
The objective of this research is to describe the consequence of thermal ratcheting on the long-term creep property of HDPE material. The thermal ratcheting phenomenon amplifies significantly the creep strain of HDPE in comparison to the steady creep strain under constant temperature. The magnitude of creep strain of HDPE increases by 8% after just 20 thermal cycles between 28 and 50°C. The creep modulus which is inversely proportional to the creep strain depletes further under thermal ratcheting. Both properties change significantly with the number of thermal cycles. The coefficient of thermal expansion (CTE) of HDPE varies with the applied compressive load, with successive thermal cycles and with the thermal ratcheting temperature. The impact of thermal ratcheting diminishes with increase in initial steady creep exposure time-period but still the magnitude cumulative damage induced is noteworthy. The magnitude of growth in creep strain drops from 8 to 2.4% when thermal ratcheting is performed after 1 and 45 days of steady creep, respectively. There is a notable change in thickness of the material with each heating and cooling cycle even after 45 days of creep however, the thermal ratcheting strain value drops by 80% in comparison to thermal ratcheting strain after 1 day of creep and under similar test conditions.
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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 | 0.000 |
| 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".