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Record W2132705806 · doi:10.1109/cca.2005.1507290

Control of a pneumatic gantry robot for grinding: performance with conventional techniques

2005· article· en· W2132705806 on OpenAlexaff
A. Raoufi, Brian Surgenor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)GrindingPID controllerGrindController (irrigation)Pneumatic flow controlControl systemRobotBenchmark (surveying)EngineeringPneumatic actuatorEnhanced Data Rates for GSM EvolutionPressure controlControl engineeringComputer scienceActuatorMechanical engineeringControl (management)Artificial intelligenceTemperature control

Abstract

fetched live from OpenAlex

The ability of a pneumatic gantry robot to grind the edges of steel blanks was evaluated experimentally. It was found that surface quality improved after edge grinding with the applied force regulated to 30 N. Three different pneumatic circuit and force control system configurations were examined. A variance of plusmn5 N was obtained with a PLC-based open loop controller that used on/off directional valves and a manually adjusted pressure regulator. The variance was reduced to plusmn1.5 N with a PC-based closed loop PID controller that used proportional pressure valves. This level of performance with conventional industry control techniques was considered acceptable for the application at hand. However, the results are presented as a performance benchmark for measuring the value of more advanced control techniques in future studies

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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 designBench or experimental
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
Published2005
Admission routes1
Has abstractyes

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