A Framework to Improve Weight Estimation and Manufacturing Accuracy for Large Student Design Teams
Bibliographic record
Abstract
As aircraft become more complex with more components fitted in airframes, there is greater importance for precise weight prediction and control. However, in an undergraduate setting, it is often overlooked and undervalued due to impractical methods and its seemingly time-consuming process, especially for large student groups. Many traditional methods are available for students to use from textbooks, but they are usually not applicable for fixed-wing Uninhabited Aerial Vehicles (UAV) competitions, such as AIAA Design/Build/Fly (DBF) and SAE Aero Design. These methods are either based on a historical dataset of general or on military aviation aircraft, which are not suitable for predicting the weight of small battery-electric aircraft. The traditional process includes many irrelevant categories and steps which makes it difficult for students to use and maintain. This paper aims to provide an efficient and flexible framework for large student teams working on small UAVs to predict and manage weights. It covers relevant weight families with calculation models and a weight control methodology to use throughout a design and build cycle.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".