Optimization of fastener pattern in airframe assembly
Bibliographic record
Abstract
Purpose The authors consider the problem of optimizing temporary fastener patterns in aircraft assembly. Minimizing the number of fasteners while maintaining final product quality is one of the key enablers for intensifying production in the aerospace industry. The purpose of this study is to formulate the fastener pattern optimization problem and compare different solving approaches on both test benchmarks and rear wing-to-fuselage assembly of an Airbus A350-900. Design/methodology/approach The first considered algorithm is based on a local exhaustive search. It is proved to be efficient and reliable but requires much computational effort. Secondly, the Mesh Adaptive Direct Search (MADS) implemented in NOMAD software (Nonlinear Optimization by Mesh Adaptive Direct Search) is used to apply the powerful mathematical machinery of surrogate modeling and associated optimization strategy. In addition, another popular optimization algorithm called simulated annealing (SA) was implemented. Since a single fastener pattern must be used for the entire aircraft series, cross-validation of obtained results was applied. The available measured initial gaps from 340 different aircraft of the A350-900 series were used. Findings The results indicated that SA cannot be applicable as its random character does not provide repeatable results and requires tens of runs for any optimization analysis. Both local variations (LV) method and MADS have proved to be appropriate as they improved the existing fastener pattern for all available gaps. The modification of the MADS' search step was performed to exploit all the information the authors have about the problem. Originality/value The paper presents deterministic and probabilistic optimization problem formulations and considers three different approaches for their solution. The existing fastener pattern was improved.
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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 | 0.001 | 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".