Additive Manufacturing of Inflatable Thermoplastic Extrudates Using a Pellet Extruder
Bibliographic record
Abstract
Additive manufacturing (AM) has emerged as one of the core components of the fourth industrial revolution, Industry 4.0. Among others, the extrusion AM (EAM) of thermoplastic materials has been named as the most widely adopted technology. Fused filament fabrication (FFF) relies on the commercial availability of expensive filaments; hence, pellet extruder-based EAM techniques are desired. Large-format EAM systems would benefit from the ability to print lightweight objects with less materials and lower power consumption, which is possible with the use of hollow extrudates rather than solid extrudates to print objects. In this work, we designed a custom extruder head and developed an EAM system that allows the extrusion of inflatable hollow extrudates of a relatively wide material choice. By incorporating a co-axial nozzle–needle system, a thermoplastic shell was extruded while the hollow core was generated by using pressurized nitrogen gas. The ability to print using hollow extrudates with controllable inflation allows us to print objects with gradient part density with different degrees of mechanical properties. In this article, the effect of different process parameters, namely, extrusion temperature, extrusion speed, and gas pressure, were studied using poly-lactic acid (PLA) pellets. Initially, a set of preliminary tests was conducted to identify the maximum and minimum ranges of these parameters that result in consistent hollow extrudates. Finally, the parameters were varied to understand how they affect the core diameter and shell thickness of the hollow extrudates. These findings were supported by analyses of microscopic images taken under an optical microscope.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".