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Record W2090925643 · doi:10.1115/imece2005-81277

Design and Implementation of a PKM Based 5-Axis Reconfigurable Machine

2005· article· en· W2090925643 on OpenAlexaff
Peter E. Orban, Zhuming Bi, Y. T. Lang, Marcel Verner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsTripod (photography)Control reconfigurationMachine toolActuatorKinematicsMachiningControl engineeringComputer scienceEngineeringControl theory (sociology)Embedded systemControl (management)Mechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper we present the design and implementation of a parallel kinematics based reconfigurable machine. The machine utilizes a Tripod based module with 3 degrees-of-freedom combined with a linear X-Y stage and gantry system for 5-axis machining. The tripod module uses a unique passive link mechanism with constant length leg actuators. This architecture offers good stiffness and high accuracy. The gantry system itself is reconfigurable, allowing for changing the working characteristics of the machine. The control system is based on open architecture principles. Corresponding to the mechanical reconfiguration, the control system also needs to be reconfigured to reflect the actual state of the machine. Mechanical reconfiguration also brings with it the need to verify the accuracy of the new configuration. Also discussed in the paper is the calibration methodology that ensures high production quality in each configured mode of the machine.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.227
Teacher spread0.217 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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