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Record W2092824369 · doi:10.1115/1.1445147

Numerical Analysis of Metal Cutting With Chamfered and Blunt Tools

2002· article· en· W2092824369 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

VenueJournal of Manufacturing Science and Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChamfer (geometry)Enhanced Data Rates for GSM EvolutionMaterials scienceChip formationFinite element methodChipMachiningCutting toolCarbideProcess (computing)Tool wearStructural engineeringComposite materialGeometryMetallurgyComputer scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

In high speed machining of hard materials, tools with chamfered edge and materials resistant to diffusion wear are commonly used. In this paper, the influence of cutting edge geometry on the chip removal process is studied through numerical simulation of cutting with sharp, chamfered or blunt edges and with carbide and CBN tools. The analysis is based on the use of ALE finite element method for continuous chip formation process. Simulations include cutting with tools of different chamfer angles and cutting speeds. The study shows that a region of trapped material zone is formed under the chamfer and acts as the effective cutting edge of the tool, in accordance with experimental observations. While the chip formation process is not significantly affected by the presence of the chamfer, the cutting forces are increased. The effect of cutting speed on the process is also 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.

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 score0.082
Threshold uncertainty score0.295

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 head0.010
GPT teacher head0.201
Teacher spread0.192 · 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