Effects of non-planar slicing techniques and carbon fibre material additives on the mechanical properties of 3D-printed drone propellers
Bibliographic record
Abstract
Propeller parameters and geometry can dramatically influence the performance of a drone and its ability to complete a mission. Though many off-the-shelf propeller choices exist, operators in the field may not be able to stock suitable options for any possible scenario and are often forced to fly with a suboptimal propeller. Modern desktop 3D printers are relatively portable, highly capable, and simple to operate, offering the chance to rapidly manufacture propellers tailored to specific missions. This research evaluates how two recent advances in fused filament fabrication 3D printing could affect the mechanical viability of printed propellers. Non-planar slicing is a model slicing technique that attempts to address roughness issues when printing the shallow three-dimensional curvature found on many propeller blades. For further improvement, polymer filaments with short-chopped carbon fibre additives were compared against their fibre-free counterparts. Test coupons were subjected to tests simulating the thrust and impact loads a propeller might experience during flight. Under thrust loading, the material with carbon fibre additives showed a significant performance advantage. During impact tests, both nonplanar slicing (65% average improvement) and carbon fibre material additives (20% average improvement) demonstrated performance gains over their more traditional counterparts.
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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 | 0.001 |
| 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".