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Record W2173960222 · doi:10.1115/1.4032090

An Accurate and Efficient Approach to Undeformed Chip Geometry in Face-Hobbing and Its Application in Cutting Force Prediction

2015· article· en· W2173960222 on OpenAlexaff
Mohsen Habibi, Zezhong Chevy Chen

Bibliographic record

VenueJournal of Mechanical Design · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsHobbingBevelFace (sociological concept)Enhanced Data Rates for GSM EvolutionChipGeometryMechanical engineeringProcess (computing)MachiningKinematicsComputer scienceMaterials scienceStructural engineeringEngineeringMathematicsArtificial intelligencePhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Due to complexities of face-hobbing of bevel gears, such as the intricate geometry of the cutting system, multi-axis machine tool kinematic chains, and the variant cutting velocity along the cutting edge, deriving the instantaneous undeformed chip geometry, as one of the most important characteristic of material removal, is a challenging process. In the present research, all these complexities have been taken into consideration to obtain an in-process model and undeformed chip geometry, and predict cutting forces. The instantaneous undeformed chip geometry is obtained using the derived in-process model. As an application of the proposed methods, cutting forces are predicted during face-hobbing by oblique cutting theory using the derived undeformed chip geometry and converting face-hobbing into oblique cutting. The proposed methods are applied on two case studies of face-hobbing of bevel gears and the chip geometry is derived and the cutting forces are predicted.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score0.295

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 head0.032
GPT teacher head0.267
Teacher spread0.235 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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