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Record W4399827438 · doi:10.21926/rpm.2402013

The Effect of Rake Angle and Cutting Edge Radius on the Orthogonal Cutting Process of Ti6Al4V Alloy

2024· article· en· W4399827438 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

VenueRecent Progress in Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRake angleEnhanced Data Rates for GSM EvolutionTitanium alloyRADIUSMaterials scienceAlloyProcess (computing)RakeMetallurgyMechanical engineeringComputer scienceEngineeringMachiningArtificial intelligence

Abstract

fetched live from OpenAlex

This paper investigates the effect of rake angle, α, cutting edge radius, r, and feed rate, f, on the cutting force and cutting zone temperature during orthogonal dry cutting of Ti6Al4V. A numerical model representative of the 2D orthogonal cutting process is developed using ABAQUS/Explicit software. Johnson-Cook, (JC) constitutive material model is used to describe material plasticity. JC damage model and energy-based fracture criterion are used to describe damage initiation and evolution. Using Minitab-19 software, Taguchi L9 orthogonal array (3 × 3) is implemented to plan simulation trails. Assuming a constant cutting speed of 500 mm/min, three levels for each factor are considered α (-5°, 0°, 5°), <em>r</em> (0.02, 0.04, 0.06 mm) and <em>f</em> (0.1, 0.2, 0.3 mm). The cutting force and cutting zone temperature are analyzed using ANOVA. Based on a 90% Confidence Interval (90% CI), the results show that only feed rate significantly affects the cutting force. However, rake angle, cutting edge radius, and feed rate do not substantially affect cutting zone temperature.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.267
Teacher spread0.261 · 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