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Extraction, Rendering and Augmented Interaction in the Wire Assembly of Commercial Aircraft

2016· article· en· W2611759146 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsComputer scienceRendering (computer graphics)XMLSoftwareWearable computerEngineering drawingEmbedded systemComputer graphics (images)EngineeringOperating system

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.118

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.249
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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