MétaCan
Menu
Back to cohort
Record W2080872924 · doi:10.1143/jjap.47.8101

Analysis and Validations of Fluid Dynamic Bearing for Spindle Motors of High-Density Optical Disc Players

2008· article· en· W2080872924 on OpenAlexfundno aff
Chien-Sheng Liu, Psang-Dain Lin

Bibliographic record

VenueJapanese Journal of Applied Physics · 2008
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsOptical discBearing (navigation)Reliability (semiconductor)Track (disk drive)Automotive engineeringNoise (video)VibrationMaterials scienceComputer scienceEngineeringMechanical engineeringAcousticsPhysicsOptics

Abstract

fetched live from OpenAlex

In recent times, the need to reduce the profiles of spindle motors in optical data storage devices without sacrificing their performance has become apparent. With the advent of high-density optical disc technology and reduction in the track pitch, spindle motors must have stable performance while tracking on narrow-pitch disks. In this paper, we present the structure and validations of the performance of a newly developed spindle motor (height 6.1 mm) using fluid dynamic bearing (FDB) for portable optical storage devices. The dynamic characteristics of the spindle motor before and after the accelerated life test are investigated experimentally. The noncontact phenomenon of the motor's shaft and FDB is also verified by using a laboratory-built apparatus. It was observed that the decrease in the axial repeatable runout (RRO) of the developed motor was up to 50% of that of the conventional spindle motor. All of the above validations prove that the developed FDB spindle motor has the potential for minimizing RRO, lowering acoustical noise, and improving reliability in portable optical storage devices.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.317

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 head0.008
GPT teacher head0.211
Teacher spread0.202 · 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

Citations6
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJapanese Journal of Applied PhysicsSame topicTribology and Lubrication EngineeringFrench-language works237,207