Post‐extrusion process for the robust preparation of highly uniform multiphase polymeric <scp>3D</scp> printing filaments
Bibliographic record
Abstract
Abstract Fused filament fabrication 3D printing demands the preparation of polymeric filaments of highly uniform dimensions in order to achieve the best possible mechanical performance. This stringent requirement is a challenging task and becomes even more difficult when multiphase hybrid polymers are used. In this work, a post‐extrusion setup is presented, allowing for the preparation of immiscible quaternary polymeric blend filaments of highly stable dimensions. The polymer blend used is bio‐based and is comprised of poly(L‐lactide), poly(ethylene oxide), poly(ether‐b‐amide), and polyamide‐11. The setup is connected to an extruder die. By incorporating a precisely localized temperature control over a pathway of 66 mm, the extrudate melt is initially transformed into a stable semisolid state. Forced convection cooling, imposed at a distance of 4–5 mm from the last static cooling zone, allows thereafter the complete solidification of the profile. Filaments with a uniform diameter of 1.75 and 2.85 mm are manufactured. The developed approach can be used to manufacture, otherwise non‐processable, complex quaternary polymer blend filaments with a highly controlled diameter in the range of 0.1–4.5 mm with no restriction regarding the filament length. The produced filaments were used to 3D print dogbone samples with very high elongation at break.
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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 | 0.001 |
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".