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Record W1999317931 · doi:10.1115/imece2003-41701

Design and Evaluation of a Pneumatic Gantry Robot for the Grinding of Steel Blanks

2003· article· en· W1999317931 on OpenAlexaff
Ashar Raoufi, Brian Surgenor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsPneumatic cylinderCompressed airPneumaticsRobotPneumatic actuatorProgrammable logic controllerActuatorGrindingController (irrigation)EngineeringProcess (computing)GrindSolenoidPneumatic flow controlMechanical engineeringControl engineeringComputer scienceCylinderElectrical engineering

Abstract

fetched live from OpenAlex

This paper describes the design and evaluation of a pneumatic gantry robot that is used to grind the edges of steel blanks as part of a finishing operation prior to being stamped. The objective of this research project is to automate the grinding process in order to reduce the frequency of cracking. The required force and degree of precision needed were thought to be within the capabilities of a pneumatically actuated robot. This would keep the cost of the apparatus down, which was important given the low capital cost of the manual operation. Furthermore, given that manipulation of the air pressure is the mechanism used to move a pneumatic actuator, the combined control of force and position is inherent to the system. A number of different pneumatic circuit configurations were examined before adopting a design that uses a combination of directional solenoid and flow control valves controlled by a Programmable Logic Controller (PLC). Pressure transducers and analog linear potentiometers are used for data acquisition. Laboratory test results are presented as well as a discussion of additional work that must be completed with the pneumatic gantry robot before field tests are conducted.

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)0.001
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 score0.679
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.062
GPT teacher head0.301
Teacher spread0.239 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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