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Record W3180748381 · doi:10.32920/ryerson.14660295.v1

Tool accessibility with path and motion planning for robotic drilling and riveting

2021· preprint· en· W3180748381 on OpenAlexaff
David Dakdouk

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsToronto Metropolitan University
FundersStrong
KeywordsMotion planningRivetAerospaceProcess (computing)AccelerationComputer sciencePath (computing)EngineeringSoftwareRobotSimulationMechanical engineeringAerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Robotic applications in aerospace manufacturing and aircraft assembly today are limited. This is because most of the aircraft parts are relatively small or have complex shapes that make tasks like robotic drilling and riveting more challenging. These challenges include tool accessibility, path planning, and motion planning. In this thesis, a process methodology was developed to overcome the tool accessibility challenges facing robotic drilling and riveting for aircraft parts. The tool accessibility was analyzed based on the Global Accessibility Area and the Global Accessibility Volume to determine the accessible boundaries for parts with zero, one and two surfaces curvatures. The path planning was optimized based on the shortest distance, least number of steps, and minimal tool orientation change. The motion planning was optimized based on the s-curve using the robot’s maximum velocity and acceleration for minimum cycle time and maximum production rate. A software application was developed to simulate the tasks.

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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.491
Threshold uncertainty score0.639

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 head0.234
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations2
Published2021
Admission routes1
Has abstractyes

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