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Record W2555275373

Design and Analysis of a Collision Detector for Hybrid Robotic Machine Tools

2015· article· en· W2555275373 on OpenAlexaff
Dan Zhang, Bin Wei

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCapacitive sensingDetectorCollisionCapacitorCollision detectionCapacitanceComputer scienceRobot end effectorFixtureProximity sensorElectrical engineeringEngineeringRobotSimulationElectronic engineeringArtificial intelligenceMechanical engineeringVoltagePhysics
DOInot available

Abstract

fetched live from OpenAlex

Capacitive sensing depends on the physical parameter changing either the spacing between the two plates or the dielectric constant. Based on this idea, a capacitive based collision detection sensor is proposed and designed in this paper for the purpose of detecting any collision between the end effector and peripheral equipment (e.g., fixture) for the three degrees of freedom hybrid robotic machine tools when it is in operation. One side of the finger-like capacitor is attached to the moving platform of the hybrid robotic manipulator and the other side of the finger-like capacitor is attached to the tool. When the tool accidently hits the peripheral equipment, the vibration will make the distance of the capacitor change and therefore trigger the machine to stop. The new design is illustrated and modelled. The capacitance, sensitivity and frequency response of the detector are analyzed in detail, and finally, the fabrication process is presented. The proposed collision detector can also be applied to other machine tools.

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: Empirical · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.240
GPT teacher head0.498
Teacher spread0.258 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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