Sustainable lunar additive manufacturing of high regolith-loaded PEKK composites for space infrastructure
Bibliographic record
Abstract
Minimizing the cost and complexity of space missions requires sustainable strategies for in-situ manufacturing using local resources. Additive manufacturing, particularly material extrusion (MEX), offers a practical route for fabricating lunar infrastructure components from regolith-reinforced thermoplastics. This work presents the development and characterization of Polyether-Ketone-Ketone (PEKK)/Lunar Regolith Simulant (LRS) composites with loadings up to 60 wt%, fabricated via twin-screw extrusion. Thermal, rheological, and microstructural analyses revealed uniform LRS dispersion and identified a critical viscosity threshold above 30 wt% that coincides with a ductile-to-brittle fracture transition. Density and porosity measurements showed that annealing increased porosity at low filler contents but reduced it at high loadings through matrix densification. Mechanical testing confirmed the interplay between filler fraction, fracture mode, and post-processing, with annealed 60 wt% composites achieving a 13.7% tensile strength improvement compared to their as-printed counterparts. A novel adapted tensile strength model was proposed, explicitly integrating volume fraction, porosity, and fracture regime, and demonstrated strong agreement with experimental results across both amorphous and annealed states. Demonstration prints of complex lunar rover wheel prototypes validated printability at high regolith contents and highlighted superior dimensional stability after annealing. These findings establish a material–process–property framework for defect-controlled additive manufacturing of high-regolith composites, supporting the design of resilient, resource-efficient structures for long-term lunar infrastructure under extreme thermal cycling, radiation, and vacuum conditions to support sustainable in-situ aerospace additive manufacturing development for future space missions. • PEKK/Lunar regolith composites fabricated with up to 60 wt% loading via twin-screw extrusion. • A novel tensile strength model integrates filler fraction, porosity, and fracture regime. • Critical viscosity threshold (>30 wt% LRS) linked to ductile–brittle transition identified. • Annealing increases porosity at low LRS but decreases it at high loadings through densification. • Complex lunar rover wheels successfully 3D-printed at 50–60 wt% LRS with high stability.
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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 | 0.000 |
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 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".