Automation of Aircraft Maintenance Risk Assessment Using a Parametric Geometry Modeler
Bibliographic record
Abstract
Maintainability is an important criterion in aircraft design, affecting customer satisfaction and operating costs. Efforts have been made to address some aspects of maintainability earlier during the aircraft development and system integration process; however, most of the proposed methods require substantial human input and are not well suited for integration with a Multidisciplinary Design Analysis and Optimization (MDAO) framework. Within the challenge of automation lies the need for a link between a geometric modeler and the maintainability analysis tool. This paper describes a method for automating a score-based maintainability assessment method, requiring three-dimensional (3D) geometry using Engineering Sketch Pad (ESP) and Computational Analysis Prototype Syntheses (CAPS). The authors propose and implement the analysis in Python by linking the analysis tool with the geometry using CAPS to extract the required data for further computations. The paper presents a validation of the method using maintenance risk scores from a previous work requiring a 3D model and demonstrates functionality by applying the approach to analyze aircraft equipment bays. The results from the automation show similarity with those obtained when computing risk scores manually, and the effect of component layout on computational time is addressed. The automated analysis enables the evaluation of multiple aircraft equipment bay configurations efficiently and quickly.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".