Effects of Punch Test Conditions on the Mechanical Response of Polyethylene Materials
Bibliographic record
Abstract
Polyethylene (PE) materials have been widely used in industrial and living fields such as natural gas pipelines, drainage pipes, sewage pipes. Punch test is an interesting tool for studying the mechanical properties of materials. However, the deformation behavior involved in punch test is complicated, it is, therefore, essential to investigate the influence of punch test conditions on the mechanical properties of PE materials. Punch tests have been carried out on PE specimens with different punching speed (0.01, 0.1, 1, 10 and 100mm/min) and different punch head diameters (4, 6, 8 and 10mm). The experimental results show that the maximum load from the load-displacement curve increases with the increase of the punch head diameter under the same punch speed. When the punch speed is slow, the force-displacement curve of PE specimens contains four typical stages, namely, elastic stage, yield stage, strain softening stage and strain hardening stage. However, the PE specimen breaks before reaching the strain hardening stage when the punch speed is fast. Similarly, the maximum load increases with the increase of punch speed when the same punch head diameters are used. Furthermore, a three-dimensional finite element (FE) model of PE specimens subjected to punch load has been established to further analyze the deformation and failure behavior. A good agreement between the simulation results and the punch test data is achieved.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".