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Record W2017481364 · doi:10.1115/1.1789531

Dynamic Compensation of Spindle Integrated Force Sensors With Kalman Filter

2004· article· en· W2017481364 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKalman filterCompensation (psychology)Bandwidth (computing)Contact forceForce dynamicsSIGNAL (programming language)Control theory (sociology)Filter (signal processing)Signal processingEngineeringComputer scienceElectronic engineeringMechanical engineeringPhysicsDigital signal processingElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a dynamically compensated Spindle Integrated Force Sensor (SIFS) system to measure cutting forces. Piezo-electric force sensors are integrated into the stationary spindle housing. The structural dynamic model between the cutting forces acting on the tool tip and the measured forces at the spindle housing is identified. The system is first calibrated to compensate the influence of spindle run-out and unbalance at different speeds. Using the cutting force signals measured at the spindle housing, a Kalman Filter is designed to filter the influence of structural modes on the force measurements. The frequency bandwidth of the proposed sensor system is significantly increased with the proposed sensing and the signal processing method.

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)0.000
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 score0.712
Threshold uncertainty score0.378

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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