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Record W2091551351 · doi:10.1081/mst-120016248

PHYSICS-BASED SIMULATION OF HIGH SPEED MACHINING

2002· article· en· W2091551351 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

VenueMachining Science and Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMachiningChip formationNonlinear systemMechanical engineeringChipConstitutive equationMaterials scienceProcess (computing)Characterization (materials science)Stress (linguistics)Empirical modellingStrain rateComputer scienceMechanicsEngineeringStructural engineeringFinite element methodTool wearPhysicsSimulationComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Computer simulation of high speed machining processes can provide a unique insight and reduce the number of design iterations required to advance and optimize the process. Predictive modeling of high speed machining of exotic materials has been hindered by the nonlinear behavior of this type of materials at extremely high strain, strain rate, and temperatures. This paper presents a physics-based modeling technology that includes the change in the material constitutive equation and the friction characterization at cutting speeds up to 400 m min−1. The dependence of the accuracy of the predicted parameters, such as the chip formation on cutting forces, chip/tool/workpiece interface temperature, stress and strain distributions are also discussed. The fundamentals of metal cutting were utilized to understand the effect of parameter changes in regimes that are outside current empirical knowledge databases.

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: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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