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Record W2034764259 · doi:10.1080/10940340008945698

SIMULATION OF CHIP FORMATION IN ORTHOGONAL METAL CUTTING PROCESS: AN ALE FINITE ELEMENT APPROACH

2000· article· en· W2034764259 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 · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFinite element methodChip formationProcess (computing)Constitutive equationMechanical engineeringMachiningLagrangianMaterials scienceCutting toolPlane stressEulerian pathApplied mathematicsStructural engineeringComputer scienceEngineeringMathematicsTool wear

Abstract

fetched live from OpenAlex

Abstract Lagrangian and Eulerian finite element formulations have been traditionally used for modeling of the orthogonal metal cutting process. In this paper it is shown that a more general formulation, the arbitrary Lagrangian-Eulerian method (ALE), may be used to combine the advantages and avoid the drawbacks of both methods in a single analysis. Due to the characteristics of the cutting process, ALE formulation offers a very efficient modeling approach for the cutting process. A comprehensive ALE model along with strain rate and temperature dependent constitutive equations and a contact/friction algorithm is used to analyze the thermo-elasto-plastic process of plane strain orthogonal cutting. Simulation results for cutting of low carbon free cutting steel are presented and compared with available experimental data obtained under similar cutting conditions. Good agreement between the numerical and experimental results is observed.

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 score0.111
Threshold uncertainty score0.337

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 communication0.0000.001
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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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