Extrusion blow molding of long fiber reinforced polyolefins
Bibliographic record
Abstract
Abstract A 58% (by weight) long glass fiber reinforced (LGF)‐HDPE master batch was blended with a typical blow molding HDPE grade. HDPE composites having between 5% and 20% (by weight) long fiber content were extruded at different processing conditions (extrusion speed, die gap, hang time). The parison swell (diameter and thickness) decreased with increasing fiber content. Although the HDPE exhibited significant shear rate dependence, the LGF/HDPE composites were shear rate insensitive. Both the diameter and weight swell results also indicated very different sagging behavior. The LGF/HDPE parisons did sag as a solid‐body (equal speed at different axial locations) governed by the orientation caused by the flow in the die. Samples taken from blown bottles showed that fiber lengths decreased to 1‐3 mm, from the original 11 mm fiber length fed to the extruder. No significant difference in fiber length distribution was found when samples for different regions of the bottle were analyzed. SEM micrographs corroborate the absence of fiber segregation and clustering or the occurrence of fiber bundles (homogeneous spatial fiber distribution) as well as a preferential fiber orientation with the direction of flow. The blowing step did not change the orientation of the fibers. Five‐percent (5%) and 10% LGF/HDPE composites could be blown with very slight variations to the neat HDPE inflation conditions. However, 20% LGF/HDPE composites could not be consistently inflated. Problems related to blowouts and incomplete weldlines were the major source of problems.
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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".