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Record W3211944777 · doi:10.1115/detc2021-68098

Mechanical Design of a New Anthropomorphic Robot for Fastening in Wing-Box

2021· article· en· W3211944777 on OpenAlexaff
Jiefeng Jiang, Fengfeng Xi, Jingjing You, Qunxing Xue

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRobotEngineeringProcess (computing)Robot end effectorAutomationFrame (networking)Industrial robotRobotic armWingComputer scienceSimulationMechanical engineeringStructural engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The fastener installation in the wing-box faces with narrow space, and it has to be done manually at present. Since manual labor has size constraints, the efficiency is low, and there may be assembly quality instability, it urgently needs automation. Automatic fastening assembly using a robot undoubtedly is an appropriate solution. The existing industrial robots, snake robots, humanoid robots can not meet the fastening assembly requirements in the wing-box. We develop a new anthropomorphic robot with multiple links to perform the inner fastening. A prismatic pair is employed to fit the arm links entering into the wing-box. A shaft with 360 degrees rotation liked human shoulder is introduced to meet the circumferential positioning around the process hole. Arm links are used for robotic end effector reaching the local fastening site. Based on the limitation of assembly position in the wing-box, the link lengths are considered and determined. By using the geometric relation with the link lengths, the joint angle variables are presented. Then, S shape arm link is designed for the compact requirement and the dimensions are determined based on the cross-section of human arm. Finally, stable frame structure is set up through the rear door frame and the bridge beam, and the whole robot is integrated.

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: Methods · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.409

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 head0.067
GPT teacher head0.274
Teacher spread0.207 · 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
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

Citations5
Published2021
Admission routes1
Has abstractyes

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