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Record W2495129274 · doi:10.4271/2016-01-2104

Unique Material Handling and Automated Metrology Systems Provides Backbone of Accurate Final Assembly Line for Business Jet

2016· article· en· W2495129274 on OpenAlexaff
Robert Flynn, Kevin Payton-Stewart, Patrick Brewer, Ryan W. Davidge

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2016
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsMetrologyLine (geometry)Jet (fluid)Computer scienceAssembly lineEngineering drawingManufacturing engineeringMechanical engineeringEngineeringOpticsPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

Figure 1 Global 7000 Business Jet. Photo credit: Robert Backus. The customer’s assembly philosophy demanded a fully integrated flexible pulse line for their Final Assembly Line (FAL) to assemble their new business jets. Major challenges included devising a new material handling system, developing capable positioners and achieving accurate joins while accommodating two different aircraft variants (requiring a “flexible” system). An additional requirement was that the system be easily relocated to allow for future growth and reorganization. Crane based material handling presents certain collision and handover risks, and also present a logistics challenge as cranes can become overworked. Automated guided vehicles can be used to move large parts such as wings, but the resulting sweep path becomes a major operational limitation. The customer did not like the trade-offs for either of these approaches. A unique conveyance system (ATLAS) based on in-floor rails was developed to offer a solution that provides highly controlled, low risk and accurate moves that allow workers and tools to remain in the assembly area. Positioners were developed, some of which include a driven passive axis (DP axis), useful in certain conditions for driving positioners in their passive axis. Accurate and rapid joins required an advanced metrology solution. Integrating this automated metrology based positioning system posed a challenge. The accuracy requirement meant that the system had to measure and accommodate slight differences between the incoming parts i.e., be an “adaptive” system. A Human Machine Interface (HMI) was developed to enable de-skilled automated metrology and to communicate with the metrology and PLC systems. The HMI presents a virtual task checklist and restricts the user from deviating from the order of operations or omitting any tasks. Established tolerances must be achieved before proceeding to the next task. A robust architecture allows failed tasks to be re-attempted without restarting the join process, resulting in a forgiving and flexible process. Integrated supervisor-override privileges make it possible to execute alignment adjustments if dictated by engineering or circumstance.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.054
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0540.041

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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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