Airframe dismantling optimization for aerospace aluminum valorization
Bibliographic record
Abstract
Seeking the most cost-effective process, dismantlers must continuously make decisions while they part and shear out a plane. The mathematical model presented optimizes the profitability of aircraft dismantling process by determining which airframe entities must be sheared and sorted prior to shred its components or which entities must be directly shredded. The model also identifies which shredded components should be sorted in order to upgrade recovered materials composition. Until now, disassembly sequences generation and disassembly planning methods have been elaborated based on assembly connection types and generic mathematical models have been published. Most of the time, these approaches are time consuming and require efforts to be adapted to a specific product. The model proposed here is aircraft-oriented and is not only based on assembly connection types and fasteners classification which are considered as being too restrictive. Knowing that an airplane is made of about 60 % of aluminum alloys and that aluminum recycling could considerably reduce the aerospace industry's ecological footprint, the model focuses on aircraft aluminum recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".