MétaCan
Menu
Back to cohort
Record W2117546266 · doi:10.1109/cca.2005.1507258

A linearized model for control of automated polishing process

2005· article· en· W2117546266 on OpenAlexaff
Liang Liao, Fengfeng Xi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPolishingNonlinear systemCoupling (piping)TorqueAutomationContact mechanicsProcess (computing)Pressure controlMechanical engineeringControl theory (sociology)EngineeringStress (linguistics)Control engineeringProcess controlComputer scienceStructural engineeringControl (management)PhysicsFinite element method

Abstract

fetched live from OpenAlex

Automation of polishing process requires control of tool pressure and tool speed. Due to friction between the tool and the part, the tool speed is affected when the tool pressure is changed, and this coupling relationship is nonlinear. In this paper, a linearized model is presented for control of automated polishing process. To do so, first, the contact stress model is introduced based on a Hertzian contact model, which relates the tool pressure to the contact stress of the contact area on the part. Second, the friction torque resulting from the contact area is derived, which relates the tool pressure to the tool speed. Then, the nonlinear coupling system is linearized with respect to the control voltages of the control valves, one for pressure and another for speed. Simulations are carried out to verify the linearized model against the original nonlinear model

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.288
Teacher spread0.275 · 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

Citations15
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Surface Polishing TechniquesFrench-language works237,207