Melting and densification of thermoplastic powders
Bibliographic record
Abstract
Abstract The present work focuses on the transformation of a loosely packed, low density powder compact, to a fully densified polymer part, when processed at temperatures above the melting (or glass transiton) point of the polymer. The purpose of this study is to dlucidate the mechanisms involved in the process and to examine the applicability of models available in the materials science literature for the description of the overall densification of molten polymer particles. The evolution of density as a function of time during sinter‐melting was measured experimentally using a heating oven. The results revealed that the overall process consists of two stages. The first stage involves particle coalescence, which depends on viscosity, surface tension and powder properties. During this stage air pockets, which eventually become bubbles, are entrapped inside the melt. The second stage involves the diffusion controlled shrinkage and eventual disapperance of the bubbles. The experimental results were compared to models commonly used for the densification of particulate compacts in the ceramics, glass and metals processing literature. Application of models based solely on viscosity and surface tension phenomena, can describe satisfactorily the process until the point where closed pores (bubbles) form. Abubble dissolution model has been successfully applied to provide predictions of density as a fuction of time for late stages of densification.
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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".