Extraction, Rendering and Augmented Interaction in the Wire Assembly of Commercial Aircraft
Bibliographic record
Abstract
In modern aircraft, the manual process of assembling electrical wire harnesses can be complex and time consuming, consisting of tens, and possibly hundreds of kilometers of wires. This can be both labor intensive and costly to produce. Subsequently, the goal of this paper is to describe the development of a prototype digital wire routing system that adds flexibility and control in the electrical wire assembly of aircraft. This includes both the software to read and extract geometrical wire information from 3D CAD drawings to an XML file format, in addition to the rendering and design of route sequences through a series of human-machine interfaces. Specifically, we demonstrate the feasibility of mobile and wearable solutions to guide the sequencing of wire bundles for factory operators, both visually and through the use of voice interaction. Indoor location tracking is provided through the use of ultra-wide band technology to update information in the operator's vicinity. A description of these features is provided in this paper, in addition to a summary of insights gathered from user testing that highlight further research opportunities to improve the system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".