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Record W2039693340 · doi:10.1177/147509020321700401

The optimum dimensioning of an underwater manipulator for weld inspection

2003· article· en· W2039693340 on OpenAlexaff
T. Asokan, Gerald Seet, Jorge Angeles

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment · 2003
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsDimensioningWorkspaceCartesian coordinate systemWeldingManipulator (device)UnderwaterProcess (computing)EngineeringComputer scienceStructural engineeringRobotMechanical engineeringArtificial intelligenceRobotic armMathematicsGeometry

Abstract

fetched live from OpenAlex

The optimum dimensioning of an underwater inspection manipulator is discussed in this paper. The inspection of a weld seam using an alternating current field measurement (ACFM) probe was identified as a five-dimensional Cartesian task. Due to the forward positioning of the required workspace, this manipulator requires two additional axes to locate its base with respect to the weld seam. This leads to a seven-axis manipulator, comprising a two-axis launching stage and a five-axis dexterous inspection stage. The optimization of the architecture of the latter five-axis inspection manipulator is the subject of this paper. In optimizing the architecture, various postures of the manipulator during the inspection process were considered, which thus allows identification of the limiting values of the joint angles for maximum dexterity. The optimum link lengths to ensure the highest dexterity were then determined from the results of the analysis. It is shown that the weld seam diameter and the manipulator condition number can be used as the deciding parameters for determining the relative link lengths of the five-axis inspection stage of the manipulator.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.012
GPT teacher head0.203
Teacher spread0.191 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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