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Record W2093029951 · doi:10.1115/msec2009-84039

Chatter Stability in Micro Milling Processes Considering Ploughing and Process Damping Effects

2009· article· en· W2093029951 on OpenAlexafffund
Ramin Rahnama, Mozhdeh Sajjadi, Simon S. Park

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChip formationShearing (physics)ChipMachiningPloughMechanical engineeringMaterials scienceProcess (computing)EngineeringStructural engineeringComputer scienceTool wearComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

Micro milling operations utilize miniature tools to remove workpiece material, in order to create the desired 3D miniature components. One of the challenges in a micro milling operation is the unstable phenomenon called regenerative chatter. The occurrence of chatter in the micro domain, as in macro machining, is detrimental to part finishes and significantly reduces the longevity of tools. There are two different cutting mechanisms in micro milling operations, which are determined by the critical chip thickness. When the chip thickness is less than the critical chip thickness, no chip forms and ploughing occurs; whereas, when the chip thickness is greater than the critical chip thickness, a chip forms and shearing cutting happens. During each rotation of the tool, the cutting mechanisms switch from ploughing to shearing and vice versa. This paper introduces a time domain chatter model to investigate the effects of the ploughing and shearing mechanisms on stability. The model also considers the effects of process damping in micro milling, especially at low spindle speeds. Several experimental tests have been performed to validate the model.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.641

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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

Citations1
Published2009
Admission routes2
Has abstractyes

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