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Record W3040018037 · doi:10.1115/1.4047625

General Cutting Dynamics Model for Five-Axis Ball-End Milling Operations

2020· article· en· W3040018037 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 Manufacturing Science and Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of British Columbia
FundersNational Key Research and Development Program of China Stem Cell and Translational ResearchNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsEnd millingMachine toolBall (mathematics)VibrationMechanical engineeringNumerical controlEngineeringComputer scienceMachiningGeometryAcousticsMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract Five-axis ball-end milling is used extensively to machine parts with sculptured surfaces. This paper presents the general cutting dynamics model of the ball-end milling process for machine tools with different five-axis configurations. The structural dynamics of both the tool and workpiece are considered for the prediction of chatter stability at each tool location along the tool path. The effects of tool–workpiece engagement and tool axis orientation are included in the model. By sweeping the spindle speeds, the chatter-free spindle speeds are selected followed by the prediction of forced vibrations in five-axis milling of thin-walled, flexible parts. The proposed model has been experimentally illustrated to predict the chatter stability and forced vibrations on a table-tilting five-axis computer numerical control machine tool.

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 score0.493
Threshold uncertainty score0.440

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 communication0.0000.001
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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