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Record W2004827858 · doi:10.1115/1.1897745

Parallel Kinematic Machines: Design, Analysis and Simulation in an Integrated Virtual Environment

2004· article· en· W2004827858 on OpenAlex
Dan Zhang, Lihui Wang, Sherman Y. T. Lang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Mechanical Design · 2004
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsNational Research Council CanadaOntario Tech University
Fundersnot available
KeywordsKinematicsCADMachine toolComputer scienceMachiningSet (abstract data type)Virtual machineFinite element methodVirtual prototypingComputer Aided DesignSimulationControl engineeringEngineeringEngineering drawingMechanical engineeringOperating system

Abstract

fetched live from OpenAlex

Selecting a configuration for a machine tool that will best suit the needs of a forecast set of requirements can be a difficulty and costly exercise. This problem can now be addressed using an integrated virtual validation system. The system includes kinematic/dynamic analysis, kinetostatic model, CAD module, FEM module, CAM module, optimization module and visual environment for simulation and collision detection of the machining and deburring. It is an integration of the parallel kinematic machines (PKM) design, analysis, optimization and simulation. In this paper, the integrated virtual system is described in detail; a prototype of a 3-dof PKM is modeled, analyzed, optimized and remote controlled with the proposed system. Some results and simulations are also given. Its effectiveness is shown with the results obtained by NRC-IMTI during the design of the 3-dof NRC PKM.

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.

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 score0.498
Threshold uncertainty score0.530

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 head0.021
GPT teacher head0.238
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