Mass Reduction Techniques for Short Backfire Antennas: Additive Manufacturing and Structural Perforations
Bibliographic record
Abstract
This paper presents novel approaches for reducing the mass of the classical short backfire (SBF) antenna by using additive manufacturing and structural perforations. We first investigated techniques to create a 3D-printed structure with a conductive coating material. This approach resulted in a significant mass reduction (70%) compared with the conventional metallic structure. We performed parametric simulation studies to investigate the effects of the manufacturing process and showed that there was practically no difference in the performance. The largest source of error was the surface roughness and the conductivity of the metal paint. In a second design, we created perforations in the structure to further reduce the mass. We performed parametric studies to optimize mass reduction and to characterize the effects of the perforations and the surface roughness introduced during the 3D-printing process on the antenna. Antenna prototypes were fabricated and tested. The masses of the perforated 3D printed antenna were approximately 30% and 20% of the original aluminum design, respectively (70% and 80% reductions in mass, respectively). The good agreement among the original design, simulation, and measurements demonstrated the effectiveness of the approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".