Experimental study on the flow and deposition of powder particles in rotational molding
Bibliographic record
Abstract
Abstract The nature of powder flow and its effect on particle deposition in rotationally molded parts were considered in this work. Experiments were carried out to observe the effects of various parameters, such as particle characteristics and operating conditions, on the deposition patterns of polyethylene powders and micropellets. The results indicate that the polymeric powders were cohesive enough to prevent size segregation at ambient temperature; however, segregation occurred when particles that had a smooth surface and regular shape were used. During processing, however, a new phenomenon of reverse cohesive segregation was observed. The results showed that the final deposition patterns are controlled primarily by the initial segregation patterns, as well as by the heating rate and rotation speed, which affect the evolution of adhesive forces between particles during heating and melt deposition process. An order‐of‐magnitude analysis was conducted to evaluate the development of cohesive forces between particles, and to estimate their effects on the movement of particles. This study provides a better understanding of the flow characteristics of polymer particles during the rotational molding process, which is very important in the development of techniques for fabricating composites and multilayered products. Polym. Eng. Sci. 45:62–73, 2005. © 2004 Society of Plastics Engineers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".