Pellet-Based Extrusion Additive Manufacturing of Lightweight Parts Using Inflatable Hollow Extrudates
Bibliographic record
Abstract
Additive manufacturing (AM) has become a key element of Industry 4.0, particularly the extrusion AM (EAM) of thermoplastic materials, which is recognized as the most widely used technology. Fused Filament Fabrication (FFF), however, depends on expensive commercially available filaments, making pellet extruder-based EAM techniques more desirable. Large-format EAM systems could benefit from printing lightweight objects with reduced material use and lower power consumption by utilizing hollow rather than solid extrudates. In this study, a custom extruder head was designed and an EAM system capable of extruding inflatable hollow extrudates from a variety of materials was developed. By integrating a co-axial nozzle-needle system, a thermoplastic shell was extruded while creating a hollow core using pressurized nitrogen gas. This method allows for the production of objects with gradient part density and varied mechanical properties by controlling the inflation of the hollow extrudates. The effects of process parameters— such as extrusion temperature, extrusion speed, and gas pressure were investigated—using poly-lactic acid (PLA) and styrene-ethylene-butylene-styrene (SEBS) pellets. The preliminary tests identified the optimal range of these parameters for consistent hollow extrudates. We then varied the parameters to determine their impact on the dimensions of the extrudates, supported by analyses of microscopic images taken with an optical microscope. Our findings reveal that pressure is the most influential factor affecting extrudate dimensions. In contrast, variations in temperature and extrusion speed had a relatively minor impact, whereas changes in pressure led to significant alterations in the extrudate’s size and shape.
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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 | 0.001 |
| 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".