MétaCan
Menu
Back to cohort
Record W2057437493 · doi:10.1115/imece2002-39109

Analysis of the Machining Process Using a Thermo-Elastic-Viscoplastic Finite Element Model

2002· article· en· W2057437493 on OpenAlexaff
N. Balihodzic, Hossam A. Kishawy, Robert J. Rogers

Bibliographic record

VenueManufacturing · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsFinite element methodViscoplasticityMaterials scienceMachiningCeramicPlane stressDeformation (meteorology)Stress (linguistics)Chip formationThrustEnhanced Data Rates for GSM EvolutionStress–strain curveComposite materialMechanical engineeringStructural engineeringTool wearConstitutive equationMetallurgyEngineering

Abstract

fetched live from OpenAlex

A plane-strain thermo-elasto-viscoplastic finite element model has been developed and used to simulate orthogonal machining. Simulations of cutting 304L stainless steel have been carried out using sharp, chamfered, and honed ceramic tools. Employing a combined thermal and mechanical stress analysis with temperature-dependent physical properties, the finite element model is used to investigate the effect of process parameters, tool geometry and edge preparation on the machining process. Stress and strain distributions within the chip and the elastic tool are presented. In addition, trends in the cutting and thrust forces, contact stress distributions and the plastic deformation beneath the machined surface are studied.

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)0.000
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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
GenreMethods

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
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueManufacturingSame topicAdvanced machining processes and optimizationFrench-language works237,207