Optimizing nozzle geometry for extrusion-based additive manufacturing to accommodate various grades of polyethylene
Bibliographic record
Abstract
In additive manufacturing via material extrusion, viscoelastic polymer melts undergo complex flow deformations that involve both shear and extensional components. These deformations, influenced by nozzle geometry, contribute to variations in pressure drop, directly affecting extrusion performance. In nozzle designs with converging and cylindrical sections, flow behavior and pressure drop play a crucial role in process reliability and operational efficiency, requiring precise control.This study examines the relationship between nozzle geometry and material properties, focusing on how polymer chain architecture, such as branching content and molecular weight, affects deformation behavior, flow characteristics and elastic response. To investigate this effect a predictive framework was developed by simulating non-Newtonian fluid flow through variable nozzle geometries, incorporating different constitutive models and experimental rheological data from various polyethylene grades. The framework aims to optimize convergence angles for maximizing flow rate while minimizing pressure drop.To validate these predictions, experiments were conducted using a capillary rheometer equipped with a custom-designed capillary die that replicates nozzle geometry. The die featured a 30 mm barrel section with a 10 mm diameter and interchangeable conical-cylindrical sections to test different convergence angles. These modifications enabled precise measurements of pressure drop, flow rate, and viscosity under controlled conditions. The findings provide practical guidelines for optimizing process parameters while advancing the fundamental understanding of polymer processing in additive manufacturing. By refining process control, these insights contribute to improved efficiency process and product quality in material extrusion- based additive manufacturing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".