MétaCan
Menu
Back to cohort
Record W3177302046 · doi:10.1115/1.4051605

Analytical Modeling of Shear Localization in Orthogonal Cutting Processes

2021· article· en· W3177302046 on OpenAlexafffund
Mohammadreza Fazlali, Mauricio Ponga, Xiaoliang Jin

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of British Columbia
FundersDefence Research and Development CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsShear bandAdiabatic shear bandShear (geology)Materials scienceShear stressSimple shearShear rateRakeFinite element methodCritical resolved shear stressMechanicsShear zoneComposite materialStructural engineeringGeologyMechanical engineeringRheologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract This paper presents an analytical thermo-mechanical model of shear localization and shear band formation in orthogonal cutting of high-strength metallic alloys. The deformation process of the workpiece material includes three stages: homogeneous deformation, shear localization, and chip segmentation. A boundary layer analysis is used to analytically predict the temperature, stress, and strain rate variations in the primary shear zone associated with the shear localization. The predictions of shear band spacing and width from the proposed model are verified by experimental characterization of the chip morphology. The rolling of shear bands on the tool rake face is discussed from the experimental observations. The cutting tool temperature, which is influenced by the heat generated during the shear band formation, is simulated and compared with finite element simulations. The proposed analytical model reveals the fundamental mechanism of the complete shear localization process in orthogonal cutting and predicts the stress and temperature variations with high computational efficiency.

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 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 score0.351
Threshold uncertainty score0.305

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations9
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Manufacturing Science and EngineeringSame topicAdvanced machining processes and optimizationFrench-language works237,207