Effect of Mold Temperature on Morphology and Mechanical Properties of Injection Molded HDPE Structural Foams
Bibliographic record
Abstract
In this study, HDPE structural foams are produced by injection molding under different mold temperatures to study the effect of this variable on average cell dimension, cell density, and skin thickness ratio. Samples are also produced by setting independently the temperature of the fixed and moving plate of the mold to detect the sensitivity of foam structure to a temperature gradient in processing. The resulting foams are also characterized in terms of mechanical properties including impact and flexural tests. It has been found that for homogeneous mold temperatures, symmetrical skin thicknesses are obtained, which increase with decreasing mold temperature. On the other hand, by keeping one mold face at a constant temperature and varying the second one, asymmetric skin thicknesses are obtained. The degree of asymmetry is found to increase as the temperature difference between both molds increased. Furthermore, decreasing mold temperature produces a small increase in average cell sizes and reduced cell density. In general, both impact strength and flexural moduli of the structural foams increase with increasing skin thickness. For the particular case of asymmetric foams, the flexural moduli are slightly higher when the load is applied on the thicker skin; while much higher impact strength is obtained when the falling weight strikes the samples on the face having the smaller skin thickness.
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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 | 0.001 |
| 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".