Bibliographic record
Abstract
In the current study completed in the Facility for Research on Aerospace Materials and Engineered Structures (FRAMES), the feasibility of implementing generative design as a means of optimizing advanced aerial drone structures was explored. By conducting relevant literature review, theoretical investigations, and experimentation, generative design demonstrated its efficacy as a design tool for various engineering structure applications. Generative design uses a series of artificial intelligence (AI) algorithms to compute various potential geometries for optimized load distribution; it is a powerful tool that provides fast and efficient topology optimized structures. This paper offers insight on the intricacies of unmanned aerial vehicle (UAV) design and discusses the various complications and advantages of using various drone geometries, manufacturing techniques, and materials. The interdependencies between geometry, manufacturing method, and material are also discussed. As such, the optimal frame type, manufacturing method, and material for optimized drone frame designs was found to be square-type, 3D-printing (MEX/FFF), and PEEK respectively. A generatively designed drone frame was created in Fusion 360 and analyzed using its own finite element analysis (FEA) capabilities; later, physical prototyping and testing verified the results gathered from FEA. This study attempts to re-introduce the feasibility and applicability of generative design in a sophisticated manner with the intention of closing gaps in novel research of drone frame optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".