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Record W2162784822 · doi:10.1109/cdc.2001.980628

Chatter suppression with adaptive control in turning metal via application of piezoactuator

2003· article· en· W2162784822 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.

Bibliographic record

VenueProceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsMachiningNonlinear systemControl theory (sociology)VibrationDisplacement (psychology)Controller (irrigation)Adaptive controlComputer scienceEngineeringControl engineeringMechanical engineeringControl (management)AcousticsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Self-excited vibration, so called chatter, is known to cause detrimental effects on the machined surface finish and to decrease the machining efficiency. In this paper, in order for chatter suppression, a piezoactuator is introduced for the regulation of the machine tool displacement. In order for the ultra-precision control in turning via piezoactuator, a model with hysteretic nonlinearity accounting for the dynamics of turning process, cutting tool and piezoactuator is presented. With this model, a robust adaptive controller for the metal cutting system of ultra-precision is proposed and the simulation results shows that the proposed adaptive controller significantly eliminates the chatter phenomena.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.782

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 head0.209
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