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Record W2128514207 · doi:10.1115/imece2008-66253

Effective Simulation of Ballscrew Drives Modeled Using Finite Element Methods

2008· article· en· W2128514207 on OpenAlexaff
Chinedum E. Okwudire, Yusuf Altıntaş

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNutFinite element methodAccelerationComputer sciencePosition (finance)Node (physics)ModalReduction (mathematics)Function (biology)Component (thermodynamics)Mode (computer interface)Control theory (sociology)AlgorithmMathematicsStructural engineeringEngineeringArtificial intelligenceGeometryPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

This paper presents a realistic and fast method of simulating ballscrew drives, taking into account the position-dependent changes in dynamics as the nut moves along the ballscrew. It achieves this by first using a strategy whereby the motion of the nut along the ballscrew is accurately and smoothly captured in simulations using the shape function of the finite element model as an interpolating function. Secondly, it applies a method based on Component Mode Synthesis with Modal Acceleration Recovery to achieve a full reduction of the ballscrew while still allowing the nut to move from node to node on the ballscrew. As a result the computational efficiency of the model is improved significantly without any considerable sacrifice of its accuracy.

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)0.000
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 score0.541
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.022
GPT teacher head0.301
Teacher spread0.278 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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