MétaCan
Menu
Back to cohort
Record W2035084964 · doi:10.1115/1.3159047

Chatter Stability of General Turning Operations With Process Damping

2009· article· en· W2035084964 on OpenAlexaff
Mahdi Eynian, Yusuf Altıntaş

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of British Columbia
FundersSandvik Coromant
KeywordsChipChip formationVibrationCutting toolEnhanced Data Rates for GSM EvolutionMachiningMachine toolEngineeringMechanical engineeringStability (learning theory)Structural engineeringControl theory (sociology)Tool wearComputer scienceAcousticsPhysics

Abstract

fetched live from OpenAlex

The accurate prediction of chatter stability in general turning operations requires the inclusion of tool geometry and cutting conditions. This paper presents regenerative chip and regenerative chip area/cutting edge contact length based dynamic cutting force models, which consider cutting conditions and turning tool geometry. The cutting process is modeled as it takes place along the equivalent chord length between the two end points of the cutting edge. The regenerative chip model is simple, and the stability can be solved directly. However, the three-dimensional model considers the effect of tool vibrations at the present and previous spindle revolutions on the chip area, chord length, and force directions and is solved using Nyquist stability criterion. The penetration of worn tool flank into the finish surface is considered as a source of process damping. The effects of the nose radius, approach angle of the tool, and feedrate are investigated. The proposed stability model is compared favorably against the experimental results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.223
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations148
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Manufacturing Science and EngineeringSame topicAdvanced machining processes and optimizationFrench-language works237,207