3D printing LDPE/lunar regolith simulant composite: manufacturing with in-situ resources on the moon
Bibliographic record
Abstract
Additive manufacturing is essential for space missions, enabling on-demand production of components where resupply from Earth is limited. Fused deposition modeling (FDM) offers a promising route for repurposing plastic packaging waste into 3D printing feedstock. Low-density polyethylene (LDPE), commonly used in space packaging, can be combined with lunar regolith simulant to increase material availability for in-situ resource utilization (ISRU). However, the method of incorporating regolith into the polymer matrix affects filament quality and printability. Here, we compare single-screw and twin-screw extrusion techniques for producing LDPE/regolith composite filaments containing up to 30 wt% regolith. Both methods successfully produced filaments suitable for FDM, though single-screw extrusion required a second extrusion step above 10 wt% regolith. Filaments were evaluated for diameter consistency and printability, including the successful fabrication of NASA-designed parts. Regolith addition enhances print performance by improving overhang formation, gap bridging, and reducing warpage. Tensile testing shows increased stiffness without compromising strength up to 20 wt% regolith. These results demonstrate that LDPE and lunar regolith can be effectively processed into printable feedstock, supporting sustainable manufacturing strategies for lunar applications and advancing terrestrial plastic waste recycling. • LDPE/regolith composite filaments with up to 30 wt% regolith. • Supports ISRU feedstock production for sustainable lunar manufacturing. • Comparison of single- and twin-screw extrusion for space composites. • Regolith improves printability: better overhangs, bridging, and reduced warpage.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".