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Record W4392907885 · doi:10.32920/25412563.v1

Robot Design and Modeling for Enclosed Area Assembly

2024· preprint· en· W4392907885 on OpenAlexaff
Feroz Balsara

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRevolute jointWorkspaceKinematicsRobotEuler anglesPlanarDeflection (physics)SimulationComputer scienceRobotic armEngineeringControl engineeringControl theory (sociology)Mechanical engineeringArtificial intelligenceComputer graphics (images)GeometryMathematics

Abstract

fetched live from OpenAlex

The objective of this research is to develop a robotic fastening system for use during aircraft wing assembly. A novel robotic system is presented, that consists of a 3-DOF serial planar robotic arm mated to a revolute joint about the horizontal axis and a prismatic joint along the vertical Z axis. This configuration allows for an extensile platform that is adaptable to any wingbox. The formulation of the robot configuration is presented and validated using forward kinematics and visualization of the workspace. The dynamics of the system are modelled using the Newton-Euler method, and a mathematical model is developed to calculate the loads as a function of joint angles. The robot links are designed using the FEM, and a prototype system is fabricated. A control system is developed, and calibrated to meet the accuracy requirements. The system is then validated through repeatability tests and deflection tests in physical configurations.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.070
GPT teacher head0.275
Teacher spread0.205 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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