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Record W2249977607 · doi:10.1139/tcsme-2012-0006

EFFECT OF SERVO SYSTEMS ON THE CONTOURING ERRORS IN INDUSTRIAL ROBOTS

2012· article· en· W2249977607 on OpenAlexaffvenue
Mohamed Slamani, Albert Nubiola, Ilian A. Bonev

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsContouringServoIndustrial robotRobotServomechanismCartesian coordinate systemControl theory (sociology)Computer scienceServo controlTrajectoryRADIUSSimulationTracking errorMotion controlEngineeringArtificial intelligenceMathematicsControl engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

Two important aspects of the performance of a servo system, tracking errors and contour errors, significantly affect the accuracy of industrial robots under high-speed motion. Careful tuning of the control parameters in a servo system is essential, if the risk of severe structural vibration and a large contouring error is to be avoided. In this paper, we present an overview of a method to diagnose contouring errors caused by the servo control system of an ABB IRB 1600 industrial robot by measuring the robot’s motion accuracy in a Cartesian circular shape using a double ballbar (DBB) measurement instrument. Tests were carried out at different TCP (tool centre-point) speed and trajectory radii to investigate the main sources of errors that affect circular contouring accuracy. Results show that radius size errors and out-of-roundness are significant. A simple experimental model based on statistical tests was also developed to represent and predict the radius size error. The model was evaluated by comparing its prediction capability in several experiments. An excellent error prediction capability was observed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.350

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 head0.024
GPT teacher head0.223
Teacher spread0.200 · 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
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

Citations19
Published2012
Admission routes2
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAdvanced Measurement and Metrology TechniquesFrench-language works237,207